Impact of lean practices on performance measures in context to Indian ...

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JMTM 26,8

Impact of lean practices on performance measures in context to Indian machine tool industry

1218 Received 5 November 2014 Revised 5 February 2015 16 March 2015 6 May 2015 16 June 2015 Accepted 17 June 2015

Vikram Sharma Mechanical-Mechatronics Engineering Department, The LNM Institute of Information Technology, Jaipur, India

Amit Rai Dixit Department of Mechanical Engineering, Indian School of Mines, Dhanbad, India, and

Mohammad Asim Qadri Mechanical Engineering Department, Galgotias College of Engineering and Technology, Greater Noida, India Abstract Purpose – The purpose of this paper is to investigate the impact of lean production practices on performance measures in machine tool industry and determines the lean criteria that can have significant positive impact on performance. Design/methodology/approach – The research paper presents a blend of theoretical framework and practical applications. Extant literature was reviewed and to achieve the research objectives, an exploratory survey was carried out in machine tool supply chains located in the national capital region of India. Reliability test, factor analysis and stepwise multiple regression analysis bring out several lean criteria that can affect key performance measures. Findings – It was found that two lean criteria, namely, strategic partnership with suppliers and cross-functional cross-organizational design and development teams significantly influenced most of the key performance measures. Some lean criteria were found to negatively affect the overall competitive potential of machine tool firms. Originality/value – The findings can encourage the management of non-adopter firms to adopt lean thinking and to select the lean production criteria that can be implemented to have significant positive impact on key performance indicators in machine tool value chains. This study is perhaps among the first few that focus on machine tool industry in India. The paper provides useful insights to the lean production implementers, consultants and researchers. Keywords Lean production, Lean Paper type Research paper

Journal of Manufacturing Technology Management Vol. 26 No. 8, 2015 pp. 1218-1242 © Emerald Group Publishing Limited 1741-038X DOI 10.1108/JMTM-11-2014-0118

1. Introduction Indian manufacturing industry has in recent years, flourished and displayed “extra-ordinary” growth capabilities. This has become possible mainly because of improvement in living standards of the Indian middle class and increase in their disposable income. Certain liberalization steps taken by the Government of India, such as reduction of tariffs on imports, and refining the banking policies, have played an equally important role in bringing the Indian manufacturing industry to greater heights. But the manufacturing sector growth has largely been eclipsed by the growth in automobile sector. This can be attributed to large number of foreign collaborations Authors express gratitude to the experts whose valuable inputs helped in understanding relationship between the lean criteria and key performance measures used in this study.

and the quality revolution in automobile industry. According to Automotive Components Manufacturers Association (ACMA, n.d.), India is fastest growing passenger car market in Asia, second largest two-wheeler market and the largest three-wheeler market in the world. Some automobile component manufacturers such as Lucas-TVS Ltd and Rane (Madras) Ltd have received the prestigious Deming Grand Prize for quality excellence. On other hand, India ranks 16th in production and 11th in the consumption of machine tools in the world (IMTMA, n.d.). The country is yet to emerge as a key player in the global machine tools industry though it is likely to see substantial high-end machine tool manufacturing in coming years. The Indian Machine tool Industry has around 1,000 units all over the country, engaged in the production of machine tools, accessories/attachments, subsystems and parts (IMTMA, n.d.). Of these, around 20 are in the large scale sector which account for 70 percent of the turnover. The rest, fall in the SME sector of the industry. Approximately, 75 percent of the Indian machine tool producers are ISO certified. While carrying out this research, it was informed that still there is a gap between demand and supply with respect to machine tools. The industry needs to move toward increasingly sophisticated computerized numerically controlled (CNC) machining centers, driven by demand from key user segments, such as automobiles and consumer durables. Many Indian manufacturing firms striving to improve their competitive potential have adopted lean practices to improve operational efficiency. The automobile industry, in particular has taken the lead as the component suppliers and the OEMs belong to different geographical regions, and it leads to formation of complex supply chains. But the machine tool manufacturers, which can be considered focal point in machine tool supply chains, have been struggling to deal with difficult issues of achieving world-class quality and high-customer satisfaction while maintaining cost competitiveness. According to Department of Heavy Industries, Government of India report on Machine Tool Industry, this industry has been facing a difficult situation in many terms (DHI, n.d.). There are not enough large firms and there is little cooperation among small and medium players. Indian companies also lack adequate capabilities in terms of export marketing. Though India has the competitive advantage of engineering skills and low man-hour cost, yet this advantage cannot be capitalized due to lack of finance and lack of coordination between the user sector, the machine tools industry and the institutions of research. A search of “Indian Automobile Industry” using the popular Google search engine shows 4,540,000 results while a search for “Indian Machine Tool Industry” shows only 2,340,000 results; that is only 51.54 percent of the former. Thus, as the Indian machine tool industry prepares to make its mark globally, the need to implement lean production practices throughout the value chain has to be given high priority. The aim of this research is to determine the lean production criteria that can have significant positive impact on key performance indicators in machine tool value chains. To achieve this objective, an exploratory survey is carried out in machine tool supply chains located in the national capital region (NCR) of India. Stepwise multiple regression analysis using SPSS brings out several lean production criteria that can have significant impact on key performance indicators. The reminder of this paper is organized as follows: literature review is carried out in the next section, which is followed by section on research methodology adopted for this research. Factor analysis is given in Section 4 and hypothesis testing is given in Section 5. This is followed by discussion on findings in the penultimate section and conclusion in the last section.

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2. Literature review Production competence leads to improved business performance. Production competence is the preparedness, skill or capability that enables manufacturers to prosecute a product-market specific business strategy (Schmenner et al., 2009; Schmenner and Vastag, 2006). With competition getting ever more intense, the organizations realized that the quality consciousness alone would not guarantee sustained business growth and started exploring the lean concepts (Corbett, 2011). The lean concept aims at an optimized use of available resources in order to minimize waste (Anand and Kodali, 2009) and creation of high-quality goods and services at the lowest possible cost with maximum customer responsiveness (Kumar et al., 2013). A lean manufacturing philosophy requires respect for people, continuous improvement, a long-term view, a level of patience, a focus on process and ability to understand where the individual is in his or her development (Ahmad, 2013). Today, changing customer and technological requirements also force manufacturers to develop lean capabilities (Saleeshya et al., 2013). Perhaps, the foremost guiding principle of lean is “value for money” from the customers’ view point. Value is preciousness of a product or service that the customer is ready to pay for. The value stream is the system-wide view of all the activities taking place in an organization’s customer order fulfillment process. Value stream mapping (VSM) aims at identifying and eliminating all the non-value adding activities in an organization, thereby streamlining the processes and making the system look leaner (Mohanraj et al., 2011; Bhamu et al., 2012; Kuhlang et al., 2013). Under the lean lens, all non-value adding activities are regarded as some form of wastes that divert resources from the value adding activities. Some researchers suggest that, the VSM can serve as a good starting point for any enterprise that wants to be lean (Belokar et al., 2012). The goal of lean manufacturing system is doing more with less of time, space, human effort while giving the customer what they want in a highly economical manner (Paranitharan et al., 2011). Lean has been praised for empowering employees, and it has been criticized for intensifying work and impairing the health and well-being of employees (Hasle, 2014). People in the organization should possess the lean mindset and act in the lean way in order to make a lean initiative successful (Wong and Wong, 2011). Migrating lean to engineering processes such as product development is ongoing in the industry as the cost and value of products is determined primarily in the product development stage (McManus et al., 2007). Efforts have also been directed by certain original equipment manufacturers in automobile and aerospace industry to implement lean throughout the supply chains. Study by Hallam and Keating (2014) in US and UK industry investigates the use of lean enterprise self-assessment utilizing the Lean Enterprise Self-Assessment Tool developed by Massachusetts Institute of Technology and University of Warwick, as a means for measuring their current state of leanness in leadership/transformation processes, life-cycle processes and enabling infrastructure. The study reveals that a clear opportunity related to lean enterprise transformation exists in raising the maturity of these enterprises in understanding their current value streams and defining their future value streams. The leanness measure should utilize the fuzzy-logic methodology since lean is a matter of degree (Bayou and De Korvin, 2008). There are series of factors that can affect the success of lean adoption decision, such as a deep-rooted culture of total quality, the role of top management, a lean organizational structure, the lean leader role and institutional support (Martínez-Jurado and Moyano-Fuentes, 2014).

Few researchers have analyzed lean implementation in SMEs (Panizzolo et al., 2012; Sahay et al., 2011). A research by April et al. (2010) brings out that SMEs find it difficult to implement productivity improvement tools, particularly those associated with lean manufacturing. Also, SMEs suffer from scarcity of resources as compared to the larger companies that have more success due to greater access to resources. Still, SMEs can deploy soft technologies such as lean and six sigma for achieving dramatic results in cost, quality and time by focussing on process performance (Kumar et al., 2006). Leadership, management, finance organizational culture and skills and expertise, among other factors; are classified as the most pertinent issues critical for the successful adoption of lean manufacturing within SMEs environment (Achanga et al., 2006). Forza (1996) explores the differences between the traditional and the lean production plants and concludes that lean production plants use more teams for problem solving, take employees’ suggestions more seriously, rely more heavily on quality feedback both from workers and supervisors, document production procedures more carefully and have employees able to perform a greater variety of tasks including statistical process control. In Indian SMEs, applications of advanced manufacturing strategies have been far fewer (Sahay et al., 2011). Traditionally, SMEs have been skeptical of benefits of lean (Achanga et al., 2006). Ghosh (2013) examine the state of lean adoption in Indian manufacturing plants and its impact on operational performance. It was found that lean manufacturing is a multi-dimensional construct, and a majority of the respondents have implemented many dimensions of lean such as focus on customer needs, pull system, setup-time reduction, total productive maintenance, supplier performance, statistical process control and cross-departmental problem solving. Lean implementation resulted in higher productivity, reduced lead time, improved first-pass correct output, reduced inventory and space requirement. It was also found that first-pass correct output, reduced manufacturing lead time and increased productivity are the three main drivers of lean implementation. Though India has the competitive advantage of engineering skills and low man-hour cost, this advantage has not been capitalized due to lack of finance and lack of coordination among the user, the machine tools industry and the research institutions. According to Eswaramoorthi et al. (2011) lean implementation in the machine tool sector is still in the infant stage. Lean manufacturing is not the most “popular” operational or quality performance improvement methodology adopted by Indian organizations (Garza-Reyes et al., 2012). Review of research on lean implementation, indicates that it can be employed to become profitable and competitive as it reduces lead time and inventories, and cuts operating costs. Yet most of the lean implementation initiatives fail due to inadequate lean strategic plans. Lack of management support, insufficient training and resistance to change by the workforce are some other reasons for such failures. Review of extant literature reveals that the Indian machine tool supply chains have not caught the interest of researchers so far. A manufacturing company should demonstrate the potential benefits of lean not only within their own company but also their suppliers. The Indian machine tool industry is likely to see substantial high-end machine tool manufacturing and is set to become a key player at the global level. The competitiveness paradigm has opened the gate for revisiting various established strategies of supply chain management (SCM) and reassessment of their viability under the lean lens. As international markets grow increasingly competent, competition no longer takes place between individual businesses, but between entire value chains. SCM has been

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considered an effective strategy for integrating suppliers and customers with the objective of improving responsiveness and flexibility of manufacturing organizations. But now, the field of SCM is reaching a new stage. After a period dominated by enthusiasm for the newness of the idea of managing the stream of products across the whole chain, from supply through manufacturing to end users, it is now realized that “one size does not fit all” (Thakkar et al., 2012). Schmenner and Swink (1998) argue for the better coordination of the supply chain. The smoother the links and the faster the flow from initial materials to the end customer, the more productive all aspects of the supply chain should be. Arkader (2001) examine advances and barriers to renewed buyer-supplier relations under lean lens for auto parts suppliers in the Brazilian automotive industry. Suppliers perceived advances in the relationships as far as operational issues were concerned, but less so in terms of strategic issues. Anderson et al., (2000) examine the demand volatility amplification and its implications for the machine tool supply chains. The researchers found that volatility hurts productivity and lowers average worker experience. Further, it was inferred that machine tool firms that use smoother forecasting policies tend to impose less of their own demand volatility upon their supply base resulting in overall cost reduction. Wen et al. (2014) apply lean manufacturing with SCM to shorten lead time, reduce inventories and to identify bottleneck and suggest need for continuous improvements in production and SCM. Nag et al. (2014) affirm that in manufacturing industries, the levels of inventories at all stages (i.e. raw material, work-in-process and finished goods inventories) indicate the firm’s competitive positioning, strategies, internal processes and relationships with suppliers and downstream customers. The researchers identify three streams of research on supply chain strategy: Fisher’s model and its variations, lean and agile paradigms and push/pull systems. Wieland (2013) propose a model that enables a company to select the supply chain strategy based on risk probability and proposes that resilience is appropriate in the case of high-value chain risk. Chiarini and Vagnoni (2014) analyze the differences between Fiat’s world-class manufacturing system and Toyota’s Toyota Production System from a strategic management, management accounting, operations management and performance measurement dimension. It was found that Fiat’s WCM have a “grand strategy” focussed on quality and cost savings where quality must be reached with no trade-off with other strategies. Safety is pursued above all else and Fiat’s WCM cannot be implemented without this first achievement. A particular system called “cost deployment” measures wastes and losses on processes. The performance measurement system is structured and fosters day-by-day management as well as computer-based management. Furthermore, the performance measurement system is based on a complex and formal auditing and benchmarking process. According to Kennedy et al. (2013), though lean is powerful means to create value through reduction of waste, application of lean tools has received more attention in traditional manufacturing industries only and it needs to be explored in other sectors such as food manufacturing too. Iris and Cebeci (2014) examine relationship between ERP utilization and lean manufacturing maturity of Turkish SMEs and found that that effective usage of specific ERP modules can contribute toward applying lean principles, and vice versa. Karim and Arif-Uz-Zaman (2013) propose a methodology for implementing lean manufacturing strategies and a leanness evaluation metric using continuous performance measurement. Continuous performance measurement matrices in terms of efficiency and effectiveness are proved to be appropriate methods for continuous evaluation of lean performance. For lean success; not only is it necessary to implement

most of the technical tools but an organization’s culture needs transforming too. Furthermore, the alterations need to be implemented throughout an organization’s value chain (Bhasin and Burcher, 2006). Taj and Morosan (2011) investigate the impact of lean operations and design on the Chinese manufacturing performance, using lean assessment data from 65 plants in various industries. Exploratory factor and regression analyses are used to examine the associations among operations practice, production design and operations performance. Research indicated significant gaps in lean manufacturing practices among different industries, with the petroleum and hi‐tech industries performing relatively better. Overall a positive impact of lean operations on the performance of the Chinese manufacturing sector was established. Doolen and Hacker (2005) developed a survey instrument to assess the implementation of lean practices within an organization. A cross-section of electronic manufacturers in the Pacific Northwest was used for the exploratory study. The researchers found that electronic manufacturers are subject to a variety of challenging conditions that limit the applicability of lean production practices. Shah and Ward (2007) in their research define and develop measures of lean production. Rahman et al. (2010) studied the impact of lean strategy on operational performance in Thai manufacturing companies. Using a survey questionnaire, data were collected against 13 lean practices from 187 middle and senior managers belonging to 187 Thai manufacturing firms. The results indicate that JIT has a higher level of significance in LEs compared with SMEs, whereas for waste minimization there is a higher level of significance for SMEs compared with LEs. Flow management has a much lower level of significance for both SMEs and LEs. With respect to ownership, JIT is highly significant to operational performance for all three ownership groups (Thai, foreign and joint venture). Foreign-owned companies show a higher level of significance on operational performance for both waste management and flow management than Thai and joint venture companies. Lucato et al. (2014) explore the implementation performance of lean principles in Brazil and concluded that performance of lean initiative implementation is not uniform among the companies located in the researched area. Outcomes also show that the degree of implementation of the lean practices by multinational companies was higher than that for the national firms. Pettersen (2009) cautions that lean production is not clearly defined in the reviewed literature. This divergence can cause some confusion on a theoretical level, but is probably more problematic on a practical level when organizations aim to implement the concept. Thus, it is important for an organization to acknowledge the different variations, and to adapt the concept to suit the organization’s needs. Thanki and Thakkar (2014) found that the status of lean implementation and awareness in Indian industries is not so encouraging and the reason for that is, the human-related issues are not tackled properly. Quality and process technology, are the two key areas where industries are indicating inadequate efforts and poor insight. Khanchanapong et al. (2014) found that there is a complementary effect of manufacturing technologies and lean practices on operational performance of manufacturing firms in Thailand. Saurin et al. (2011) develop a framework for assessing the use of lean production practices in manufacturing cells. Jasti and Kodali (2014) emphasize on the need of empirical research in lean by collecting the samples from developing and undeveloped countries. Bhamu and Sangwan (2014) affirm that Indian automotive industry has been the focus of lean research as also the simultaneous adoption of leanness in supply chains. But there is lack of standard lean implementation framework.

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What we measure dictates what we do (Carreira, 2007). Little attention has been paid to performance evaluation and hence to measures and matrices of performance (Gunasekaran, 2001; Arif-Uz-Zaman and Ahsan, 2014). Due to the increasing complexity of supply chains, researchers and practitioners recognize the need for measuring and monitoring the performance, in particular, in those contexts where supply chains are considered a key factor of corporate success (Bigliardi and Bottani, 2014). But there are hardly any valid key performance indicators for lean machine tool supply chains. To study the impact of leanness on performance, a basic question that needs to be addressed is: what are the vital performance matrices for companies working in lean supply chain environment? The review of extant literature shows that there is little research on lean implementation in India and there is even little research on lean implementation in the machine tools sector. 3. Research methodology The main objective of the research was the identification of useful criteria which greatly influence the performance measures in the machine tool industry. For this, a survey questionnaire was adapted from similar research carried out in the automobile industry (Sharma et al., 2008; Sahay et al., 2011). The questionnaire was developed in English, as it is the most commonly used official language for written communication in private sector companies. The reliability and validity of survey instrument was established. The respondents were asked to rate on a five-point scale to measure the lean supply chain criteria in the questionnaire. The basic framework of research is shown in Figure 1. The objective of the research is identification of lean criteria which contribute more to the key performance indicators in machine tool supply chains. It was assumed that the machine tool supply chains comprise of machine tool manufacturers, component manufacturers and raw material suppliers. To fulfill the research objectives, a comprehensive questionnaire was designed to carry out a survey on lean production practices in Indian machine tool supply chains. A pilot survey was conducted to access the appropriateness of the questionnaire. The questionnaire was sent to 70 firms drawn from the list of Confederation of Indian Industries, The Associated Chambers of Commerce and Industry of India, Indian Machine Tool Manufacturers Association and Indiamart.com. After repeated mailing and the follow-up, we received 117 responses, with a response rate of 28.3 percent. Out of the 117 responses received, 50.4 percent are from the machine tool manufacturers, 35.8 percent are from the component manufacturers and 13.6 percent are from raw material suppliers. Responses to the survey questionnaire were collected from different executives at different levels so as to capture their views about lean, criteria about lean and their influential role. It was assumed that machine tool supply chains comprise of machine tool manufacturers, component manufacturers and raw material suppliers. It was also assumed that the lean practices of machine tool industry located in the NCR of India are representative of lean practices of machine tool industry all over the country. It was assumed that the machine tool manufacturers comprises manufacturers of the following types of machinery and its accessories: (1) metal cutting machinery such as lathes, milling machines, drilling/boring machines; and (2) metal forming machinery such as presses, punches, forges jigs and fixtures, etc.

Research identification

Introduction Objectives Investigate the lean practices in machine tool supply chains

Issues Understand the lean production criteria that positively affect performance

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Literature review Identification of gaps in literature

Conceptual framework of research

Designing framework for assessment of lean practices in machine tool industry Lean production criteria

Matrices for measuring performance

Pilot testing and validation

Survey questionnaire

Data collection

Data analysis

Inferences and conclusion

Based on technology, machine tools can be classified into CNC and some NC machines and conventional machines. The machine tool component industry includes manufacturers of the CNC systems, servo motors, spindles, bearings, guide ways and ball screws, cast iron products such as beds, columns and saddles and hydraulic systems. The interviewer was an academician having more than 12 years of experience in teaching manufacturing and operations management. Respondents comprised of employees working at the post of managing director, director, works manager, manager and engineer having more than two years of experience in the firm. In total, 8.45 percent of the respondents work in the purchasing function, 28.17 percent in design function, 38.03 percent work in manufacturing function, 4.23 percent work in planning and scheduling, 17.6 percent work in quality function and 3.52 percent work in information systems department. The number of employees in these organizations varied from less

Figure 1. Framework of research

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than 50 to over 1,000. In total, 92.25 percent organizations have ISO certification while 98.59 percent have an interactive web site. Only 16.90 percent have separate SCM department. SPSS for Windows was used in data analysis. In addition, to the analysis of survey questionnaire, the response received was validated through personal interviews by the research team. As 71.7 percent of the companies did not respond for our questionnaire, we were concerned about the non-response bias in the study. This issue has been addressed as follows. The issue was validated by using χ2 test with 95 percent confidence level and it was found that: (1) The distribution of the response group by geographical area shows no significantly different pattern relative to population data (Table I). (2) The distribution of respondents by ownership (Table II). (3) There is no significant difference in responses received before and after reminder. The following table provides the comparison of percentage of respondents without reminder and after reminder under various categories (Tables III-V). (4) Further, the characteristics, experiences and opinions of respondent organizations after reminder are not significantly different to those obtained by the first mailing. Hence providing validity to the results of the representative sample size and eliminating the non-response bias. It was essential to ascertain the reliability of the measures for lean production. A measure is reliable to the degree it gives consistent results (Cooper and Schindler, 2003). The most frequently measured form of reliability by the researchers is “internal consistency reliability.” It is the degree to which instrument items are homogenous and reflect the same underlying constructs. Cronbach’s α coefficient was used for estimating internal consistency reliability. Its value ranges from 0 to 1. The generally agreed limit for Cronbach’s α is 0.7, although it may decrease to 0.6 in exploratory research (Hair et al., 2006). Cronbach’s α was used to assess reliability of each scale.

Region

Table I. Distribution of the response group by geographical area

Response group (%)

Eastern 5 11.8 Western 25 29.4 Northern 30 17.6 Southern 40 41.2 2 Notes: No significant difference in distributions at 95 percent confidence level ( χ value ¼ 1.778; df ¼ 3; p-value ¼ 0.6196)

Ownership Table II. Distribution of respondents by ownership

Population (%)

Population (%)

Response group (%)

Public limited 20 17.7 Private limited 80 82.3 Notes: No significant difference in distributions at 95 percent confidence level ( χ2 value ¼ 0.0041; df ¼ 1; p-value ¼ 0.9483)

The results of reliability test for lean production practices in machine tool value chains are summarized in Table VI. The results of reliability test for performance measures are summarized in Table I. Cronbach’s α value for this study ranged from 0.650 to 0.801. These values of α in excess of 0.7 indicate acceptable internal consistency associated with all the measures. Hence the scales can be considered to be reliable. After determining the reliability, the validity has to be judged. Cooper and Schindler (2003) define validity as the extent to which a test measures what we actually want Geographical distribution

Without reminder (%)

Eastern Western Northern Southern Notes: No significant difference in distributions df ¼ 3; p-value ¼ 0.951)

Ownership

After reminder (%)

9.0 16.7 27.3 33.3 45.4 33.3 18.2 16.7 2 at 95 percent confidence level ( χ value ¼ 0.381;

Without reminder (%)

Impact of lean practices on performance measures 1227 Table III. Difference in responses received before and after reminder (based on geographical distribution)

After reminder (%)

Table IV. Difference in Public limited 27.3 16.7 responses received Private limited 72.7 83.3 before and Notes: No significant difference in distributions at 95 percent confidence level ( χ2 value ¼ 0.243; after reminder df ¼ 1; p-value ¼ 0.622) (based on ownership)

Participation by management level

Without reminder (%)

After reminder (%)

Managing Director 9.0 16.7 Director 18.2 16.7 Works Manager 27.3 33.3 Manager 27.3 16.7 Engineer 18.2 16.7 Notes: No significant difference in distributions at 95 percent confidence level ( χ2 value ¼ 0.434; df ¼ 4; p-value ¼ 0.980)

Scale Procurement practices Design and engineering practices Quality management practices Inventory management practices Information management practices Marketing management practices Distribution management practices Customers management practices

Cronbach’s α coefficient

Number of items

0.752 0.716 0.711 0.801 0.764 0.650 0.706 0.679

17 16 10 7 11 5 6 10

Table V. Difference in responses received before and after reminder (based on participation by management level)

Table VI. Reliability analysis for lean production practices

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to measure. In other words, it is the ability of the research instrument to measure what it is purported to measure. One widely accepted classification of validity is: content validity; criterion-related validity; and construct validity. In this research, content and construct validity were employed. The content validity of a measuring instrument (the composite measurement scales) is the extent to which it provides adequate coverage of the investigative questions guiding the study. The content validity of the instrument used in this research was ensured by discussing the research instrument with highly acclaimed academicians as well as senior managers from machine tool industry. Construct validity attempts to identify the underlying constructs being measured and determine how well the test represents it. It tries to find reasons for variance in the measures. It investigates adequacy of the instrument after establishing in theoretical sense that the constructs are meaningful. A widely used method of determining construct validity is factor analysis and has been used in this research as follows. 4. Factor analysis In this research, factor analysis was employed to access the validity of proposed model constructs. Hair et al. (2006) defines factor analysis as generic name given to a class of multivariate statistical methods whose primary purpose is to define the underlying structure in a data matrix. According to Cooper and Schindler (2003), factor analysis is a computational technique employed for reducing to a manageable number, many variables that belong together and have overlapping measurement characteristics. Factor analysis constructs a new set of variables based on relationship in the correlation matrix. While this can be done in a number of ways, the most frequently used approach is principal components analysis. This method transforms a set of variables into a new set of variables or principal components that are not correlated with each other. These linear combination of variables, called factors, account for variance in the data as a whole. The best linear combination makes up the first principal component and is the first factor. The second principal component is defined as the best linear combination of variables for explaining the variance not accounted for by the first factor. Thus, there may be a third, fourth and kth component, each being the best linear combination of variables not accounted for by the previous factors. The process is continued until all the variance is accounted for or may be stopped after a small number of factors have been extracted. In this research, for each of the item scales, factor analysis was used to reduce the total number of items to a smaller set of underlying factors. Principal component analysis was used to extract factors with eigenvalues greater than 1. Varimax rotation was used to facilitate interpretation of the factor matrix by making factors as pure as possible (each variable loads on to as few factors as possible). Bartlett test of sphericity and Kaiser-Meyer-Olkin measure of sampling adequacy were used to validate the use of factor analysis. The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial correlations among variables are small. Bartlett’s test of sphericity tests whether the correlation matrix is an identity matrix, which would indicate that the factor model is inappropriate (Table VII). In total, 17 procurement management practices were reduced to five underlying factors referred to as P1-P5 (Table VI). These factors address the need of strategic partnership with suppliers, supplier involvement and performance management, etc. In total, 16 design and engineering practices were reduced to four factors referred as DE1-DE4 (Table VI). These factors reflect the cross-functional and cross-organizational design and development teams, and improving production processes. Ten quality

Factors Procurement P1: supplier involvement

Percentage of variance explained Scale items

23.11

P2: simplified purchasing and E-procurement

16.89

P3: supplier performance management

16.24

P4: strategic partnership with suppliers

15.59

P5: onsite suppliers, supplying standardized parts Design and engineering DE1: cross-functional and cross-organizational design and development teams

9.32

21.66

DE2: optimizing design and manufacturing function

19.42

DE3: process and technology integration

16.59

DE4: improving production processes

13.39

Quality Q1: working for quality improvement in TQM environment

39.54

Q2: quality audit training and emphasis on continuous quality improvement

28.95

Factor loading

Reduce uncertainty for suppliers Supplier training and development Supplier integration in ongoing processes Active communication with suppliers Pull system Build commodity teams Emphasis on E-commerce in procurement Simplified purchasing processes Enterprise resource planning Outsourcing non-core functions Measure and manage supplier performance Cost quality delivery targets for suppliers Long-term relationships with key suppliers Reducing supply base Vendor rating and vendor certification Onsite suppliers Using common standardized parts

0.900 0.863 0.607 0.598 0.764 0.702 0.679 0.634 0.611 0.838 0.769 0.692 0.759 0.647 0.689 0.734 0.647

Cross-functional design development teams Involving suppliers and customers in design process Visual control to eliminate delay Total productive maintenance Designing based on commonality of parts Group technology Value stream mapping Advanced flexible automated system CAD/CAM systems Computer integrated manufacturing Focus on eliminating shop floor problems Single minute exchange of die Cellular manufacturing Poka-Yoka for mistake proofing 5S Production leveling

0.856 0.787 0.652 0.612 0.897 0.835 0.699 0.846 0.723 0.681 0.820 0.657 0.635 0.621 0.572 0.559

Quality circle Visibility of quality department Quality documentation and records Statistical process control Working with suppliers for quality Benchmarking to improve quality Quality audit Training in quality awareness Failure mode effect analysis Kaizen

0.925 0.864 0.860 0.751 0.729 0.713 0.915 0.823 0.800 0.731

(continued )

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Table VII. Factor analysis of lean production practices

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Table VII.

Factors

Percentage of variance explained Scale items

Inventory I1: supplier involvement in inventory management

36.33

I2: optimizing inventory levels

30.35

Information INF1: maintaining central information database for SC

32.91

INF2: maintain and share customer information in SC

29.35

INF3: in-depth information sharing within departments and with SC partners

21.22

Vendor managed inventory Just-in-time approach Collaborative planning Economic order quantity Good inventory visibility throughout the SC Regular on hand inventory analysis Reduce inventory not at cost of responsiveness

Factor loading

0.854 0.823 0.768 0.636 0.549 0.911 0.865

Centralized coordination of information system Regular upgradation of information needs Ability to track movement of goods in SC Inter-company information access Sharing costing information Customer information is integrated with SC Maintain common database IT managed logistic activities Discard obsolete information Share CAD files with suppliers Intra company in-depth information access

0.845 0.727 0.645 0.621 0.585 0.824 0.794 0.719 0.598 0.769 0.687

Marketing M1: marketing management

51.42

Promotional planning Emphasis on pull strategy Use of forecasting tools Customer feedback Principal of postponement

0.829 0.804 0.766 0.689 0.663

Distribution D1: distribution management

53.68

Collaborative plan forecast distribution Milk run system Use shared distribution facilities Optimize transportation Outsource distribution Consolidate warehousing

0.829 0.838 0.816 0.741 0.698 0.574

Customer C1: efforts to meet customer expectations

38.69

C2: customer service employee training

27.84

C3: proactive interaction with customers

21.69

Maintain database of customer profile Understand customer expectations Study demand patterns Study feedback from customers Product customization Customer service employee training Feedback shared with SC partners Minimize response time to customer query Regular customer contact programs Customer care services upgraded regularly

0.881 0.835 0.815 0.795 0.715 0.835 0.757 0.885 0.685 0.649

management practices were reduced to two principal components, namely, Q1 and Q2. These include working for quality improvement in TQM environment and quality audit, training and emphasis on continuous quality improvement. Seven inventory management practices were reduced to two underlying factors referred as I1 and I2. These include supplier involvement in inventory management and inventory optimization. In total, 11 information management practices were reduced to three components, namely, INF1, INF2 and INF3. These address the issues of maintaining central information database for the supply chain, sharing customer information and sharing in-depth information within organization at inter-department level as well as with supply chain business partners. Five marketing management practices were combined into a single factor referred as M1. Six distribution management practices were also combined into a single factor, namely, D1. Ten-customer management practices were reduced to three underlying factors, namely, C1-C3.

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5. Hypotheses The key performance indicators for machine tool supply chains were developed in consultation with industry practitioners. Researchers suggest that financial indicators such as cost saving and profits should not be the only criteria for performance measurement rather a balanced approach considering customer expectations, business process perspective and learning perspective too should be used to develop the key performance indicators for any industry type. Thus, a balanced score card was developed and listed in Table VIII. The hypotheses for this study were built based on literature review and then discussed with experts from machine tool firms. Many a research studies have discussed the benefits of implementing supply chain practices to various type of industry and performance measurement is considered vital for continuous improvement of supply chains (Senvar et al., 2014). Koh and Bayraktar (2007) found that SCM practices such as outsourcing and multi-suppliers and strategic collaboration and lean manufacturing have a direct significant positive impact on operational performance and an indirect

KPIs Quality of design (QOD)

Explanation

Design is simple, least expensive, conforms to product needs, meets environmental considerations as well as profit considerations Quality of Manufactured product meets design specifications, conformance (QOC) incoming material meets order specifications and material rejection rate is low Information is timely, accurate, brief, relevant, Quality of information (QOI) reliable, complete Quality of material Synchronized lead times and capacities in supply chain, flow (QOMF) optimized transportation function, no product damages during material flow and product reaches right place at right time Quality of Trust among supply chain partners, long-term relationships (QOR) collaborative relationships and high responsiveness Overall competitive Low-cycle time, high return on assets (ROA) and potential (OCP) market share, Cost saving and On time delivery

Balanced score card perspective Learning and growth perspective

Business process perspective

Customer perspective Financial perspective

Table VIII. Key performance indicators for machine tool industry

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impact on organization performance. Tan et al. (1999) through their research concluded that SCM practices such as quality management, supply base management, customer relationship management, analyzing competitor’s strategies and determining future customer requirement can have direct impact on corporate performance. Six hypotheses were framed for this research and are enumerated here: H1. Lean production practices significantly affect design quality. H2. Lean production practices significantly affect quality of conformance. H3. Lean production practices significantly affect information quality. H4. Lean production practices significantly affect quality of material flow. H5. Lean production practices significantly affect quality of relationships. H6. Lean production practices significantly affect firm’s competitive potential. Stepwise multiple regression analysis was used to test hypotheses and develop models that relate the seven measures of quality and competitive potential to 17 independent variables comprising lean SCM practices in the machine tool value chains. Durbin-Watson test was used to verify that the residuals were independent. Normal probability plots were used to verify that the residuals were normally distributed. The models were developed using SPSS tool and is given in Table IX. Consider Model 1, coefficient of determination (R2) is 0.481. Thus, in the above model, the independent variables explain 48.1 percent of quality of design. The R2 is adjusted to reflect model’s goodness of fit for the population. In multiple regression, F-test has an overall role for the model and each of the independent variables is Model 1

Table IX. Results of stepwise multiple regression

Model 2

DVs QOD QOC IVs P1 0.362*** P2 P3 P4 0.401*** 0.353*** DE1 0.441*** 0.497*** DE2 DE3 0.359*** DE4 Q1 0.226** 0.211*** Q2 I1 INF1 INF2 M1 D1 C1 C2 F-value 23.626*** 29.386*** R2 0.481 0.664 Adjusted R2 0.461 0.643 Notes: ***p o0.01; **p o0.05

Model 3

Model 4

Model 5

Model 6

QOI

QOMF

QOR

OCP

0.425*** 0.298*** 0.359***

0.357*** 0.419***

−0.841** −0.416*** 0.689*** −0.328** 0.337***

0.256** 0.995*** 0.326***

0.314***

0.327***

0.560*** 36.224*** 0.562 0.554

34.121*** 0.652 0.626

−0.702*** 0.563*** 39.658*** 0.652 0.589

21.338*** 0.650 0.626

evaluated with a separate t-test. Analysis of variance measures whether or not the equation represents a set of regression coefficients that, in total are statistically different from zero. The critical value of F is found using degree of freedom (df), with df for numerator equaling k, the number of independent variables, and for denominator, n−k−1, where n is the number of observation. The equation is statistically significant at less than the 0.05 level of significance. Regression coefficients for the model is shown in Table IX. Hence, the equation may be constructed as: QOD ¼ 0.401P4+0.441DE1+0.359DE3+0.226Q1+2.319. In multiple regression, standard error is a measure of sampling variability of each regression coefficient. “t” value gives statistical significance of each regression coefficient. The coefficients are statistically significant at less than 0.05 significance level. Thus, regression coefficients are both individually and jointly statistically significant. The Model 1 shows that four lean production practices: lean value chain development teams (DE1); process automation (DE3); continuous quality improvement in TQM paradigm (Q1); and long-term partnership with key vendors (P4) have significant positive impact on quality of design. Thus, the first hypothesis is accepted. In the Model 2, regression coefficients are both individually and jointly statistically significant. This model shows that the four lean production practices: lean supplier management (P1); lean value chain development teams (DE1); long-term partnership with key vendors (P4); and lean production processes (DE4) have significant positive impact on quality of conformance. Thus, H2 is accepted. The regression Model 3 is statistically significant. Thus, three lean production practices, namely: in-depth information sharing within departments and with value chain partners (INF2), lean value chain development teams (DE1), long-term partnership with key vendors (P4) has significant positive relationship with quality of information. This analysis lends support for H3. In the regression Model 4, the regression coefficients are significant both individually and jointly. Three lean value chain practices, namely, lean transportation and distribution (D1), in-depth information sharing within departments and with SC partners (INF2) and maintaining lean inventory levels (I1) have direct significant positive impact on quality of material flow at various positions in supply chain. This finding bolsters acceptance of H4. In Model 5, the overall regression model and the individual regression coefficients are significant at 0.01 level. Hence, the Model 5 is statistically significant. Five lean value chain practices, namely, customer service employee training (C2), long-term partnership with key vendors (P4), in-depth information sharing within departments and with SC partners (INF2), lean supplier management (P1) and lean value chain development teams (DE1) have direct significant positive impact on quality of relationships among value chain partners in Indian machine tool supply chains. Thus, H5 cannot be rejected. In the Model 6, regression coefficients are both individually and collectively statistically significant. Three lean production practices, namely, lean design and engineering, maintaining central information database and lean production processes, have positive correlation with overall competitive potential, which lends a support to H6. Thus, H6 is accepted. Interestingly, four lean production practices, namely, IT-based procurement practices based for a pull-oriented system, efforts to meet customer expectations, long-term partnership with key vendors and process automation show significant negative relation with competitive potential in this regression model. 6. Discussion All the six hypotheses tested to study the impact of lean practices on various key performance indicators were not rejected. This analysis supports the assertion that lean

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production practices influence performance measures of quality and competitive potential of Indian machine tool supply chains. Four lean production criteria; DE1, DE3, Q1 and P4 were found to be significant in the regression Model 1. This reflects the fact that having lean value chain development teams (DE1) can have a positive impact on quality of design. A well integrated R&D department is more efficient than the isolated R&D department of any company. Within a firm, R&D department should have strong linkages with various other departments as various functional departments can contribute to its success (Sharma et al., 2008). For example, marketing and after sales service department can provide information regarding what features the customers desire and to what extent. The marketing department at the same time can acquaint their customers with the various design features of their product. Production engineers decide manufacturing processes and the machines required for manufacturing of a product and can provide valuable information regarding the anticipated difficulties in implementing a design. The design department defines the raw material and part specifications to be procured by the purchasing department, which in turn keeps a track of vendors and their performance. Naturally design department will have much bearing on the quality of the final product. On the supplier side, R&D department shall have strong communication links with R&D departments of its vendors. Vendors can even get their designs validated. This would enable them to discuss the desired features and specifications of the product. Practice P4 too has significant positive impact on design quality. On the customer side, R&D department will have direct or indirect contact with their customers and hence a better understanding of their requirements and preferences. Supply chain partners should be willing to share critical information and engineering drawings with supply chain partners. Thus, while adopting the proposed model, lean value chain development teams should be set up with full-fledged vendor participation. Lean criterion DE3 has significant impact on quality of design (Model 1). This provides evidence to support the assertion that machine tool firms should acquire advanced flexible automation systems to have competitive advantage over rivals. Moreover, using CAD/CAM systems and working in computer integrated manufacturing (CIM) environment, improves quality of design as it helps to work in concurrent engineering environment where the design department can exchange design ideas, views, designs and drawing with other departments and improve their designs iteratively. Q1 (continuous quality improvement in TQM paradigm) is having direct positive impact on quality of design which shows that quality practices such as quality function deployment, and quality circle too can lead to improved design. Four lean criteria such as P1, DE1, P4 and DE4 bear significant positive relationship with quality of conformance (Model 2). Active communication with suppliers, providing necessary training and reducing uncertainty in terms of order frequency and order size leads vendors to work whole-heartedly to achieve best levels of conformance quality by making investment in latest technologies and initiatives. Reducing supply base is important, as it gives them confidence that the OEM is dependent on them and they get consistent large-sized orders. Developing long-term partnership with a few strategic suppliers based on mutual benefit and trust is key to success of a lean value chain. Not surprisingly, lean production practices such as efforts on eliminating shop floor problems, Poka-Yoka for mistake proofing and 5 S have a positive impact on quality of conformance. Such an outcome bolsters previous research work. Machine tool companies’ continuous focus on eliminating shop floor problems has reduced rejection rates greatly.

Four lean production criteria, namely, INF2, DE1 and INF1and P4 have a positive impact on quality of information (Model 3). Quality of information has assumed high importance in manufacturing supply chains. This analysis highlights the fact that lean production practices such as maintaining and sharing customer information in supply chain, setting up cross-function and cross-organizational design and development teams in supply chain and maintaining central information database for supply chain business partners improves quality of information flow in the supply chain. Most of the companies have either deployed or are in process deploying suitable information technologies. Customer information either collected by after sales service department or marketing department is shared internally with other departments as well as with supply chain business partners. Setting up lean value chain development teams facilitates sharing of information in a timely manner. The outcome also demonstrates need to maintaining central information database as compared to distributed database, since it facilitates access to timely, relevant and accurate information. Three Lean criteria, D1, INF2 and I1 have significant positive impact on quality of material flow (Model 4). This demonstrates the importance of lean transportation and distribution management in the value chain. Lean transportation and distribution variables such as collaborative planning forecast and distribution, using shared distribution facilities, optimizing transportation function and outsourcing distributions can significantly help in reaching right product, at right place, at right time, in right condition. Interestingly, the variable INF2, that is, in-depth information sharing within departments and with supply chain partners has significant impact on quality of material flow. This finding again highlights the need to maintain IT managed logistic activities and there should be willingness to share in-depth information with supply chain business partners. Maintaining lean inventory levels (I1) by following vendor managed inventory, just-in-time approach, collaborative planning and maintaining good inventory visibility in the value chain casts a positive impact on material flow in the supply chain. Five lean criteria, namely, C2, P4, INF2, P1 and DE1 show significant positive impact on quality of relationship among the partners in the value chain. This finding demonstrates the importance of customer service employees’ training. Also machine tool firms are setting lean targets for suppliers, and are at the same time keen to develop long-term strategic partnership with key vendors. In-depth information sharing with value chain partners (INF2) by sharing CAD files or costing information helps in developing stronger trust-based relationship. Lean supplier management and lean value chain development teams also contribute in strengthening relationships. Model 6 brings out that lean criteria DE2, P2, INF1, C1, P4, DE4 and DE3 have significant impact on overall competitive potential. Thus, lean design and engineering (DE2) has positive impact on overall competitive potential. This demonstrates the need of employing group technology, and VSM in the value chain. Grouping of parts into part families based on similarity of design attributes or manufacturing attributes or both can result in cost saving and higher return on assets. VSM is a proven tool for eliminating all those activities which do not add value from the customer perspective, thus lowering cycle time and improving ROA. That, DE2 has significant impact on competitive potential also supports the notion that machine tool companies should work to improve their design and engineering function to meet world-class standards. The need to continuously improve production processes using lean tools such as cellular manufacturing, SMED and 5 S is highlighted by DE4 having positive correlation with competitive potential. The model highlights the need for maintaining

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central information database for the supply chain (INF1), as timely and accurate information can be a major source of competitive potential for complex machine tool value chains. IT-based procurement practices for a pull-oriented system (P2) shows negative correlation to overall competitive potential of the supply chain. This phenomenon is seen to occur as E-procurement has not yet stabilized in procurement practices related to machine tool parts and components. In machine tool manufacturing, a large number of parts and sub-assemblies have to be brought together at one place and assembled. Due to the fragmented nature of the industry and the small size of the firms, most of the players have not implemented any of the latest E-procurement technologies. Most of the small scale manufacturers have failed to capitalize on the available E-procurement technologies largely due to financial constraints and lack of trust among supply chain partners. Thus, OEMs and component manufacturers have failed to realize the benefits of E-procurement in terms of competitive potential. An effort to meet customer expectations (C1) also shows a negative correlation to competitive potential in this sector. This can be attributed to the situation where demand presently exceeds supply. In order to remain competitive, the manufacturers save on resources related to efforts to meet customer expectations. P4, that is, long-term partnership is negatively correlated to competitive potential. Strategic partnership calls for risk sharing with the suppliers. This is likely to reduce the potential of low-cost realization through competition. The benefits of economies of scale have not accrued to the machine tool component manufacturers due to highly fragmented market structure. Process automation (DE3) is also having inverse relationship to competitive potential. One reason can be that this function is highly capital intensive and first tier and second tier component manufacturers do not look at it as critical factor to enhance competitiveness. 7. Conclusion The aim of this research was to investigate key lean production practices of Indian machine tool value chains and to determine the practices that can have significant impact on key performance measures. For this, a survey questionnaire was adapted from similar research carried out in automobile industry and reliability was checked using Cronbach’s α method. Responses were collected from machine tool firms of India. Using exploratory factor analysis the number of lean production practices was reduced to 17 factors from 72. Stepwise multiple regression models brought out several lean value chain criteria that can have significant positive impact on key performance measures in machine tool supply chains. Thus, the research makes use of scientific methodology to put forth results of survey carried out to comprehend the lean implementation practices in the Indian machine tool industry. The methodology can be adopted by researcher for further research. Such outcomes highlight the significance of lean implementation throughout the machine tool value chain as a strategy to improve quality performance and competitive potential. Though, there exists some studies on lean implementation by the Indian machine tool firms (Eswaramoorthi et al., 2011), there discussion is limited to examining the extent or level to which lean practices have been adopted and the ranking the hurdles faced in lean implementation. Our research goes a step further to highlight the relationship between lean practices and key performance measures of the machine tool industry thus bringing out those critical lean practices that can significantly affect the key performance measures of this industry. The study can be used to encourage the non-adopter to implement those critical lean practices that can positively affect key performance indicators.

The research highlights several research and managerial implications. The researchers can use this study to develop their own research models and lean implementation models. The survey instrument developed for this research can be adapted for studying lean practices in other industry types. For the industry, the research outcomes demonstrate the need for machine tool firms to have cross-functional, cross-organizational teams working in CIM and total quality management environment to improve quality of design. Discussing designs with suppliers and having close proximity to suppliers too makes positive impact on quality of design. For machine tool manufacturing, a large number of parts have to be assembled together. The quality of machine tool naturally depends on quality of components assembled. Reducing supply base to develop strategic partnership with key suppliers and providing economy-of-scale along with fiscal and technical support helps in improving conformance quality. Lean criteria such as Poka-Yoka, 5 S, and emphasis of eliminating shop floor problems have a positive impact on quality of conformance. Information exchange among value chain partners should be timely, accurate, brief, relevant, reliable, and complete. Results of hypothesis test reveal that the machine tool firms need to adopt lean value chain practices such as maintaining and sharing customer information in supply chain, setting up cross-function and crossorganizational teams and maintaining central information database for supply chain partners to ensure high quality of information in the value chain. Right product should reach right place at right time and in right condition. Statistical analysis demonstrates that collaborative planning forecast and distribution, using shared distribution facilities, optimizing transportation function and outsourcing distribution function can positively impact quality of material flow in machine tool value chains. In-depth information sharing within departments and with supply chain partners too has significant impact on quality of material flow. This research emphasizes on having long and trusting relationship with strategic supply chain partners. Lean practices such as group technology, and VSM, optimized design and engineering functions, cellular manufacturing, SMED and 5 S show positive correlations with competitive potential. The model highlights the need for maintaining central information database for the supply chain, as timely and accurate information can be a major source of competitive potential for complex machine tool value chains. Limitations of this research generate scope for further studies. The sample size can be increased to include machine tool firms from other parts of the country or other developing countries. Lean implementation can be further explored in other industries in India such as automobile or consumer goods supply chains. Similar research can be carried out in future as results are likely to vary with time. Introducing sub-criteria and additional lean value chain criteria can provide added insight to lean implementation tactical and operational issues. The research makes use of exploratory approach. Structural equation modeling using path analysis can be used in future, to test conceptual or theoretical models. Confirmatory factor analysis can be used to test whether measures of a construct are consistent with researchers’ understanding of the nature of that construct. Such analysis can bring additional insight to lean implementation issues of machine tool industry. The survey has been conducted among machine tool firms that are located in the NCR of India. Thus the findings cannot be generalized to the machine tool industry in other parts of country or other countries. Neither can the findings be generalized to other industry types such as automobile industry or consumer goods industry. The limitation can be overcome by conducting a nationwide survey of machine tool industry or the entire manufacturing sector. This shall improve the sample size as well. Another limitation of this research is that although the models emphasize on the application of lean practices for performance

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Dr Amit Rai Dixit is serving as an Associate Professor of Mechanical Engineering at the Indian School of Mines Dhanbad, India. He has over 13 years of teaching and research experience. He holds a BTech degree in Mechanical Engineering, and ME degree in Production and PhD in the field of cellular manufacturing systems. He has presented several papers in international conferences and journals. His current research interest includes advanced production systems. Dr Mohammad Asim Qadri is a Professor in Mechanical Engineering Department of Galgotias College of Engineering and Technology, Greater Noida, India. He did his BSc in Mechanical Engineering and MSc in Mechanical Engineering from the Jamia Millia Islamia, New Delhi and Aligarh Muslim University, Aligarh, respectively. He obtained his PhD from the Jamia Millia Islamia in the area of green supply chain management. His research interests include green supply chain management, optimization techniques, operations management among others.

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