The TQM Journal

The TQM Journal Volume 21, Issue 6, 2009, pp.549-635

Six Sigma in the Indian software industry: some observations and results from a pilot survey Rupa Mahanti, Jiju Antony (pp. 549-564) Keywords: Communication technologies, Computer software, Critical success factors, India, Quality, Six sigma ArticleType: Research paper Barriers faced by engineers when applying design of experiments Martín Tanco, Elisabeth Viles, Laura Ilzarbe, Ma Jesus Alvarez (pp. 565-575) Keywords: Experimental design, Industrial engineering, Statistical methods of analysis ArticleType: Literature review The relationship between quality management and the speed of new product development Hongyi Sun, Yangyang Zhao, Hon Keung Yau (pp. 576-588) Keywords: Consumer satisfaction, Continuous improvement, Customer service management, Manufacturing industries, Product development, Total quality management ArticleType: Research paper A proposed framework for combining ISO 9001 quality system and quality function deployment Paulo A. Cauchick Miguel, José Celso Sobreiro Dias (pp. 589-606) Keywords: ISO 9000 series, Product development, Quality function deployment, Quality systems ArticleType: Research paper Total quality management in Indian industries: relevance, analysis and directions Raj Kumar, Dixit Garg, T.K. Garg (pp. 607-622) Keywords: India, Manufacturing industries, Total quality management ArticleType: Research paper Does size matter for Six Sigma implementation?: Findings from the survey in UK SMEs Maneesh Kumar, Jiju Antony, Alex Douglas (pp. 623-635) Keywords: Continuous improvement, Critical success factors, Performance measures, Six sigma, Small to medium-sized enterprises, United Kingdom ArticleType: Research paper Book Review

Voice of the Customer: Capture and Analysis Vol : 21 Issue: 6 Author(s): K. Narasimhan

Lean Six Sigma for Supply Chain Management: The 10-Step Solution Process Vol : 21 Issue: 6 Author(s): K. Narasimhan

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1754-2731.htm

Six Sigma in the Indian software industry: some observations and results from a pilot survey Rupa Mahanti

Six Sigma in the Indian software industry 549

Kolkata, India, and

Jiju Antony Centre for Research in Six Sigma and Process Excellence (CRISSPE), Department of DMEM, Strathclyde Institute for Operations Management, University of Strathclyde, Glasgow, UK Abstract Purpose – The aim of this paper is to present the results from an empirical investigation of Six Sigma in the Indian software industry Design/methodology/approach – The paper begins with a review of literature of Six Sigma and its role in the software industry. The importance of Six Sigma in the software domain is presented, followed by presentation of the results from an empirical investigation of Six Sigma in the Indian software industry Findings – The research reflects the status of Six Sigma application and implementation in the software industry, identifies the commonly used statistical and non statistical and software engineering tools and frameworks used within software business; and determines the critical success factors (CSFs) for a successful Six Sigma initiative in the software/IT industry. The most important factor was management commitment and involvement. Documentation management and suppliers’ involvement were found to be the least important factors. Research limitations/implications – This study was carried out with some boundaries like the number of companies, available resources, time constraints, etc. Practical implications – This paper dispels the myths concerning the unsuitability of Six Sigma in the software arena. At the same time it highlights the status of Six Sigma implementation in Indian software organizations and the critical success factors for implementation of Six Sigma. Originality/value – Little research has been carried out in terms of empirical survey relating to the application of Six Sigma in the software industry like that demonstrated in this paper. The paper will be valuable for quality professionals and management personnel in software organizations. Keywords Six sigma, Computer software, Critical success factors, Communication technologies, Quality, India Paper type Research paper

Introduction Software quality is often seen as an elusive and mysterious subject; it is perhaps the most ignored topic in the world of software development (Kenett and Baker, 1999). For many business leaders, software quality is often viewed as a luxury; something that can be sacrificed, if necessary, for added functionality, faster development, or lower costs. However, the quality of software is of paramount importance to everyone, including users and developers. Because of fierce global competition, many software companies are suffering financial setbacks, and hence they are trying to control costs (Phan et al., 1995). Software organizations are also fighting for survival and excellence

The TQM Journal Vol. 21 No. 6, 2009 pp. 549-564 q Emerald Group Publishing Limited 1754-2731 DOI 10.1108/17542730910995837

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in an atmosphere of global competition. To maintain sound competition, the software companies need to differentiate their products in ways that are meaningful to their customers. Quality is a proven way to achieve this differentiation (Humphrey, 1989). Successful software development organizations have found that an organizational commitment to quality expedites software development process, reduces costs, and allows new features to be added with greater ease. This is because an organization that develops low-quality software, whether for internal use or for sale, is essentially always looking backward, spending time and money fixing defects in “completed” software (Bessin, 2004). Both practitioners and academicians agree that software quality improvement techniques lead to a reduction in software development costs and therefore, software quality is one of the critical issues (Kan et al., 1994; Weinberg, 1996; Yang, 2001). An abundance of tools, techniques, and philosophies have been developed for minimizing errors and improving overall software quality (Parzinger and Nath, 1998). A number of quality standards, methodologies and frameworks such as ISO 9000, TQM, Malcolm Baldridge National Quality Award, Six Sigma, Capability Maturity Model (SEI-CMM), Capability Maturity Integration Model (SEI-CMMI), Team Software Process (TSP), People Software Process (PSP), People Capability Maturity Integration Model (P-CMMI) have been embraced by organizations to improve their products and services. Jovanoic and Shoemaker (1997) argued that ISO 9000 is appropriate for software development processes as well. Jalote (2000) found the Software Engineering Institute’s Capability Maturity Model (CMM) to be a widely used framework for quality management in software companies. Organizations that have acquired the fifth level of CMM and PCMM are expected to maintain very high quality standards (Harter et al., 2000). Issac et al. (2004) proposed a descriptive TQM model for quality management in the software industry. Six Sigma is the new star in the quality world (Tennant, 2001). Six Sigma is a business improvement strategy that seeks to find and eliminate causes of defects or mistakes in business processes by focusing on outputs that are of critical importance to customers (Snee, 2000). Six Sigma has both management and technical components. The focus of management component is to select the right people for Six Sigma projects, select the right process metrics, provide resources for Six Sigma training, provide clear direction and guidance with regard to project selection, etc. The focus of technical component is on process improvement by reducing variation, creating data that explains process variation and using statistical tools and techniques for problem solving. The concept of implementation of Six Sigma methodology was pioneered at Motorola in the 1980 s with the aim of reducing quality costs. After Motorola, Six Sigma has been exploited by many organizations such as GE, Honeywell, Sony, Caterpillar, J P Morgan, American Express, Common Wealth Health Corporation, Lloyds TSB to name but a few here. Six Sigma has its roots in manufacturing. The proven potential of Six Sigma to provide competitive advantage to manufacturing industries has initiated the implementation of Six Sigma to the software industry as well. Although the application of Six Sigma in the software industry is in its infancy stage, a number of sources in the existing literature have discussed about the applicability of Six Sigma in software development projects (Antony and Fergusson, 2004; Binder, 1997; Hong and Goh, 2003; Mahanti and Antony, 2006).

The implementation of Six Sigma in the software industry is still in its infancy. The software industry is currently still young, without sufficient knowledge and adequate standards to guarantee fault-free software. Typical software processes operate at between 2.3 and 3.0 sigma (Mahanti and Antony, 2006). The best software processes operate at 4 to 5 sigma. If we use a single line of source code to represent a unique opportunity for defect, it means a typical software development process generates a theoretical average of 66,803 defects per million lines of source code. The large defect number suggests significant effort wasted in producing defects during the development process. The final product delivered to the customer is only 93.332 percent defect free. On the other hand, by deploying the Six-Sigma methodology, a Six-Sigma process claims that only an average of 3.4 defects per million lines of source code is produced. The final product delivered to the client is 99.9997 percent defect free (Hong and Goh, 2004). Software Six Sigma is the application of statistical and non-statistical tools to the software process, and software work products throughout the software development life cycle to measure, analyze and reduce defects, cycle time, schedule slippage, effort slippage, effort variation and schedule variation (Mahanti, 2005). Though surveys have been conducted in the implementation of various software process improvement and quality methodologies like TQM, Capability Maturity Model standards and ISO standards in Indian software organizations no survey research has been conducted in connection to the implementation of Six Sigma in Indian software organizations (Jalote, 2000; Issac et al., 2004; Issac et al., 2004). The findings present in this paper are possibly the first of its kind in the Indian software organizations. This paper begins with a review of literature on Six Sigma and its role in the software industry, followed by the importance of Six Sigma in the software business and finally the presentation of results from an empirical investigation of Six Sigma in the Indian software industry. Six Sigma and its role in the software industry: a review of literature Six Sigma starts from a practical problem, translates it into a statistical domain, works out a statistical solution and then translates it back to a practical solution. Sigma is a statistical unit of measure, which reflects process capability of any process. Sigma value is perfectly correlated to such characteristics as defects-per-unit, parts-per million defective and the probability of a failure/error. Six Sigma methodology: . focuses on the customer and is based on data; . integrates well with other software quality initiatives like CMM and CMMI; . is measurable, unlike other quality systems; and . is an effective approach for removing defects from products. Six Sigma is based on two basic methodologies: (1) Define, Measure, Analyze, Improve and Control (DMAIC). (2) Design for Six Sigma (DFSS). DFSS follows the Define, Measure, Analyze, Design, Optimize and Verify methodology (DMADOV). The success of Six Sigma in the manufacturing domain has been reported all over the world. A report from the Black & Decker Corporation illustrates this. In January 30, 2003, the company announced that, despite the weak economic conditions, by focusing


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on Six-Sigma in the process of restructuring, operating profit for the Power Tools and Accessories segment increased 38% from the fourth quarter a year ago (Appliance Magazine.com, 2003). However, the application of Six Sigma in the software domain has been a subject of much debate and conjecture and is still less widely accepted than CMM, CMMI or GQM (Goal Question Metric). One aspect of resistance to the application of Six Sigma to the software process is as follows. An example is a study by Binder (1997), which identifies three major difficulties involved in applying the Six Sigma model to software. These are classified as “processes”, “characteristics” and “uniqueness”. “Processes” refers to the relative “fuzziness” of software process compared to manufacturing processes where the application of the Six Sigma model is well established and documented. “Characteristics” highlights the difficulties in taking meaningful measurements such as what constitutes a fault, fault densities, etc. “Uniqueness” refers to the observation that, unlike manufactured products that are generally mass produced, software products are generally “one-off” and identical copies can be easily made. Six Sigma aims to align business products within customer specifications using a data-driven approach. However, a software product is essentially intangible until it reaches system integration and test phase. There are difficulties in applying Six Sigma to the software industry, due to its statistical foundations in manufacturing and assumptions on process variation. Another barrier to the successful application of Six Sigma to software is a lack of adequate product and process metrics (Janieszewski and George, 2004). In addition, software development is a cognition-intensive activity and a software process itself is an intellectual process; one must first visualize and document it, for example, before one can measure and manage it. However, Six Sigma does not specifically address this situation (Card, 2000; Hong and Goh, 2003; Hong and Goh, 2004). The fit between Six Sigma and software project management methodology is not always obvious. Some of the common Six Sigma tools do not easily lend themselves to software projects. Part of the reason is possibly that engineering and manufacturing have evolved over hundreds of years, software development is only a few decades old (Aggarwal, 2004). Software development processes can be fully characterized by three simple measurements (Janieszewski and George, 2004): (1) time – the time required to perform a specific task; (2) size – the size of the product produced; and (3) defects – the number of defects, the type of defects, time to eliminate defects, etc. A total of 90 percent of the processes in a software services company are repeatable and can be improved by the process improvement. Success of Six Sigma in the manufacturing domain has encouraged its application in the software domain. In the 1999 SEI survey of high maturity software organizations, it is shown that among the 36 top maturity organizations surveyed worldwide out of the millions, only seven organizations institutionalized the Six Sigma practice as part of the organization’s standard software process and four organizations followed it frequently (Paulk et al., 2000). Less than 20 percent of 194 companies monitoring software quality stated that they use Six Sigma to improve the quality of internally developed applications (Information Week, 2003). In a survey carried out in the UK software industry out of 15 companies that responded to the Six Sigma survey, ten companies were applying the

principles of Six Sigma. The companies participating in the survey regarded requirement analysis as a potential area to improve followed by operation, maintenance and testing (Antony and Fergusson, 2004). Adoption and application of Six Sigma tools in the software industry requires Six Sigma training tailored to the intended environment of use to improve the software quality and creation and selection of meaningful metrics that give insight into how to meet business goals. The need for training tailored to the intended environment of use even more critical in software, because learning is maximized when the problems and examples are directly relevant to the students immediate needs and because software is different (Gack, 2003). An industrial black belt training programme typically takes six months (Goyal and James, 2003). In each DMAIC cycle, Six Sigma experts analyze their processes to find out where and how defects occur, measure them and eliminate problems. Hallowell (2003) suggested that Design for Six Sigma (DFSS) in software development supplements the usual define, measure, analyze, improve, control (DMAIC) process. Six Sigma achieves dramatic improvement in business performance through a precise understanding of customer requirements and the elimination of defects from existing processes, products, and services. Six Sigma is a methodology to organize the tools of the trade in a way that they can be executed on business issues that really matter, by people who really care (Hayes, 2004). The Software Engineering Institute (SEI) found Six Sigma is a feasible enabler of domain specific process improvement technology. Most software organizations follow the CMM (Capability Maturity Model) or CMMI (Capability Maturity Model Integration) for software process improvement. CMM is a reference model for inducting the software process maturity into different levels (Paulk et al., 1993). The maturity of an organization’s software process helps to predict a project’s ability to meet its goals. Projects in Level 1 organizations experience wide variations in achieving cost, schedule, functionality, and quality targets. As maturity increases, the difference between targeted results and actual results decreases across projects, the variability of actual results around targeted results decreases and targeted results improve as the maturity of the organization increases. CMM is a good framework for technical process topics but does not get at the issue of management accountability and organizational behavior change. Six Sigma actually addresses the tools and the root causes of the lack of needed change, management accountability and organizational behavior (Hayes, 2004). Six Sigma is also not a complete replacement for CMM, technology tools, or other emerging best practices (Hayes, 2004). In fact, Six Sigma and Capability Maturity Model (CMM) are complimentary (Card, 2000; Murugappan et al., 2003). In fact, Six Sigma directly contributes to Maturity Level 5 – continuous optimization of key process areas. Also there are organizations, where implementation of Six Sigma has helped in attaining higher CMM levels. Northrop Grumman Mission Systems, for example, used Six Sigma to help in its move from Maturity Level 3 of the SEI CMMI models to Level 5 in just one year (Heinz, 2004). While the CMM focuses on transformation of the organization and does not explicitly require better results, Six Sigma drives deeper into the process and requires measurements in place for process improvement. Six Sigma can also be used in conjunction with a model-based approach like CMMI. It fits well with CMMI’s measurement and analysis process area (Janieszewski and George, 2004; Gack and Robison, 2003).


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Goal question metric (GQM) together with other practical software measurement (PSM) paradigms shows compatibility and consistency with the objective of Six Sigma. GQM matches well with the Define-Measure phases of Six Sigma. An integration of Six Sigma and GQM can strengthen the Define and Measure phases of Six Sigma. A good Six Sigma program incorporates GQM and other PSM paradigms into one cohesive program. No matter which software life cycle model is chosen, the DMAIC framework can always be integrated, although more work needs to be done in this area. Significance of Six Sigma in IT Software development for business critical systems has emerged as a core discipline that every company has to perform in. Software projects are often positioned at the critical interface between a company’s products and / or services and the company’s customers. However, software projects are associated with a high degree of risk. About 25 percent of software projects are cancelled because they are late, over budget, have unacceptably low quality or experience some combination of these problems. A Standish Group survey of 8,000 software projects found that the average project exceeded planned budget by 90 percent, its schedule by 120 percent and project cancellation of 25 percent due to some combinations of delays, budget overruns, or poor quality. Requirement failures (reflecting needs not originally recognized or correctly understood, leading to substantial and costly rework late in the software development cycle) are associated with 80 percent of failed (late or cancelled) software projects. Execution failures (incorrect and overly optimistic estimates, leading to long delays and cost overruns) are a factor in 65 percent of failed software projects. Execution failures (leading to poor software quality, heavily back-loaded costs, and very high levels of rework – commonly 40 percent of total cost) are a factor in 60 percent of failed software projects. A division of Hewlett Packard decided to release its product despite a continuing incoming defect trend during the test phase. This resulted in a costly update after the release, a steady need for defect repairs and a product with a bad quality reputation (Grady, 1996). Nowadays software is performing more critical tasks than ever before. Software failures in mission critical systems often jeopardize public safety. A discovery by Britain’s nuclear regulatory agency that a power plant scheduled to begin operation was potentially unsafe due to inadequate software designed to manage the reactor in the event of an emergency (Schwartz, 1996). Software failures have caused disasters in the past. In February 1991, a software failure in Patriot missile’s radar system allowed an Iraqi Scud to penetrate their air defense system and slam into an American barracks in Saudi Arabia, killing 28 people during the Gulf War (Schmitt, 1991). The benefit of Six Sigma to mission critical systems is rather significant. The above points accentuate the necessity to decrease defects in complex and mission critical software. Owing to the extremely high costs involved in achieving the Six Sigma standard, it is unlikely that a software development team will achieve a true Six Sigma level. However this doesn’t diminish the value of Six Sigma and having minimal defects as a goal. However software failures can cause customer dissatisfaction which may result in software companies losing business to their competitors (Hong and Goh, 2003). Although the true statistical meaning of Six Sigma i.e. 3.4 defects per million opportunities does not hold, in this case Six Sigma for software is more about using the methodology to achieve “continual process improvement” than it is about achieving a statistical Six Sigma process output.

Research methodology Research methodology can be viewed as the process taken to accomplish the key objectives of the research undertaken. The authors have undertaken a survey-based approach to assess the status of Six Sigma in the Indian software industry. A survey is a means of “gathering information about the characteristics, actions, or opinions of a large group of people, referred to as a population” (Tanur, 1982). In a survey-based approach, data are collected by means of questionnaires or interviews. The choice of data collection method such as mail questionnaire, telephone interviews or face-to-face interviews is significant because it affects the quality and cost of the data collected. For example, mail questionnaires are very good for gathering factual data, but they are less effective when sensitive data and complex data are needed. In general, quality and cost are highest with face-to-face interviews or telephone interviews whereas quality and cost are lower with mail questionnaires and group administration. The purpose of survey research is to find out what situations, events, attitudes or opinions are occurring in a population. Survey research aimed at description asks simply about the distribution of some phenomena in a population or among subgroups of a population. Exploratory surveys should be used as the basis for developing concepts and methods for more detailed, systematic descriptive or explanatory surveys (Babbie, 1973, Fowler, 1984). In short, the whole purpose of an exploratory survey is to elicit a wide variety of responses from individuals with varying viewpoints in a loosely structured manner as the basis for design of a more careful survey. Exploratory surveys are different from pilot studies. The pilot study is a small-scale rehearsal of a systematic survey aimed at testing questions, question flow, and questionnaire format with representatives of the target population. Exploratory surveys frequently are used prior to pilot studies to determine what concepts should be measured and how to measure them best. To establish the broad view of Six Sigma within the organization and to develop issues for the semi-structured interviews, a survey questionnaire was developed. The questionnaire was designed and adapted based on the published work of similar studies carried out by Antony and Fergusson (2004); Antony and Banuelas (2002); and another study carried out in the US industry (Dusarme, 2003). The questionnaire used in this study consisted of two parts: the background of the company and the CSFs of Six Sigma. The first part was primarily aimed to understand some of the fundamental issues such as the size of the company, current status of Six Sigma, common Six Sigma tools and techniques used within software industry, financial gains generated from Six Sigma initiative to date and so on. A total of 19 critical success factors (CSFs) and 39 variables were derived mainly from the literature (Adams et al., 2003; Antony and Banuelas, 2002; Breyfoggle, 1999; Snee and Hoerl, 2003; Pande et al., 2000) and discussions with software quality professionals. The second part consisted of these 19 CSFs with 39 variables. All factors were ranked on a five-point scale (1 ¼ not very important, 2 ¼ not important, 3 ¼ important, 4 ¼ very important and 5 ¼ critical). The objectives of this empirical study were to: . understand the status of Six Sigma application and implementation in software industry (that is, key metrics used to measure the service performance, six sigma capability level of the core business processes, criteria used to measure the success of the Six Sigma program etc.);


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identify the most and least commonly used statistical and non statistical and software engineering tools, techniques, methodologies and frameworks used within software business; and determine the critical success factors (CSFs) for a successful Six Sigma initiative in the Indian software/IT industry

An e-mail survey was used to gather survey data. The advantages of the e-mail survey approach to data collection are (Nueman, 2003; Sarantakos, 1998, Bachmann et al., 1996; Kittleson, 1995; Sproull, 1986): . inexpensive; . possibility of very rapid surveying; . results are produced quickly; . questionnaires are completed in the respondents’ convenience; . anonymity is greatly assured; and . respondents are at liberty to provide objective views on sensitive issues, and so on and so forth. In this study, a total of 100 questionnaires were sent to software companies. The list of companies was obtained from NASSCOM database as well as using search engines (www.google.com). The criteria used to select the companies were Six Sigma certification, CMM certification, service areas and employee strength. The sample chosen was a representative sample. The response rate from the companies was about 20 percent (i.e. 20 companies). However, just 12 companies were actively applying the principles of Six Sigma. The distribution of the respondents to the questionnaire was master black belts (5 percent), green belts (10 percent), black belts (10 percent), project managers (15 percent), general managers (5 percent), Vice-President-Quality (10 percent) and others (45 percent) as shown in Figure 1. Results of the empirical investigation The service areas of the companies participating in the survey comprized of Internet, software consultancy and services, data warehousing, IT enabled services, data mining, embedded technology, training and education, advanced databases, software vendor, telecommunication, ERP, mainframe technology, engineering design services and transportation sector services. 55 percent of the companies participating in the

Figure 1. Percentage distribution of employees participating in the study

survey had multiple service areas. The rest, 45 percent, had only one service area. Of the companies 50 percent had software consultancy and services as one of their service areas. Also, 60 percent of the participants of the Six Sigma survey were big companies with more than 1,000 employees. Of the respondents 10 percent hailed from companies with number of employees between 501 and 1,000. Also, 25 percent of the respondents were companies with 301-500 employees. The remaining 5 percent of the companies had less than 100 employees. It was also observed that more than 80 percent of all the companies (Six Sigma and non-Six Sigma companies) participated in the survey had formal quality management system in place. Of all respondents 25 percent had total quality management in place. The participants were also asked to prioritize the key attributes that are important in the software development process. These attributes were derived from the literature (Pressman, 2001) and through interactions with professionals in the software industry. The participants were asked to assign a rank in the range of 1 to 11 with 1 being the most important and 11 being the least. The average scores are as follows: . functionality – 1; . correctness – 1.1; . reliability – 1.4; . consistency – 1.8; . cost, timeliness, efficiency – 2.2; . integrity – 2.35; . maintainability – 2.9; . usability – 3; . complexity, reusability – 3.8; . interoperability – 5.23; and . portability – 5.7.


About 66.5 percent of the companies have been involved with the Six Sigma program for one to three years. Approximately 16.5 percent of the companies had implemented Six Sigma for three to five years. A total of 8.5 percent of the companies had implemented Six Sigma for over five years and the remaining 8.5 percent of the companies claimed to have been using Six Sigma for three to six months. Figure 2 shows the Six Sigma implementation experience.

Figure 2. Percentage distribution of Six Sigma implementation experience of companies

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Figure 3. Percentage of companies and their respective bottom line savings

The results of the analysis have revealed that 41.67 percent of the respondents implementing Six Sigma have completed more than 30 Six Sigma projects. 33.33 percent have completed between five and ten Six Sigma projects. The rest, 25 percent, have completed less than five Six Sigma projects. Of the companies 25 percent had made a bottom line savings of more than Rs. 1 million through the implementation of Six Sigma. Approximately 17 percent of companies reported a bottom line saving of Rs 500K-1 million. A total of 8.33 percent reported a bottom line saving of Rs. 50 to 100K; 8.33 percent had observed a bottom line saving of Rs 250 to 500K; and 41.67 percent of companies did not give a value for bottom line saving achieved by the implementation of Six Sigma initiatives. Figure 3 shows the bottom line savings achieved by the respondents implementing Six Sigma. Of the 12 companies implementing Six Sigma, 66.67 percent of companies were at Capability Maturity Level 5. A total of 16.67 percent of companies were at Capability Maturity Level 4 and 16.67 percent had not attained CMM certifications. It was also found that 25 percent of the respondents implementing Six Sigma had their core processes operating between 3 and 4 sigma capability levels. Of the respondents, 25 percent implementing Six Sigma claimed to have their core processes operating above five sigma capability levels. Overall, 8.5 percent of the respondents implementing Six Sigma had their core processes operating at less than 3 sigma capability levels. The remaining 41.5 percent of the respondents implementing Six Sigma were not sure of the sigma capability levels of their core processes. The commonly used metrics to measure the service performance in the software industry were: . number of customer complaints; . defect (bug) rate; . cost of poor quality (COPQ); . defect per million opportunity (DPMO); . process capability indices – Cp and Cpk; and . access time.

Less frequently used metrics included: . schedule variance; . effort variance; . SLA compliance; and . schedule slippage. The survey results revealed that “number of customer complaints” was the most popular metric with 11 out of 12 respondents (i.e. 91.67 percent) using it. 66.67 percentof the respondents were using defect (bug) rate as one of the key metrics. 51.67 percent of the respondents were using process capability as one of the metrics to measure service performance. Of the respondents, 50 percent were using DPMO as a metric: 33.33 percent of the respondents were using COPQ as the metric; and 25 percent of the respondents were using access time as a metric. The commonly used metrics are depicted in Figure 4. Most companies measured the success of the Six Sigma program in terms of: . amount of net savings achieved; . process capability improvement; . reduction in process variation; . reduction or elimination of defects; . number of trained black belts; . employee satisfaction/increase in morale; . using balance scorecard; . reduction in project effort; . reduction in cycle time; . reduction in process variation; and . reduction in schedule slippage.


The most commonly used tools/techniques/models/methodologies/frameworks include: . statistical process control (SPC); . control charts;

Figure 4. Commonly used metrics by companies

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fishbone diagram; gap analysis; inspection; CMM (capability maturity model); regression; process mapping; quality function deployment (QFD); failure mode and effect analysis (FMEA); and process capability analysis (PCA).

The least commonly used tools/techniques/models/methodologies/frameworks include: . SERVQUAL for measuring service quality; . service blueprinting; . team software process (TSP); . personal software process (PSP); . simulation; . agile technologies; . objectives/principles/attributes (OPA); and . lean. The most common skills that the companies were looking for in selecting people for Six Sigma projects were: . problem solving skills; . presentation and communication skills; . leadership skills; . mentor and teaching skills; and . management and organizational skills. Continual motivation to the people involved in Six Sigma was deployed by most companies through the enthusiastic participation of the senior executives, by giving employees an opportunity of becoming candidates for further leadership experience, by showing the examples of cases from successful companies and giving the employees special training. Critical success factors (CSFs) for implementation of Six Sigma in software industry The respondents were asked to rank the 19 CSFs on a scale of 1 to 19 (1 being the most important and 19 being the least important). The results of the analysis showing the mean scores and standard deviation of each essential ingredient which are required for the successful deployment of Six Sigma are given in Table I. The analysis of the study has shown that management commitment and involvement is the most critical success factor.

Ranking

CSFs

Mean

Standard deviation

1 2 3 4 5 6 6 8 9 10 11 12 13 14 15 16 17 18 19

Management commitment and involvement Linking Six Sigma to business strategies Project planning and management Understanding the Six Sigma methodology Project prioritization and selection Training and education Employees’ commitment Integrating Six Sigma with the financial infrastructure Organizational infrastructure Customers involvement Cultural change Linking Six Sigma to software process improvement Knowledge Sharing Team communication Risk management Linking Six Sigma to input quality Software productivity Document management Suppliers involvement

1.50 3.00 3.08 3.42 3.92 4.00 4.00 4.12 4.42 5.00 5.83 6 6.25 6.33 6.75 7.17 7.75 8.083 10.17

0.595119 1.291 1.977 1.6285 3.148 2.9156 3.488 3.184 3.82 3.488 5.595 6.56 5.12 6.196 6.03 6.09 7.34 5.24 4.616

The analysis of the study has also shown that linking Six Sigma to business strategy is the second most critical success factor. Six Sigma builds a sense of exigency by putting emphasis on rapid completion of projects in four to six months (Snee and Hoerl, 2003). Quite naturally, project planning and management has been the third most important factor. Documentation management and suppliers involvement were found to be the least important factors in the successful deployment of Six Sigma in the software industry. Conclusion Six Sigma is a customer-centric, data-driven business strategy and a systematic methodology that focuses on reducing process deviation, centering, making the process mean match with the process target and optimizing the development process. Its application leads to breakthrough improvements in software quality, product performance, productivity, cost savings and customer satisfaction. This article presents the results of a Six Sigma pilot survey carried out in the Indian software industry. At the same time it dispels the myths concerning the unsuitability of Six Sigma in the software domain. The paper also presents the CSFs which are essential for successful deployment of Six Sigma in software business. A total of 19 CSFs, were considered in the study. This study was carried out with some boundaries such as the number of companies, available resources, time constraints and so on and so forth. Different positions of the respondents may have different opinions. Employees with varying backgrounds in addition to quality professionals (such as team leaders, project managers, module leaders, analysts, human resource managers, etc.) within the company have been suggested to complete the questionnaires so as to get a broader picture on the application of Six Sigma within each business process. The limited sample size of the current study, calls for a survey on a larger scale for greater validity


Table I. Critical success factors for implementation of Six Sigma in the Indian industry

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of the findings from this research. Because of limited budget and time constraints, a face-to face interview could be carried out for only five of the companies responded to the survey questionnaire. An e-mail survey was carried out for the rest of the companies. The scaled questions have disadvantages because respondents often do not use the whole scale, whatever response they tick, we do not know why a particular response was chosen. Semi-structured interviews with people at different levels of Six Sigma knowledge and expertise have been conducted to obtain a deeper understanding of the Six Sigma practices in the software industry. References Adams, C., Gupta, P. and Wilson, C. (2003), Six Sigma Deployment, Butterworth-Heinemann, London. Antony, J. and Banuelas, R. (2002), “Key ingredients for the effective implementation of a Six Sigma program”, Measuring Business Excellence, Vol. 6 No. 4, pp. 20-7. Antony, J. and Fergusson, C. (2004), “Six Sigma in the software industry: results from a pilot study”, Managerial Auditing Journal, Vol. 19 No. 8, pp. 1025-32. Aggarwal, M. (2004), Six Sigma Meets Software Development, available at: www.isixsigma.com/ library/content/ (accessed 15 April, 2004). Appliance Magazine.com (2003), Black & Decker Announces Q4 and Full-year Financials, available at: www.appliancemagazine.com/news.cfm?newsid¼3216 Babbie, E.R. (1973), Survey Research Methods, Wadsworth Publishing, Belmont, CA. Bachmann, D., Elfrink, J. and Vazzana, G. (1996), “Tracking the progress of e-mail versus snail-mail”, Marketing Research, Vol. 8, pp. 31-5. Bessin, G. (2004), “The business value of software quality”, The Rational Edge, pp. 1-8. Binder, R.V. (1997), “Can a manufacturing quality model work for software?”, IEEE Software, Vol. 14 No. 5, pp. 101-5. Breyfoggle, F.W. (1999), Implementing Six Sigma: Smarter Solutions Using Statistical Methods, Wiley, New York, NY. Card, D.N. (2000), “Sorting out Six Sigma and CMM”, IEEE Software, May/June, pp. 11-13. Dusarme, D. (2003), Six Sigma Survey: Big Success – What about the Other 98 Percent?, available at: www.qualitydigest.com (accessed 15 April, 2004). Fowler, F.J. Jr (1984), Survey Research Methods, Sage Publications, Beverly Hills, CA. Gack, G.A. and Robison, K. (2003), “Integrating improvement initiatives: connecting six sigma for software, CMMI, personal software process, and team software process”, Software Quality Professional, Vol. 5 No. 4, pp. 5-13. Grady, R.B. (1996), “Software failure analysis for high return process improvement decisions”, Hewlett-Packard Journal, Vol. 47, pp. 15-24. Goyal, N. and James, V.F. (2003), Medical Transcription Case Study, available at: www.isixsigma. com/library/content/c030203a.asp Hallowell, D. (2003), “Understanding and integrating the voice of the customer”, paper presented at the Six Sigma in Software Conference, San Jose, CA, January. Harter, D.E., Krishnan, M.S. and Slaughter, S.A. (2000), “Effects of process maturity on quality, cycle time, and effort in software development”, Management Science, Vol. 46, pp. 451-66. Hayes, B.J. (2004), Six Sigma for Software . . . More Than a New Tool, available at: www. isixsigma.com/library/content

Heinz, L. (2004), Using Six Sigma in Software Development, available at: www.sei.cmu.edu/newsat-sei/features/2004/1/feature-3.htm Hong, G.Y. and Goh, T.N. (2003), “Six-Sigma in software quality”, The TQM Magazine, Vol. 15 No. 6, pp. 364-73. Hong, G.Y. and Goh, T.N. (2004), “A comparison of Six-Sigma and GQM approaches in software development”, International Journal of Six Sigma and Competitive Advantage, Vol. 1 No. 1, pp. 65-75. Humphrey, W. (1989), Managing the Software Process, Addison-Wesley, Reading, MA. InformationWeek (2003), “Software quality”, InformationWeek, 26 May. Issac, G., Rajendran, C. and Anantharaman, R.N. (2004), “A holistic framework for TQM in the software industry: a confirmatory factor analysis approach”, Vol. 11 No. 3, pp. 35-60. Jalote, P. (2000), CMM in Practice, Addison-Wesley, Reading, MA. Janieszewski, S. and George, E. (2004), Six Sigma and Software Process Improvement, pp. 1-12, available at: www.SoftwareSixSigma.com (accessed 15 April, 2004). Jovanovic, V. and Shoemaker, D. (1997), “ISO9001 standard and software quality improvement”, Benchmarking for Quality Management and Technology, Vol. 4, pp. 149-59. Kan, S.H., Basili, V.R. and Shapiro, L.N. (1994), “Software quality: an overview from the perspective of total quality management”, IBM Systems Journal, Vol. 33, pp. 4-19. Kittleson, M.J. (1995), “An assessment of the response rate via the postal service and e-mail”, Health Values, Vol. 18, pp. 27-9. Kenett, R.S. and Baker, R.S. (1999), Software Process Quality: Management Control, Marcel Dekker, New York, NY. Mahanti, R. (2005), “Six Sigma for software”, Software Quality Professional, Vol. 8 No. 1, pp. 12-26. Mahanti, R. and Antony, J. (2006), “Six Sigma in software industries: some case studies and observations”, International Journal of Six Sigma and Competitive Advantage, Vol. 2 No. 3, pp. 263-90. Murugappan, M., Keeni, G. and Blending, C.M.M. (2003), “Blending CMM and Six Sigma to meet business goals”, IEEE Software, Vol. 20 No. 2, pp. 42-8. Neuman, W.L. (2003), Social Research Methods: Qualitative and Quantitative Approaches, 5th ed., Pearson Education, Upper Saddle River, NJ. Pande, P., Neuman, R. and Cavanagh, R. (2000), The Six Sigma Way: How GE, Motorola and other Top Companies are Honing their Performance, McGraw-Hill, New York, NY. Parzinger, M.J. and Nath, R. (1998), “TQM implementation factors for software development: an empirical study”, Software Quality Journal, Vol. 7 Nos 3-4, pp. 239-60. Paulk, M.C., Goldenson, D. and White, D. (2000), The 1999 Survey of High Maturity Organizations, Special Report, CMU/SEI-2000-SR-002, Software Engineering Institute, Los Alamitos, CA, February. Paulk, M.C., Curtis, B., Chrissis, M. and Weber, C.V. (1993), Capability Maturity Model for Software, Version 1.1, CMU/SEI-93-TR-24, DTIC Number ADA263403, Software Engineering Institute, Los Alamitos, CA. Phan, D.D., George, J.F. and Vogel, D.R. (1995), “Managing software quality in a very large development project (case study)”, Information and Management, Vol. 29, pp. 277-85. Pressman, R.V. (2001), Software Engineering A Practitioner’s Approach, Tata McGraw-Hill Publishing Company Limited, New Delhi, India.


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Sarantakos, S. (1998), Social Research, 2nd ed., Macmillan, London. Schmitt, E. (1991), Computer Failure Let Scud Through, The Morning Call, Allenton, PA. Schwartz, E.I. (1996), “Trust me, I’m your software”, Discover, Vol. 17, pp. 78-81. Snee, R.D. (2000), “Impact of Six Sigma on quality engineering”, Quality Engineering, Vol. 12 No. 3, pp. 9-14. Snee, R.D. and Hoerl, R.W. (2003), Leading Six Sigma, FT Prentice-Hall, Englewood Cliffs, NJ. Sproull, S. (1986), “Using electronic mail for data collection in organizational research”, Academy of Management Journal, Vol. 29, pp. 159-69. Tanur, J.M. (1982), Advances in Methods for Large-Scale Surveys and Experiments, Behavioral and Social Science Research: A National Resource, Part II, National Academy Press, Washington, DC. Tennant, G. (2001), Six Sigma: SPC and Six Sigma in Manufacturing and Services, Gower Publishing Company, Aldershot. Weinberg, M.G. (1996), Quality Software Management – 2, Dorset House Publishing, New York, NY. Yang, Y.H. (2001), “Software quality management and ISO 9000 implementation”, Industrial Management & Data Systems, Vol. 101 No. 7, pp. 329-36. Further reading Janiszewski, S., George, E. and Integrating, P.S.P. (2004), “TSP and Six Sigma”, Software Quality Professional, Vol. 6 No. 4, pp. 4-13. Corresponding author Jiju Antony can be contacted at: [email protected]

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Barriers faced by engineers when applying design of experiments

Barriers faced by engineers

Martıń Tanco, Elisabeth Viles, Laura Ilzarbe and Ma Jesus Alvarez TECNUN, University of Navarra, Gipuzkoa, Spain

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Abstract Purpose – The purpose of this article is to provide an extensive review of the barriers faced by engineers when applying design of experiments (DoE). The aim is to help new practitioners learn from the past and avoid possible barriers that they may encounter when applying DoE in industry. Design/methodology/approach – An exhaustive literary review was carried out to find articles in which hindrances to the application of DoE were mentioned. The information is organised and grouped into 16 barriers with this end in mind. Findings – The 16 barriers can be classified into three different groups: business barriers; educational barriers; and technical barriers. It is shown that DoE can be successfully applied without overcoming every barrier, although it is inconvenient to do so. Practical implications – Although DoE is commonly found in statistics and quality literature, it is clearly underused in industry. The paper brings together ideas from those with experience in DoE to detect the reasons behind this anomaly. Originality/value – Very little material has been published regarding the difficulty of applying DoE. Unfortunately, what is available is repetitive, unstructured and incomplete. The paper is intended to encourage discussion between practitioners and experts, in order to find a way to define, categorise and eventually overcome the most problematic barriers. Keywords Industrial engineering, Experimental design, Statistical methods of analysis Paper type Literature review

Introduction Engineers engage in a variety of activities such as developing new products, improving previous designs and maintaining, controlling and improving ongoing manufacturing process, among others. Experimentation is frequently carried out in unison with those activities. Therefore, since variation is ever-present in these activities, most engineers (and scientists) end up using statistics, regardless of their background (Gunter, 1985). Lye (2005) defines the Design of Experiments (DoE) as a methodology for systematically applying statistics to experimentation. Although DoE provides a quick and cost-effective method to understand and optimise products and processes (Antony, 2002), not enough industries carry out experimentation with a pre-established statistical methodology. Surveys (Gremyr et al., 2003; Bergquist and Albing, 2006; Tanco et al., 2008) and numerous articles (Hoadley and Kettering, 1990; Hahn and Hoerl, 1998; Costa et al., 2006; Antony et al., 1998) reflect the existence of a substantial gap between theoretical development of DoE and its application in industry. Although very little has been published regarding the difficulty of DoE application, we share Costa’s (2006) opinion that the material available is indeed useful for identifying barriers. Therefore, the purpose of this research is to detect the barriers to the application of DoE by means of an extensive literature review. In section 2, we present the methodology used for the bibliographic review. In section 3, we present the barriers hindering the widespread use of DoE among


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engineers. In section 4, an analogy to better understand the barriers and the difficulty in overcoming them is given. Conclusions can be found in section 5. Review scope In order to detect the barriers to the widespread use of DoE, we started with a brainstorming session among our group of researchers. We tried to pinpoint the barriers based on our experience with DoE application. Afterwards, an exhaustive review of relevant literature of the last 25 years was carried out. Our first approach was to search for any mention of problems with DoE application in the journals that publish DoE-related articles. Then, in order to augment the collection of detected problems, we decided to resort the ISI Web of KnowledgeSM database[1]. Barriers to the widespread usage of DoE Limited information about these barriers is available in industry literature. Furthermore, the information is scattered, repetitive and extremely subjective. Therefore, we used affinity diagrams to classify the barriers into fewer groups. In spite of the considerable effort taken to classify and define them, some of the barriers may not seem entirely exclusive or independent from each other. Even though some barriers may be partly caused or affected by the others, we have chosen to include them separately in an effort to find a practical solution. The 16 barriers shown in Table I can be classified into three different groups. The following section provides a brief explanation of each barrier. (1) Business barriers: Those barriers which are inherent to business systems and are common to all the initiatives that must be launched at companies. The most common ones are low managerial commitment and resistance to change. Since business barriers are generally the most difficult to overcome, it is recommended to pay special attention to these factors when introducing DoE for the first time. (2) Educational barriers: Those barriers related to the education or training of engineers. Unfortunately, one needs both a background in theoretical statistics 1. Business barriers

2. Educational barriers

3. Technical barriers

1.1. Resistance to change

2.1. Publications don’t reach engineers 2.2. Poor statistical background

3.1. Limited software aid

1.2. Low commitment of managers 1.3. Insufficient resources

2.3. DoE is not taught to engineers at universities

1.4. Absence of teamwork skills 2.4. DoE is badly taught 1.5. Negative image of statistics 2.5. Poor statistical consultancy Table I. Barriers to the widespread usage of DoE

3.2. Statistical jargon is used to explain DoE 3.3. Lack of methodologies to guide users through experimentation 3.4. Previous negative experiences with DoE 3.5. Absence of theoretical developments to solve real industrial problems 3.6. DoE is not widely used because it is a complex tool

and specific training to apply these techniques. All aspects that influence the education of DoE are included in this group. (3) Technical barriers: Those barriers which are inherent to DoE. Included in this group, among others, are existing problems with methodologies that guide users through the experimentation process, software support and the absence of theoretical development to solve real industrial problems. Business barriers Resistance to change Resistance to change is an almost inevitable consequence of the introduction of new business techniques (Owen, 2003). The danger of systematically rejecting all new initiatives is what is known as the fifth discipline of Senge (Senge, 1990): If you only know how to hammer nails, you will always try to solve everything with a hammer. When you fail, the reason is that the hammer is too small.

Engineers often perform one factor at a time (OFAT) experiments. Thus, engineers must be convinced that what they have been doing for years can be improved upon (Czitrom, 1999). Responses from engineers with high resistance to change may include the following (Owen, 2003; Steinberg and Hunter, 1984; Amstong et al., 1990): . “Experimental design tells me what I already know”; . “I need to make additional effort to prove what I already know”; . “It sounds good, but it is not applicable to my job”; . “It looks great, but when can I use it?”. Low commitment of managers Any statistical method will ultimately fail unless management is receptive to facts and data (Schmidt and Lausnby, 2005). Too many managers are unaware of the importance of statistical techniques in processes and product development. They do not instinctively think statistically, mainly because they are not convinced that statistical thinking adds any value to management and decision-making (John and Johnson, 2002). This barrier is mainly due to the lack of managerial training, since business schools de-emphasise one of the key aspects in the field of quality and systems thinking: understanding through data and its statistical analysis (Schippers, 1998; McAlevey and Everett, 2003). On the other hand, some managers do recognise the value of experimentation, but they only use experiments to confirm their initial assumptions (Schoemaker and Gunther, 2006). Insufficient resources Many engineers believe that applying DoE requires more resources (time, cost, etc.) than traditional approaches, although they usually appreciate the technique’s problem-solving potential (Amstong et al., 1990; Owen, 2003). They believe that DoE is too resource-demanding and compromises equipment availability (Bergquist and Albing, 2006; Owen, 2003). Moreover, when off-line experiments are unfeasible and production load is high, they believe that applying DoE is too expensive.


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Absence of teamwork skills Since experimentation is a team process, its success depends on involving the right people and having them work as a team (Knowlton and Keppinger, 1993), which is not easy. Moreover, one of the common obstacles when it comes to teamwork is the lack of communication within the group (Antony et al., 1998). Poor interpersonal relationships and lack of communication may cause the project to fail (Amstong et al., 1990). When people with different backgrounds such as engineers, managers and statisticians form a group, they have to bridge a gap of knowledge and experience to successfully work as a team during each step of the process (Tay and Butler, 1999). Negative image of statistics Many engineers are reluctant to apply DoE because it requires the use of statistics, planning and discipline (Anderson and Kraber, 1999). They see statistical methods as irrelevant and arcane (Gunter, 1985). Not only does statistics lack visibility, it lacks influence as a discipline. Some engineers see statisticians as only compilers of data (Marquardt, 1987). Statistics tends to be among the least popular subjects at universities. Hogg (1991) said that since statistics is generally badly taught, it is easy to understand why even the most intelligent students are averse to something that seems unnecessarily complicated and not very useful. Consequently, most engineers have a negative image of statistics (Hoadley and Kettering, 1990). The word statistics tends to invoke fear and resistance towards its use (Antony et al., 1998; Anderson and Kraber, 1999). Educational barriers Publications do not reach engineers Engineers, especially those from small and medium enterprises (SME), do not have access to books and articles that explain DoE in detail (Amstong et al., 1990). Moreover, the publications that are available tend to be of limited usefulness since they are generally focused on mathematical problems rather than on the whole experimentation process. Few practical experiences (especially in services), and even fewer examples of failures in experimentation, are published (Bergquist and Albing, 2006). Furthermore, most of the material is in English, which has become the international language of science and of peer-reviewed publications. Unfortunately, many engineers and statisticians in the developing world lack adequate English reading skills and, therefore, cannot use such materials (Romeu, 2006). Poor statistical background Another factor that hinders the use of DoE is the lack of familiarity that practicing engineers have with statistical concepts related to DoE methods (Chen, 1991; Bergquist and Albing, 2006). Unfortunately, the education that engineers receive in statistics is all too often deficient and inadequate (Antony et al., 1998; Romeu, 2006; Costa et al., 2006). Results of a survey carried out by Romeu (2006) of several quality, reliability and manufacturing engineers in New York and Florida (via the Isostat ASA group) and members of the European Network for Business and Industrial Statistics (ENBIS) support this hypothesis. The majority (66 per cent) of graduated engineers (BS level) have either deficient training in statistics or none whatsoever.

Statistics courses are generally inadequate and overly focused on probability theory and hypothesis testing. Moreover, statistics education is presented almost exclusively as deductive, to the detriment of inductive development. The only experiments students participate in, if any, are based on demonstration and are often of limited educational value (Bisgaard, 1991). Consequently, the statistics needed to implement DoE is generally misunderstood and wrongly applied. We must address the following questions (Gunter, 1985; Viles, 2006): What type of statistics should engineers study?; Is one statistics course enough? Does this training provide the understanding and tools necessary to meet the productivity and quality challenges of today? DoE is not taught to engineers at universities Although statistical experimentation design is considered to be an essential component of engineering training, engineers and scientists receive little or no training in DoE at the university level (Gunter, 1985). Consequently, many often leave universities without a proper understanding of the power of statistics and are likely to regard statistics as useless to their future careers (Bisgaard, 1989). Most statistics professors believe that one course is not enough to teach the concepts that engineers and scientists need. However, we cannot expect a student to sit through more than a one-semester course if it does not appear to be either useful or relevant (Bisgaard, 1991). DoE is badly taught The way in which DoE is taught is generally inadequate (Funkenbusch, 2005; Kenett, 1987; Wilson, 2002). Engineering teachers have also been traditionally unaware of how vital experimentation is to an engineer’s daily work (Bisgaard, 1991). The most common criticisms of the teaching of statistics in the USA are that it is too academic in focus, excessively theoretical and divorced from the real problems that can appear in the industrial and business world. We share Romero’s opinion (Romero et al., 1995) that these criticisms also describe the teaching of statistics in most European countries. Planning is not generally emphasised, so courses tend to be overly focused on choosing the design and analysis (Coleman and Montgomery, 1993). As much as 70-80 per cent of most DoE training courses and text books are dedicated to analysis and design selection. Courses are organised and taught as a kind of logical cookbook of statistical formulas rather than in response to the real needs of practical engineering (Gunter, 1985). Unfortunately, working with practical examples or real student experimentation is not encouraged. It doesn’t help that professors usually lack practical experience, since teaching DoE through textbook examples does not fully shed light on how to identify and formulate problems, identify factors and determine the performance of physical experiments (Coleman and Antony, 2000). If problem-solving skills using statistical methods and DoE had been successfully taught to engineers, today all engineering teachers would be including it in their classes (Bisgaard, 1991). There is a clear consensus that academia needs to change the way it teaches business statistics (John and Johnson, 2002). Poor statistical consultancy Most statisticians do not seem to become involved deeply enough in the field of science to understand scientific problems in their contexts (Hoadley and Kettering, 1990; Box


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et al., 2005). Moreover, consultants tend to oversell the technique or to create unrealistic expectations of the benefits reaped from DoE. They seem to convince companies that experimental design is the answer to all of their problems (Owen, 2003). Consultation inaccuracy such as creating unrealistic expectations and failing to go to the core of the problem are common among consultants. Moreover, many SMEs cannot afford these services.

570 Technical barriers Limited software aid In the past, the spread of DoE was hampered by both a lack of proper training and tools to help implement DoE in industry (Joglekar and Kackar, 1989). Although many commercial software products which aid in experimentation analysis are available nowadays, they are not adequate enough to satisfy industrial needs (Costa et al., 2006; Antony et al., 1998). Most commercially available DoE software programmes are made up of little more than catalogues of standard designs presented in a logical sequence. They lead to the erroneous application of statistical methods and are poor at handling special technical features (Tay and Butler, 1999). Finally, they are too focused on design and analysis selection and do not cover the entire experimentation process. Statistical jargon is used to explain DoE Most statistics textbooks for engineers are written by statisticians (Amstong et al., 1990). Consequently, many references in these books are written in statistical jargon or terminology that is unfamiliar to engineers and technicians (Hoadley and Kettering, 1990; Schneider, 2006). Specific language (jargon) can be an obstacle to students when learning DoE (Wilson, 2002). These explanations do not ease the application of DoE, although they must be understood by the appropriate manufacturing personnel (Amstong et al., 1990). Lack of methodologies to guide users through experimentation Tay and Butler (1999) present a review of DoE methodologies, which states that two basic types of methodologies are in use today: (1) Classical techniques: Developed in the UK and the USA during the 1920s. Includes full-factorial designs, fractional factorial designs, D-optimal designs and Response surface designs. (2) Taguchi methods: Developed by engineer Genechi Taguchi in Japan during the 1950 s. His original work is documented in his two-volume book (Taguchi, 1987). A number of novel designs and analyses are promoted, as are orthogonal arrays and signal-to-noise ratio analysis. Both types have their proponents and opponents, and the discussion between the two has become heated at times (Box et al., 1988). Tay and Butler also emphasise that there is a lack of experimental planning and managerial guidance in the field. This is in part due to the fact that industry literature gives little attention to the methodologies needed to carry out DoE implementation, focusing rather on data analysis. In order for experimental design to be successfully applied in today’s industrial environment, a mixture of statistics, planning, engineering, communication and teamwork skills are required (Antony, 1999).

Moreover, existing material is limited to a collection of unstructured, unorganised and uneven elements (Romeu, 2006). There is a real need to develop a simplified series of operational steps that lead to a proper solution (Amstong et al., 1990). Previous negative experiences with DoE Negative experiences with DoE may make companies reluctant to use DoE again. The majority of negative DoE experiences can be classified into two groups. First, those related to technical issues, such as (Coleman and Montgomery, 1993; Anderson and Kraber, 1999): . choosing unreasonably large or small designs; . inadequate and/or inaccurate measurement of responses or factors; . running experiments in an incorrect order; . lacking awareness of assumptions: knowing how to evaluate them, knowing alternatives when they are needed; and . undesirable combinations of variable control levels in the design. Second, mistakes that may be due to non-technical issues such as (Leoń et al., 1993; Knowlton and Keppinger, 1993; Box and Liu, 1999; Gunter, 1993; Robinson, 2000; Peace, 1993; Anderson-Cook, 2006). . not choosing the right factors and appropriate levels; . underestimating non-statistical aspects in planning and conducting experiments; . lack of planning; and . carrying out one-shot experimentation, instead of iterating when possible. Absence of theoretical developments to solve real industrial problems Most of the development of DoE is mathematically complex. The complexity of some problems, due to restrictions in the factors and available experiments, makes it impossible for engineers to find a solution using the current DoE technique. DoE is not widely used because it is a complex tool Some engineers believe that DoE is a difficult technique because of the inherent complexity of the tools involved (Costa et al., 2006; Antony et al., 1998; Tay and Butler, 1999). Unfortunately, the development of DoE is often too mathematically complex for those not well-versed in mathematics (Schmidt and Case, 2002). It doesn’t help that many engineers don’t use DoE frequently: many users work with DoE fewer than twice a year. This makes it quite difficult to develop an intuitive feel for procedures that are used so rarely (Grant, 2006). Successful application of DoE analogy To better explain some concepts; we believe it is useful to present an analogy, shown in Figure 1. As can be seen, in order to successfully apply DoE, it is necessary to climb three consecutive ladders. Each ladder represents a group of barriers. The order in which they are presented is not important, but successful application of DoE cannot be accomplished unless all ladders are climbed. This concept is of great importance as


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Figure 1. Ladder analogy for the application of DoE

many people believe that good DoE training is enough to guarantee success. However, many problems may arise when experimenting, such as worker resistance to change. In the figure there are a total of 16 rungs, which represent each of the barriers identified and explained previously. Theoretically, to climb the ladder you need all the rungs, which means that you need to overcome every barrier. Sometimes you may have to climb a ladder which is missing some rungs, although it is inconvenient to do so. However, if there were too many rungs missing, you could not have climbed it. This analogy illustrates the fact that you can successfully apply DoE without overcoming every barrier, although it is inconvenient to do so. Conclusions There is still a gap between the theoretical development of DoE and its application in industry. Therefore, an analysis of the obstacles hindering the application of DoE has been presented. In it we have identified 16 barriers from industry literature, which have been classified into three main groups. Each of the barriers have been briefly explained, which can be useful in warning practitioners and DoE experts about which barriers they may face when applying DoE. “There is so much agreement on the need for change” (Kettering, 1995). However, when thinking of solutions, we must bear in mind Senge’s principle (Senge, 1990): “Today’s problems are caused by yesterday’s solutions”. The aim of this article is to help practitioners and DoE experts learn from the past and find a way to detect and prevent the possible barriers practitioners may encounter when applying DoE in industry. Furthermore, this paper is intended to help encourage discussion between DoE practitioners and experts, in order to increase the use of DoE among engineers. Note 1. Recognised as a research platform, to find, analyse and share abstracts and references on the web.

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Schoemaker, P.J.H. and Gunther, R.E. (2006), “The wisdom of deliberate mistake”, Harvard Business Review, June, pp. 109-15. Senge, P.M. (1990), The Fifth Discipline – The Art and Practice of the Learning Organizations, Random House, London. Steinberg, D.M. and Hunter, W.G. (1984), “Experimental design: review and comment”, Technometrics, Vol. 26, pp. 71-97. Taguchi, G. (1987), System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Cost, UNIPUB/Kraus International, White Plains, NY. Tanco, M., Viles, E., Ilzarbe, L. and Alvarez, M.J. (2008), “Is design of experiments really used? A survey of Basque industries”, Journal of Engineering Design, Vol. 19 No. 5, pp. 447-60. Tay, K.-M. and Butler, C. (1999), “Methodologies for experimental design: a survey, comparison and future predictions”, Quality Engineering, Vol. 11, pp. 343-56. Viles, E. (2006), “Statistics for industrial engineers”, Scientific Computing World, June/July, pp. 40-1. Wilson, R.J. (2002), What Does This Have to Do with Us? Teaching Statistics to Engineers, ICOTS6, Cape Town. Corresponding author Martin Tanco can be contacted at: [email protected]



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The relationship between quality management and the speed of new product development

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Hongyi Sun, Yangyang Zhao and Hon Keung Yau Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong Abstract Purpose – The speed of new product development (NPD) has been a key factor in a firm’s degree of competitiveness. The tools and philosophy of quality management have been widely used to improve and control product quality. However, there is a lack of literature on the relationship between quality management and NPD. This paper aims to report on a study that investigates the influence of quality management on the speed of NPD. Design/methodology/approach – The philosophy of quality management refers to total quality management (TQM). Tools for quality management include teamwork, continuous improvement (CI), value analysis (VA) and quality function deployment (QFD). This study begins by comparing literature in concurrent engineering (CE) and TQM, which leads to several common principles and five hypotheses. The hypotheses are tested using survey data from 700 manufacturing companies in 20 countries. Findings – The research reveals that TQM, Team, VA and QFD are positively correlated with the speed of NPD, meaning that the tools and philosophy of quality management have a positive influence on the speed of NPD. However, no relationship is found between CI and the speed of NPD. Research limitations/implications – This paper tests hypotheses using survey data. It reveals the empirical relationship between quality management and the speed of NPD but does not provide details regarding the mechanism of influence between the two. Consequently, case studies should be conducted in the future to probe into the details. Additionally, new quality methods like Six Sigma can also be included in a future study, since Six Sigma covers both quality and NPD. Practical implications – This study proposes that companies that have implemented TQM and other quality management tools will have a better foundation for implementing new NPD approaches like CE and design for manufacturing and assembly. This is especially encouraging for those original engineering manufacturing (OEM) firms that would like to change from OEM to original design manufacturing/original brand manufacturing (ODM/OBM). OEM companies typically implement TQM but invest very little in NPD. Originality/value – This paper fills the gap in research exploring the links between quality management and NPD. It addresses the concern over whether quality management may hinder NPD. The critical issues for implementing quality management such as culture change, learning, change management, and team building can all be applied to implementing NPD methods such as CE. The result also supports the concept of “design the quality into products”. It is beneficial for employees in quality and NPD to share and work together. Keywords Total quality management, Product development, Consumer satisfaction, Manufacturing industries, Customer service management, Continuous improvement Paper type Research paper


The research reported in this paper is fully supported by a Strategic Research Grant (SRG 7002298) from the City University of Hong Kong, Hong Kong, PRC.

1. Introduction Over the past two decades, there have been a number of fundamental trends affecting manufacturing industries. Among these, there has been a significant trend towards speeding up the rate of new product development (NPD). Some firms have made substantial progress in reducing NPD cycle times (Ali, 2000); specific acceleration techniques can be used in this effort (Gonza´les and Palacios, 2002; Langerak and Hultink, 2008). Today, reduction of time in NPD offers a new source of competitive advantage. Companies that develop new products faster successfully obtain the competitive advantage (Gupta et al., 1992; Yam et al., 1996). Realising the importance of NPD, researchers have conducted a huge amount of research on critical factors for successful NPD (Balachandra and Friar, 1997; Cooper and Kleinschmidt, 1995; Poolton and Barclay, 1998; Koufteros, 1995; Song and Montoya-Weiss, 1998). However, based on the review of 47 studies regarding the determinants of new product performance across several disciplines, Montoya-Weiss and Calantone (1994) conclude that “much research on the drivers of new product performance has been disjointed and lacking with respect to concise conclusions in which factors should command the most attention.” More researchers (Millson et al., 1992; Nijssen et al., 1995; Droge et al., 2000) suggest that a group of practices (as opposed to individual items) should be studied, since these approaches are not separated but work together. In this study, the TQM philosophy and some quality tools are studied in the context of NPD. TQM applies an all-encompassing, quality-focused management approach to providing products and services that satisfy customer requirements. TQM is a philosophy that stresses a systematic, integrated, and consistent perspective involving everyone and everything. NPD should be influenced if a company has implemented TQM. This is because the philosophy and some tools of NPD are similar to those of TQM, as elaborated in the literature section. 2. Literature review and hypothesis formulation 2.1 Concurrent engineering (CE) and total quality management (TQM) Product development previously followed a sequential, over-the-wall approach that normally lead to a longer period of NPD. However, the new philosophy suggests an integrated approach called Current Engineering (CE). Wu and Grady (1999) define CE as “the consideration, during the design phase, of the factors associated with the life cycle of the product”. This technique bridges the gap between upstream design and downstream manufacturing by enhancing intensive information sharing and teamwork. Bullinger and Warschat (1995) state that parallelisation, standardisation and integration are the three keys to a CE-oriented product development process. CE suggests that organisational structures be more open and that teamwork and information have common ownership, be shared freely and be easily and freely accessible. CE is therefore the integration of all company resources needed for product development, like people, information and tools and resources. Many organisations have formed some development teams under the CE approach to implement their ideas and to convert research to company profit and have gotten very good results over the past few years. One component of CE is Design for Manufacturing and Assembly (DFMA). This means design for ease of manufacturing and assembly. The main objective is to reduce the number of separate parts to reduce the product cost. DFMA emphasises effective

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communication and information flow. DFMA includes not only assembly and manufacturing but also other considerations such as testing, maintenance and environmental issues. This reflects a typical parallel approach that aims to shorten the time of NPD. This has been demonstrated in the context of many companies; for example, Japanese automakers, Ford and Brown & Sharp (Fujimoto, 2000). Wu and Grady (1999) claim that DFMA has the potential to reduce manufacturing cost, increase quality and reliability and shorten manufacturing time. In addition to CE and DFMA, many other critical success factors (CSFs) associated with the success/failure of NPD have been identified. Lynn et al. (1999) identify 11 key factors. Lester (1998) carried out a study and found a range of potential problems that can derail well-intentioned NPD efforts. By working through these problems, he discovered 15 CSFs in five areas of NPD. Poolton and Barclay (1998) identified a set of six variables that have consistently been identified in the literature as being associated with successful NPD. Cooper (1999) studied hundreds of cases that revealed what makes the difference between winners and losers in the NPD process. He extracted 12 common denominators of successful new product project and seven possible reasons (blockers) offered by managers for why these success factors are invisible and why projects seem to go wrong or end up not being well-executed. There are other studies on CSFs or drivers for NPD (Balachandra and Friar, 1997; Cooper and Kleinschmidt, 1995; Spivey et al., 1997). Their CSFs are not all the same. However, some factors are the same as those required in quality management, especially in the TQM philosophy and some of its tools. Lockamy and Khurana (1995) found that QFD can be viewed as the application of TQM to NPD, just as Just in Time (JIT) is the application of TQM to the production process. King (1989) describes QFD as one of the 14 concepts of TQM. According to these studies, a thorough understanding of the TQM philosophy is a prerequisite for the successful use of QFD. The comparison is shown in Table I.

Philosophy Customer-oriented Parallel process Cross-functional integration External integration Management support Coordination and communication

Table I. Common factors in CE and TQM

CE for NPD

TQM for quality

Voice of customer Product design and process planning Designers, manufacturers and marketers Customer and supplier involvement Yes Yes

Customer-focused Production and quality control Process engineers, quality engineers and designers. Customer and supplier involvement Yes Yes

Hypothesis H1

Tools/approaches Team Yes Continuous improvement (CI) Quality function deployment (QFD) Yes Value analysis (VA) Yes

Yes Yes Yes Yes

H2 H3 H4 H5

Quality management systems based on TQM aim to enhance product quality, providing organisations with a means of achieving higher quality processes. As a direct consequence of this, customer satisfaction is improved (Pfeifer, 2002). These are the requirements for both existing and new products. TQM aims for quality principles to be applied broadly throughout an organisation or set of business processes. If a company implements TQM, this should influence NPD (if any is ongoing) as well. Therefore, it is reasonable to propose the relationship between quality management tools and NPD in this way. H1. Companies that have implemented the TQM philosophy develop new products faster. 2.2 Team Teamwork in the manufacturing industries has increased significantly. Increases in product and process complexity and shorter product lives have been the major reasons for this shift (Funk, 1992). Many articles have stated that teamwork is one of the major elements of CE, which provides a systematic and integrated approach to the introduction and design of products (Kinna, 1995; Harding et al., 1999). Functions such as design and engineering are integrated into teams so that continuous and complete information can be exchanged. As the commencement of each distinct stage is not dependent upon the full completion of the preceding stage, overlapping activities can take place, leading to concurrency in product development. In addition, teamwork quality is significantly related to improved performance in terms of NPD (Dayan and Benedetto, 2009). The application of CE and NPD depends on the ability to build, empower and nurture teams. Well functioning teams help the integration of all factors like customer satisfaction, user needs, technology base, material, manufacturing capability, and support capabilities. Successful teams overcome the shortcomings of hierarchical structures and generate quality decisions (Spivey et al., 1997). Henke et al. (1993) emphasise that the key discriminators are not the way the team is managed, but the decision-making process used by the team. Musselwhite (1990), in another research study, reported that over 80 per cent of the projects using a cross-functional team met or exceeded commercial exceptions, compared with 60 per cent of projects headed by technical line management, 50 per cent of projects headed by technical project management and only 20 per cent of one-person operations. These studies have demonstrated the fact that properly forming a development team can help a company be successful in NPD. When team members discuss and share ideas and provide different solutions to problems at different stages, the best results possible will be achieved, including decreased development cycle time, decreased cost and more efficient use of resources. The above discussion suggests that in a product development process with cross-functional teams, training, group decision-making, the commitment of team members and top management and high project importance, product designs may be achieved in less time and with lower costs. H2. Companies that have implemented a team structure should develop new products faster.


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2.3 Continuous improvement Kaizen is a Japanese word meaning continuous improvement (CI). Imai, who introduced the term kaizen in 1986, defined it as “ongoing improvement involving everyone – top managers and workers”. It is an umbrella concept covering most of those “uniquely Japanese” practices that have once achieved worldwide fame (Masaak, 1986). In order to introduce improvements in innovation performance, CI has been used to change the NPD processes that some organisations use (Dooley and Johnson, 2001). NPD can take advantage of kaizen by reducing product cycle-time, increasing product quality and simplifying the development process. As production technology advances and customer demand for product variety increases, the rate at which new products are introduced into the market is more rapid. Thus, the product life cycle is much shorter. The speed-to-market in today’s fast-paced, competitive environment is significantly positively associated with new product success (Kate and William, 1999). The improvement of existing products through the addition of new features, the design of more user-friendly operations or even the reduction of redundant parts can create more new products. These new products can be upgraded again into yet more new products according to market demand. Hence, the traditional product life cycle of development, growth, maturity and decline is now shortened to produce more and more new products. Obviously, the time that it takes to modify an existing product so that it becomes new is shorter than the time required to develop a new product from scratch. This CI, kaizen, has reduced product cycle-time and hence sent products into the marketplace more quickly. In fact, this strategy has been employed by many companies in recent years. Intel launched several new products each year just by improving the clock speed of one CPU. By using the planning, doing, checking and action (PDCA) cycle and TQM tools, kaizen can simplify the process of NPD, resulting in better quality and lower price (Sirvanci and Durmaz, 1994). We have discussed in the previous section the fact that teamwork presents a positive contribution to NPD. However, cross-functional teams may encounter many problems, often in terms of conflict-resolution. Kaizen enables management to take a systematic and collaborative approach to cross-functional problem solving by establishing a corporate culture in which everyone can freely admit these problems (Masaak, 1986). New technologies and machines play important roles in NPD nowadays. However, without proper operation and utilisation by individuals, they do not benefit the company. The set-up of teams that share expertise and build up a kaizen culture – one that continuously improves every process and every procedure in every way – helps push the company toward success. H3. Companies that have implemented CI should develop new products more quickly. 2.4 Value analysis The purpose of value analysis/value engineering (VA/VE) is to simplify products and processes (Fowler, 1990). Its objective is to achieve equivalent or better performance at a lower cost while maintaining all functional requirements defined by the customer. VA does this by identifying and eliminating unnecessary costs. Technically, VA/VE deals with products already in production and is used to analyse product specifications and requirements, as shown in production documents and purchase requests. Typically, purchasing departments use VA as a cost-reduction technique. Performed

before the production stage, VE is considered a cost-avoidance method. In practice, however, there is alternation between the two for a given product. This occurs because new materials, processes and so forth require the application of VA techniques to products that have previously undergone VE. NPD is the means by which companies continually renew themselves. New products are the lifeblood of manufacturing organisations. For many companies today, new products will represent as much as half of their sales in the next three to four years. Even more importantly, most of their sales and profit growth will stem from new products. This indicates the importance of VA to the development process for new products (Prasad, 1998; DellIsola, 1997). H4. Companies that have implemented VA should develop new products more quickly. 2.5 Quality function deployment Quality function deployment (QFD) (Karbhari, 1994; Zairi and Youssef, 1995; Abdul-Rahman et al., 1999; Tsai et al., 2002) is a systematic product development methodology that insures that products and services are designed with the “Voice of the Customer” in mind. When a cross-functional team uses QFD, dynamic products become a reality. Applying QFD leads to a reduction in cycle time, engineering changes, scrap and rework and warranty returns. QFD links customer requirements or “whats” with the appropriate engineering design characteristics, or “hows”, so that the voice of the customer is translated into product designs and specifications. Building quality is an important step in moving from customer requirements to production requirements. QFD offers product development teams the opportunity to achieve significant improvements over traditional product development practices. QFD may also enable the firm to cut product costs and reduce time-to-market. QFD can be used to develop innovative products (Miguel, 2007). It offers product development teams the opportunity to achieve significant improvements over traditional product development practices. QFD creates an information-intensive atmosphere in which communication increases and ideas are freely exchanged. This has a positive impact on developing product concepts and devising designs that meet customer quality and performance objectives. QFD may also enable the firm to cut product costs and reduce time-to-market. QFD is able to simplify the manufacturing process, but overall product costs appear to be only slightly less when QFD is applied than when traditional practices are used. The reason for this small improvement in product costs and time-to-market may be a lack of experience with QFD. For many organisations, this was a first attempt to apply QFD and the first application of QFD to a particular product. As companies gain experience with QFD and learn to apply it more effectively, product costs and time-to-market may decline. H5. Companies that have implemented QFD should develop new products more quickly. Based on the formulation of the five hypotheses, a framework is proposed as shown in Figure 1.


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582 Figure 1. The five factors hypothesised to influence the speed of NPD (SNPD)

3. Methodology and empirical data The research reported in this paper is based on data from the International Manufacturing Strategy Survey (IMSS). IMSS was initiated by the London Business School and Chalmers University of Technology and is being co-ordinated by the Instituto de Empresa in Spain. A researcher network in more than 20 countries carried out the survey. The questionnaire was first designed by a Swedish team in the 1980 s and modified for the first international survey in the period 1992-1993. Based on experiences in 20 countries during the first round of the survey, it was modified for the second round of survey distribution. The questionnaire was discussed in a workshop convened by IMSS researchers. For details of the IMSS project, please refer to Lindberg et al. (1998). The questionnaires were sent separately to companies in individual countries. All of the data were sent to the co-ordinator in Spain and then distributed to all participants. The survey has been conducted four times: in 1992, 1997, 2000 and 2005. This project uses the data from the 1997 survey, since TQM was at its peak then. Also, later rounds of the survey do not cover TQM tools. Data from 23 countries/regions were available when this research was conducted. The participating countries and the sample sizes are shown in Table II. In total, the sample size is 700. The distribution of the company sizes, with the percentage of sampled companies in each size group, is as follows: , 100, 14 per cent; 101-500, 40 per cent; 501-1000, 26 per cent; 1001-3000, 11 per cent; and . 3000, 6 per cent. The companies that participated in the survey are in the International Standard Industry Classification (ISIC) 38 group. Products in the ISIC 38 industry include metal products, machinery, electrical machinery, appliances and suppliers, transport equipment, professional and scientific measuring and controlling equipment and photographic and optical goods. The above-mentioned questionnaire covers 300 variables that concern strategy, practice and performance. The research reported here focuses on those quality philosophies and tools that may influence the speed of new product development. They were measured on a 1-5 scale including 1: None, 2: Little, 3: Some, 4: Much and 5: Very Much, as shown in Table III. The time of NDP varies a lot from product to product. For example, it may take a couple of weeks to develop a small toy but several months or even years to develop a car. So the absolute measures of NPD time are not comparable. As a result, the percentage change in the speed of NPD was used as a relative measure here.

Country

Sample

Argentina Australia Brazil Canada Chile China Denmark Finland Germany Hong Kong Hungary Italy Japan Mexico Netherlands New Zealand Norway Peru South Korea Spain Sweden UK USA Sum

31 55 27 38 10 30 27 14 28 14 38 71 29 29 29 32 13 8 50 33 27 24 43 700


Table II. Participating countries and sample size

1 ¼ None 2 ¼ Little 3 ¼ Some 4 ¼ Much 5 ¼ A lot Total quality management program (TQM) Quality function deployment (QFD) Continuous improvement (CI) Value analyses/redesign of products (VA) Implementing team approach (Team)

1 1 1 1 1

2 2 2 2 2

3 3 3 3 3

4 4 4 4 4

5 5 5 5 5

Table III. Measures of quality philosophy and tools

4. Results Hypothesis testing was based on the Pearson correlation. Table IV shows the correlation coefficients between quality management and the speed of NPD. The results of the tests with regard to the five hypotheses are discussed below.

H1 H2 H3 H4 H5

TQM Team CI VA QFD

SNPD

Test result

0.15 * * 0.14 * / 0.14 * 0.15 * *

Accepted Accepted Rejected Accepted Accepted

Note: * Significant at the level of 0.05; * * significant at the level of 0.01

Table IV. Correlation coefficients for hypotheses testing

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The correlation between the TQM philosophy and the speed of NPD is 0.15. The correlation is positively significant at a level of 0.01. Therefore, H1 is accepted. This implies that companies with TQM tend to develop new products more quickly. From Table IV, it is also evident that the correlation between team and the speed of NPD is 0.14. This correlation is positively significant at the level of 0.05. Therefore, H2 is accepted. This means that teamwork also helps companies’ speed up NPD. However, the correlation between CI and the speed of NPD is not significant. Therefore, H3 is rejected. Additionally, VA is correlated with the speed of NPD (r ¼ 0.14, p , 0.05). As a result, H4 is accepted. This means VA is also a useful tool for shortening the time of NPD. Finally, QFD is also significantly correlated with the speed of NPD (r ¼ 0.15, p , 0.01). Thus, H5 is also accepted. This suggests that QFD is an effective tool for speeding NPD. The research reveals that four of the five hypotheses were validated by the data. This implies that companies with TQM and relevant tools should experience faster NPD. The only hypothesis that was rejected is the one about continuous improvement. There is little understanding of the practical issues surrounding the adoption of CI in NPD processes (Caffyn and Grantham, 2003). So far, no empirical research has been found about CI and speeding up NPD. The topic will be left for future research. The results and their implications will be discussed in the next section. 5. Discussions and conclusions This research reveals that there is positive correlation between quality management and the speed of NPD. It should be pointed out that the correlation coefficients are rather slight, although they are positive and significant. This is perhaps due to the fact that TQM and relevant tools are mostly implemented in production areas for quality improvement. They do influence the design department, but to a limited extent. Nevertheless, the results imply great usefulness for NPD. It is well known that CE is the most efficient method of NDP. This research tells us that various quality tools such as TQM, team, VA and QFD also support NPD. The result further supports the conclusion that there are commonalities between CE and TQM, as shown in Table I. The result also has useful implications for practice and research. First, a company may not be able to implement all of the methods at once. This research may suggest a pattern in terms of the adoption/implementation of organisational practices. It is suggested that a company first implements basic methods, such as the team approach and the TQM philosophy and then progresses to more complicated methods like CE and DFMA. Compared with TQM, CE is relatively new, and companies have limited experiences with it. This is especially true for those companies in developing countries. Since the basic managerial principles and philosophy are the same, the experience of implementing TQM can be used in CE implementation. The major issues surrounding TQM implementation, such as cultural changes, learning-curve effects, change management and team building, are all the same to CE implementation. Second, the result is useful to original engineering manufacturing (OEM) companies transferring from OEM to original design manufacturing/original brand manufacturing (ODM/OMB). These companies worry that they do not have enough resources and expertise when it comes to implementing CE for NPD. This project reveals that if companies have implemented TQM and employed other quality tools,

they should have a good basis for speeding up NPD as well. This is especially useful for most Hong Kong and Chinese companies, which are mostly OEM-based and are eager to increase their NPD capability. Finally, it must be pointed out that speed is not the only goal of NPD. The speed of NPD development may not guarantee the quality of the NPD. In fact, Lukas and Menon (2004) found that NPD that is too slow or too rapid leads to quality problems in the new product. Wheelwright and Clark (1992) suggested that NPD should aim for speed, efficiency and quality. Langerak et al. (1999) suggested that NPD is a trade-off process in which time-to-market, quality, cost and customer value should all be considered. There is a new tendency to incorporate quality improvement into NPD, as demonstrated by the new trend of transferring from Six Sigma for quality improvement to Design for Six Sigma. “Design the quality into products” has become the new slogan in NPD, as well as in quality management. However, TQM philosophy and tools have not been studied enough in the context of NPD. Bellary and Murthy (1999) tried to link TQM and NPD in a conceptual model. However, the empirical research linking quality and NPD is not substantial. This paper will, we hope, trigger more research on the quality management on NPD, visa versa.

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Dooley, K. and Johnson, D. (2001), “Changing the new product development process – reengineering or continuous quality improvement”, Measuring Business Excellence, Vol. 5 No. 4, pp. 32-8.

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Droge, C., Jayaram, J. and Vickery, S.K. (2000), “The ability to minimize the timing of new product development and introduction: an examination of antecedent factors in the North American automobile supplier industry”, Journal of Product Innovation Management, Vol. 17, pp. 24-40. Fowler, T.C. (1990), Value Analysis in Design, Van Nostrand Reinhold, New York, NY. Fujimoto, T. (2000), “Shortening lead time through early problem-solving – a new round of capacity-building competition in the auto industry”, in Ju¨rgen, U. (Ed.), New Product Development and Production Networks, Springer, Berlin. Funk, J. (1992), The Teamwork Advantage: An Inside Look at Japanese Product and Technology Development, Productivity Press, Cambridge, MA. Gonza´les, F.J.M. and Palacios, T.M.B. (2002), “The effect of new product development techniques on new product success in Spanish firms”, Industrial Marketing Management, Vol. 31 No. 4, pp. 305-15. Gupta, A.K., Brockhoff, K. and Weisenfeild, U. (1992), “Making trade-offs in new product development process: a German/US comparison”, Journal of Product Innovation Management, Vol. 9 No. 1, pp. 11-18. Harding, J.A., Omar, A.R. and Popplewell, K. (1999), “Applications of QFD within a concurrent engineering environment”, International Journal of Agile Management Systems, Vol. 1 No. 2, p. 88. Henke, J.W., Karchenberg, A.R. and Lyons, T.F. (1993), “Perspective: cross-functional teams; good concept, poor implementation!”, Journal of Product Innovation Management, Vol. 10, pp. 216-29. Karbhari, V.M. (1994), “Total quality design: an approach for customer satisfaction in critical advanced technologies”, Benchmarking for Quality Management & Technology, Vol. 1 No. 1, pp. 5-8. Kate, L.G. and William, R. (1999), “Key factors in increasing speed to market and improving new product success rates”, Industrial Marketing Management, Vol. 28, pp. 319-26. King, R. (1989), Better Design in Half the Time, Methuen, Cambridge, MA. Kinna, R. (1995), “Teamworking and concurrent engineering – a success story”, World Class Design to Manufacture, Vol. 2 No. 3, pp. 5-9. Koufteros, X. (1995), “Time-based competition: developing a nomological network of constructs and instrument development”, dissertation, The University of Toledo, Toledo, OH. Langerak, F. and Hultink, E.J. (2008), “The effect of new product development acceleration approaches on development speed: a case study”, Journal of Engineering and Technology Management, Vol. 25, pp. 157-67. Langerak, F., Peelen, E. and Nijssen, E. (1999), “A laddering approach to the use of methods and techniques to reduce the cycle time of new-to-the-firm products”, The Journal of Product Innovation Management, Vol. 16 No. 2, pp. 173-83. Lester, D.H. (1998), “Critical success factors for new product development”, Research Technology Management, Vol. 41 No. 1, pp. 36-43. Lindberg, P., Voss, C. and Blackmon, K. (Eds.) (1998), International Manufacturing Strategies: Context, Content and Changes, Kluwer Academic Press, Kingston upon Thames.

Lockamy, A. and Khurana, A. (1995), “Quality function deployment: total quality management for new product design”, International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 73-84. Lukas, B.A. and Menon, A. (2004), “New product quality: intended and unintended consequences of new product development speed”, Journal of Business Research, Vol. 57 No. 11, p. 1258. Lynn, G.S., Abel, K.D., Valentine, W.S. and Wright, R.C. (1999), “Key Factors in increasing speed to market and improving new product success rates”, Industrial Marketing Management, Vol. 28, pp. 320-9. Masaak, I. (1986), Kaizen: The Key to Japanese Competitive Success, Random House, New York, NY. Miguel, P.A.C. (2007), “Innovative new product development: a study of selected QFD case studies”, The TQM Magazine, Vol. 19 No. 6, pp. 617-25. Millson, M.R., Raj, S.P. and Wilemon, D. (1992), “A survey of major approaches for accelerating new product development”, Journal of Product Innovation Management, Vol. 9, pp. 53-63. Montoya-Weiss, M.M. and Calantone, R. (1994), “Determinants of new product performance: a review and meta-analysis”, Journal of Product Innovation Management, Vol. 11, pp. 397-417. Musselwhite, W.C. (1990), “Time-base innovation: the new competitive advantage”, Training and Development Journal, Vol. 44 No. 1, pp. 53-6. Nijssen, E.J., Arbouw, A.R.L. and Commandeur, H.R. (1995), “Accelerating new product development: a preliminary empirical test of a hierarchy of implementation”, Journal of Product Innovation Management, Vol. 12 No. 2, pp. 99-109. Pfeifer, T. (2002), Quality Management – Strategies, Methods, Techniques, Hanser, Munchen. Poolton, J. and Barclay, I. (1998), “New product development from past research to future applications”, Industrial Marketing Management, Vol. 27, pp. 197-212. Prasad, B. (1998), “A method for measuring total value towards designing goods and services”, The TQM Magazine, Vol. 10 No. 4, pp. 258-75. Sirvanci, M.B. and Durmaz, M. (1994), “Cycle-time and cost improvement by designed experiments”, The International Journal of Quality & Reliability Management, Vol. 11 No. 6, pp. 21-6. Song, X.M. and Montoya-Weiss, M.M. (1998), “Critical development activities for really new versus incremental products”, The Journal of Product Innovation Management, Vol. 15 No. 2, pp. 124-35. Spivey, W.A., Munson, J.M. and Wolcott, J.H. (1997), “Improving the new product development process: a fractal paradigm for high-technology products”, Journal of Product Innovation Management, Vol. 14 No. 3, pp. 203-18. Tsai, Y.C., Chin, K.S. and Yang, J.B. (2002), “A hybrid QFD framework for new product development”, The Asian Journal on Quality, Vol. 3 No. 2, pp. 138-58. Wheelwright, S.C. and Clark, K.B. (1992), Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency, and Quality, Free Press, New York, NY. Wu, P. and Grady, P. (1999), “A concurrent engineering approach to design for assembly”, Concurrent Engineering, Vol. 7 No. 3, pp. 231-43. Yam, C.M.R., Chin, K.S. and Tang, P.Y. (1996), “New product development strategies for Hong Kong manufacturing industries”, The International Journal of Human Factors in Manufacturing, Vol. 6 No. 3, pp. 233-41.


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Zairi, M. and Youssef, M.A. (1995), “Quality function deployment: a main pillar for successful total quality management and product development”, International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 9-23.

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Further reading Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1988), Multivariate Data Analysis, Prentice Hall, Englewood Cliffs, NJ. Kochan, T.A. (1988), “On the human side of technology”, ICL Technical Journal, Vol. 6, pp. 391-400. About the authors Hongyi Sun is an Associate Professor in the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong. Dr Sun’s research and teaching interests include quality management, management of technological innovation and entrepreneurship and manufacturing strategy. Hongyi Sun can be contacted at: [email protected] Yangyang Zhao is a Research Assistant in the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong. She is interested in management of technology and innovation. Hon Keung Yau is an Instructor at the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong. Dr Yau’s research interests include quality management and organisational learning.



A proposed framework for combining ISO 9001 quality system and quality function deployment

Combining ISO 9001 quality system and QFD 589

Paulo A. Cauchick Miguel Universidade Nove de Julho – Uninove, Saõ Paulo, Brazil, and

Jose´ Celso Sobreiro Dias Fundaçaõ de Ensino Octa´vio Bastos, Saõ Paulo, Brazil Abstract Purpose – ISO 9001 certification assures that a company employs a quality system, which provides trust for the customers but this system does not assure the quality of the products. It is then necessary to apply other methods and tools to achieve the demanded quality. This paper aims to propose a framework for combining ISO 9001 requirements with quality function deployment (QFD). Design/methodology/approach – A theoretical framework is developed followed by an empirical application. The framework consists of three components: quality assurance items, critical operational functions, and requirements of the ISO 9001: 2000 quality management system. The framework is then applied in a company that produces surge arresters. Findings – Main results indicate that the proposed framework may assist in developing products and prioritising quality assurance items, critical operational functions, and ISO 9001: 2000 requirements. The empirical application provided an effective case of QFD full usage. In addition, the application was useful to the company not only for achieving a better organizational quality structure, but also for recording company knowledge through QFD. Research limitations/implications – For more extensive empirical validation further replications among other samples are needed for external validation of the findings. Originality/value – Although QFD is extensively explored in the literature, this paper is one of the few published studies that report and discuss the use of QFD with ISO 9001. In addition, the proposed framework may be useful for practitioners and academics, who deal with the subject of quality. Keywords ISO 9000 series, Quality function deployment, Quality systems, Product development Paper type Research paper

1. Introduction The contribution of QFD to new product development activities has been pointed out by several authors in recent years. Since its creation in the 1960 s, numerous successful applications have been reported in a wide range of industries. A comprehensive review of such applications can be found in Chan and Wu (2002). Nevertheless, to achieve product The authors acknowledge the cooperation of the company where the study was conducted. Nevertheless, this paper reflects only the view of the authors, not the official view of the company. The authors also wish to express their gratitude to the referees who did contribute to the improvement of this article as well as to Ms Ann Puntch for English revision. One of the authors is also affiliated to the Production Engineering Department of the Polytechnic School from the University of Saõ Paulo – USP and this institution should also be acknowledged.


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quality, QFD alone might not be enough. In order to obtain design quality, quality assurance must be in place and purposeful quality control points established to assure design requirements. Additionally, a documentation system and a quality assurance structure should also exist in order to actually establish processes, procedures and management resources. These are necessary to implement quality assurance and control. Quality assurance has evolved over recent decades. It began in order to correct defects in products and then to prevent defective products being made in the first place, especially in the quality assurance activities for machinery (Ozawa, 1998). Later on, the establishment of a quality assurance system was needed for standardization, which serves not only to prevent the recurrence of trouble in production but also to permit complete control of the process. This process is accomplished by the use of standards. They are a kind of guarantee concerning job performance, control methods and procedures, materials, quality of parts, technical specifications, production and inspection guidelines (Ozawa, 1998). To cover that, the ISO 9001 standard checks whether a company has a quality assurance system in place and, if so, a certificate of compliance may be issued by a certification body. One of the principal criticisms of the ISO 9001 standard is that certification itself does not assure product quality. In fact, the certification process is an assessment of the “quality of operations”. If operations were well established by effective procedures and documentation, the quality of the output (product) would be a consequence. Therefore, certification of the quality management system is a necessary condition to achieve product quality, but it is not sufficient. In this sense, the ISO 9000 series underwent an overall review and a new set of standards was issued. This can be considered as an attempt to increase the effectiveness of the standards and the certification process in order to assure product quality. In this context, it would be important to have a quality management system which considers the “quality of the product” and “the quality of operations”, as identified by Akao (2001). Such a system considers/has three components: quality assurance items, critical operational functions, and ISO 9000 requirements. It considers the product quality information generated by the application of quality deployment as an input to define quality assurance items. Further, the critical operational functions can be identified by using narrowly defined QFD (also called work deployment). Finally, ISO 9001 requirements are then introduced to yield a framework by using QFD principles, which are then applied to integrate these components by creating interrelated matrices. This framework establishes a quality management system capable of assuring not only the quality of the output (product) but also the operations. The main objective of this article is to demonstrate this proposal. It considers a quality management system that combines ISO 9001 requirements and QFD. The theoretical framework for this research comes from the literature (Akao and Hattori, 1998; Akao, 2001). The present proposal may be regarded as an alternative proposal to Akao’s. One of the main outputs of this framework is to focus on those ISO 9001 requirements that really affect customer requirements. In addition, to illustrate the application of the framework, an empirical application was carried out at a company that produces electronics. 2. Theoretical framework This section highlights the theory behind the development of the present proposal. It starts with QFD concepts followed by an overview of ISO 9000.

Finally, some remarks on combining QFD and ISO 9000 found in the literature are outlined. 2.1 QFD concepts The term “quality function deployment” as used today, is a generic name for practices which can be accurately broken down into two parts: quality deployment – QD and narrowly defined quality function deployment – NDQFD (Akao, 1998), as illustrated in Figure 1. QD can be defined as translating user demands (customer requirements) into quality characteristics (product attributes). This determines the design quality of a completed product (usually a system). Then design quality should be systematically deployed to each product sub-system and then into all components and processes. NDQFD can be defined by systematically deploying the job functions and operations that are necessary to achieve quality into step-by-step details (i.e. procedures, work instructions, etc.). As can be seen in Figure 1, the term “broadly defined QFD” encompasses QD and NDQFD. In the USA, QFD and QD are not distinguished and they are treated as synonyms (Akao, 1998), as also confirmed in the survey by Christiano et al. (2000). As a result, the majority of QFD applications in the USA are, in fact, only quality deployment. This also occurs in some countries in Europe, e.g. in Sweden (see Ekdahl and Gustafsson, 1997) and in the UK (see Martins and Aspinwall, 2001) as well as in the developing countries. In Brazil, for instance, companies usually employ solely QD, as identified by Cauchick Miguel and Carpinetti (1999) and Cauchick Miguel (2003).


2.2 QFD application A proposal for the sequence in applying QFD can be found in the work of Cheng et al. (1995); it can be divided into five main steps. The steps are briefly described as follows:

Figure 1. QFD definition

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Figure 2. Example of a QFD conceptual model

(1) Determination of QFD project goals. First, it is necessary to determine which goals the QFD project aims at achieving. These goals are usually related to quality, technology, reliability, costs, and market. Examples of projects goals can be: achieve a market share of 10 percent in the first two years after product introduction (market goal), increase product life-span to 5,000 cycles (quality goal), and reduce customer complaints about the product by a quarter (reliability goal). (2) Definition of the QFD conceptual model. A QFD conceptual model is the set of matrices for the development of a given product which is represented graphically. The location of the matrices in the QFD conceptual model depends on the cause-effect relationships of/in the product development process, i.e. how the final product is obtained. Figure 2 illustrates a conceptual model for developing flexible films for packaging, whose further details can be seen in Cauchick-Miguel et al. (2001, 2003). (3) Development of the House of quality (HOQ). The basic concept of QFD is to translate customer requirements into product design or engineering characteristics. This is accomplished by using the HOQ, a matrix that relates customer requirements (CRs) into quality characteristics (QCs). Figure 3 shows an example of this matrix. The HOQ presented in Figure 3 shows: Part (1) CRs in rows and part (2) QCs in columns. Part (3) shows their relationships within the matrix, i.e. the relationship ratings between CRs and QCs. The conventional HOQ employs a rating scale, e.g. 1 – 3 – 9, or 1 –5 – 9 to indicate the degree of the

Combining ISO 9001 quality system and QFD 593 Figure 3. Customer requirements £ quality characteristics

relationship between CRs and QCs, i.e. weak – moderate –strong. Although the conventional approach to prioritising QCs is easy to understand and use, there are several methodological issues associated with it. Part (4) shows the determination of the degree of importance of CRs and quality planning. There is also the incorporation of the correlations among QCs into a decision process to determine appropriate QCs (not shown in the matrix), i.e. the consideration of trade-offs among the QCs. Part (5) is dedicated to the QC prioritization and determining the planned specification of more important quality characteristics. (4) Development of other matrices. Depending on the type of product under development, other matrices can be produced. In fact, one of the main potentials of QFD usage is the application of such matrices. Unfortunately, only the first matrix (HOQ) is used by companies, as identified by the literature (Christiano et al., 2000; Martins and Aspinwall, 2001; Cauchick Miguel, 2003). Examples of other matrices can be found in Cauchick Miguel et al. (2001, 2003). (5) Definition of an action plan. The main purpose of this step is to put the matrix information into practice, i.e. to incorporate quality information into a product, since QFD can be used to develop a new product or to improve an existing one. In the latter case, an action plan is required as a result of the QFD effort. In the former case, the action plan can be implemented through narrowly defined QFD. However, only a very few companies have applied NDQFD. Usually, they use other organizational practices to deploy functions (the necessary work to obtain quality). The companies may use ISO 9001 procedures or others (e.g. ISO TS 16949, TL 9000, AS 9001, depending on to which industrial sector the company belongs). A case of an implementation of these steps is presented elsewhere (Cauchick-Miguel et al., 2001). Having discussed QFD principles, attention is turned to ISO 9000, highlighted next. 2.3 Outlining ISO 9000 The continued growth of ISO management system certification standards is revealed in a recently published ISO Survey (ISO, 2007). The organization carries out an annual survey to indicate the impact of its best-known standards. As of the end of December

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2007, 951,486 ISO 9001: 2000 certificates had been issued in 175 countries. ISO 9001 establishes a quality system as a quality management structure, within which responsibilities, procedures, processes and the necessary resources must be defined. Typical ISO 9001 requirements are those usually expressed by elements such as management responsibility, resource management, product and service realization, and measurement, analysis and improvement. These ISO 9001 requirements are usually implemented through a quality manual, procedures, work instructions, records, etc. in a documented quality management system. The quality system is, in fact, the organizational structure related to quality through which operational (job) functions and quality assurance can be obtained by implementing ISO 9001 requirements. A quality system is also defined as a network of operational procedures and controls necessary to obtain a product with the required quality (Feigenbaum, 1961). In another definition, QFD is a way to systematically communicate the information related to quality and to make explicit the jobs necessary to obtain it (Cheng et al., 1995). The second definition is, in fact, the concept of narrowly defined QFD by Akao (1990). When comparing these two definitions with ISO 9001 requirements, it can be concluded that they are almost the same, i.e. the ISO 9001 standard considers narrowly defined QFD, although it does not use this term. Therefore, if the definitions are almost the same, it would be important to consider quality deployment linked to narrowly defined QFD, as argued by Akao (2001). In this sense, a combination of QFD and the ISO 9001 seems appropriate to obtain a more effective quality management system. 2.4 Combining ISO 9000 and QFD A review of the literature (Dias and Cauchick Miguel, 2001), identified 462 QFD articles published in 71 international journals. Articles were divided into three groups: (1) QFD applications (140); (2) conceptual (159); and (3) discussion papers (163). Not a single article referring to QFD and ISO 9000 was identified. More recently, another literature review was offered by Chan and Wu (2002). In that, 647 references were listed and an analysis done by authors (Chan and Wu, 2002) showed nine functional fields of QFD, seven applied industries, and an additional section about QFD methodological development. Within the “quality management” session only one citation relating QFD and ISO 9000 was found (Kanji, 1998), demonstrating that this kind of study is not common. Even the work of Kanji (1998) was superficial and, in fact, did not present a strong relationship (or combination) between ISO 9001 and QFD. One of the first publications that attempted to relate QFD to a quality management system more effectively was provided by Akao and Hattori (1998). These initiatives are outlined in the next section. 3. Research background Akao and Hattori (1998) first proposed a framework that combined QFD with a quality system. This was later improved further and presented by Akao (2001). Figure 4 shows Akao’s proposal for establishing a quality system based on ISO 9001 and QFD. The right-hand side of Figure 4 shows a series of matrices, which consist of the relationships among operational functions, quality elements, demanded quality, and


Figure 4. QFD supported ISO 9000 quality system

ISO 9000 requirements. The development starts by obtaining the initial parameters for quality deployment (customer requirements transformed into product attributes) as well as by identifying critical operational functions through narrowly defined QFD, as shown in the left-hand side of Figure 4. 3.1 Developing an alternative framework A preliminary application of Akao’s proposal (shown in Figure 4) was conducted previously (Dias and Cauchick-Miguel, 2000). A matrix which related job functions and ISO 9001 requirements was created. As a pilot study, it was conducted for only one specific job function: “purchasing, inspection and control of raw materials” (to produce electronic ceramics). The application permitted a definition of the relevant job functions and then it was possible to document the necessary operations within the quality system. Based on these results, the combination between ISO 9000 requirements and QFD, based on Akao’s proposal, was proven to be feasible and effective. However, there were some drawbacks in the Akao’s model, discussed in a later work (Dias and Cauchick-Miguel, 2000). These drawbacks have led to further development, i.e. to develop an alternative framework to Akaos’s (for more details of this development see Dias and Cauchick Miguel, 2002). The proposal intended to analyze the relationship among three components: critical operational functions, quality assurance items and ISO 9001 requirements. QFD was then employed to integrate those components into a quality system. The framework is presented in the next section. 4. Further development of the framework Further development of the framework is presented in Figure 5. It consists of three parts: (1) Quality deployment – QD (part 1); (2) Narrowly-defined QFD – NDQFD (part 2); and (3) a quality system (part 3).

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Figure 5. Further model development

Each of these three parts in the model is outlined next. 4.1 Part 1 – Quality deployment Quality deployment is defined in the beginning of section 2 and it can be developed as shown in section 2.2 of this paper. Quality assurance items are derived from quality deployment. These items identify which issues are important for establishing quality control points, e.g. quality control features to be checked via a quality control plan. These items are extracted from the most important quality characteristics (product attributes) prioritized from the HOQ as well as from the process control parameters. Those correspond to the QD output. 4.2 Part 2 – Narrowly defined QFD NDQFD performs an operational function deployment that identifies which functions are important to create documentation of operations (e.g. procedures, internal work instructions, records, etc.). Relevant operational functions can be extracted by verifying the assurance purpose of each function (Akao, 1998). A first level of deployment could be the main company functions such as “sales”, “marketing”, “R&D”, “production”, “logistics” and distribution”, “customer services” and so on. A second level deploys the activities of each function, while a third level deploys these activities into their respective tasks. 4.3 Part 3 – Quality system considering three components The proposed quality system framework is a quality management system where quality assurance items, operational functions and ISO 9001 requirements are interrelated by QFD. As mentioned earlier in the paper, quality assurance items come from QD, operational functions come from narrowly defined QFD and the ISO 9001 requirements are the elements from the standards. By combining the three components, the following matrices can be obtained: (1) Quality assurance items versus operational functions; (2) Operational functions versus ISO 9001 requirements; (3) ISO requirements versus quality assurance items; (4) Quality assurance items versus ISO 9001 requirements;

(5) ISO 9001 requirements versus operational functions; (6) Operational Functions versus quality assurance items. Each face of the framework is a QFD matrix (see this representation in part 3 of Figure 5). As in any QFD application, it is necessary to perform a “conversion process.” The conversion process is when a series of results from one deployment table is transferred to another deployment table by multiplying the relative importance by the relationship intensity (“strong”, “moderate” or “weak”) between the two tables. It is worth mentioning that the conversion process in the proposed framework can be executed in both directions (see the arrows in Figure 5). The result is the relative importance of each item in the second deployment table and vice-versa. Thus, the output result can be calculated by a multiplication of matrices, as below: Matrix A ¼ [aij]mxn and B ¼ [bjk]nxp, AxB results in matrix C ¼ [cik]mxp; hence cik ¼ ai1 x b1k þ ai2 x b2k þ ... þ ain x bjk, for every i [ {1, 2,..., m} and every k [ {1, 2,..., p}. Then, the multiplication in the transposed matrix is: 2 h

der11

der 12

:::

r 11

6 i 6 r 21 der 1n · 6 6 6 : 4 r m1

r 12

:::

r 22

:::

:

:::

r m2

:::

r 1n

3

7 r 2n 7 h 7 7 ¼ dsa11 : 7 5 r mn

dsa21

::: dsa1n

i

where: der1n

¼ the nth element of the transposed matrix of input data relative to the matrix ab or ba;

r ij

¼ elements of the relationship matrix (strong, moderate, weak);

dsa1n

¼ the nth element of the matrix of output data (absolute weight) of matrix ab or ba.

This approach enables us to analyze the impact of one deployment table on the other and vice-versa. Then, a set of results can be obtained. These results are the absolute (or relative) importance of each deployment table, making possible a further detailed analysis. From the framework, it is still necessary to implement an effective quality management system considering: . What should be done: established by the activities in the process represented by the quality assurance items; . Who and how it should be done: determined by the relevant operational functions; and . The relationship among quality assurance items and relevant operational functions: defined by the development of an ISO 9001 quality system considering the previous points.


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Figure 6. Global view of the proposed framework

Figure 6 shows a general overview of the framework. In order to implement the proposed framework, the following steps are suggested: (1) Identification of customer quality requirements and application of QD. The output of this step is the quality assurance items;

(2) Design of the quality system considering stakeholder needs. This includes the revisions and validations of the system. The output of this step is the determination of procedures, instructions, records, etc., based on ISO 9001 requirements; (3) Allocation of the necessary resources to establish the relevant operational functions, either for the administrative or production processes. The output of this step is the operational function deployment (application of narrowly defined QFD); (4) Establish the relationship among quality assurance items (output of step (1)), ISO 9001 organizational procedures (output of step (2)), and relevant operational functions (output of step (3)). The output of step (4) is the relationship among the components aiming at reviewing the procedures and/or establishing new ones; (5) Compare product quality with the objectives, to determine whether customer needs have been met, and assess their satisfaction. Corrective actions are taken as needed. A final process analysis will allow establishing preventive actions to assure product quality and reliability. Having presented the framework, its application to developing surge arresters is presented in the following section. 5. Application of the model This work was conducted in a small-sized electronic company with annual revenue of US$ 1.2 million that employs about 100 people. The technology in electronic ceramics is provided by a research centre in France. The main component of electronic ceramics is non-linear resistors (called varistors) used for absorbing atmospheric discharges. Typical products are surge arrestors suitable for overvoltage protection of electrical distribution networks, substations, catenaries, and vehicles. The manufacturing processes demands rigid quality control with rather long manufacturing lead times, usually involving high production costs. Effective inspection of materials is vital. For example, the costs involved in scrapping a batch of parts from the sinterization process can reach over US$ 10 thousand. Figure 7 shows a typical processing scheme for electronic ceramics. 5.1 Part 1 – quality deployment (QD) QD is represented in Figure 8. It was developed by a multidisciplinary team, composed of seven members from the following functional areas: . quality (2); . sales (1); . production (2); and . company top management (2). The QD model consists of eight matrices that are successively deployed in order to identify quality assurance items. First, the HOQ was developed (customer requirements versus product quality characteristics). Customer requirements were deployed from 31 first-level requirements


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Figure 7. Flow diagram for varistor production

that led to 139 requirements in the second level. The deployment of product quality characteristics resulted in 36 first-level characteristics with 93 on the second level. Figure 9 illustrates a set of results from the relative weight of customer requirements. Due to the large number of customer requirements (96 at the second hierarchical level), the figure was used to present them clearly. However, the most important requirements are circled in the figure (“resistance to high discharges”, “suitable quality of raw material”, “have a uniform layer”, etc.). Although it was possible to identify some relevant customer requirements, interpreting those requirements and the time required to carry out data collection were one of the difficulties when using QFD, as identified by the literature (Kim et al., 2000; Myint, 2003). The next deployment consisted of two large groups (see Figure 8). The first group refers to aspects related to the production of surge arresters and their main components, such as the trigger, the polymer basis and the support. These components have a direct influence on the product quality characteristics mentioned earlier. The next group of matrices refers to the main component of the surge arresters: the electronic ceramics, called varistors. After developing all the matrices shown in Figure 8, it was possible to identify which quality issues, i.e. product quality characteristics, manufacturing process parameters, and specifications of raw materials, should become quality assurance items.


Figure 8. Quality deployment (set of matrices) for surge arresters

Figure 9. Customer requirements results

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Developing the matrices was one of the major difficulties in applying QFD. This development was very time-consuming. It was estimated that 126 hours are required for QD development. The next step consisted of establishing the operational functions.

602

5.2 Part 2 – operational functions Then it was necessary to deploy the NDQFD operational functions. These operational functions are related to the job activities essential for product development. A tree diagram was used to help deploy these functions. Operational deployment was conducted considering the following company functional areas: . purchasing; . financing; . marketing and sales; . materials; . production; and . human resources.

Table I. Operational functions deployment

Figure 10. Example of a process technical procedure

Level 1

Level 2

Purchasing

Acquire quotations (at least three companies) Request purchasing of national material Request purchasing of imported material Send out invoice Fill in import declaration Prepare incoming material report (...)

An example of a deployment for the job function of purchasing activities is shown in Table I. QD application does not finish/end with the development of the matrices. The relevant information should be forwarded to the production area. This is usually recorded in a “process technical procedure”. Figure 10 illustrates a process technical procedure for this empirical application. QD and NDQFD should converge into process technical procedures (Cheng et al., 1995). It is worth mentioning that the company had already been working step 2, shown in section 4.3, so its implementation ran more smoothly. Even so, it was estimated that 33 hours were required to develop step 2.


5.3 Part 3 – three component quality system This consists of the third part of the framework presented in Figure 5. It relates quality assurance items, operational functions and ISO 9001 requirements. The quality assurance deployment table was consolidated from QD. Table II shows part of this deployment table. Similarly, the operational functions deployment table was consolidated from NDQFD. An example was presented earlier in Table I.

Level 1

Level 2

Contamination

Maximum level (ppm)

Current

High current, wave 6/15, 10 kA, one hour (% variation) Alternate medium current (mA) High intensity short duration (kA) Maximum alternate current (kA) Discharge nominal current, wave 8/20 (kA)

Dimensions

Minimal gap (mm) Maximum block height (mm) Maximum body height (mm) Minimal edge angle (degrees) (...)

Level 1

Level 2

Resource management

(...) Provision of resources Human resources

Table II. Quality assurance deployment

Level 3

Generalities Competency and training

Infra-structure Work environment Product realization

Product planning Process related to customers (...)

Customer requirements determination Critical analysis of product requirements Communication with the customer

Table III. ISO 9001 deployment

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ISO 9001: 2000 requirements are the elements of the last deployment tables that are necessary to complete the framework. In fact, the ISO 9001 standard does suggest a structure of requirements based on activities (processes) that are interrelated through the inputs (requirements) and outputs (results). Table III shows part of such a table where some of the requirements are deployed in three levels. Figure 11 shows part of a matrix that relates quality assurance items and ISO 9001 requirements. 5.4 Developing a computing support system The deployment tables shown earlier were, in fact, only parts from the actual tables. Combining all the deployment tables results in very large matrices. Some of them can reach considerable proportions, e.g. 15 square meters. Working large matrices is a difficulty well-cited in the literature (e.g. Chou, 2004; Dikmen et al., 2005; Marsot, 2005). To overcome that problem, it was necessary to develop a better way to deal with the matrices. Since the calculation could involve a two-way conversion process, commercial software (e.g. QFD Capturew) was not suitable. In this phase of the project, due to the costs involved, a specific development would not be viable. So, all matrices were developed by using Microsoft Excelw. The matrices were constructed in a way that consisted of various interconnected spread sheets. A display interface was then developed by using form tools and control box to index and visualize all the relationships and the results from input and output data. The interface mode allowed the visualization to stay within the monitor area without using rolling bars. Lastly, it was necessary to use tools such as search function and index function in order to execute cross-references. Navigation was possible using an internet browser (e.g. Explorerw) and the interface is run from a CD ROM. As a result, many different

Figure 11. Matrix: QAI £ ISOR

simulations could be performed; a quick result display was possible, including input and output data, as well as the results of correlation in the matrices. This interface has proven to be very useful and an effective achievement of the model application. Further work will involve developing a better interface and a possible commercial version.

Combining ISO 9001 quality system and QFD

6. Conclusions and future work Companies seek to establish an organizational structure that permits output quality to be obtained. While ISO 9000 certification assures that a company employ a quality system, it does not assure the quality of new products. Besides, companies aiming at achieving quality assurance when developing their product usually apply product development methods such as QFD. In this sense, this work has demonstrated the development of a framework by integrating QFD with a quality management system. The model also included a relationship from three components: quality assurance items, critical operational functions, and elements of the quality management system based on ISO 9001: 2000, whose result is a series of interrelated matrices within this triad. An empirical application of the model was also presented as well as some results of its use for developing surge arresters. Concerning the empirical application, it can be considered as two-fold. On the one hand, it is understood that the application of the framework provided an effective case of QFD research. On the other hand, it was very useful to the company where the application was performed not only with respect to QFD usage but also in terms of recording company knowledge that QFD allowed to be explicitly revealed. Of course, the model does suffer from some constraints, but its current version can be considered successful. It is then understood that other applications are necessary to further empirically validate the framework. Future work will concentrate on further applications and enhancement of the framework in addition to the development of an interface that enables it to display the results in an easier and more user friendly way.

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References Akao, Y. (1990), Quality Function Deployment – QFD – Integrating Customer Requirements into Product Design, Productive Press, Portland, OR. Akao, Y. (1998), Introduction to QFD. Quality Function Deployment Advanced Class, QFD Institute, Novi, MI. Akao, Y. (2001), “Quality management system by QFD”, Proceedings of the 7th International Symposium on QFD, Tokyo, pp. 1-6. Akao, Y. and Hattori, Y. (1998), “Quality system based on ISO 9000 combined with QFD”, Proceedings of the 4th International Symposium on Quality Function Deployment, Sidney, August. Cauchick Miguel, P.A. (2001), “Pilot project of QFD implementation to develop BOPP flexible films”, CD ROM of the 3rd Brazilian Congress of Product Development Management, Floriano´polis, SC, Brazil (in Portuguese). Cauchick Miguel, P.A. (2003), “The state-of-the-art of the Brazilian QFD applications at the top 500 companies”, International Journal of Quality & Reliability Management, Vol. 20 No. 1, pp. 74-89. Cauchick Miguel, P.A. et al. (2003), “Quality deployment for developing flexible films for packaging”, Polymers Science and Technology Journal, Vol. 13 No. 2, pp. 87-94.

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Cauchick Miguel, P.A. and Carpinetti, L.C. (1999), “Some Brazilian experiences on QFD application”, Proceedings of the 5th International Symposium on QFD, Belo Horizonte, MG, Brazil, pp. 229-239. Chan, L.K. and Wu, M.L. (2002), “Quality function deployment: a literature review”, European Journal of Operational Research, Vol. 143 No. 1, pp. 463-97. Cheng, L.C. (1995), QFD – Quality Planning, FCO, Belo Horizonte (in Portuguese). Chou, S.M. (2004), “Evaluating the service quality of undergraduate nursing education in Taiwan – using quality function deployment”, Nurse Education Today, Vol. 24 No. 4, pp. 310-8. Cristiano, J.J. (2000), “Customer-driven product development through quality function deployment in the US and Japan”, Journal of Production Innovation Management, Vol. 17, pp. 286-308. Dias, J.C.S. (2000), “Inspection and control of raw material applied to electronic ceramics through the quality chart”, Transactions of the 6th International Symposium on Quality Function Deployment, Novi, MI, pp. 115-128. Dias, J.C.S. and Cauchick Miguel, P.A. (2001), “QFD: historical profile and application chronology”, Product and Production, Vol. 5 No. 2, pp. 25-56 (in Portuguese). Dias, J.C.S. and Cauchick Miguel, P.A. (2002), “Development of a quality management system by integrating ISO 9001 and QFD - Part 1: a proposed model”, Proceedings of the 8th International QFD Symposium, Munich, September 4-6, pp. 233-244. Dikmen, I., Birgonul, M.T. and Kiziltas, S. (2005), “Strategic use of quality function deployment (QFD) in the construction industry”, Building and Environment, Vol. 40 No. 2, pp. 245-55. Ekdahl, F. and Gustafsson, A. (1997), “QFD: the Swedish experience”, Transactions of the 9th Symposium on Quality Function Deployment. Novi, MI, June. Feigenbaum, A. (1961), Total Quality Control Engineering and Management, McGraw-Hill, New York, NY. ISO (2007), The ISO Survey – 2007, International Organization for Standardization, Geneva. Kanji, G.K. (1998), “An innovative approach to make ISO 9000 standards more effective”, Total Quality Management, Vol. 9 No. 1, pp. 67-78. Kim, S.H., Jang, D.H., Lee, D.H. and Cho, S.H. (2000), “A methodology of constructing a decision path for IT investment”, Journal of Strategic Information Systems, Vol. 9 No. 1, pp. 17-38. Marsot, J. (2005), “QFD: a methodological tool for integration of ergonomics at the design stage”, Applied Ergonomics, Vol. 36 No. 2, pp. 185-92. Martins, A. and Aspinwall, E. (2001), “Quality function deployment: an empirical study in the UK”, Total Quality Management, Vol. 12 No. 2, pp. 575-88. Myint, S. (2003), “A framework of an intelligent quality function deployment (IQFD) for discrete assembly environment”, Computers and Industrial Engineering, Vol. 45 No. 2, pp. 269-83. Ozawa, M. (1998), Total Quality Control and Management – The Japanese Approach, JUSE Press, Tokyo. Corresponding author Paulo A. Cauchick Miguel can be contacted at: [email protected]



Total quality management in Indian industries: relevance, analysis and directions Raj Kumar

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Mechanical Engineering Department, Shri krishan Institute of Engineering and Technology, Kurukshetra, India, and

Dixit Garg and T.K. Garg Mechanical Engineering Department, National Institute of Technology, Kurukshetra, India Abstract Purpose – The purpose of this paper is to analyze the various factors important for total quality management implementation in various manufacturing organizations and to assess their relevance for Indian manufacturing organizations. Design/methodology/approach – A literature review was conducted for important factors and a survey approach was used to collect relevant data from industries. Further data were used to establish a model. Findings – It is shown that customer focus must be the prime objective for various industries to achieve total quality management. All the factors must be used systematically to achieve total quality management (TQM) and it can be done efficiently by using a model having four phases to implement TQM. Originality/value – The paper will be useful for manufacturing as well as service industries that are in the starting phase of TQM implementation or have already failed to implement TQM at their works. Keywords Total quality management, Manufacturing industries, India Paper type Research paper

1. Introduction It is known fact that concept of quality has been around for a very long time, but the stress on the word quality in every aspect of life i.e. in business, service or social life has increased in the last few decades. Quality has awakened all the nations, industries and organizations around the world. The word “quality” means different things to different people. The ranges of meanings include that quality is excellence, value, conformance to specifications, conformance to requirements, fitness for use, customer satisfaction, meeting and exceeding customers’ expectations and minimizing the loss imparted to society. Successful companies over the years have not fundamentally redefined the word quality; they have expanded it to design and service quality. Incorporating the customer’s requirements into the product design and services requires companies to change the way they treat their customers. Companies now need to translate the words and ideas of customers into product and service specifications. Indian companies are also participating in the quality race, although slowly. They are facing a challenge from the multinational companies since the Government of India implemented the policies of liberalization, privatization and globalization. In the light of this, the Indian companies are in dire need of new ideas, approaches and techniques for attaining a competitive edge


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Many organizations have started realizing the importance of total quality management (TQM) and new quality system improvement standards (Dalu, 2000). Meeting the customer requirements is the measurement standard and continuous improvement of work process is the method of meeting the competition. A focus is needed on analyzing various techniques and strategies to be adopted by the Indian companies. This paper examines the way the Indian Industries understand the total Quality Management approach. Further, a survey helps to point out the important factors for TQM implementation in Indian industries and their relation with customer satisfaction. The paper also reveals the prescriptive model for TQM implementation. 2. Review of literature Total quality management is considered by many as an important quality and business performance improvement tool. The popularity of concept has led to an explosion of TQM-related literature. There is enormous amount of literature relating to Total Quality Management from innumerable researchers and practitioners. An inclusive review of this literature helps to differentiate the literature in four categories like conceptual articles, survey articles, case studies and empirical or modelling work. Over the past few years a number of studies have been reported in literature, which have examined and compared quality management practices in different countries around the world and use suitable concept in some industries. Some of the widely cited case studies include Dalu et al. (2000); Kannan et al. (2002); Baisya et al. (2004). All these studies belong to industrial sectors like automobile, electronics, textile industries etc. A number of surveys has been conducted by various authors in the field of quality management considering various techniques of quality management. Some widely cited surveys include Chaudhry et al. (2000); Murthy and Shrivastav (2000); Robinson (2001); Khond and Dabade (2004); Antony et al. (2004). Ho et al. (2001); Mehra et al. (2001); Taylor et al. (2003); Lai et al. (2003); Khanna et al. (2002); Heizer et al. (2004); Shrivastava et al. (2004); Mohanty et al. (2006); and Dahlgaard and Dahlgaard (2006); and conducted empirical studies with various objectives like implementation of TQM in less and more experienced firms of US, various critical factors affecting total quality management at the business unit level, literature available on total quality management and using the literature search, field expert, identifies the future role of TQM in businesses facing global markets. Authors also provided various models and checked their validity in the scenario of TQM. In total, 30 important factors were founded by researchers, which were further divided into ten main factors for detailed study: (1) Customers’ satisfaction; (2) Managements’ effective participation; (3) Employees’ effective participation; (4) Reward schemes; (5) Communication system; (6) Vendors’ power; (7) Statistical quality control; (8) Fast result techniques;

(9) Quality planning and cost involved; and (10) Analytical techniques. 3. Research questions, hypotheses and design 3.1 Research questions A review of the literature leads us to the following research questions: RQ1. What is the extent up to which various factors of total quality management are being followed in Indian industries and their relation with customer satisfaction? RQ2. What type of comprehensive framework is required for successful implementation of total quality management in industries? 3.2 Research hypotheses First research question can be broken down into sequence of specific Hypotheses to understand the importance and impact of various factors. To achieve the goal stated above, hypothesis were set and discussed accordingly. Commonality in importance of various factors in Indian industries. Literature survey force to set following hypothesis: X2) of two H1. There is no significant difference between two sample means (X1; independent industrial groups on TQM factors i.e. all industries accept the importance of factors of total quality management. For the relation of various factors with customer focus and satisfaction H2 was set. Customer satisfaction can be taken as prime objective of industries now days which leads to set following hypotheses: H2. Factor (2) to Factor (10) have positive effect and strong relation with Factor (1), i.e. customer’s satisfaction. 3.3 Research methodology Total quality management researchers are often faced with challenges of how to collect relevant information to answer their research question. The instrument used to test stated hypotheses was a mail survey and a questionnaire (Appendix) on TQM in industries was prepared covering all the important factors group wise. This questionnaire was then pre-tested with academics and practitioners to check its content validity, terminology and modified accordingly. The questionnaire (Appendix) was then sent to 150 manufacturing companies and 75 companies responded (response rate ¼ 50 percent), which are from various sectors like automobile engineering (AUTO), textile engineering (TEXT), electrical and electronics engineering (E&CE), light weight engineering (LIG) and heavy weight engineering (HEV) works in India. 4. Findings and discussion 4.1 Hypothesis testing (H1) This hypothesis was checked by comparing the mean results of survey for all the industries combining them in various industrial groups like AUTO – TEXT etc. As


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there are five industrial sectors so total fifteen groups were compared on the basis of t-test method for two means and for comparison between mean values combined degree of freedom has been calculated for all 15 industrial groups at 5 percent level and also at 1 percent and 2 percent levels (for those values which did not satisfies the hypotheses at 5 percent level) with the help of soft wares like MINITAB and SPSS. Table I represents the combined degree of freedom values. Taking the standard value for various industrial combinations, there means for different factorial groups had been compared and results were tabulated in Table I (column represents the mean values of survey results factor vise): . When calculated “t” is , tabulated “t” – H1: Hypothesis accepted at 5 percent level or level mentioned against figure this is represented by positive sign. . When calculated “t” is . tabulated “t” – H1: Hypothesis rejected at 5 percent level or level mentioned against figure, this is represented by negative sign. From Table II following observations can be made: . Factor (1) – Customers’ power, Factor (2) – Management’s effective participation and Factor (9) – Quality planning and cost involved are acceptable to all industrial combinations. . Factor (4) – Reward scheme is acceptable to all except AUTO – E&EC combination. . Factor (6) – Vendors’ power is acceptable to all except AUTO – E&EC, E&EC – CHE and LIG – CHE. . Factor (8) – fast result techniques is acceptable to ten industrial combinations. . Factor (3) – Employees’ effective participation and Factor (5) – Communication scheme are acceptable to eight different industrial combinations. . Factor (7) – Statistical quality control and Factor (10) – Analytical techniques is acceptable to five various industrial combinations. 4.2 Hypothesis testing (H2) The analytic procedure adopted for calculations involved in H2 included the calculation of descriptive statistics, reliability analysis and correlation measures. The reliability analysis of a measurement instrument determines its ability to yield consistent measures. Reliability was operationalized as internal consistency, which is the degree of inter-correlation among the items that comprise a scale. Cronbach’s alpha was then calculated for each scale. Hypothesis testing was accomplished using correlation measures and p-values. SPSS 11.5 (Mohanty et al., 2006) and MINITAB 14

Table I. Composite score of industries for various factors from F(1)– F(10)

AUTO TEXT E & EC HEV LIG CHE

F(1)

F(2)

F(3)

F(4)

F(5)

F(6)

F(7)

F(8)

F(9)

F(10)

24 25 24 58 115 124

82 89 86 202 415 421

131 144 144 341 689 748

19 21 20 48 94 97

36 49 45 86 183 201

19 17 22 62 100 97

89 74 88 230 449 445

112 108 112 241 546 535

40 39 41 94 180 194

32 23 22 64 249 161

Factor (1)

0.11(þ) 0.31(þ) 0.277(þ) 0.245(þ) 0.06(þ) 0.405(þ) 0.35(þ) 0.31(þ) 0.00(þ) 0.062(þ) 0.046(þ) 0.244(þ) 0.00(þ) 0.33(þ) 0.255(þ)

Combined groups

AUTO – TEXT AUTO – E&EC AUTO – HEV AUTO – LIG AUTO – CHE TEXT – E&EC TEXT – HEV TEXT – LIG TEXT – CHE E&EC – HEV E&EC – LIG E&EC – CHE HEV – LIG HEV – CHE LIG – CHE

1.75 (þ) 0.058(þ) 0.42(þ) 0.28(þ) 0.81(þ) 1.48(þ) 1.26(þ) 0.53(þ) 0.15(þ) 0.33(þ) 0.24(þ) 0.69(þ) 0.43(þ) 0.87(þ) 0.29(þ)

Factor (2) 0.37(þ) 1.38(þ) 2.34 (2) at all 0.68(þ) 2.52 (2) at all 1.44(þ) 2.17 (2) at all 0.75(þ) 2.08 (2) at all 1.63(þ)at 2% 0.277(þ ) 2.51(2) at all 2 (þ)at 2% 2.94 (2) at all 2.13 (2) at all

Factor (3) 0.40(þ) 2.56 (2) at all 0.305(þ ) 0.086(þ ) 0.531(þ ) 1.54(þ) 0.41(þ) 0.147(þ ) 0.148(þ) 1.57(þ) 1.51(þ) 1.196(þ ) 0.33(þ) 0.698(þ ) 0.487(þ )

Factor (4) 0.188(þ ) 2.18 (2) at all 2.28 (2) at all 2.08 (2) at all 4.47 (2) at all 1.21(þ) 0.85(þ) 1.105(þ ) 2 (þ) at 2% 0.05(þ) 2 (þ) at 2% 3.2 (2) at all 3.4 (2) at all 8.6 (2) at all 1.41(þ)

Factor (5)

Factor (7)

Factor (8)

1.30(þ) 2.5 (2) at all 2.9 (2) at all 3.44 (2) at all 2.1 (2) at all 1.195(þ ) 0.185(þ ) 0.00(þ ) 1.025(þ ) 1.21(þ) 0.160(þ) 0.927(þ ) 1.17(þ) 2.2 (2) at all 0.0(þ) 1.70(þ) 0.250(þ) 2.6 (2) at all 0.799(þ ) 1.66(þ ) 0.84(þ) 0.16(þ) 3.1 (2 at all 0.80(þ) 1.49(þ) 5.2 (2) at all 1.53(þ) 1.68(þ) 1.41(þ ) 2.5 (2) at all 0.934(þ ) 34.3 (2) at all 2.5 (2) at all 2.24 (2) at all 5.2 (2) at all 8.1 (2) at all 1.55(þ) 2.8 (2) at all 0.0(þ) 1.27(þ) 3.9 (2) at all 1.30(þ) 3 (2) at all 10 (2) at all 0.956(þ )

Factor (6)

Factor (10) 0.221(þ) 3.2 (2) at all 0.00(þ) 4.1 (2) at all 3.7 (2) at all 2.5 (2) at all 0.116(þ) 3 (2) at all 2.7 (2) at all 2.5 (2) at all 2.2 (2) at all 1.69(þ) 5 (2) at all 4.5 (2) at all 0.503(þ)

Factor (9) 0.82(þ) 0.06(þ) 0.272(þ) 0.453(þ) 0.053(þ) 0.0(þ) 0.215(þ) 0.404(þ) 0.0(þ) 0.147(þ) 0.269(þ) 0.0(þ) 0.136(þ) 0.214(þ) 0.33(þ)


611

Table II. T-test calculations for various industrial groups for different categories of factors affecting TQM

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software’s were used to get desired results. Composite score of various industries (shown in Table I) is acceptable for various calculations to be performed, as the Cronbach’s alpha (a) is 0.94 (a . 0 .7) (Kumar and Venkatesh, 2002). The squared multiple correlations (R 2) for each factor give the communality of factors and it can be used to assess how good or reliable a variable is for measuring the relation. Although there is no hard and fast rules regarding how high the R 2 should be but literature suggests that it should be least greater than 0.05. In present case most of the R 2 exceeds this value. Finally, correlations show the facts about various hypotheses. The score of Factor (3) – customer focus was used as key data to get relation with other nine factors. Table III shows various measures like Cronbach’s alpha, R 2 and p-values. Table IV shows the correlation between customers’ power and other factors. In final scale all the factors have significant positive weights ( p , 0.05). All the results show that H2 is acceptable i.e. all the factors have positive and strong relation with customer satisfaction. For all correlations p , 0.05 (italicized numbers indicate relation between Factor I i.e. customer satisfaction and other factors) 5. A comprehensive framework for TQM implementation process for Indian industries The previous discussion covers the relevance and analysis of the TQM in Indian industries and it has been observed that many of the Indian firms that are taking strategic initiatives to implement TQM in their business units are not able to sustain

Table III. Cronbach alpha, R 2, and p-values for various factors in comparison with Factor (1) – customers’ satisfaction

Factors

a

R2

p-values

Factor Factor Factor Factor Factor Factor Factor Factor Factor

0.7 0.5848 0.9861 0.9490 0.9837 0.5000 0.5917 0.9542 0.8526

0.997 0.999 0.998 0.994 0.967 0.970 0.992 0.999 0.859

0.002 0.002 0.001 0.004 0.017 0.003 0.004 0.001 0.141

(2) (3) (4) (5) (6) (7) (8) (9) (10)

Factor

Table IV. Correlation (b) between customers’ satisfaction and other factors

Factor Factor Factor Factor Factor Factor Factor Factor Factor Factor

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1 1 1 1 1 0.983 1 1 1 0.927

1 1 1 1 0.986 1 1 1 0.944

1 1 1 0.983 1 1 1 0.925

1 1 0.988 1 1 1 0.937

1 0.970 0.988 0.994 1 0.922

1 0.993 0.980 0.986 0.925

1 1 1 0.944

1 1 0.956

1 0.926

1

these initiatives because they often lack in articulating the critical factors that are needed for continual pursuance. As a result, total quality management activities have become stand-alone types and the programmes have lost their defined objectives. The analysis of results from Table III and Table IV has shown that industries well understand the importance of various TQM factors and also able to correlate the various factors with customer satisfaction, but due to lack of a comprehensive framework, are not able to use all the factors of TQM, this is clear from Table I which shows the acceptability of different factors differs in various industries. This explains the need for a common model, which covers all the above mentioned factors using the results of Table III and Table IV. The model covers all the aspects while dealing with TQM and shows the directions to organizations to contemplate the introduction of TQM and to identify their specific course of action and priorities. Figure I shows the model of TQM implementation process that was derived from cumulative findings of research. The model suggests that the introduction of TQM consists of four stages: (1) preparation and awareness; (2) focus; (3) planning and implementation; and (4) development and backup. 5.1 Stage 1: preparation and awareness Stage one covers the preparation of management and employees to understand and implement the efforts for TQM with gaining thorough knowledge of TQM and its implementation. Management’s commitment for quality in all aspects through continuous improvement, working environment, leadership, transparency in systems are certain crucial preparations which are required for management. Attention is required to educate and train the employees for quality work and creating a sense of self-belongingness by engaging them in small group activities and team works. This creates good understanding between employees and management, which will be helpful in further stages and make employees a decision maker than an ordinary worker. TQM is impossible in Indian industries with present mindset and it is very important to change the attitude from short gains to continuous improvement. Table III and Table IV shows that for management Cronbach alpha (a) is 0.7, R 2 is 0.991 and have strong relationship with customer satisfaction this shows that managements’ understand the preparation required for TQM but lacking behind due to Human resource management which states that employees should be given priority as they are the internal customers’. For Employees Cronbach alpha (a) is 0.585, R 2 is 0.999 and have strong relationship with customer satisfaction shows that they are lacking in self-belongingness and this can be improved only by taking interest in training and education programs, by giving regular and valuable suggestions for day-to-day problems etc. 5.2 Stage 2: focus Customer satisfaction must be taken as the focus for successful total quality management implementation programme. Customer satisfaction as an index of a product’s quality covers two important areas:


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Figure 1. A comprehensive framework for TQM implementation process

(1) the actual performance of a product compared against the ‘expectation’ fostered in a customer’s mind during selling process; (2) the level of customer support provided after the delivery of the product. 5.3 Stage 3: planning and implementation In this stage start up the planned implementation programme step by step. Quality is people and if an institution is really people oriented, it needs plenty of words to describe the way people ought to treat one another. Communication is an expression of

trust and confidence in people and induces cooperation involving people assisting each other, not because they perceive their long-term goals to be identical but because they seem to realize that their own welfare lies in not harming each other’s interest. Table III and Table IV shows that for communication has Cronbach alpha (a) is 0.949, R 2 is 0.994 and a strong relationship with customer satisfaction. Communicating the company’s total-quality-management programme to vendors involves the same basic principles that are used to communicate the programme internally, i.e. vendors must be led to appreciate the benefits to be gained by using total quality management. For this purpose companies can publish attractive brochures and even conduct vendor clinics for encouraging their suppliers to join with them in specific quality improvement and quality management programmes. All these programmes are based on the principles of benefit from good purchaser-vendor relationship. Vendor development has Cronbach alpha (a) is 0.984, R 2 is 0.967 and a strong relationship with customer satisfaction. For improving productivity and quality in any organization, the key techniques are based on quantitative data. These techniques using quantitative data for the control of a process is called statistical process control (SPC). There are many quantitative techniques for the process control and improvements but these are generally referred as seven basic tools. Some of these techniques are not statistical in strict sense but commonly grouped under statistical techniques. SQC has Cronbach alpha (a) is 0.5, R 2 is 0.97 and a strong relationship with customer satisfaction. Small value of a shows that employees are not trained and involved to solve SQC problems at shop floor. Analytical techniques covers design of experiment (DOE) and failure mode and effect analysis (FMEA); these are the techniques which put direct impact on quality of design and has Cronbach alpha (a) is 0.853, R 2 is 0.859 and a strong relationship with customer satisfaction. Manufacturing a quality product, providing a quality service, or doing a quality job – one with a high degree of customer satisfaction – is not enough. The cost of achieving these goals must be clearly managed, so that the long-term effect of quality costs on the business or organization is a desirable one. These costs are a true measure of the quality effort. A competitive product or service based on a balance between quality and cost factors is the principal goal of responsible management. This objective is base accomplished with the aid of competent analysis of the cost of quality and planning. Vendor development has Cronbach alpha (a) is 0.984, R 2 is 0.967 and a strong relationship with customer satisfaction. 5.4 Stage 4: development and backup Rewards are the form of employee’s involvement in which the organization identifies and recognizes employees who have made positive contribution in the success of the organization. The reward should be commensurate to the situation and level of achievement, i.e. higher the achievement, the higher the reward. Recognition and rewards can be in many forms but it is always better to develop new ideas to suit the local situation for recognition. Thus, integrating the efforts at various levels and using the above factors of TQM implementation as the foundation and pillars of an implementation strategy an organization can plan a transition to total quality management culture. By using the above model, it is hoped that Indian companies shall be able to implement TQM in a systematic manner.


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6. Conclusion Quality is essence of all the business and the manufacturing activities is clear in the mindsets of the Indian organizations. It is evident that total quality management like many other management techniques propounded from time-to-time is based on a serious philosophy of continuous improvement and customer satisfaction. It cannot be applied hurriedly and results cannot be achieved overnight. It needed complete change in mind set which required enough time to believe on the concepts of TQM. It is a common observation that managers, in the initial euphoria of discovering yet another management technique, are impatient to apply it and expect quick results. In such cases disillusionment occurs rapidly causing the technique to be termed a fad, that’s what is happening with TQM in Indian industries. For effective implementation of total quality management, sincere efforts with clarity of objectives are being put in by Indian organizations. Sometimes they also employ external consultants also for bringing out the much-needed changes for improving quality of their products and services. This paper takes the current and important TQM implementation phenomenon, and draws extensively from the literature to build model, which can help a lot while adopting the TQM as whole not as a part, to get life time results. This recommended path develops a self-reinforcing cycle of commitment, communication and culture change. The model presented here provides a direct approach to top management to implement TQM programme through customer satisfaction as main focus. The new operating environment of the future will provide a set of challenges on various levels. A clear focus on defining and managing the customer side, process emphasis, and creating knowledge through innovation will comprise the new business environment. Under this new environment, TQM systems will shift towards a philosophy of quality based strategic management systems. In general, it is strongly recommended that the Indian industry must make all efforts to implement TQM, may be in a phased manner. This will help in making industries competitive on global level. References Antony, J., Fergusson, C., Waraood, S. and Tsang, H.Y. (2004), “Comparing total quality management success factors in UK manufacturing and service industries: some key findings from survey”, Journal of Advances in Management Research, Vol. 1 No. 2, pp. 32-45. Baisya, R.K. and Sarkar, R. (2004), “Customer satisfaction in service sector: a case study of the airline industry”, Journal of Advances in Management Research, Vol. 1 No. 2, pp. 73-9. Chaudhry, P.E. and Chaudhry, S.S. (2000), “Managerial perceptions of quality control in Japanese business”, Production and Inventory Management Journal, Vol. 4, pp. 34-9. Dalu, R.S. and Deshmukh, S.G. (2000), “An exploratory study of quality management practices in small and medium scale industry”, Industrial Engineering Journal, Vol. 29 No. 12, pp. 21-7. Dahlgaard, J.J. and Dahlgaard, S.M. (2006), “Lean production, six sigma quality, TQM and company culture”, The TQM Magazine, Vol. 18 No. 3, pp. 263-81. Heizer, J. and Nathan, J. (2004), Total Quality Management Manufacturing and Services, Thomson South Western, Perth. Ho, D.C.K., Duffy, V.G. and Shih, H.M. (2001), “Total quality management: an empirical test for mediation effect”, International Journal of Production R & S, Vol. 39 No. 3, pp. 529-48.

Khanna, V.K. and Vrat, P. (2002), “TQM practices in the Indian Automobile Sector”, Productivity, Vol. 43 No. 3, pp. 245-9. Khond, M.P. and Dabade, B.M. (2004), “TQM: a perspective of Indian manufacturing environment study”, Industrial Engineering Journal, Vol. 33 No. 10, pp. 21-7. Kumar, A. and Venkatesh, Y.D. (2002), “Implementation of Kaizen concept – a case study”, Industrial Engineering Journal, Vol. 31 No. 8, pp. 11-13. Lai, K.H. and Cheng, T.C.E. (2003), “Initiatives and outcomes of quality management implementation across industries”, Omega: The International Journal of Management Science, Vol. 141 -154. Mehra, S., Hoffman, J.M. and Sirias, D. (2001), “TQM as a management strategy for the next millennia”, International Journal of Operations & Production Management, Vol. 21 No. 5, pp. 855-76. Mohanty, R.P., Shrivastava, R.L. and Lakhe, A. (2006), “Linkages between total quality management and organisational performance: an empirical study for Indian Industry”, Production Planning & Control, Vol. 17 No. 1, pp. 13-20. Murthy, P.V.R. and Shrivastav, A.K. (2000) in Kohli, U. and Sinha, D.P. (Eds), “Empowering people for TQM through redesigning organisations”, Human Resource Development: Global Changes & Strategies in 2000, Proceedings of 23rd IFTDO World Conference on Human Resource Development, New Delhi, November, pp. 327-343. Robinson, P. (2001), “Continuous improvement principles”, Industry, Vol. 2.0, pp. 25-8. Shrivastava, R.L., Bhagade, S.S. and Lakhe, R.R. (2004), “Thinking performance measures with TQM factors & practices”, Productivity, Vol. 44 No. 4, pp. 586-94. Taylor, W.A. and Wright, G.H. (2003), “A longitudinal study of TQM implementation: factors influencing success and failure”, Omega: The International Journal of Management Science, Vol. 31, pp. 97-111. Further reading Nirmala, S. (1998), “Making TQM and work – some critical issues”, Productivity, Vol. 39 No. 2, pp. 280-5. Ong, K.H., Harvey, G.M., Shehab, R.L., Dechert, J.D. and Darisipudim, A. (2004), “The effects of three statistical control charts on task performance”, Production, Planning & Control, Vol. 15 No. 3, pp. 313-23.

(The Appendix follows overleaf.)


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Figure A1.

Appendix. Questionnaire


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Figure A1.

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Figure A1.


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About the authors Raj Kumar is Assistant Professor at Mechanical Engineering Department, Geeta Institute of Management and Technology, Kanipla, Kurukshetra (Haryana) and obtained his Master’s degree and Doctorate degree from NIT Kurukshetra. His areas of interest include industrial engineering, quality control, and total quality management in manufacturing sectors and service sector. Raj Kumar can be contacted at:[email protected]

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Dixit Garg is Assistant Professor at Mechanical Engineering Department, National Institute of Technology, kurukshetra (Haryana). He is a doctor in industrial engineering area and has published more than 60 papers in various journals and conferences of international and national repute. T.K. Garg is Professor at Mechanical Engineering Department, National Institute of Technology, Kurukshetra (Haryana). He has published more than 80 papers in various journals and conferences of international and national repute.



Does size matter for Six Sigma implementation?

Six Sigma implementation

Findings from the survey in UK SMEs Maneesh Kumar and Jiju Antony

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Department of Design Manufacture and Engineering Management, Strathclyde Institute of Operations Management, University of Strathclyde, Glasgow, UK, and

Alex Douglas Liverpool Business School, Liverpool John Moores University, Liverpool, UK Abstract Purpose – The purpose of this paper is to identify the quality initiatives implemented in UK manufacturing small and medium-sized enterprises (SMEs) and to perform a comparative analysis of quality management practices within Six Sigma firms against the non-Six Sigma manufacturing SMEs. Design/methodology/approach – To achieve the research objective, a survey-based approach is adopted by designing a short questionnaire addressing the issues of quality practices in SMEs. The paper encompasses the survey results from the first phase of Doctoral study to identify Six Sigma and non-Six Sigma companies. Findings – The response rate from the survey is 12.7 per cent out of 500 companies identified through the use of random sampling technique within the FAME and Dun & Bradstreet database of manufacturing SMEs. Data analysis was carried out using SPSS and Microsoft Excel. The findings from the study reveal that there is a significant difference in the performance of the Six Sigma/Lean firms against ISO certified companies. However, it is interesting to reflect on the findings of critical success factors (CSFs) of the sample firms. There is no significant difference in the perceived importance of the identified CSFs’ variables in the Six Sigma and ISO certified SMEs. Research limitations/implications – The focus of the study is only on UK manufacturing SMEs encompassing 64 firms. The small sample size and focus on manufacturing sector limits its generalisability to the entire SME population. Future study should focus on performing a comparative study of manufacturing and service based SMEs in UK or Europe. Originality/value – The novelty of the paper lies in conducting a comparative study on the performance of Six Sigma and non-Six Sigma UK SMEs and drawing out valuable lessons for academics, consultants, researchers and practitioners of continuous improvement initiatives like Lean and Six Sigma. Keywords Six sigma, Small to medium-sized enterprises, Critical success factors, Performance measures, Continuous improvement, United Kingdom Paper type Research paper

Introduction From 1980s onward, with the globalisation of the world market, a continuous trend towards downsizing of large firms and business outsourcing to smaller firms seems to be the latest trend. With the beginning of the new millennium, the degree of productivity demonstrated by small firms will be vital to a continued economic surge (Kuratko et al., 2001). The small and medium-sized enterprises (SMEs) constitute the


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bulk of enterprise with the major contribution to private sector output and employment in all economies of the world (Antony et al., 2005). SMEs contribution to world economy can be adjudged from the following: . European Union – SMEs are economically important with 98 per cent of an estimated 19.3 million enterprises defined as SMEs, providing around 65 million jobs (66 per cent) and more than half (52 per cent) of private sector turnover (EUROSTAT, 2003); . The OECD countries – SMEs represent over 95 per cent of enterprises in each of the 30 member countries and generate over half of private sector turnover (OECD, 2000); . Of all enterprises in China, 99 per cent are SMEs, providing employment to 75 per cent of total workforce (China Service SMEs, 2002). . In UK, SMEs economic significance and contribution in generating income and sustaining employment has been widely recognised by the government and policy makers (Jayawarna et al., 2003). According to the recent survey by Small Business Service (SBS), an agency of the Department of Trade and Industry (DTI), out of 4.3 million business enterprise, 99.9 per cent are SMEs (99.3 per cent were small (0-49 employees) with only 0.6 per cent (26,000) of medium sized (50-249 employees)) and 6000 (0.1 per cent) large companies (. 250 employees) (DTI, 2005). In terms of employment and annual turnover, SMEs account for 58.5 per cent and 51.3 per cent respectively (DTI, 2005). To adhere to one common definition of SME, this research considers an organisation to be an SME if it has less than 250 employees as stated by European Commission (2003) and Department of Trade and Industry (DTI) (DTI, 2005). In regards to the “quality” effort in SMEs as compared to large firms, there has not been a great deal of research (Kuratko et al., 2001). A few articles that mention the quality effort in SMEs tend to be conceptual with little empirical findings. “Quality” has emerged as a key management concern since the beginning of the 1980 s and has become essential to the success and survival of any business, large or small (North et al., 1998). Organisations not delivering reliable, defect-free products or services have ceased to be serious competitors. In recent years, thinking about quality issues has spawned a host of quality management strategies. In the quest for quality, organisations have pursued formalised change programmes or quality initiatives such as: Total Quality Management (TQM), continuous improvement methodologies such as Kaizen (Hamel and Prahalad, 1994); breakthrough improvement methodologies such as Business Process Re-Engineering (BPR) (Grover et al., 1995); and more recently Six Sigma (Kumar et al., 2006). Six Sigma has evolved significantly and continues to expand since its inception at Motorola in the mid-1980s to improve the process performance, enhance business profitability and increase customer satisfaction. Six Sigma is considered one of the most effective improvement drives among a large number of multinational organisations, with its adoption showing an upward trend (Desai, 2006). Six Sigma is a highly structured process improvement framework that uses both statistical and non-statistical tools/techniques to eliminate process variation and thereby improve process performance and capability. The aim of Six Sigma is to keep

the distance between the process average and the nearest tolerance limit to at least six standard deviations and thus reduce variability in products and processes in order to prevent defects (Wiklund and Wiklund, 2002). Six Sigma aims at achieving 3.4 defects per million opportunities (DPMO) with an assumption that the process mean shifts by 1.5 standard deviation off the target value. It provides business executives and leaders with the strategy, methodology, infrastructure, tools and techniques to change the way businesses are run. The adoption of Six Sigma as a business strategy by large multinational corporations such as General Electric, Honeywell, Motorola, Seagate Technology, Caterpillar, Raytheon, ABB, Bombardier and Sony, to name a few, has resulted in publication of reports in the professional magazines and journals about the success achieved by these organisations after the implementation of Six Sigma. In spite of a number of Six Sigma success stories in large organisations, many SMEs are yet to be convinced of the benefits from the introduction, development, implementation and deployment of Six Sigma. The objective of this research is to investigate into the quality practices of SMEs and compare the differences in performance of Six Sigma and non-Six Sigma firms. Literature review Once an owner of the business (in small firms) is convinced of the advantages conferred by Six Sigma and visualises the benefits, it is much easier to implement Six Sigma and to realise its benefits (Adams et al., 2003). In small companies, the top management team need to be visibly supportive of every aspect of a Six Sigma initiative and they must demonstrate by their active participation, involvement and by their actions that such support is more than lip service (Adams et al., 2003; Tennant, 2001). Snee and Hoerl (2003) argue that there is nothing inherent in Six Sigma that makes it more suitable for large companies. They also suggest that the greatest barrier to implementation in small companies to date has been the way major Six Sigma training providers have structured their offerings. More recently, as more and more sets of deployment guides and training materials have become available, the pricing structures have begun to change. Researchers and practitioners have proposed frameworks or guidelines for Six Sigma deployment in SMEs (Spanyi and Wurtzel, 2003; Gupta and Schultz, 2005; Schwinn, 2003; Waxer, 2004; PQA, 2003). The following points may be taken into account for the successful deployment of Six Sigma in SMEs. . visible management buy-in, commitment and support for Six Sigma deployment (Henderson and Evans, 2000; Antony, 2004); . linking Six Sigma to business strategy and customers (Henderson and Evans, 2000; Antony, 2004; Antony and Fergusson, 2004); . understanding the customer requirements; . shared understanding of core business processes and their critical characteristics; . training, rewarding and recognising the team members (Antony, 2004; Antony and Fergusson, 2004); . communicating the success and failure stories (Goldstein, 2001);


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.

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. .

selecting the right people and the right projects (Antony, 2004; Antony and Fergusson, 2004; Goldstein, 2001); monitoring cost of quality for identifying non-value added activities within the small business, reducing overheads to minimum and decimating the indirect costs (Huxtable, 1995); conducting monthly performance reviews (Goldstein, 2001); keeping everyone aware of Six Sigma through company meetings, postings and everyday activities.

The aforementioned factors may be considered as critical to the success of a Six Sigma program within SMEs. The idea of identifying Critical Success Factors (CSFs) as a basis for determining the information needs of managers was popularised by Rockart (1979). Research methodology A survey-based approach is used to identify and understand the continuous improvement (CI) initiatives prevalent or commonly and widely practised in SMEs. The survey instrument was constructed drawing upon prior literature on continuous improvement initiatives in SMEs and large organisations (Antony and Banuelas, 2002; Ghobadian and Gallear, 1996; Lee and Oakes, 1995; Snee, 2004; Wessel and Burcher, 2004; Yusof and Aspinwall, 1999; Antony et al., 2005; Antony et al., 2007; Kumar, 2007). The survey instrument was designed with the purpose of identifying Six Sigma and non-Six Sigma companies within UK and understanding their quality management practices. The primary data collection method used to achieve the research objectives was postal questionnaires with the self-addressed return envelop targeted to managing directors, operations directors, quality managers, and production engineers within the sample. Sampling method and procedure The questionnaire was mailed to 500 manufacturing SMEs in the UK, randomly chosen from the FAME and Dun & Bradstreet database. After sending three reminders to sample companies, 75 questionnaires were returned with only 64 completed and valid responses. This resulted in the response rate of 12.8 per cent, which is considered as an average response rate in researching manufacturing SMEs. Findings from the survey Demographic information The demographic details pertaining to sample companies includes information on the type of firm (local, joint venture, or part of multi-national corporation (MNC)); location of firm within UK; type of manufacturing industry which include 13 categories; size (small or medium); annual turnover ranging from less than £1 million to over £50 million; and position of the respondents including CEO/Managing Director, departmental head, quality manager and others. These variables may also be termed as control variables, used in the later part of analysis to understand the quality practices within the sample firms.

Among the 64 responding SMEs, 49 firms (76.56 per cent) are local, 14 (21.88 per cent) firms are part of MNC and one being a joint venture company. Geographically, majority of the SMEs are located across UK (43 or 67.1 per cent). The distribution of the 64 manufacturing firms by different industry is presented in Table I. It can be gauged from the table that the sample is representative of different kinds of manufacturing companies ranging from aerospace, automotive, electronics and semiconductors to food, paper and plastic manufacturing industry. One of the control variables included in the survey is the size of company, i.e. small (, 50 employees) and medium-sized company (50-249 employees). Of the respondents, 25 percent are small firms, whereas 75 percent of the respondents are medium-sized firms. A clustered bar chart is plotted for size of the company against its annual turnover, as shown in Figure 1. Out of 64 companies, four companies were not happy to discuss there annual turnover and thus not plotted in the chart. The figure shows that there is a significant variation in annual turnover within each sub group (small and medium).


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History of quality initiatives in SMEs The respondents were asked to list the quality initiatives implemented in the past or those currently deployed across their business functions. As depicted in Table II, a majority of the SMEs were ISO certified followed by implementing Lean, Investors in Industry specialisation Automotive Textiles Chemical Aerospace Electrical Pharmaceuticals Printing/paper Mechanical Food Electronics & semiconductor Others

Count 2 2 2 3 3 3 5 6 7 7 24

Table I. Industry specialisation of the sample firms

Figure 1. A clustered bar chart plot of size against company’s annual turnover

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628 Table II. History of quality initiatives in SMEs

Quality initiatives undertaken Six Sigma TQM Lean Kaizen BPR Theory of constraints ISO 9000 Investors in People (IIP) European Foundation for Quality Management (EFQM) Others No initiative undertaken

Count

%

10 5 17 7 1 1 49 10 0 9 8

15.6 7.8 26.6 10.9 1.6 1.6 76.6 15.9 0 14.3 12.5

People (IIP) and Six Sigma. None of the SMEs in the sample had implemented the European Foundation for Quality Management (EFQM) assessment model, which further confirms the argument in the literature that EFQM is not suitable for SMEs. The model is bureaucratic and time consuming, making it difficult for SMEs to allocate scarce resources for its implementation and follow-up. From the analysis, it was found that 12.5 per cent of the responding companies do not have any kind of quality improvement methodology or system in place. The focus in these firms is more on productivity and meeting the customers’ deadline. The majority of the respondents in other category were implementing British Retail Consortium (BRC) certification, especially within the food industry. Further in-depth analysis revealed that out of 49 certified ISO firms, 17 of the firms have implemented Lean and 10 of the 17 Lean firms have gone down the route of Six Sigma. This gives an indication that ISO may be the foundation or building block before embracing Lean and Six Sigma. This is an area of further research. Customer focused measures in the firm Respondents were given the option of multiple answers in order to capture all the measures existing within SMEs to understand the customer issues and problems. The results of the analysis are shown in Table III. The majority of the firms (89.1 per cent) used customer complaints as a medium to understand the critical business issues followed by criteria such as delivery time (60.9 per cent) and customer survey (59.4 per cent). This indicates that rather than using proactive measures to capture voice of customer such as survey and focus group, SMEs prefer to operate in reactive mode by addressing the complaints from their key customers. Customer satisfaction measures used

Table III. Measures used to capture voice of customers

Customer complaints Delivery times Surveys Repeat business Sales data Others

Count

%

57 39 38 30 28 15

89.1 60.9 59.4 46.9 43.8 23.4

The respondents were also asked to cite the three most important criteria that helped the firm to win customer loyalty. The criteria used to win orders were divided into seven categories and the results from the analysis shows that manufacturing quality, product reliability, and on-time delivery of the final product are the three most important criteria that SMEs focus on to win customer orders, as shown in Figure 2. Criteria used to win customer loyalty were also tested against the size of the firm that identified manufacturing (mfg.) quality, product reliability, and on-time delivery as the three most important factors irrespective of the size of the firm.


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Reasons for not implementing Six Sigma in SMEs Large organisations have been implementing and reaping the benefits of Six Sigma in the last two decades. However, its application in SMEs is still less evident in the literature. It is important to understand the perception of Six Sigma and factors hindering its implementation from the SMEs perspective. Firms were asked to state the reasons for not implementing Six Sigma as an initiative to drive continuous improvement efforts within their firms. As depicted in Table IV, the majority of the firms were discouraged to implement Six Sigma due to lack of knowledge of the system to start the initiative. This was followed by other reasons such as lack of resources, not sure if relevant, never heard, and cost issues. In the SMEs literature, the most common reason cited for not embarking on continuous improvement (CI) initiatives like TQM, Lean or Six Sigma is the availability of resources, commitment from the top management to invest in the

Figure 2. Criteria used to win customer loyalty

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Reasons for not implementing Six Sigma

Count

630

Lack of knowledge of system to kick off Not sure if relevant Availability of resources Never heard Cost issue Other competing initiatives ISO is accepted and necessary Leadership desire Suitable for large company Bureaucratic

12 9 8 7 7 6 5 5 3 2

Table IV. Reasons for not implementing Six Sigma in SMEs

required resources for successful implementation, and considering ISO certification as a destination to CI efforts. This study further enriches the literature by providing in-depth information on the reasons for not implementing Six Sigma. Critical success factors (CSFs) study The concept of identifying and applying CSFs to business problems is not a revolutionary new field of work (Caralli, 2004). It dates back to the original concept of success factors, as a basis for determining the information needs of managers, proposed by Daniel (1961) and popularised by Rockart (1979). CSFs are those factors which are critical to the success of any organisation, in the sense that, if objectives associated with the factors are not achieved, the organisation will fail – perhaps catastrophically so (Rockart, 1979). The respondents were asked to rate the importance of CSFs within the company, with 1 corresponding to “not important at all” and 5 as “very important”. In order to find the gap between the importance of CSFs and its actual practice in-company, a similar rating scale (1 represents “very poor practice” and 5 corresponds to “very good practice”) was used to measure the extent of implementation of CSFs within the firms. From Table V, it was found that management involvement and commitment is considered the most important factor and vision and plan statement and IT and Critical success factors

Table V. Gap analysis of CSFs of quality practices in SMEs

Management involvement and commitment Communication Link QI to employee Cultural change Education and training Link QI to customer Project selection Link QI to business Link QI to supplier Project mgmt skill Organizational infrastructure Vision and plan IT and innovation

Importance

Practice

GAP

Sig. *

4.73 4.70 4.44 4.38 4.27 4.22 4.19 4.14 4.14 4.03 3.97 3.97 3.83

3.97 3.59 3.36 3.19 3.27 3.36 3.22 3.28 2.97 3.17 3.57 3.46 3.17

0.76 1.11 1.08 1.19 1.00 0.86 0.97 0.86 1.17 0.86 0.40 0.51 0.66

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.003 0.002

Note: *Test performed at 5 per cent significance level

innovation received the lowest mean value of importance. Most of the variables had a mean importance equal to or greater than four. On the contrary, in practice within the company, each of these variables was found to be less applicable with mean practice value less than four for all factors. A t-test was performed to identify whether the mean value for importance and actual practice of CSFs are statistically different from each other. The result of the analysis shows that each factor is statistically significant in terms of application and perceived importance of CSFs within SMEs. It can be inferred from Table V that even though the company has got the quality systems or initiatives in place, still there is a huge gap in the level of importance and practice of CSFs, which may result in the poor organisational performance of the company. Comparison of CSFs between Six Sigma/Lean companies against ISO certified companies, details provided in Table VI, revealed that there is no significant difference in terms of importance of the CSFs in Six Sigma and ISO certified companies. SMEs implementing ISO perceive the importance of these CSFs in a similar way as firms implementing Lean and Six Sigma. From the CSFs findings, it clearly illustrates that irrespective of type of initiatives a SME is undertaking, management involvement and commitment is the most important factor to make the initiative successful followed by communication, employee involvement, culture change, training and focus on voice of customers. The top seven CSFs are related to the soft side or the human side of implementation rather than application of tools and techniques. The result reflects that it is the softer factors that make any change program successful rather than focusing more on the application of tools and techniques.


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Barriers to implementation of quality initiatives in SMEs Companies were asked to identify the top five inhibiting factors that were felt to be barriers to quality initiative implementation. The results of the analysis showed that

Critical success factors

Six Sigma/lean company Na Importance

ISO 9000 company N Importance

Management involvement and commitment Communication Link QIb to employee Cultural change Education and training Link QI to customer Project selection Link QI to business Link QI to supplier Project mgmt skill Organizational infrastructure Vision and plan IT and innovation

17 17 17 17 17 17 17 17 17 17 17 17 17

30 30 30 30 30 30 30 30 30 30 30 30 30

4.88 4.82 4.44 4.41 4.47 4.38 4.25 4.06 4.00 4.00 3.71 3.94 3.56

Note: a This sample includes company implementing lean or Six Sigma; Initiative

b

4.67 4.67 4.43 4.37 4.20 4.17 4.23 4.10 4.23 4.10 3.97 3.83 3.93

QI stands for Quality

Table VI. Comparison of CSFs between Six Sigma/lean against ISO certified SMEs

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about 71.2 per cent percent of the responding firms stated lack of resources as one of the impeding factors to the successful introduction of quality initiatives in UK SMEs. Lack of resources covered a large number of aspects including financial resources, human resources, and time. This was followed by lack of knowledge, poor training/coaching, internal resistance, poor employee participation, to name a few (see Table VII). Lack of resources is the most common impeding factors, as cited in the SMEs literature on CI initiatives that deters the progress of any change management programme in SMEs. The findings are similar to other researchers work on SMEs (Antony et al., 2005; Antony et al., 2007; Kumar, 2007). Comparing the benefits of Six Sigma against ISO-certified surveyed companies The respondents were asked to rate the benefits that quality initiatives had brought to their organisations since implementation. The respondents were asked to rate on a Likert scale of 1 to 5, where 1 ¼ negative benefit, 3 ¼ some benefit and 5 ¼ crucial. Table VIII summarises the key benefits gained from the implementation of Six Sigma and is compared against the performance of ISO certified companies with respect to variables mentioned in Table VIII. Testing of the mean performance of Six Sigma/Lean organisations against ISO certified firms revealed the significant differences in performance of an ISO certified SME as compared to a firm implementing Six Sigma. Performance of seven Lean firms out of 17 (SMEs not implementing Six Sigma) were also recorded with respect to the variables mentioned in the table and it was Barriers to implementation of QI

Table VII. Barriers to implementation of quality improvement initiatives in SMEs

Availability of resources Lack of knowledge Lack of training Internal resistance Poor employee participation Inadequate process control techniques Changing business focus Lack of top mgmt commitment Poor delegation of authority Poor supplier involvement Poor project selection

Performance measures

Table VIII. Performance measures of Six Sigma/lean company vs non- Six Sigma/lean company

Reduction in scrap rate Reduction in cycle time Reduction in delivery time Increase in productivity Reduction of cost Increased profitability Increased Sales Reduction of customer complaints Reduction of employee complaints

SS/lean org. Mean Std dev. 3.52 3.38 3.24 3.79 3.50 3.40 3.50 3.65 3.27

0.829 0.875 0.872 0.726 0.777 0.770 0.900 0.950 1.072

Count

%

42 35 33 32 27 24 21 18 17 16 5

71.2 59.3 55.9 54.2 45.8 40.7 35.6 30.5 28.8 27.1 8.9

Non-SS/lean org. Mean Std dev. 2.82 2.80 2.84 2.84 2.88 2.35 3.04 3.07 3.00

0.872 0.940 0.926 0.746 0.752 0.797 0.889 0.961 1.087

Sig. *value 0.000 0.003 0.002 0.000 0.000 0.000 0.003 0.003 0.024

revealed that the mean performance of these firms were above ISO certified SMEs but below firms implementing Lean and Six Sigma. This analysis gives an indication that Lean firms implementing Six Sigma have realised more benefits as compared to SMEs implementing Lean on its own. Six Sigma firms are performing much better on the operational metrics like reduction in scrap rate, cycle time, delivery time and increase in productivity. Even in the strategic measures of organisational performance, i.e. reduction in cost, increased profitability and increase sales, Six Sigma and Lean firms out perform ISO certified SMEs. Conclusion This study presents the results of the survey conducted in UK manufacturing SMEs to investigate into their quality practices and measure its impact on the organisational performance of SMEs. Results of the survey revealed that factors critical to success of quality initiatives are equal in importance, irrespective of type of initiatives implemented by the firm. Management Commitment and Strong Leadership is required to make any change initiatives successful in the organisation. It should also be linked to employees in terms of training, making resources available and establishing good communication with them. However, the operational and strategic performance metrics of SMEs implementing Six Sigma differs significantly to a ISO certified companies. This gives an indication that Six Sigma is beneficial for all type of firm, irrespective of the size of the firm. This statement needs to be further validated by conducting in-depth case-studies in SMEs implementing Six Sigma and compare with the performance of non-Six Sigma firms. The second phase of this research project will address the aforementioned issues. It is imperative for SMEs to have a strong management commitment and good leadership skills before embarking on the programme. Research had shown that Six Sigma initiative in many organisations have failed either due to lack of understanding of how to get started or due to failure to link the initiative to strategic business goals and measurable objectives. Management in such organisations are weak and often involved in fire-fighting, paying inadequate attention to softer issues such as leadership, culture change, employees training and education. If Six Sigma is only considered as implementation of statistical tools and techniques to solve complex problems in an organisation, it is doomed to fail due to its very weak linkage to strategic business objectives. References Adams, C., Gupta, P. and Wilson, C. (2003), Six Sigma Deployment, Butterworth-Heinemann, Burlington, MA. Antony, J. (2004), “Six Sigma in the UK service organisations: results from a pilot survey”, Managerial Auditing Journal, Vol. 19 No. 8, pp. 1006-13. Antony, J. and Banuelas, R. (2002), “Key ingredients for the effective implementation of Six Sigma program”, Measuring Business Excellence, Vol. 6 No. 4, pp. 20-7. Antony, J. and Fergusson, C. (2004), “Six Sigma in the software industry: results from a pilot study”, Managerial Auditing Journal, Vol. 19 No. 8, pp. 1025-32. Antony, J., Kumar, M. and Labib, A. (2007), “Gearing Six Sigma into UK manufacturing SMEs: an empirical assessment of critical success factors, impediments, and viewpoints of Six Sigma implementation in SMEs”, Journal of Operations Research Society, doi:10.1057/ palgrave.jors.2602437.


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Book Review : Voice of the Customer: Capture and Analysis The Reviewers K. Narasimhan, Regional Advisor (India), The Emerald Group Publications, UK RR 2009/1 Review Subject: Voice of the Customer: Capture and Analysis K.Yang Publisher Name: McGraw-Hill Place of Publication: Publication Year: 2008 ISBN: 978-0-07-146544-1 Price: £53.99 (hardback) Article type:Review Pages: 427 pp. Keywords: Emerald Journal: The TQM Journal Volume: 21 Number: 6 Year: 2009 pp. 636-637 Copyright: © Emerald Group Publishing Limited ISSN: 1754-2731 Creating products and delivering services which customers consider of superior value (functional, psychological and/or of convenience) has become more important in this knowledge and global age. Capturing and analyzing voice of the customer (VOC) has become the vehicle for organizations irrespective of whether they are pursuing a technology push or market pull strategy. This book shows how to achieve this in practice with the aid of examples. Kai Yang is the Executive Director of Enterprise Excellence Institute, Michigan, USA. He is also a Professor of Industrial and Manufacturing Engineering at Wayne State University, Detroit. He has wide experience in this field, and has co-authored the book Design for Six Sigma: A Roadmap for Product Development. The book comprises 11 chapters and is well supported by 165 illustrations (106 figures and 59 tables) and 4 pages of references. Chapter 1 introduces the key concepts of value, innovation and the VOC and their inter-relationships. It also gives an overview of the rest of the chapters.

Chapter 2, the longest chapter (80 pages), deals with the product development process from opportunity identification to manufacturing process preparation in five stages. Concepts introduced include product life cycle cost, theory of inventive problem solving (TRIZ), quality function deployment (QFD), design of experiments, four domains (customer, functional, physical and process) in the design process, information and knowledge mining, value-stream mapping, and lean operation techniques (one-piece flow, work cells, and pull-based production). Chapter 3 starts with a discussion of customer value and its components, before moving on to discussion on how to collect and analyze customer value data, how customer value changes over time, and how break-through products can be developed by capturing these changes. Finally, discusses how to link VOC information to product design specifics in a clear and precise manner. The next two chapters respectively deal with quantitative and qualitative methods used for capturing the VOC. Topics covered in the quantitative methods to collect VOC data include types of customer surveys, TEN stages of customer survey, design of survey instruments, sampling methods and internet surveys. The latter chapter deals in some depth with ethnographic methods of answering “What do customers really want?” Useful case studies and examples are included. Chapter 6 focuses on explaining how to analyze qualitative VOC data by methods such as “Affinity Diagram”, “Arrow diagrams”, to derive “Critical to customer satisfaction” metrics. It also briefly explains how to arrive at “Critical-to-Quality characteristics from the raw VOC data collected. Chapter 7 deals in depth the QFD methodology with clear and concrete examples. The next three chapters deal respectively with the related topics of brand development, value engineering and TRIZ. The final chapter briefly covers some of the basic descriptive statistical methods, commonly used probability distribution models (normal, Binomial and Poisson), and process capability indices. The mathematics involved in some chapters may put off readers averse to mathematics. A KJ Method (Affinity) diagram is very complicated and illegible and does not add any value. Overall, the book is a very well though out and a study of that really helps to acquire the skills necessary to develop products and services that will be appreciated and valued by a customer.

Book Review : Lean Six Sigma for Supply Chain Management: The 10-Step Solution Process The Reviewers K. Narasimhan, Regional Advisor (India), The Emerald Group Publications, UK RR 2009/2 Review Subject: Lean Six Sigma for Supply Chain Management: The 10-Step Solution Process J.W. Martin Publisher Name: McGraw-Hill Place of Publication: Publication Year: 2007 ISBN: 0-07-147942-2 Price: £21.99 (hardback) Article type:Review Pages: 429 pp. Keywords: Emerald Journal: The TQM Journal Volume: 21 Number: 6 Year: 2009 pp. 637-638 Copyright: © Emerald Group Publishing Limited ISSN: 1754-2731 The book comprises ten chapters, two appendices and a comprehensive glossary. The first appendix contains important supply chain metrics and the second contains key lean six sigma (LSS) concepts, listed by chapters. The introduction explains the reasons for writing the book and its goals, and gives a synopsis of the ten chapters. The chapters commence with key objectives and end with a summary. James W. Martin, a Master Black Belt and President of Six Sigma Integration, INC, a Lean Six Sigma Consulting firm, is an adjunct instructor at the Providence College Graduate School of Business, Rhode Island, USA. He has worked with diverse organizations including in retail sales, banking and insurance, and manufacturing. Chapter 1 presents 20 (12 key supply chain operational and eight key financial) metrics that should be identified to ensure business alignment, prior to creating LSS improvement projects. It is emphasized that it is imperative to understand the importance of Voice-of-the-Customer and match that with the Voice-ofthe-Business. A 10-step solution process (dealt in detail in the subsequent chapters) is also introduced. Deploying LSS Projects using Lean Tools is the theme of Chapter 2; it shows how the sales and operating plan (S&OP) team facilitates the communication process. It also shows the importance and role of lead-

time in reducing supply chain costs that can account for 50 to 70 percent of an organization's budget; and briefly touches upon the various ways of reducing the lead-time. Lead-time reduction strategies are discussed in depth in Chapter 4, with the aid of value stream mapping. Chapter 3 deals with the concept of demand management, appropriate tools and methods (aggregating demand, forecasting models and errors) to accurately estimate demand. Chapters 5 and 6 focus respectively on LSS applications to material requirement planning (MRP II) and how inventory models (fixed-order and make-to-order) are used to identify LSS projects to identify and eliminate excess and obsolete inventory investment. The concepts of “lean supply chain” and third-party logistics are first introduced in Chapter 7, and then the importance of integrating third-party suppliers into the LSS initiative is explained. Chapter 8 shows how to arrive at the root cause of problems by applying LSS tools such as process flow charts, cause-and-effect diagrams, time series plots, etc. Some of the operations research methods like queuing analysis, linear programming, simulation, etc. are just touched upon. The application of these tools and methods are explained with the aid of an example from a distribution call center. Chapter 9 explains the importance of conducting pilot studies to test the recommended solutions and reduce the risk of implementation by effectively communicating process changes based on facts. The concluding chapter uses a simple inventory model (Microsoft Excel based) to show the relationships between key process input variables and key process output variables and their impact on inventory turns ratio and investment. Finally, 25 LSS supply chain applications are briefly discussed. This book with 190 illustrations is of real use to practitioners and academics teaching supply chain management