Lean, Six Sigma and Lean Six Sigma

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Lean, Six Sigma and Lean Six Sigma: an analysis based on operations strategy ab

ac

ac

Everton Drohomeretski , Sergio E. Gouvea da Costa , Edson Pinheiro de Lima Andrea da Rosa Garbuio

& Paula

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Industrial and Systems Engineering, Pontifical Catholic University of Parana, Curitiba, Brazil. b

Graduate School of Management, FAE University Center, Curitiba, Brazil.

c

Electrical Engineering, Federal University of Technology – Parana, Curitiba, Brazil. Published online: 15 Oct 2013.

To cite this article: Everton Drohomeretski, Sergio E. Gouvea da Costa, Edson Pinheiro de Lima & Paula Andrea da Rosa Garbuio (2014) Lean, Six Sigma and Lean Six Sigma: an analysis based on operations strategy, International Journal of Production Research, 52:3, 804-824, DOI: 10.1080/00207543.2013.842015 To link to this article: http://dx.doi.org/10.1080/00207543.2013.842015

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International Journal of Production Research, 2014 Vol. 52, No. 3, 804–824, http://dx.doi.org/10.1080/00207543.2013.842015

Lean, Six Sigma and Lean Six Sigma: an analysis based on operations strategy Everton Drohomeretskia,b*, Sergio E. Gouvea da Costaa,c, Edson Pinheiro de Limaa,c and Paula Andrea da Rosa Garbuioa a

Industrial and Systems Engineering, Pontifical Catholic University of Parana, Curitiba, Brazil; bGraduate School of Management, FAE University Center, Curitiba, Brazil; cElectrical Engineering, Federal University of Technology – Parana, Curitiba, Brazil

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(Received 3 April 2012; accepted 12 August 2013) There is a growing need for operations management models that contribute to the continuous improvement of company processes, among them we highlight lean manufacturing, Six Sigma and, more recently, Lean Six Sigma (LSS). This article aims (1) to identify and analyse the differences and complementarities in the production decision areas for each one of the three models; (2) to identify the competitive priorities that lead to the best performance as a result of policies followed in the decision areas as a result of the adopted model. First, a theoretical conceptual model was developed based on a review of the literature, followed by a exploratory research questions applied to manufacturing companies that use the lean, Six Sigma or LSS manufacturing models in southern Brazil. The main results show that there are differences in the models in relation to the importance of the decision areas and the performance achieved in the competitive priorities. Individually, lean manufacturing, Six Sigma and LSS have varying degrees of importance in the Facilities, Vertical Integration and Production Planning and Control decision areas. The performance dimensions with the best performance are speed, quality, reliability and cost. Keywords: lean manufacturing; Six Sigma; Lean Six Sigma; operations strategy; survey

1. Introduction Organisations now face constant change in the external environment driven by heightened competition, more demanding consumers and a relatively unstable economic climate in many countries. Running operations at the lowest cost, with greater reliability and speed and a superior ability to change and continuously improve, are some of the pillars in the development of operations strategy in organisations that seek to survive in this competitive environment (Hayes and Pisano 1996; Ward and Duray 2000; Voss 2005; Datta and Roy 2011). Leong, Snyder, and Ward (1990) observe that content and process frameworks characterise operations strategy definitions. Performance dimensions and decision areas delimit the content framework. Process framework covers the design, implementation, management and review aspects. Appendices 1 and 2 present detailed definitions of performance dimensions and decision areas. The expression continuous improvement is quite popular and the concept is associated mainly with the total quality movement, present in models such as Six Sigma and other approaches like lean manufacturing; in this case, continuous improvement – also known as kaizen – is one of the fundamental foundations for lean systems. Caffyn (1999) conceptualises continuous improvement as a broad process centred on incremental innovation that involves the entire organisation. Continuous improvement is simple, easy to understand and requires a low investment; it is now considered one of the most efficient ways to increase the competitiveness of an organisation (Bessant et al. 1994; Shah and Ward 2007; Pettersen 2009). In this way, Chen, Li, and Shady (2010) point out that the implementation of programmes like kaizen helps to make operations more flexible. Nevertheless, there are difficulties in effectively implementing this concept in companies. And that is what stimulates the interest in finding new models and strategies. For decades now, the number of continuous improvement models has been growing based on the concept of improved quality and/or processes aimed at reducing waste, simplifying the production line, improving quality, etc. Some of these include the following: total quality management (TQM), lean manufacturing, Six Sigma and Lean Six Sigma (LSS). However, often times these models are unable to solve all of the company problems. To solve this deficiency, companies are adopting hybrid programmes like LSS (Bhuiyan and Baghel 2005). Individually, lean manufacturing and Six Sigma are unable to reach the improvement rates that LSS is achieving (Antony 2011). As a result, *Corresponding author. Email: [email protected] Ó 2013 Taylor & Francis

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some companies have been using both models in parallel for years, while others focus only on LSS as the only model for continuous improvement and operations management. The use of models that enhance operational performance by reducing waste, for example, adds value and gives companies a competitive edge. LSS is one of the models used and, when well applied, leads organisations to improve their performance. Among the performance impacts, we can highlight improved quality, reduced cycle time, in addition to creating value for stakeholders in all areas of the organisation (George 2002; Näslund 2008). Based on this affirmation, the following research questions were formulated: (1) is there a difference in the importance of the decision areas as a result of using lean manufacturing, Six Sigma or LSS?; (2) if there are, what are the decision areas most affected by each one of the models, in other words, which decision areas – and consequently the policies created and adopted – are more relevant to the implementation of each one of the models?; (3) for each one of the models, what are the performance dimensions (competitive priorities) that get the best results as a result of policies adopted in the different decision areas? Thus, to answer the proposed research questions, this article has two main objectives: (1) to identify and analyse the differences and complementarities in the production decision areas for each one of the three models; (2) to identify which competitive priorities get the best performance as a result of the policies adopted in the decision areas based on the adopted model. Thus, this article contributes principally to identifying the main characteristics of each model in relation to the decision areas and competitive priorities. 2. Literature review This section aims to present a brief discussion of operations strategy, the concept, application, tools and differences between the lean manufacturing and Six Sigma systems, as well as discuss the results of the combined application of lean and Six Sigma, that is, LSS. 2.1 Operations strategy The concern that organisations have regarding the importance of operations strategy could already be seen in the studies carried out by Skinner (1969, 1974). Skinner demonstrated that organisations sought to visualise the competitive threat, not only thinking in terms of how to increase productivity, but also how to compete and see the problem, this involved the efficiency of the entire manufacturing process; not only the work force. Along the same lines, Hayes and Wheelwright (1984) conceptualise the set of world-class manufacturing practices as being the best practices for achieving superior performance. They include the following: the skills and capacity of the work force; managerial technical competence; competence to meet clients’ expectations regarding quality; work force participation; investment in strategic development and developing flexible operations that are capable of responding quickly to the demands and changes in the market. In a competitive environment manufacturing companies need a performance edge in their production systems, Hayes and Wheelwright (1985) determined certain characteristics that set companies apart from one another, classifying them into four stages of operational effectiveness. In the first stage, production can offer a small contribution to the success of the organisation, while in the fourth, production provides an important source of competitive advantage. Leong, Snyder, and Ward (1990) found that there is a distinction among the studies on operations strategy and propose two complementary models, the process model and the content model. The process model shows how an operations strategy is formulated, implemented and reviewed. For this model, the literature presents different structures, like for example, those of Leong, Snyder, and Ward (1990), Hill (1983) and Slack (1991). The majority of these frameworks present the variables and logic in the process of developing an operations strategy, but they are unable to arrive at a level of detail in the operationalisation of the process. In terms of strategy content, Leong, Snyder, and Ward (1990) propose the content model, which involves the decision areas that have medium and long-term importance to operational functions and the performance dimensions based on the corporate objectives. For Hill (1983), the performance dimensions are separated by qualifying criteria and order-winning criteria. The operations system can be developed by adjusting the strategy and taking appropriate actions in manufacturing decision areas (Leong, Snyder, and Ward 1990). The pioneers in classifying decision areas were Hayes and Wheelright (1984). The authors classified them under two broad headings: structural and infrastructural. Maslen and Platts (1997) took it one step further by proposing they be divided into structural, infrastructural and human decision areas, considering that they are important for capacity building in manufacturing.

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In the context of the discussion on decision areas, Mills et al. (2002) divided the decision areas into two large groups: structural, related to long-term investments with high capital investments, and infrastructural, related to specific aspects of the organisation that include a large number of ongoing decisions, that do not require large volumes of investment (this is the classification of decision areas that will be used in this study). Table 1 demonstrates these divisions. To align the operations strategy with the business strategy, it is important to identify the competitive priorities of each product or family of products in relation to the market. There are various approaches for defining the most important competitive priorities (also known as performance dimensions). Table 2 illustrates some of the classifications for competitive priorities. This study used the classification put forth by Slack (1991), which considers competitive priorities to be:

• • • • • •

Quality: offer products that meet project specifications; Reliability: meet delivery deadlines; Flexibility: have the capacity to adapt operations whenever necessary and respond quickly, whether it is due to changes in demand or needs of the production process; Speed: strive to achieve a shorter interval of time than the competitor between the start of the production process and delivery to the client; Cost: offer products at a cost lower than the competitor; Innovation: design new products and launch more diverse products in faster development times than competitors.

The six competitive priorities presented are of great importance for choosing the operations management model, in the specific case of this study, lean manufacturing, Six Sigma or LSS. 2.2 Lean manufacturing Lean manufacturing originated as a result of the crisis in Japan after World War II. Unemployment rates were high and the market was essentially destroyed and productivity in Japan was inferior to that of America. Based on this problem, a systematic process was begun to banish waste (Womack and Jones 1996). Holweg (2007), in a vast study that sought to discuss the genealogy of lean production, points out that lean is the result of a dynamic learning process that adapted practices from the automotive and textile sectors in response to an environment with various contingencies in Japan at that time. In other words, it is not a package of resources but rather a model that helps organisations have a clear vision of improvements. Along the same lines, Furlan, Vinelli, and Dal Pont (2011) say that lean is not a simple package of implementing improvements. To implement lean, it is necessary to adapt the techniques to the characteristics of the organisation, clients and suppliers. For Staatsa, Brunnerb, and Upton (2011), lean aims to reduce human effort, stocks, delivery time and production space to meet the demands of the market while delivering high-quality products at the lowest price. The gains from implementing lean can be seen in the productivity results reached. In a study seeking to propose a model for quantifying the results of lean, Wan and Chen (2008) demonstrate that time, cost and value are the results generated by lean and directly contribute to organisational strategy. Chen, Li, and Shady (2010) highlight that with the implementation of lean the company cannot only increase its flexibility, but it can also improve its overall competitiveness. From 1968 to 1978, the productivity of American industries increased by roughly 23.6%, while Japanese industries showed an increase of 89.10% (Teresko “It came from Japan!” [Industry Week, February 1, 2005]).

Table 1. Decision areas in operations management. Areas of Structural Decision

Areas of infrastructural decision

Capacity Facilities Process technology operations function Vertical Integration

Organization Quality policy PPC Human resources Introduction of new products Performance Measurement Systems Performance Measurement and Reward (PMR)

Source: Adapted of Mills et al. (2002).

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Table 2. Competitive priorities. Wheelwright (1978)

Leong, Snyder, and Ward (1990)

Slack (1991)

Garvin (1993)

Efficiency Reliability Quality Flexibility Speed Cost

Quality Delivery performance Cost Innovation

Quality Reliability Flexibility Speed Cost Innovation

Quality Reliability Flexibility Speed Cost Services

Pepper and Spedding (2010) report that various actions are now being taken to incorporate Japanese industrial practices into American companies. One example is New United Motor Manufacturing, Inc. (NUMMI), a joint venture of American and Japanese car assemblers located in the USA. Also, Mitsubishi set up a joint venture with Volvo (NedCar) to benefit from the experiences of NUMMI, aiming to lower operational costs and improve quality. One of the starting tools for applying lean is value stream mapping (VSM). A study carried out in an Indian company, Singh, Garg, and Sharma (2010) showed that with the application of VSM it was possible to identify the various points of improvement necessary for lean to achieve the expected results. The studied company managed to reduce the work in process (WIP) by nearly 89%, the stock of finished products by 17.85% and reduce processing time by 12.62%, among other significant gains. Pepper and Spedding (2010) report that lean should be identified as a holistic philosophy, since although VSM is considered an important tool for identifying activities that add value, lean is based on a much larger set of tools like Single-Minute Exchange of Die (SMED), the 5’s and others. Lean manufacturing has five key principles (Womack and Jones 1996; Mason-Jones, Naylor, and Towill 2000; Bendell 2006; Dahlgaard and Dahlgaard-Park 2006; Shah and Ward 2007; Thomas, Barton, and Chuke-Okafor 2009) presented in Figure 1. For Furlan, Vinelli, and Dal Pont (2011), many companies that opted for the implementation of lean, also implemented TQM, generally achieving superior performance when compared to the isolated implementation of lean and TQM. Pettersen (2009) points out that lean differs from TQM in that it focuses on process, continuous improvement and has less emphasis on facts. The integration of the quality management system with lean can generate a series of benefits. Chiarini (2011) carried out a nine-year study in European companies that were ISO 9001 certified in addition to being at a mature stage in the implementation of lean. The author identified that the integration of lean and the ISO 9001 management system allows for the organisation to increase its efficiency and standardise its lean practices, like the functioning of kanban, total productive maintenance (TPM), kaizen events, lean metrics and others.

Figure 1. Characteristics of lean manufacturing.

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2.3 Six Sigma Six Sigma was launched by Motorola, in 1987. In 1988, when Motorola won the Malcolm Baldrige National Quality Award, Six Sigma became recognised as the reason for the company’s success. Between the 80s and 90s, Motorola achieved gains of 2.2 billion dollars as a result of applying the model. For Snee (2000), Six Sigma is a business improvement strategy that seeks to identify and eliminate the causes of defects or errors in business processes by concentrating on activities that are relevant to clients. The key components to the success of implementing Six Sigma are related to the commitment of top management, the supporting infrastructure, training and statistical tools (Henderson and Evans 2000; Van Iwaarden et al. 2008; Brun 2011; Gutiérrez 2012). The combination of Six Sigma with other strategies can further increase the benefits generated. One example is the integration of Six Sigma and TQM, the combined application of the two strategies enables organisations to gain a competitive advantage (Su and Kano 2003). Six Sigma principles define the content. Methodologies as systematised sets of techniques and procedures define the Six Sigma process view. Implementation is based on principles and process methodology selection, which constitute a success factor for Six Sigma projects (Bañuelas and Antony 2002; Lynch, Bertoline, and Cloutier 2003; Gupta 2005). Among the methodologies applied during the Six Sigma implementation phase, DMAIC is the most widely known. For Andersson, Henrik, and Håkan (2006), DMAIC is used for implementing Six Sigma projects in situations where the process is being studied. The main purpose of DMAIC is to guide the Six Sigma model application considering its five constitutive steps. Table 3 based on Kumar and Sosnoski (2009) shows the DMAIC steps and the corresponding quality tools to be applied. For Näslund (2008), Six Sigma implementation involves the following characteristics:

• • • • • • • •

an understanding of project expectations from the shop floor; leadership of top management; disciplined application of DMAIC; fast application of the project (3–6 months); clear definition of results to be reached; supplying of infrastructure to implement improvements; focus on the consumer and the process; focus on the statistical approach to improvement.

Chakravorty (2009) presents a model for the implementation of Six Sigma. According to the author, the first step is to conduct an analysis of the market strategy, the second step is to select a high level team for the implementation of improvements, the third step is focused on selecting the tools, the fourth step is for identifying opportunities for improvement, the fifth and sixth steps are for the implementation and control of results achieved. Mahanti and Antony (2009) conducted a survey among Indian software companies and found that the application of Six Sigma made it possible for those companies to not only produce better quality software, but they were also able to improve product performance, achieve greater productivity, reduce costs and increase customer satisfaction. The application of Six Sigma can generate a drastically reduces a company’s costs. For example, General Electric achieved a saving of US$ 2 billion in 1999, and DuPont/Yerkes plant in New York achieved a saving of US$25 million in 2000 (Kwak and Anbari 2006). Thus, Six Sigma, in addition to improving quality, drastically reduces the organisation’s costs. In relation to the maintenance of the Six Sigma model, Van Iwaarden et al. (2008) found that the maintenance of Six Sigma depends on a culture focused on quality in the organisation as a whole. Since according to the authors, the companies that do not use quality tools before the implementation of Six Sigma end up interrupting the programme, mostly due to the need for more complex statistical tools. Table 3. DMAIC methodology. N°

Phase

Tools

1 2 3 4 5

D – Define M – Measure A – Analyse I – Improve C – Control

Pareto analysis; Project charter Descriptive statistics; Process capability analysis Detailed process map; Fish-bone diagram Experimentation; New process Statistical process control

Source: Kumar and Sosnoski (2009).

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2.4 Lean Six Sigma (LSS) LSS emerges from the integration of the consecrated lean manufacturing production system with the efficient Six Sigma improvement methodology. Snee (2010) defines LSS as a business strategy and at the same time a methodology that increases process performance, resulting in greater client satisfaction and results. In a theoretical conceptual study, Arnheiter and Maleyeff (2005) found that LSS leads to an incremental increase in the level of quality of the products and reliability of processes and thus supports the implementation of lean practices like kanban, TPM and others. Along these same lines, Bendell (2006) showed that the integration of lean and Six Sigma led to reductions in waste, process variability and errors while contributing to improvements in the business processes. Chen, Li, and Shady (2010) identify that the LSS eliminated rework time, improved productivity, increased system flexibility and consequently reduced inventory levels between work stations. In relation to the comparison of the tools used by Six Sigma, lean and the tools common to both models, Antony, Escamilla, and Caine (2003), Pepper and Spedding (2010) and Salah, Rahim, and Carretero (2010) identified four common tools, as illustrated in Figure 2. Shah, Shandrasekaran, and Linderman (2008) conducted a survey of 2215 companies that combined the implementation of lean tools with Six Sigma projects and identified that certain lean practices have a greater influence on Six Sigma projects than others depending on how they are implemented. They affirm that lean and Six Sigma should be seen as complementary strategies. Su, Chiang, and Chang (2006) carried out a case study on a help-desk service company in the area of information technology. As the main results, the authors found that with the implementation of LSS the company reduced the service time by nearly 52%, in addition to reducing the cost of operations. In the same study, Su, Chiang, and Chang (2006) listed the benefits and some differences between lean and Six Sigma, as shown in Table 4. Pepper and Spedding (2010) propose the integration of lean manufacturing and Six Sigma. According to the authors, both the pull production system and Six Sigma require that the organisation has a culture focused on continuous improvement. The integration of lean and Six Sigma allows employees to have greater autonomy in relation to operational processes making the process of continuous improvement more solid. It also allows the organisation to obtain enhanced performance through the application of tools that contributes to the continuous improvement of processes. This makes it so the objectives developed by the organisational strategies are reached. One example of the benefits of integrating lean and Six Sigma is that the Six Sigma practices increase the quality of products, which facilitates the process of reducing inventory (Pepper and Spedding 2010). Table 5 demonstrates the synergy between lean and Six Sigma. Snee (2010) shows that the main objectives of Six Sigma and lean are aligned, that is, both to seek and to improve processes. Figure 3 presents the improvement objectives for the integration of Six Sigma and lean.

Figure 2. Six Sigma and lean common tools. Source: Antony, Escamilla, and Caine (2003), Pepper and Spedding (2010), Salah, Rahim, and Carretero (2010).

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Table 4. The benefits and challenges for Six Sigma and Lean. Methodology Six Sigma

Lean

Benefits

Cycle time reduction WIP reduction Cost reduction Productivity improvement Shorten delivery time Space saving Less equipment needed Less human effort Statistical or system analysis not valued Process incapability and instability People issues

Challenges

Uniform process output Defect reduction Cost reduction Productivity improvement Culture change Customer satisfaction Market share growth Product/service development System interaction is not considered because processes are improved independently Lack of specific speed tools Long project duration

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Source: Adapted of Su, Chiang, and Chang (2006).

Table 5. Synergies between lean and Six Sigma. Lean

Six Sigma

Establish methodology for improvement Focus on customer value stream Use a project-based implementation understand current conditions Collect product and production data document current layout and flow Time the process Calculate process capacity and takt time create standard work combination sheets evaluate the options plan new layouts Test to confirm improvement

Policy deployment methodology

Reduce cycle times, product defects, changeover time, equipment failures, etc. Source: Adapted of Pepper and Spedding (2010).

Figure 3. Improvement objectives of the LSS. Source: Adapted of Snee (2010).

Customer requirements measurement, cross- functional management project, management skills knowledge discovery. Data collection and analysis tools process mapping and flowcharting Data collection tools and techniques, SPC data collection tools and techniques, SPC process control planning Cause-and-effect, FMEA Team skills, project management statistical methods for valid comparison, SPC Seven management tools, seven quality control tools, design of experiments

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Thomas, Barton, and Chuke-Okafor (2009) conducted a case study in a small engineering company located in the UK. The authors report that with the integrated implementation of LSS on the production line where the pilot was implemented, there was a 55% reduction in scrap costs, an increase in overall equipment effectiveness (OEE) from 34 to 55%, a 34% increase in the time available for production and a 12% reduction in energy consumption per year. Jeyaraman and Teo (2010) conducted a survey in companies from the electronics segment to analyse the critical factors for successful LSS deployment. For this, through a survey of the literature, the authors identified the 25 factors that lead to success in the implementation of LSS, the main factors are linked to the involvement of senior management, a reward system for employees and frequent dissemination of results to all employees. Laureani, Antony, and Douglas (2010) conducted a case study in a call centre company to analyse the benefits of LSS. The authors found that LSS reduced call time, decreased operator turnover and streamlined the process. The authors found that with the implementation of LSS in a service company, the annual turnover fell from 35 to 25% and the company saw a reduction of US$ 1.3 million per year in the hiring process, training and dismissal, among others. Corbett (2011) conducted a study in two different companies that won the Baldrige Quality Award in order to identify the contribution of LSS for maintaining performance levels. The author was able to identify that the use of LSS practices enabled the companies to incorporate a culture of continuous improvement in all sectors, including employee training, the involvement of management, employee involvement, understanding value, which resulted in reductions in cost and improved quality, all helping to increase the scores for the Baldrige Award criteria. Besseris (2011) conducted a case study in a shipping company. The study sought to analyse the benefits that the application of lean, Six Sigma and green techniques generates for the company (green techniques are techniques for the optimum use of resources and reducing waste generation and emissions). By experimenting with a combination of the techniques, the author found that the company managed to reduce the delivery time (by increasing speed with improvements made with Six Sigma), reduce the emission levels of gaseous pollutants and reduce costs by reducing fuel consumption. Antony (2011) conducted research with professionals and academic researchers from the area to identify the differences between lean and Six Sigma. The main differences identified were as follows:

• • • •

Six Sigma requires longer training time than lean; Six Sigma requires a larger investment than lean; Lean seeks to reduce the inefficiency of the process and Six Sigma aims to improve the effectiveness of the process; Six Sigma seeks to eliminate defects in the process and increase the capability and stability of the process.

3. Research methodology and design The exploratory survey was chosen as a research methodology to gain a better understanding of the contribution of lean manufacturing, Six Sigma and LSS in operations strategy. The results of isolated deployment of lean and Six Sigma have been reported more and more in recent years. However, understanding the differences and similarities of lean, Six Sigma and LSS in operations strategy reveals gaps that call for further exploration through exploratory studies. 3.1 Research design This research adopted Forza’s (2002) proposed methodological structure and process, which is organised in six phases: (1) synthesising a conceptual model from a literature review; (2) survey design; (3) survey refinement and testing; (4) data collection; (5) data analysis; and (6) final report. Thus, to achieve the objectives of this study, the research protocol was based on three steps:

• •

Initial Step: literature review involving four specific areas of knowledge: operations strategy, lean manufacturing, Six Sigma and LSS. The initial stage was completed with the development of a theoretical conceptual model (TCM) that guided the remaining steps of the survey (presented in Section 3.2 and shown in Figure 4); Intermediate step: preparation of the questionnaire for the exploratory survey, containing questions related to the TCM developed and the conducting of a pilot test for possible adjustments of the issues and their application to verify if the research hypotheses can be validated empirically. In this phase, the observation elements were identified and variables were measured so the questionnaire can be applied to the population of interest in the study. The analysis of questionnaire results made it possible to understand the relationship between the determinants;

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R1 DECISION AREAS COMPETITIVE PRIORITIES

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STRUCTURAL Capacity Facilities Process Technology Vertical Integration INFRASTRUCTURAL Organization Quality PPC Human Resources New Product Introduction PMS

Cost

Quality Reliability Speed Flexibility Innovation

R2

Figure 4. Conceptual model of the LSS theory.



Final Step: application and statistical analysis of the results of the exploratory survey to validate and improve upon what was developed in the previous steps.

3.2 Theoretical conceptual model To summarise the literature review, we developed a TCM that combines improvement models as lean manufacturing, Six Sigma and LSS and operations strategy defined by decision areas and performance dimensions. The conceptual model development for identifying and testing hypotheses is based on Malhotra and Grover (1998) recommendations that clearly define constructs delimited by a theoretical domain, which reflect the theme being studied. Figure 4 shows the conceptual model. The model presented in Figure 4 suggests two relationships: the first relationship (R1) aims to test the impacts from adopting the models in structural and infrastructural decision areas, and to identify the differences in the level of importance that each decision area has for each one of the models; the second relationship (R2) aims to identify, from the actions implemented in the decision areas, which competitive priorities result in the best performance. Note that the different actions are a consequence of the different models. 3.3 Building and validating the research tool The TCM presented proposes two relationships that have been translated into two hypotheses: H1: the adoption of lean manufacturing, Six Sigma or LSS models has a positive correlation with operations strategy decision areas.

Regarding hypothesis H1, it could be said that the adoption of the three models led to improved resource allocation, the development of processes (operations and management), the implementation of improvements and changing projects, and the formulation and management of the right policies. For example, a Six Sigma initiative improves process stability through decisions and activities related to Production Planning and the Control decision area. H2: the decision areas have a positive correlation with the performance dimensions.

We could see that in hypothesis H2 the decision areas resources, activities, processes, projects, systems and policies have a direct impact on performance, and if the adopted models positively influence decision areas, they will produce a

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positive impact on performance. Operations performance is directly connected to decision areas and connected to operations models through the mediation of decision areas. To test the hypotheses that unfolded from TCM, a questionnaire was developed with questions related to the operational management model of the companies and their relationship with the decision areas and performance dimensions. The questionnaire contains a total of 91 questions, organised into five blocks of questions:

• •

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• • •

Block 1 identifies which operations management model is adopted by the organisations that took part in the study. It consists of only one issue, which is the first variable of the study. Respondents had to choose from among four options: Six Sigma, lean manufacturing, LSS and one other; Block 2 identifies the company with eight questions related to the work area, number of employees, annual turnover and firm size, as well as the position of the respondent, his/her time in that position, educational background and years since graduation; Block 3 includes questions related to qualifying criteria and order-winning criteria. There are 12 questions so that respondents can compute the importance of each performance dimension for their clients and their performance in relation to the competition. In block 3, it was possible to identify the strategic context of the surveyed companies; Block 4 has 10 questions, which relate to the operations management model of the companies and the structural and infrastructural decision areas; Block 5 includes 60 questions, which contain the decision areas and performance dimensions.

Each question represents a variable of analysis. As there are 91 questions, there is the same number of variables. To facilitate the tabulation of the data, we developed a code for each item. Both nominal scales (multiple choice) and interval scales (the five-point Likert scale) were used for this study. In the first and second blocks, there are multiple choice questions. In block 3, a scale of 5 points of correlation (interval) was used. For blocks 4 and 5, the five-point Likert scale was used (1 – Strongly Disagree 2 – Disagree 3 – Agree or Disagree 4 – Agree 5 – Strongly Agree). This type of scale measures the respondent’s perception for each question. Through this perception, the variables are measured according to the level of agreement. The higher the level of agreement, the greater the relationship revealed by the question. A pilot test was conducted in order to improve the first version of the questionnaire. We looked at aspects such as clarity in the wording of questions, possible resistance to answering certain questions, the appropriateness of the response options, suitability of the sequence of thematic blocks and the time required for filling them out. The pilot survey was tested on 18 respondents who were not part of the population; they included professors with experience in the area, consultants and industry professionals with expertise in operations management and knowledge of the models studied. The suggested improvements were incorporated into the survey. In the test, it was determined that, on average, the respondents took 15 min to complete the questionnaire. The internal consistency of the data was checked, looking at the equivalence, homogeneity and inter-correlation of the items used in the measure. This means that items of a measure should align and be capable of measuring the same construct independently (Forza 2002). The consistency measure used was Cronbach’s alpha, which corresponds to a correlation coefficient of one item with another (Cronbach 1951). Cronbach’s alpha is expressed in terms of ρ, the Table 6. Summary of data collection instruments and data analysis. Block Theme

Scale

Method of Analysis

1 2 3

Identification of operations management model Identification of company and the respondent Identification of the level of importance of competitive priorities

4

Level of change necessary in the decision areas of the company for the implementation of the operations management model Classification of how much the actions in the decision areas from the use of the production management model adopted by the company contribute to the competitive priorities

Multiple choice Multiple choice Likert (1–5), 1 for less important, up to 5 for more important based on the market and competition Likert (1–5), attributed for level of agreement (1 if you agree less up to 5 if you agree more) Likert (1 to 5), attributed for level of agreement (1 if you agree less up to 5 if you agree more)

Descriptive statistic Descriptive statistic Non-parametric correlation test (Spearman-R) and cross bivariate tabulation Non-parametric correlation test (Kruskal–Wallis H Test)

5

Non-parametric correlation test (Kruskal–Wallis H Test)

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average correlation between ‘n’ items of measure in the instrument, where values over 0.75 are considered high (Peterson 1994). For the pilot test, the value obtained for Cronbach’s alpha coefficient was 0.955099, a good level of consistency. To confirm the result, the alpha was recalculated excluding each of the variables, and still, the lowest value recorded was 0.953578, which is a good level of consistency. With the data consistency information and the adaptations made based on the respondents’ suggestions from the pilot test, the data collection instrument was restructured. The choice of the range of questions for each of the five blocks in the questionnaire and the data analysis instruments for each block are summarised in Table 6. After the collection instruments were validated and the data were analysed, the survey was conducted and the data were analysed.

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4. Collected data and analysis The companies that participated in the study were selected because they either use or are in the process of implementing the Six Sigma, lean manufacturing or LSS models in their operations. The geographical delimitation criterion for the questionnaires was companies located in southern Brazil. Data collection was performed electronically with questionnaires completed in whole or in part, and returned by professionals who participate effectively in the implementation of the model for the management of their organisations. Thus, respondents had the necessary knowledge to answer the survey instrument. A total of 178 questionnaires were sent and 95 were sent back over a period of two months representing a return rate of 53.37%. For data processing, statistical methods were used to analyse data from the completed questionnaires. The operational procedures for processing the data included spreadsheets for organising and creating simple graphs, and the software Statistica, to analyse the data statistically. Parametric and non-parametric correlations were used to determine the possible relationships between variables. The statistical verification was performed by applying the Spearman correlation coefficient, Kruskal–Wallis H tests as well as bivariate cross-tabulation. As in the pilot test, the consistency of the Cronbach alpha coefficient was measured using Statistica software, for the 95 questionnaires answered, and the value was 0.956516488. Moreover, the alpha was recalculated deleting each of the variables, and still, the lowest value recorded was 0.955520. Based on these results, the data were considered consistent. The single question in block 1 identified that of the 95 companies studied, 11 use the Six Sigma model, 60 use lean manufacturing, 17 use LSS and 7 use another similar model, such as: ISO 9000, Deployment Guidelines and their own Operational Management Systems. Of the 95 questionnaires received, 7 do not relate to the models studied and were not included in the analyses; thus, 88 companies took part in this study. According to the National Classification of Economic Activities of the Brazilian Institute of Geography and Statistics, findings that showed 80% of the companies surveyed are from five industry sectors, they are metal products (16 companies), machinery and equipment (16), food and beverages (15), manufacture of modes of transportation (15), machinery and electrical appliances, and precision electronics and communications (8). This shows a diversification in the groups of respondents. However, around 53% of the respondents are from linked activity sectors (metal products, machinery and equipment, and manufacturing of modes of transportation). 4.1 The strategic context: importance vs. performance matrix This section presents the strategic context of the companies that participated in the survey by identifying their competitive priorities from the perspective of the market and the competition. Block 3 was included in the survey to create the importance and performance matrix. The measurement scales were inverted and reduced, that is, the respondent classified the least important criterion for order winning, on a scale from 1 to 5. Note that the data obtained in this study are related to the perception and experience of employees at the 88 companies surveyed. Table 7 shows the number of respondents who indicated that competitive priority contributes to the organisation achieving its competitive advantage for each competitive priority. Thus, order-winning competitive priorities were identified (Hill 1983). Based on the data, the most significant performance dimensions are reliability and quality, with average percentages of 74 and 70%, respectively, followed by speed with an average percentage of 59%. These findings indicate that for the companies studied, the order-winning criteria are speed, quality and reliability, making them indicators that on average represent a competitive advantage for more than 58% of companies. The performance dimensions flexibility, innovation and cost are performance dimensions that make a difference for more than 41% of companies on average; thus, they may be considered order qualifiers or in certain cases, order winners.

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Table 7. Competitive priorities and competitive advantage. Competitive priorities and competitive advantage

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Speed Quality Flexibility Reliability Cost Innovation

Number

% Average

52 62 38 65 37 38

59 70 42 74 42 43

Thus, it was found that the order-winning competitive priorities of the companies surveyed are reliability, quality and speed. These competitive priorities are based on the perception of what customers consider most important. It is worth noting that the competitive priorities considered most important from the point of view of the respondent companies are in line with the expectations generated from the deployment of one of the three models of operations management studied (lean manufacturing, Six Sigma and LSS). As presented in the literature review, the adoption of these models goes beyond reducing costs. With proper management and use of resources, the operations become more reliable, since the reduction in cycle time, for example, increases speed (Snee 2010). In conclusion, quality is one of the competitive priorities that generate the highest expectations among companies that deploy one of the three models, as the models are structured in order to significantly reduce defects. 4.2 Non-parametric correlation analysis Blocks 4 and 5 of the survey include questions that are intended to (1) verify the occurrence of a positive correlation between the model of operations management used in the company and the decision areas of the organisations (R1), (2) determine whether there is a positive correlation between the decision areas and the performance dimensions (R2). 4.2.1 Validation of the R1 relationship The objective of R1 was to test the impact the adopted models had on decision areas. Since in relationship R1 of TCM, the parametric data were correlated with the non-parametric data (numerical and interval), the statistical test used was the H test by Kruskal–Wallis one-way analysis of variance (Bisquerra, Sarriera, and Martínez 2004). With these assumptions, the hypotheses of the statistical test were defined. For R1:

• •

H0: p > 0.05, there are no differences among decision areas with the adoption of one of the three models; H1: p < 0.05, there are differences among decision areas with the adoption of one of the three models.

Once the hypotheses and the significance levels were determined, tests were carried out using Statistica software to evaluate the data relating to block 4 of the survey. The relationship results showed specifically for which relations H0 was rejected, that is, where there was a significant difference regarding the relationship of the management model adopted and the decision areas of the company. Doing the Kruskal–Wallis H test for each decision area, a significant difference was found between the Six Sigma, lean and LSS models in the decision areas Facilities, Vertical Integration and PPC. For the other decision areas, there was no significant difference in the actions taken. That is, regardless of the model used by the companies, the intensity with which they took actions was similar in the decision areas Capacity, Technology of Manufacturing Processes, Organisation, Quality, Human Resources, Introduction of New Products and Performance Measurement Systems (PMS). Table 8 shows the ‘p’ values of the Kruskal–Wallis H test obtained using the Statistica software. Although the three models studied have different characteristics yet similar objectives, it is acceptable that many of the decision areas overlap. For example, the cultural issue relates to the decision areas of Human Resources and Organisation. It is worthy to note that the research aimed to identify how necessary it is to take action in each decision area and not what actions should be taken. Both the Six Sigma and lean models value the qualifications of employees and check the profile of people that can carry out the duties and implement the model tools more effectively. A considerable change between the two models in the area of Human Resources is related to the degree of autonomy and employee participation. With lean, employees of all levels are encouraged to solve problems and continuously improve their

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Table 8. ‘p’ values, H-Kruskal Wallis test. Decision area

Model

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Capacity Facilities Process Technology Vertical Integration Organisation

Decision area

0.38 0.02 0.13 0.03 0.18

Model

Quality PPC Human Resources New products PMS

0.81 0.02 0.65 0.28 0.27

activities. While in Six Sigma, the emphasis is on training a smaller group of employees in the use of statistical and quality tools, forming a group of experts who will lead improvements, aligned with the results obtained by Gutiérrez (2012). Considering that LSS is the combined application of lean manufacturing and Six Sigma, it is important to analyse some of the differences between lean and Six Sigma in the decision areas. Some of the differences between lean and Six Sigma in Facilities are in the approach of lean related to continuous improvement of processes as a result of changes in layout and reducing excessive movements, for example (Womack and Jones 1996; Staatsa 2011). While Vertical Integration in Six Sigma projects calls for decisions aimed at the supply system and consequently its suppliers, lean requires a greater integration of the supply chain so that its objectives are met, such as more frequent and reliable delivery of materials (Shahin 2006; Näslund 2008). Finally, the PPC is an area of great importance for the lean system and that is why the area is going through major changes to adapt to the new production management system, from the process of demand management, capacity planning, signalling of the need for materials and the quantity to be produced, among others (Pepper and Spedding 2010; Singh, Garg, and Sharma 2010). In Six Sigma, the improvement projects are carried out without direct dependence on changes in the PPC area. 4.2.2 Validation of the R2 relationship To test the second relationship, the following question was asked for each decision area, ‘In relation to Capacity (and again for each of the decision areas), do the actions taken as a result of using the improvement management model and production management of your company contribute to the improvement of the following performance indicators: speed, quality, flexibility, reliability, cost and innovation?’ For each decision area and performance dimension, respondents had to mark the answer on a scale from 1 (‘if you agree less’) to 5 (‘if you agree more’). The Spearman correlation test (R) was carried out, because the two are interval scales. For R2:

• •

H0: R = 0. There is no positive correlation between the variables. H1: R ≠ 0. There is a positive correlation between the variables, where R is the correlation coefficient of Spearman.Significance level (α) = 0.05

Based on the results from the correlations of R1, the block of five questions from the survey instrument was tested, that is, the R2 relationship between the decision areas and the six competitive priorities. Table 9 illustrates the correlation test between R1 and R2. Table 9. Summary of correlations between decision areas and performance dimensions with a significance level α < 0.05. AD  CP Capacity Facilities Process technology Vertical Integration Organisation Quality PPC Human Resources New products PMS

Speed

Quality

Flexibility

Reliability

Cost

Innovation

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes No Yes Yes Yes Yes Yes Yes Yes

Yes Yes No Yes Yes No Yes Yes Yes Yes

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The results indicate that all of the decision areas correlate with at least part of the six competitive priorities: speed, quality, flexibility, reliability, cost and innovation. To arrive at more detailed conclusions, a bivariate cross-tabulation was carried out for all correlations. The set of data in Tables 10 and 11 shows the tabulation for all 10 decision areas. The tabulation shows the average percentage of interaction between the decision areas and performance dimensions, within a 95% confidence interval. The first part of the analysis focused on the decision areas that show no differences in the use of the models individually, that is, the intensity of the actions taken in these areas was essentially the same for all of the models used (Six Sigma, lean or LSS). They are as follows: Capacity, Process Technology, Organisation, Quality, Human Resources, New Products and PMS. For the decision area Capacity, the following competitive priorities stood out the following: speed and reliability (80%), cost (73%) and quality (71%). This means that the majority of companies studied achieved positive results in these performance dimensions when investing in capacity, regardless of the management model used. The confidence interval limits can be considered. These results are aligned with the results obtained by Laureani (2010), Snee (2010) and Antony (2011). Manufacturing Process Technology had high average percentages in the following performance dimensions: speed (81%), quality (79%), flexibility (73%) and reliability (71%). In the decision area Organisation, there was no performance dimension index over 70%, taking into account that the methodology was not studied in isolation, since with lean and LSS the Organisation area directly helps flexibility performance, for example.For the decision area Quality, the most significant dimensions were Quality (83%) and Reliability (75%), that is, companies that invested in quality had higher performance indicators for Quality and Reliability than Products and Processes. For Human Resources, the quality dimension stood out the most with 70%, that is, companies that invest in people, recruiting, training and communication have a high return on the quality indicator. This finding corroborates with the literature, because for the implementation of any one of the three models studied, there is a need for constant training of employees and this means that the focus of organisations is the people. For the decision area Introduction of New Products, performance dimensions like innovation (76%) and reliability (71%) stood out with a high average percentage. Results showed that when policies were created affecting new products, innovation and reliability indicators tended towards positive results, that is, for the companies surveyed, product innovation results in increased reliability. In the case of lean more specifically, this model shows the need for a Table 10. Cross tabulation for decision areas and the competitive priorities.

Capacity

Process technology

Facilities

Vertical integration

Organisation

CP  DA Performance objective/model



% Average



% Average



% Average



% Average



% Average

Speed Quality Flexibility Reliability Cost Innovation

73 65 60 73 66 54

80 71 66 80 73 59

68 64 64 62 62 51

75 70 70 68 68 56

74 62 66 65 59 55

81 79 73 71 65 60

63 65 61 64 59 51

70 72 68 71 66 57

56 60 54 60 59 43

62 66 59 66 65 47

Table 11. Cross tabulation for decision areas and the competitive priorities. Quality

CP  DA Performance objective/model



% Average

Speed Quality Flexibility Reliability Cost Innovation

53 74 53 67 56 49

60 83 60 75 63 55

PPC

Human resources

New products

PMS



% Average



% Average



% Average



% Average

72 50 63 62 58 40

81 56 71 70 65 45

51 62 45 52 56 46

57 70 51 58 63 52

58 60 62 64 60 68

64 67 69 71 67 76

62 68 61 67 68 56

69 76 68 74 76 62

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Table 12. Cross tabulation for facilities decision area and the competitive priorities. Decision area: facilities Six Sigma

Performance objective/model

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Speed Quality Flexibility Reliability Cost Innovation

Lean manufacturing

LSS

Number

% Average

Number

% Average

Number

% Average

9 9 9 8 7 7

82 82 82 82 73 64

39 38 38 43 39 35

68 67 67 75 68 61

16 13 13 14 12 5

100 81 81 88 75 31

longer product cycle. In other words, after the start of production of a new product, it is important to allow time for the results of the improvements to be incorporated. In order to detail the information of these decision areas, bivariate cross-tabulation was used again, testing the performance dimensions of each methodology individually, Six Sigma, lean and LSS. The results of the three areas that show significant differences for the three models – Facilities, Vertical Integration and PPC – are presented in Tables 12–14, which are also represented graphically for better understanding in Figures 5–7. For the decision area Facilities, analysing the Six Sigma model individually, it was found that the competitive priorities speed (82%), quality (82%), flexibility (82%), reliability (82%) and cost (73%) have a higher average percentage. While for the lean manufacturing model, the most outstanding dimension is reliability (75%). That is, approximately 87% of respondents pointed to this dimension as having the most significant results for the model in this decision area. With respect to LSS, it was found that the performance dimensions with the highest average percentage were the same as those for the Six Sigma model, the most notable being speed at 100%. For the decision area Vertical Integration in the case of the Six Sigma model, the priorities speed (82%), quality (82%) and cost (82%) had a mean percentage value over 70%, the most notable being reliability at 100%. In lean methodology, the competitive priorities with the greatest return were reliability (73%) and quality (70%). In LSS, the dimensions with the highest average percentages were quality (81%), reliability (75%) and cost (75%). Table 13. Cross tabulation for Vertical Integration decision area and the competitive priorities. Decision area: vertical integration Six Sigma

Performance objective / model Speed Quality Flexibility Reliability Cost Innovation

Lean manufacturing

LSS

Number

% Average

Number

% Average

Number

% Average

9 9 7 11 9 7

82 82 64 100 82 64

38 39 38 41 33 32

68 70 68 73 59 57

10 13 11 8 12 7

63 81 69 50 75 44

Table 14. Cross tabulation for PPC decision area and the competitive priorities. Decision area: production planning and control Six Sigma

Performance objective/model Speed Quality Flexibility Reliability Cost Innovation

Lean manufacturing

LSS

Number

% Average

Number

% Average

Number

% Average

11 8 10 9 9 4

100 73 91 82 82 36

43 28 38 38 34 28

78 51 69 69 62 51

14 10 11 11 12 5

88 63 69 69 75 31

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Figure 5. Facilities decision area and the competitive priorities.

Figure 6. Vertical integration decision area and the competitive priorities.

For the PPC decision area, the Six Sigma methodology presented speed (100%), flexibility (91%), quality (73%), reliability and cost (82%). For lean, speed stood out as the competitive priority with the highest return (78%). In the LSS model, the dimensions speed (88%) and cost (75%) had the highest average percentages. Analysing the ten decision areas studied, the Six Sigma methodology had significant results in the dimensions speed, quality, flexibility, reliability and cost – it is worth noting that most of the average percentages were above 80%. While lean manufacturing showed positive results in speed, quality and reliability at a confidence interval of up to 87%. Finally, LSS showed significant results in the dimensions speed, quality, flexibility, reliability and cost. It is worth noting that the three models studied can be complemented by other techniques. For example: (1) the implementation of TQM can complement the quality of performance; (2) process technologies and information technologies can improve performance in terms of speed, reliability and flexibility; (3) techniques like activity-based costing can lead to improvements in operational efficiency which have an impact on cost; (4) life cycle management and an integrated product development process can increase the number of new product launches and reduce launch time, thus having an impact on innovation.

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Figure 7. PPC decision area and the competitive priorities.

5. Conclusions The results showed that the surveyed companies that deploy lean manufacturing, Six Sigma or LSS models achieve superior performance in competitive priorities like quality, reliability and speed. Among the three competitive priorities with the highest averages, reliability was found to be the predominant main objective. It is therefore clear that organisations benefit from incremental cost reduction as a result of increased speed, reliability and improved quality. In this sense, an enterprise that decides to adopt a LSS model as an initiative in terms of its operations strategy tends to produce substantial improvements in the speed and time performance categories through decisions related to the Facilities decision area. Nevertheless, if a company decides to implement Six Sigma in its operations strategy, it also tends to impact speed and time, but the most affected decision area is Production Planning and Control. The decision areas Facilities, Vertical Integration and PPC showed significant differences between the Six Sigma, Lean and LSS models. That is, the strategy to operationalise these three areas differs depending on the model adopted by the organisation. While the other decision areas – Capacity, Manufacturing Process Technology, Organisation, Quality, Human Resources, Introduction of New Products and Performance and Rewards Measurement Systems – show the least amount of difference in their implementation strategy for the three models analysed. An important finding in this study is that although the literature stresses that one of the main objectives of the three models studied is cost reduction, the results reveal other competitive priorities for the companies surveyed, when analysed from the perspectives of market and competition. These results are based on the perception of the survey respondents. Another key point is that the application of LSS makes it possible to reach a wider range of competitive priorities compared to the isolated application of the models. That is, in addition to cost reduction, it allows the organisation to be faster, have superior quality, to offer greater reliability in products and services and flexibility to serve customers. As a suggestion for future work, we recommend that the research sample be broadened to increase the validity of the results since the respondent companies are located in the southern region of Brazil, from a wide variety of industry sectors. This directly resulted in the diversification of the competitive priorities given greater importance. It is important to note that over 50% of the companies surveyed are from the metal mechanic sector, which is of great economic importance for southern Brazil, in addition to being a segment that uses production improvement techniques with greater intensity. We also recommend applying this research protocol to small and medium-size enterprises (SME), because the presented results are mainly related to large companies. It is important to assess how SMEs adopt the three different models proposed, and how these models impact companies’ performance. SMEs could define a substantial part of the supply chains of large companies or operations networks, and their operations policies are interrelated. Kumar, Antony, and Tiwari (2011) observed that the implementation of Six Sigma projects is different in SMEs and large companies. Furthermore, we suggest the application of multiple cases to prove that these analyses were significant and to perform a cross-sectional study in companies that are in the process of implementing the three models to track changes in decision areas during implementation.

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We can conclude that the three proposed models have significant influence on performance dimensions through a specific set of policies and decisions in the structural and infrastructural decision areas in operations strategy. The present study allows operations managers to foresee the impact of the models adopted and to also use them in a normative way; that is, setting performance targets and adopting the appropriate set of processes and techniques. Understanding the role of decision areas in adopting lean, Six Sigma or LSS will improve resource allocation and the review of operations strategy policies.

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References Andersson, Roy, E. Henrik, and T. Håkan. 2006. “Similarities and Differences between TQM, Six Sigma and Lean.” The TQM Magazine 18 (3): 282–296. Antony, J. 2011. “Six Sigma vs Lean: Some Perspectives from Leading Academics and Practitioners.” International Journal of Productivity and Performance Management 60 (2): 185–190. Antony, J., J. L. Escamilla, and P. Caine. 2003. “Lean Sigma.” Manufacturing Engineer 82 (4): 40–42. Arnheiter, E. D., and J. Maleyeff. 2005. “The Integration of Lean Management and Six Sigma.” The TQM Magazine 17 (1): 5–18. Bañuelas, R., and J. Antony. 2002. “Critical Success Factors for the Successful Implementation of Six Sigma Projects in Organizations.” The TQM Magazine 14 (2): 92–99. Bendell, T. 2006. “A Review and Comparison of Six Sigma and the Lean Organizations.” The TQM Magazine 18 (3): 255–262. Bessant, J., S. Caffyn, J. Gilbert, R. Harding, and S. Webb. 1994. “Rediscovering Continuous Improvement.” Technovation 14 (1): 17–29. Besseris, G. J. 2011. “Applying the DOE Toolkit on a Lean-and-green Six Sigma Maritime-operation Improvement Project.” International Journal of Lean Six Sigma 2 (3): 270–284. Bhuiyan, N., and A. Baghel. 2005. “An Overview of Continuous Improvement: From the Past to the Present.” Management Decision 43 (5): 761–771. Bisquerra, R., J. Sarriera, and F. Martínez. 2004. Introduction to Statistics (in portuguese). 1st ed. Porto Alegre: Artmed. Brun, A. 2011. “Critical Success Factors of Six Sigma Implementations in Italian Companies.” International Journal of Production Economics 131: 158–164. Caffyn, S. 1999. “Development of a Continuous Improvement Self-assessment Tool.” International Journal of Operations & Production Management 19 (1): 1138–1153. Chakravorty, S. S. 2009. “Six Sigma Programs: An implementation Model.” International Journal of Production Economics 119: 1–16. Chen, J. C., Y. Li, and B. D. Shady. 2010. “From Value Stream Mapping Toward a Lean/Sigma Continuous Improvement Process: An Industrial Case Study.” International Journal of Production Research 48 (4): 1069–1086. Chiarini, A. 2011. “Integrating Lean Thinking into ISO 9001: A First Guideline.” International Journal of Lean Six Sigma 2 (2): 96–117. Corbett, L. M. 2011. “Lean Six Sigma: The Contribution to Business Excellence.” International Journal of Lean Six Sigma 2 (2): 118–131. Cronbach, L. 1951. “Coefficient Alpha and the Internal Structure of Tests.” Psycometrika 16 (3): 297–335. Dahlgaard, J. J., and S. M. Dahlgaard-Park. 2006. “Lean Production, Six Sigma Quality, TQM and Company Culture.” The TQM Magazine 18 (3): 253–281. Datta, P. P., and R. Roy. 2011. “Operations Strategy for the Effective Delivery of Integrated Industrial Product-service Offerings: Two Exploratory Defence Industry Case Studies.” International Journal of Operations and Production Management 31 (5): 579–603. Forza, C. 2002. “Survey Research in Operations Management: A Process Based-perspective.” International Journal of Operations & Production Management 22 (2): 152–194. Furlan, A., A. Vinelli, and G. Dal Pont. 2011. “Complementarity and Lean Manufacturing Bundles: An Empirical Analysis.” International Journal of Operations & Production Management 31 (8): 835–850. Garvin, D. A. 1993. “Manufacturing Strategic Planning”. California Management Review 35 (4): 85–106. George, M. 2002. Lean Six Sigma – Combining Six Sigma Quality with Lean Speed. New York: McGraw-Hill. Gupta, P. 2005. “Innovation: The Key to a Successful Project.” Six Sigma Forum Magazine 4 (4): 13–17. Gutiérrez, L. J. G., O. F. Bustinza, and M. B. Barrales. 2012. “Six Sigma, Absorptive Capacity and Organizational Learning Orientation.” International Journal of Production Research 50 (3): 661–675. Hayes, R. H., and G. P. Pisano. 1996. “Manufacturing Strategy: At the Intersection of Two Paradigm Shifts.” Production and Operations Management 5 (1): 25–41. Hayes, R., and S. Wheelwright. 1984. Restoring our Competitive Edge: Competing Through Manufacturing. New York, NY: Wiley. Hayes, R. and S. Wheelwright. 1985. “Competing through Manufacturing.” Harvard Business Review Jan–Feb.: 99–109. Henderson, K. M., and J. R. Evans. 2000. “Successful Implementation of Six Sigma: Benchmarking General Electric Company.” Benchmarking: An International Journal 7 (4): 260–81. Hill, T. 1983. “Manufacturing Strategic Role.” Journal of the Operational Research Society 34 (9): 853–860.

Downloaded by [Everton Drohomeretski] at 17:55 09 February 2014

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E. Drohomeretski et al.

Holweg, M. 2007. “The Genealogy of Lean Production.” Journal of Operations Management 25: 420–437. Jeyaraman, K., and L. K. Teo. 2010. “Conceptual Framework for Critical Success Factors of Lean Six Sigma Implementation on the Performance of Electronic Manufacturing Service Industry.” International Journal of Lean Six Sigma 1 (3): 191–215. Kumar, S., J. Antony, and M. K. Tiwari. 2011. “Six Sigma Implementation Framework for SMEs – A Roadmap to Manage and Sustain the Change.” International Journal of Production Research 49 (18): 5449–5467. Kumar, S., and M. Sosnoski. 2009. “Using DMAIC Six Sigma to Systematically Improve Shopfloor Production Quality and Costs.” International Journal of Productivity and Performance Management 58 (3): 254–273. Kwak, Y., and F. Anbari. 2006. “Benefits, Obstacles, and Future of Six Sigma Approach.” Technovation 26 (5–6): 708–715. Laureani, A., J. Antony, and A. Douglas. 2010. “A. Lean Six Sigma in a Call Centre: A Case Study.” International Journal of Productivity and Performance Management 59 (8): 757–768. Leong, G., D. Snyder, and P. Ward. 1990. “Research in the Process and Content of Manufacturing Strategy.” OMEGA International Journal of Managment Science 18 (2): 109–122. Lynch, D. P., S. Bertoline, and E. Cloutier. 2003. “How to Scope DMAIC Projects.” Quality Progress 36 (1): 37–41. Mahanti, R., and J. Antony. 2009. “Six Sigma in the Indian Software Industry: Some Observations and Results from a Pilot Survey.” The TQM Journal 21 (6): 549–564. Malhotra, M. K., and V. Grover. 1998. “An Assessment of Survey Research in POM: from Constructs to Theory.” Journal of Operations Management 16 (4): 407–425. Maslen, R., and K. Platts. 1997. “Manufacturing Vision and Competitiveness.” Integrated Manufacturing Systems 8 (5): 313–322. Mason-Jones, R., B. Naylor, and D. R. Towill. 2000. “Lean, Agile or Leagile? Matching your Supply Chain to the Marketplace” International Journal of Production Research 38 (17): 4061–4070. Mills, J. F., K. W. Platts, A. D. Neely, A. H. Richards, and M. C. S. Bourne. 2002. Creating a Business Winning Formula. Cambridge: Cambridge University Press. Näslund, D. 2008. “Lean, Six Sigma and Lean Sigma: Fads or Real Process Improvement Methods?” Business Process Management Journal 14 (3): 269–287. Pepper, M. P. J., and T. A. Spedding. 2010. “The Evolution of Lean Six Sigma.” International Journal of Quality & Reliability Management 27 (2): 138–155. Peterson, R. A. 1994. “A Meta-analysis of Cronbach’s Coefficient alpha.” Journal of Consumer Research 21 (2): 381–391. Pettersen, J. 2009. “Defining Lean Production: Some Conceptual and Practical Issues.” The TQM Journal 21 (2): 127–142. Salah, S., A. Rahim, and J. A. Carretero. 2010. “The Integration of Six Sigma and Lean Management.” International Journal of Lean Six Sigma 1 (3): 249–274. Shah, R., A. Shandrasekaran, and K. Linderman. 2008. “In pursuit of Implementation Patterns: The Context of Lean and Six Sigma.” International Journal of Production Research 46 (23): 6679–6699. Shah, R., and P. T. Ward. 2007. “Defining and Developing Measures of Lean Production.” Journal of Operations Management 25: 785–805. Shahin, A. 2006. “Critical Success Factors: A Comprehensive Review.” Proceedings of the International Conference on Problem Solving Strategies & Techniques – PSST 2006, Tehran. Singh, B., S. K. Garg, and S. K. Sharma. 2010. “Lean Implementation and Its Benefits to Production Industry.” International Journal of Lean Six Sigma 1 (2): 157–168. Skinner, W. 1969. “Manufacturing – Missing Link in Corporate Strategy.” Harvard Business Review 47 (3): 136–145. Skinner, W. 1974. The Focused Factory. New Approach to Managing Manufacturing Sees our Productivity Crisis as the Problem of How to Compete.” Harvard Business Review 52 (3): 113–121. Slack, N. 1991. The Manufacturing Advantage. London: Mercury Books. Slack, N., and M. Lewis. 2008. Operations Strategy. 2nd ed. Harlow: Prentice Hall. Snee, R. D. 2000. “Impact of Six Sigma on Quality Engineering.” Quality Engineering 12 (3): 9–14. Snee, R. D. 2010. “Lean Six Sigma – Getting Better all the Time.” International Journal of Lean Six Sigma 1 (1): 9–29. Staatsa, B. R., D. J. Brunnerb, and D. M. Upton. 2011. “Lean Principles, Learning, and Knowledge Work: Evidence from a Software Services Provider.” Journal of Operations Management 29: 376–390. Su, C.-T., T.-L. Chiang, and C.-M. Chang. 2006. “Improving Service Quality by Capitalising on an Integrated Lean Six Sigma Methodology.” International Journal of Six Sigma and Competitive Advantage 2 (1): 1–22. Su, C-T. and N. Kano. 2003. “A Comparison of TQM and Six Sigma.” Proceedings of the 33rd JSQC Conference, Nagoya, Japan, 15–18. Thomas, A., R. Barton, and C. Chuke-Okafor. 2009. “Applying Lean Six Sigma in a Small Engineering Company – A Model for Change.” Journal of Manufacturing Technology Management. 20 (1): 113–129. Van Iwaarden, J., T. Van Der Wiele, B. Dale, R. Williams, and B. Bertsch. 2008. “The Six Sigma Improvement Approach: A Transnational Comparison.” International Journal of Production Research 46 (23): 6739–6758. Voss, C. A. 2005. “Paradigms of Manufacturing Strategy Re-visited.” International Journal of Operations & Production Management 25 (12): 1223–1227. Wan, H., and F. Chen. 2008. “A leanness Measure of Manufacturing Systems for Quantifying Impacts of Lean Initiatives.” International Journal of Production Research 46 (23): 6567–6584.

International Journal of Production Research

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Ward, P. T., and R. Duray. 2000. “Manufacturing Strategy in Context: Environment, Competitive Strategy and Manufacturing Strategy.” Journal of Operations Management 18: 123–138. Wheelwright, S. 1978. “Reflecting Corporate Strategy in Manufacturing Decisions.” Business Horizons Feb.: 57–66. Womack, J. P., and D. T. Jones. 1996. Lean Thinking. New York: Simon and Schuster.

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Appendix 1. Performance dimensions

Orientation

Description

Doing the activities right

Do not commit mistakes; the products should be in conformity with their design specifications. When the manufacturing provides this capability to the production process, it gives to the process a quality competitive advantage Lead time, defined as the total amount of time between the placing of an order and the receipt of the goods ordered, should be lower than the competitors. When the manufacturing provides this capability to the operations system, it gives to the system a speed competitive advantage Keep delivery promises. Developing that manufacturing capability implies in correctly estimates the delivery dates (or alternatively being able to accept the client required deadlines); clearly communicating that dates to the client; and finally, to deliver the products on time. When the manufacturing provides this capability to the operations system, it gives to the system a dependability competitive advantage. Adapt or reconfigure the production system; being able to attend the client changing demands or to reconfigure the operations due changes in the production process or in the supply chain. This capability means that the manufacturing system is able to change in the right pace. When the manufacturing provides this capability to the production process, it gives to the process a flexibility competitive advantage. Design new products; being able to launch a more diversified collection of products in reduced product developing times, than the competitors. When the manufacturing provides this capability to the operations system, it gives to the system an innovation competitive advantage Manufacture the products at low cost; being more efficient than the competitors. In the long term, the only way to achieve this advantage is through the negotiation of low cost resources and efficiently running the production process. When the manufacturing provides this capability to the production process, it gives to the process a cost competitive advantage

Doing the activities faster

Doing the activities on time

Able to change the activities

Able to produce unique products

Doing the activities with low costs

Performance dimension Quality

Speed

Dependability

Flexibility

Innovativeness

Cost

Source: Slack and Lewis (2008) and Slack (1991).

Appendix 2. Decision areas

Structural decision area Product design Capacity Facilities Manufacturing process technology

Design for manufacturing; design for assembly; design and manufacturing processes specifications Capacity flexibility, shift work management, temporary labour, subcontracting policies Size, localization and manufacturing resource ‘focus’ Automation level, technology selection, layout, maintenance policy, internal process development capability (Continued)

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Vertical integration

E. Drohomeretski et al.

Make-vs.-buy strategic decisions, suppliers and procurement policies, suppliers’ dependence level Manufacturing vision, development paths, and best practices

Capabilities Infra-structural decision areas Organisation Structure, organisational and management processes, levels of centralization/decentralisation; planning and control systems; roles–responsibilities–autonomy; communication and learning processes Quality policy Quality policies, Quality models, systems and processes, Quality techniques, procedures and tools Production planning and Materials and production planning and control systems control Human resources Recruitment, training and development policies. Organisational culture, leadership and management styles. Reward policies. Competencies management model New products introduction Manufacturing and assembly design directives. Product development cycles and matrix. Organisational issues Performance measurement Performance indicators structure and use. Financial and non-financial measures. Relationand rewards ships between manufacturing performance and the rewards systems and processes Information systems Data and information acquisition, analysis and use processes and systems Continuous improvement Manufacturing operations processes continuous improvement system, processes and systems procedures development Source: Slack and Lewis (2008), Mills et al. (2002) and Hayes and Wheelwright (1984).