Assessment of Lean Supply Chain Practices in Indian Automotive Industry

Abstract

The purpose of this study is to explore the effect of lean practices on performance measures in the automotive industry and identify the lean criteria that can have significant impact on automotive supply chain. The identified lean practices can serve as a template to enhance the performance of a supply chain. The present study offers a multi-criteria decision-making approach to identify the effective performance practices in automotive lean supply chain. The decision-making trial and evaluation laboratory (DEMATEL) was applied on a matrix of observed values and the actual effect of proposed practices was observed. Further it was confirmed with the help of fuzzy-Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multicriteria optimization and compromise solution, with pronunciation). The criteria which had the most impact are proposed for achieving the future goals of leanness. It was found that among the lean criteria considered, quality management, information management and customer management practices influence the key performance measures more than others. Although DEMATEL and fuzzy-VIKOR were applied for situation leading to setting up of priorities of factors that considered affecting automotive manufacturer, the proposed methodology can be applied in diverse industrial settings. The present study may help decision-makers to device the appropriate strategy in identifying major practices that influence the lean supply chain.

Keywords

Lean supply chain DEMATEL fuzzy-VIKOR automobile

Introduction

The manufacturing sector is facing challenges from three directions (Jasti & Kodali, 2015). First one is the adoption of advance manufacturing technologies. Second one is from the varied demand of customers (Avittathur & Swamidass, 2007; Koh, Demirbag, Bayraktar, Tatoglu, & Zaim, 2007; Neto, Pereira, & Borchardt, 2015). Third one is to meet the environmental regulations (Srivastava, 2007; Zhu, Sarkis, & Lai, 2008). The firms are competing in the different dimensions of performance to achieve lean objectives. The dimension of performance includes delivery, quality, price and flexibility (Gunasekaran & Ngai, 2012; Jabbour, Junior, & Jabbour, 2014). The history of lean is as old as automotive industry. Toyota company has developed lean strategy in the 1950s (Ohno, 1988). Toyota has strong lean techniques and practices in place. In manufacturing across the globe, lean techniques being used to coordinate all the activities within and outside the organization (Bevilacqua, Ciarapica, & Paciarotti, 2015; Mckinsey & Company, 2017). Lean techniques are originally evolved as methodology in making production process efficient. For past one decade, lean implementation has also grown significantly in the domain of services (Narayanamurthy & Anand, 2016).The lean philosophy was introduced many decades ago and continued evolving from continuous production lines, to the interchangeable parts to Toyota Production System (TPS). All this constituting together formulates a lean system. Lean production is an important element of any lean system (Sundar, Balaji, & Kumar, 2014).

Lean literature exhibits that most of the research revolves around measuring the impact of shop floor practices on operational performance (Marin-Garcia & Bonavia, 2015; Marodin, Tortorella, Frank, & Filho, 2017b). Lean can help firms to integrate with its external suppliers and customers, which is called as lean supply chain management (Marodin, Frank, Tortorella, & Fetterman, 2017a). The main purpose of lean supply chain is to obtain value and eliminate the waste in the supply chain (Afonso & Cabrita, 2015; Perez, Castro, Simons, & Gimenez, 2010). The exchange of accurate information with suppliers regarding the demand, schedules and their performance is very critical for focal firms (Adebanjo, Laosirihongthong, & Samaranayake, 2016; Huang, Yen, & Liu, 2014). Similarly exchange of information in the downstream of supply chain is also crucial. This downstream information exchange includes: demand in distribution layers (dealer, distributor, retailer, etc.), current inventory level, and current and upcoming product offerings to the market (Prajogo, Oke, & Olhager, 2016; Soosay & Hyland, 2015; Swenseth & Olson, 2016; Yan, Wu, Ye, & Zhang, 2017).

Purchasing is one of the important functions of an industry like automobile. Purchasing function becomes the central for adding value by its contribution in implementing quality standards at supplier end, negotiating at best price, alignment of purchases to the firm policies and disposal of allied products like packaging (Huang & Wu, 2016; Krause, Vachon, & Klassen, 2009; Lindgreen, Vanhamme, van Raaij, & Johnston, 2013). Purchasing department has a crucial role in communicating any changes in the product design or at engineering level to its suppliers. Firms can create the capabilities when product design and engineering are integrated in their supply chain. This integration of product design and supply chain practices needs to balance the current resources and capabilities of a firm (Khan, Stolte, Creazza, Hansen, & Zhou, 2016; Lin & Zhou, 2011). Customers perceived and rate the firm from their products offering and unique style of operations. The marketing practices like green supply chain management, offering green products, helps firms to attract customers and make their supply chain more visible and lean (Green, Zelbst, Meacham, & Bhadauria, 2012; Lummus, Duclos, & Vokurka, 2003; Manrodt, Abott, & Visatek, 2005).Customer look for quality everywhere. Quality management principles of a firm have a direct impact on its supply chain elements (Thai & Jie, 2018; Vanichchinchai & Igel, 2011).

Lean production comprises of a unified set of undertakings to achieve high level production requirements with minimum work-in-process, inventory and finished goods (Lewis, 2000; Liker, 2004). Lean manufacturing eco-system is an imperative philosophy for current scenario of uncertainty (Dennis, 2008; Holweg, 2007). Lean is viewed as a management system which is governed by principles and practices (Hines, Holweg, & Rich, 2004; Marodin, Frank, Tortorella, & Saurin, 2016; Pettersen, 2009; Shah & Ward, 2007). The principles include the legal aspects and ideal situations. The principles include ‘providing value extracted from customer’s insights’, ‘continuous flow’, ‘pulling the demand from customers’, etc. (Liker, 2004; Papadopoulou & Ozbayrak, 2005). On the other hand, lean practices help lean principles to be adopted on shop floor. The lean practices include ‘one piece flow’, ‘jidoka’, ‘kanban system’ and ‘standardized work’ etc. (Abdulmalek & Rajgopal, 2007; Bhasin, 2011; Saurin, Marodin, & Ribeiro, 2011; Sezen, Karakadilar, & Buyukozkan, 2012; Soriano-Meier & Forrester, 2002). Firms with lean practices on shop floor are likely to have better operational performance (Marodin et al., 2016). Many firms instituted the improvement at shop floor within the organization which is not sufficient. Firms need to extend this improvement scale to both the sides of supply chain (Adebanjo et al., 2016; Behrouzi & Wong, 2011a, b; Prajogo et al., 2016; Yan et al., 2017). In a supply chain, the firm acts as an interdependent entity that help to pull out the information regarding consumer requirements, raw material vendors and component suppliers (Cagliano, Caniato, & Spina, 2006; Ozelkan, Gary Teng, Johnson, Benson, & Nestvogel, 2007; Scherrer-Rathje, Boyle, & Deflorin, 2009; Vonderembse, Uppal, Huang, & Dismukes, 2006).

Globally, the automotive sector has a waste management market which may grow at compounded annual growth (CAGR) of 3 per cent from 2017 to 2022 (Business Wire, 2018). India has become the significant market for automotive industry worldwide (The Economic Times, 2018). Automobile industry is one of the biggest in India today. There is 339.52 per cent growth observed in production of automobiles from 2001–2002 to 2014–2015 (Community.data.gov.in [GOI], 2016). Total production rose at a CAGR of 5.56 per cent between financial year 2012–2017 (IBEF, 2018). Automobile manufacturing is energy intensive including the extraction of raw materials and assembly process (Fysikopoulos, Anagnostakis, Salonitis, & Chryssolouris, 2012). Therefore, it is important to identify the prevalent sources of waste in the automotive supply chain and prioritize them to achieve required leanness (Vanalle & Santos, 2014; Vanalle, Lucato, & Santos, 2011).

In this view, the present study attempts to establish the priorities about the factors leading to high concentration of non-value-adding operations in automobile industry. The study presents the ideas for industries those are planning to implement lean practices in the form of application of multi-criteria decision-making (MCDM) tool in prioritizing the factors impacting lean practices. The study is addressing the following research questions:

RQ1: Which are the criteria that impact the lean supply chain implementation in a firm?

RQ2: What is the ranking of the affecting criteria’s?

RQ3: What is significance of identified criteria’s?

The remainder of the article is organized as follows: the second section discusses the related literature review. The third section describes the objective of the study. The fourth section displays the rationale of the studies and methodology adopted for the study is delineated in the fifth section. The sixth section presents the analysis using the decision-making trial and evaluation laboratory (DEMATEL) and fuzzy-Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multicriteria optimization and compromise solution, with pronunciation). The seventh section covers the results and discussion. The eighth section presents the concluding remarks followed by managerial implications in the ninth section. Finally, the tenth section discusses about the limitations and future scope of the study.

Literature Review

The term lean means a series of solutions to eliminate the waste, rework, alleviate non-value-adding activities and improve value-added ones (Wee & Wu, 2009). Lean focusses on the elimination of wastes in a manufacturing process along with improvements (Motwani, 2003; Rahman, Laosirihongthong, & Sohal, 2010; Tapping, 2006; Wee & Wu, 2009). Lean approach helps companies to gain a competitive advantage (Qrunfleh & Tarafdar, 2013). The focus of lean approach is on cost reduction by eliminating non-value-added (NVA) activities and using lean tools in order to have sustainable and optimal supply chain (Arif-Uz-Zaman & Ahsan, 2014).

Table 1.

Review of Recent Literature Discussing the Interaction Between Lean Principles and Supply Chain Performance

S. No.	Author(s) and Year	Research Objectives	Level of Analysis	Tool of Analysis	Approach Adopted	Setting	Major Findings
1	Tortorella, Giglio, and Limon-Romero (2018)	To investigate and recognize the practices of lean supply chain management impact on supply chain performance.	Firm	Factor analysis and data envelopment analysis	Survey	Manufacturing firms from Brazil	The levelled scheduling, value stream mapping and pull system significantly affect the efficiency of high performing firms. The firms facing delivery related issues on both sides of supply chain incline to improve the information flow with respect to schedule, transportation and products and hence they have impact on performance of the supply chain.
2	Tortorella, Miorando, and Tlapa (2017)	Explore the impact of contextual variables (Supply chain level, age of lean management practices, plant size and level of onshore suppliers) on the amount of lean supply chain practices.	Supply chain	χ2 τεστ	Survey	Manufacturing firms from Brazil	High level of lean supply chain implementation firms has higher levels of buyer control over low power suppliers. The first and second tier suppliers have more impact of lean supply chain practices as compared to third and fourth tier. There is not a significant difference between low and high lean supply chain implementation in the case of onshore suppliers. The effect of lean initiatives is visible only if firm has practiced it at least 5 years.
3	Shah and Ganji (2017)	Identify the influence of lean production techniques on the performance of service industries.	Firm	Cross-case comparison and synthesis	Case study	Baked food firms from the UK	The performance of the firm is influenced by lean production techniques by avoiding defects and adopting just-in time. Value stream mapping, 5S, Kanban and automation also helps in improving the product quality. The lean techniques help the firm to speed up the activities whereas flexibility is taken care of with the implementation of just-in-time. Cost is strongly influenced and reduced with lean practices like just-in-time. Kaizen method helps the firm to decrease the mistakes and its impacts on cost measures.
4	Mohammaddust, Rezapour, Farahani, Mofidfar, and Hill (2017)	Formulating the supply chain network design using path-based analysis including leanness, responsiveness philosophies and risk mitigation strategies. Validate the model with a case.	Supply chain	Mathematical modelling	Scenario based and case study	Automobile firm from Iran	The supply chain designer needs to choose the smallest expensive arrangement to reduce the special effects of risks. The path-based model formulation helps in handling supply chain risk mitigation such as emergency stock and reserved capacity.
5	Prajogo et al., (2016)	Evaluate the supply chain processes that denote the relationship between supply logistics integration and firm’s operational performance.	Firm	Structural equation model	Survey	Manufacturing firms from Australia	No important direct association among supply logistics integration and firm performance exists. Through the association is completely mediated by upstream supply performance along with internal lean manufacturing processes. Lean manufacturing processes impact positively upstream supply performance.
6	Marodin et al., (2016)	Identify the arrays of lean manufacturing implementation and the association between time of lean activities, firm position in the supply chain and size of the firm.	Supply chain	Multivariate analysis	Survey	Automotive firms from Brazil	The lean adopted firms have shown better performance in terms of inventory, lead time and turnover. The first two tier firms were leaner than the third tier. The big size firms have a high amount of use of lean tools than smaller and medium sized enterprises. The practices like total productive maintenance, one-piece-flow, problem-solving and multi-functionality matures in a time of less than two years, whereas standardized work is applied by every firm whether they have taken lean initiative or not.
7	Bortolotti et al. (2016)	Examine the features of supply network affecting the addition of lean initiatives to supply network and interfaces between lean initiatives and features to identify how to have encouraging environment for lean addition/extension programmes	Supply chain	Cross- case comparison and synthesis	Case study	Andalusian aeronautics industry from Spain	There is a repetitive effect between supply network features and lean practices. The choice of lean practices and their intention of implementation depend on the first-hand experience of firms involved in the value chain. Distance between lean knowledge provider and addressee also decide the adoption and implementation of lean practices.
8	Sharma et al. (2015)	Examine the effect of lean manufacturing practices on performance indicators in machine tool industry. To conclude the lean criteria those have encouraging effect on performance.	Supply chain	Factor and stepwise multiple regression analysis	Survey	Machine tool firms from India	Key performance indicators are influenced by cross-firm design and development team along with strategic partnership with suppliers. IT based practices of procurement in pull oriented structure indicate negative association to overall supply chain. Single minute exchange of die, 5S, Value- Stream –Mapping, Design and Engineering have positive impact on machine tool supply chain performance. Design quality is affected by value chain development teams.
9	Vlachos (2015)	Investigate the adoption and implementation of lean thinking in food supply chains.	Supply chain	Cross- case comparison and synthesis	Interviews	Small to medium sized firm from UK	The firm can deploy the lean in three stages: (a) planning for lean; (b) analysis of lean and (c) lean operations and control. The planning stage starts from discussion with a change agent. Change agent gives the knowledge about lean and its importance. The lean lever is to be identified followed by forget the grant strategy. Analysis stage requires the comments of expert, management and operational processes. Last stage of operations and control redefine the value, use the value stream and develop the pull strategy.
10	Arif-Uz-Zaman and Ahsan (2014)	Develop a fuzzy-based performance assessment approach for lean supply chain along with supply chain metrics.	Supply chain	Triangular fuzzy method	Case study	A textile supply chain of a firm from Bangladesh	The competitive supply chain performance is affected largely by cost and lowest by flexibility. In the five SCOR processes, planning and execution plays a vital role to generate the profit for supply chain and to become lean.
11	Qrunfleh and Tarafdar (2013)	Investigate the part of strategic supplier partnerships and postponement on the relation of agile and lean supply chain strategy and responsiveness.	Firm	Structural equation model	Survey	Manufacturing firms from the USA	Strategic supplier partnership entirely mediates between lean supply chain strategy and supply chain responsiveness. On the other hand postponement partly mediates the relationship of agile supply chain strategy and supply chain responsiveness. Supply chain responsiveness has a positive impact on firm performance.
12	Guimarães and Crespo de Carvalho (2013)	To identify the lean practices in the outsourcing and understanding the parallel evolution of both the practices.	Supply chain	Meta-analysis	Literature review	Classification of cases in lean healthcare from 15 cases	There is an association between lean and supply chain practices in healthcare. This association places a supply chain management verdict as outsourcing as a lean practice for improving the operational efficiency. There is a match between lean maturity level and different outsourcing drivers such as transformational, strategic and transactional. The cultural differences in the firms are the main constraints when it comes to the implementation of lean and outsourcing practices.
13	Jasti and Kurra (2017)	Investigate the reliability and validity of current lean supply chain management frameworks in Indian manufacturing industry.	Firm	χ2 test	Survey	Manufacturing firms from India	Lean supply chain management frameworks illustrate the unidimensionality. Most of the existing frameworks demonstrate a high reliability level. The greater part of the constructs had a high mode and mean concluded from frequency distribution analysis. It is determined that there is a need of new lean supply chain management structure for Indian manufacturing sector.
14	Manzouri Rahman, Saibani, and Mohd Zain (2013)	Examine the likelihood of executing lean practices in Halal food supply chain and obstacles to their implementation.	Firm	Structural equation model	Survey	Halal food firms from Malaysia	Most of the firms have not opted from lean supply chain related activities. On the other hand very few firms (14 %) wanted immediately to apply lean practices at their workplace. Uncertainty and market competition was critical barrier in executing lean supply chain among lean organizations, whereas in the non-lean firms the lack of purchaser awareness was the key obstacle.
15	Kumar, Agarwal, & Sharma (2016)	Find the critical factors whose contribution to the evolution of green supply chain due to lean strategies.	Firm	Meta-analysis	Literature review	Aviation and related sectors from India	The proposed framework focuses four pillars of lean practices on strategic focus, measurability, completeness and timeliness. Strategic focus discusses about environmental competencies of suppliers and products whereas measurability includes the green practices in the entire supply chain. On the other hand completeness includes process improvements to become a leaner supply chain and products, whereas timeliness is about identification of the measures with greater profits for the cost incurred.

Source: The authors.

A supply chain include the events related to movement and conversion of products, flow of information and services right from raw materials to end consumer (Ballou, 2009). Supply chain is a complex network of associated firms in the form of different stakeholders and their management becomes a challenging task (Ellram & Cooper, 2014; Lambert, García-Dastugue, & Croxton, 2005). The lean practices and principles adoption by firms has helped them to achieve significant results (Arif-Uz-Zaman & Ahsan, 2014). Therefore, deployment of lean practices along the supply chain called lean supply chain management (Anand & Kodali, 2008b; Li, Ragu-Nathan, Ragu-Nathan, & Rao, 2006; Shah & Ward, 2003; Vitasek, Manrodt, & Abbott, 2005). Lean supply chain is a system of interconnected and interdependent departments that operate together to accomplish the organizational supply chain objectives. These objectives must be regularly monitored to achieve success across the supply chain. Albright, Hudgens, Juras, and Petty (2015) asserted that the Jenny’s cycle components (firm name) supply chain model is a three-tier model and the company operates as per the lean principles. The motorcycle components are delivered for assembly just-in-time (JIT) and just-in-sequence (JIS) and helps in avoiding any inventory cost and reduce the waiting time. Also to avoid any stock-out or line stoppage costs, the second tier suppliers install redundant manufacturing equipment. Lean practices and principles play a vital role in firm’s strategy (Bhasin & Burcher 2006; Sisson & Elshennawy, 2015). Lean becomes much more strategic for firms as the orientation of competition changes to competitive supply chain (Li et al., 2006; Ponomarov & Holcomb, 2009; Tan, Lyman, & Wisner, 2002; Vonderembse et al., 2006). This leads to the idea of ‘lean supply chain strategy’. The lean supply chain strategy is realized in the integration of manufacturers, suppliers, stores and warehouses. This further includes the downstream of the supply chain (Simchi-Levi, Kaminsky, & Simchi-Levi, 2007).

In the upstream it is important for firms to have strategic supplier partnership (Chen & Paulraj, 2004; Li et al., 2006). The leaning of supply chain includes the entire network (Lamming, 1996). Lean manufacturing and lean design are two approaches towards lean supply chain that impact the firm performance (Jayaram, Vickery, & Droge, 2008). Supply chain strategies can be defined as lean, agile and hybrid (Towill & Christopher, 2002; Vonderembse et al., 2006). In lean supply chain, one of the objectives is responsiveness (Hines et al., 2004; Melnyk, Davis, Spekman, & Sandor, 2010). The relationship between supply chain responsiveness and lean supply chain strategy is affected by strategic supplier partnership (Qrunfleh & Tarafdar, 2013). Lean supply chain focusses on nonstop progress efforts and emphasize on reducing wastes through the chain (Sukatia, Hamida, Baharuna, & Yusof, 2012). Agile supply chain reacts quickly to the market requirements by presenting dynamism and flexibility across the channel (Naylor, Naim, & Berry, 1999; Towill & Christopher, 2002). On the other hand, hybrid supply chains are applicable where more complex products are present in the supply chain. This includes many suppliers to support the supply chain (Vonderembse et al., 2006). The ultimate aim of lean supply chain is to satisfy the customer. Lean supply chain helps in customer complaint handling and building long-term relationship with customers (Li et al., 2006; Vickery, Jayaram, Droge, & Calantone, 2003). This helps the firms to respond quickly. The information sharing speed among the lean supply chain is important (Li, Rao, Ragu-Nathan, & Ragu-Nathan, 2005; Moberg, Cutler, Gross, & Speh, 2002). The speed of information sharing across the supply will define the degree of customer satisfaction and degree of addressing the changing customer needs (Li & Lin, 2006; Stevens, 1990; Stock, Greis, & Kasarda, 2000).

Past researchers focussed on the supply chain performance using lean principles and tool. Arif-Uz-Zaman and Ahsan (2014) examined the performance of cost competitive lean supply chain using optimal metrics. Value stream mapping can be associated with the processes to increase the productivity (Vishwanath, Kadam, Vinod, & Mulla, 2014). The duration of lean initiatives helps in achieving higher levels of manufacturing performance (Wickramasinghe & Wickramasinghe, 2017). The lean tools have been applied in different industries mentioned in previous studies. Ghosh (2012) has studied the current state of lean adoption in Indian manufacturing plants and its impact on operational performance, where productivity, lead time, inventory etc. have shown great improvements after implementation of lean. Sharma, Dixit, and Qadri (2015) investigated the impact of lean production practices on performance measures in machine tool industry and found out that strategic partnership with suppliers and cross-functional cross-organizational design and development teams positively influence firm performance. Lean production action programmes give good return in the form of excellent operational processes (Losonci & Demeter, 2013). Leanness and agility are negatively correlated but they do not always oppose each other, instead complement each other (Bezuidenhout, 2016). Resilient supply chains are not cost effective as compared to lean but they are better prepared to deal with uncertainties and fluctuations in the business environment (Christopher & Peck, 2004). The lean approach in healthcare industry is to shield production from shifts in demand by keeping stocks or queues while the agile methodology is to work with flexible capacity (Aronsson, Abrahamsson, & Spens, 2011).

To substantiate the objective of this study which is to identify the criteria that affect lean implementation in automobile manufacturing firms, author(s) reviewed the relevant literature. Reviewed literature consists of research articles on the implementation of lean in supply chain, lean supply chain and its effects on product cost and quality. Furthermore, the criterion was applied to search for lean and agile supply chain and its quantification. The performance measurement and alignment of lean supply chain strategy was also studied. The focus was on attributes of lean supply chain, benefits and research approach in lean supply chain. Table 1 indicates the recent literature that deliberates the association between lean practices, principles and supply chain. A significant number of articles in the recent past indicate the emergence and importance of the area and the need for the research in this field (Table 1). From a review conducted, it was evident that not many studies have focussed on recognition and identification of lean criterion to support the supply chain especially in context to Indian automobile industry. In addition, the present study also claims to be exclusive in relation to the identification of effective lean practices in the automotive supply chain. Addressing these gaps, the present study focusses on prioritization and assessment of lean practices in highly complex automotive supply chain. DEMATEL and fuzzy-VIKOR were used to identify the lean practices which have significant impact on automotive supply chain.

It is evident from different MCDM methods that hybrid methods are the second highest in use after analytical hierarchy process (AHP). Different author(s) applied different methods for problem-solving. For instance, Omar and Fayek (2016) applied technique for order preference by similarity to an ideal solution (TOPSIS)-based techniques for ranking the aggregate in MCDM problems, whereas Otun (2016) applied a decision-making structure with the help of concordance and discordance sets for the selection of a ward for rural road construction in Nigeria. VIKOR can be applied in the areas of manufacturing engineering and business management (Kumar et al., 2017; Zavadskas, Turskis, & Kildiene, 2014). Few studies applied for the location selection by adopting an integrated approach of AHP, preference ranking organization method for enrichment of evaluation (PROMETHEE) and VIKOR (Makan, Malamis, Assobhei, Loizidou, & Mountadar, 2012; Sennaroglu & Celebi, 2018). Acuña-Soto, Liern, and Pérez-Gladish (2018) recommended a VIKOR methodology for the ranking of mathematical instructional videos. Analytic network process (ANP), AHP, DEMATEL, PROMETHEE, VIKOR and TOPSIS are the commonly used MCDM tools (Anand & Kodali, 2008b; Can & Toktas, 2018; Haldar et al., 2017; Jayakrishna, Vimal, & Vinodh, 2015; Pool, Arabzad, Asian, Fahimi, & Kazemi, 2018; Prascevic & Prascevic, 2013; Ranjan, Chatterjee, & Chakraborty, 2016; Wang & Cai, 2017; Yadav & Sharma, 2016; Wolfgang & Ralf, 2013). In this article, we used DEMATEL for the identification of critical lean supply chain practices and have been used by earlier researchers (Chang, Chang, & Wu, 2011; Shieh, Wu, & Huanag, 2010). DEMATEL method is specifically used in this study, because it helps to understand the cause and effect relationship between the variables (Chang et al., 2011; Wu & Chang, 2015). The input from DEMATEL is the set of the alternatives which are not measureable easily. VIKOR is an appropriate method from decision criteria that compromising is acceptable to existing conflicts (Opricovic & Tzeng, 2004; Shemshadi, Shirazi, Toreihi, & Tarokh, 2011). The main benefit of applying VIKOR as compared to other MCDM methods is that final score considered is overall aggregation of all the criteria’s along with their relative importance. The other methods like TOPSIS present the ranking on the basis of closeness to the ideal solution and uses linear normalization to remove units of criteria functions (Opricovic & Tzeng, 2004). VIKOR methodology presents a measure for compromise ranking with the help of Lp-metric using aggregate function (Shemshadi et al., 2011). Fuzzy set theory helps to chart the linguistic variables into numerical variables with in choice making course. A fuzzy set is characterized with the help of membership functions within the intervals [0, 1] (Bevilacqua, Ciarapica, & Giacchetta, 2006). These benefits motivated author(s) to adopt the DEMATEL and fuzzy-VIKOR approach in the present study. Table 1 presents the select studies on lean principles and supply chain performance

Objectives

The present study focusses on assessing the lean practices for automobile supply chain through different performance measures. The study also aims to explore the ranking of criteria’s those have significant impact on automobile supply chain. This study also intended to extract the lean supply chain practices direct from the working professionals and those have considerable work experience in the domain of lean supply chain and match them to the practices extracted from the literature to device a robust list of practices. Additionally, the study targets the selection of appropriate tools for the assessment of lean supply chain practices. The study also aims to derive the useful findings from practice and academic view.

Rationale of the Studies

Today customer pay for value he perceives in the products and not only products for the sake of consumption. Therefore, firms today are more focussed on enhancing value creation activities and eliminating rest of the waste (Chen, Cheng, & Huang, 2013). Technology is an important aspect of modern and lean supply chains (Kawa & Maryniak, 2019). Hence, firms are eager to consider the investments that can help in improving their supply chains. Modern supply chains demand visibility to be more responsive and value oriented (Bustinza, Parry, & Vendrell-Herrero, 2013; Caridi, Perego, & Tumino, 2013; Sithole, Silva, & Kavelj, 2016). Pull from customer to firm can be assesses precisely if a firm is able to track the customer usage (Horbach, Rammer, & Rennings, 2012). Therefore, it is critical to have appropriate information management practices at all levels and partners in the supply chain. This defines the supply chain speed. The procurement is the first bow of any supply chain and has a great impact on entire operations (Barney, 2012). Strategic sourcing and procurement function design the layout for supply chain with its level of resilience (Roberta Pereira, Christopher, & Lago Da Silva, 2014; Tassabehji & Moorhouse, 2008). The supply chain activities are also impacted by the ease of moving the products and handling during transportation. The lean and sustainable supply chains are the interface of product design and engineering considerations while manufacturing (Linton, Klassen, & Jayaraman, 2007; Nikolopoulou & Ierapetritou, 2012). In supply chains the quality of operations ensures the smooth delivery. Quality management practices have few similarities with supply chain as well as impact the supply chain activities positively (Sharma & Modgil, 2019; Vanichchinchai & Igel, 2011). Inventory at any stage in operations impact the performance of supply chain and firm (Matsebatlela & Mpofu, 2015). Many firms have brought paradigm shift in inventory management to bring their cost to the lowest level in handling the inventory (Darom, Hishamuddin, Ramli, & Nopiah, 2018). Hence, the appropriate management of inventory across the supply chain can act as competitive advantage for firms. Streamlining the processes with adequate information flow can lead to value generating supply chains (Buzby, Gerstenfeld, Voss, & Zeng, 2002; Yang, Kuo, Su, & Hou, 2015). In this view, the businesses today are more careful in adopting the technologies those are effective in communication and ensuring the flow of materials, semi-finished and finished products towards customer (Agus & Shukri Hajinoor, 2012; Schiavi & Behr, 2018; Zawislak, Fracasso, & Tello-Gamarra, 2018). Traditionally supply chain is focussed on sourcing and distribution (Caniato, Golini, & Kalchschmidt, 2013; Yu, Zeng, & Zhao, 2009). Marketing plays an important role in the process by balancing the demand information and maintaining the relationships that have an impact on supply chain operations. Marketing helps in providing the information on price, incentive, campaigns, information of sale and availability of products in the market and new requirements (Pauwels, Silva-Risso, Srinivasan, & Hanssens, 2004). The final core leg of supply chain activities belong to distribution in terms of packaging, warehousing, inventory and logistics (Ciliberti, Pontrandolfo, & Scozzi, 2008). The right distribution approach ensures the longevity and financial success of the firm (Ellinger et al., 2011). After sales services belong to customer relationship and their management in terms of different demands and requirements and meeting them on time with minimum variation (Ahn & Sohn, 2009; Forza & Salvador, 2008). In this manner the above discussed lean supply chain practices impact the performance of supply chain and hence considered in this study.

Methodology

To review the related literature with the application of MCDM, we defined the searching criteria into three categories of key words: MCDM, lean supply chain management and lean supply chain practices. A search for related research publications including three categories was piloted. The following sequence for search was used: ‘multi-criteria decision making’ OR ‘multi-attribute decision making’ AND ‘supply chain management’ OR ‘lean supply chain practices’.

After applying the search criteria, we have identified articles wherein MCDM approaches were discussed and applied by researchers to solve complex business problems. In total, 62 articles were identified, where MCDM is applied in supply chain activities. The literature review has been conducted as a first step to identify the lean supply chain practices prevailing in the different sectors. Standard databank platforms such as Emerald, Taylor & Francis, Wiley online library, Elsevier, Springer and EBESCO Host were retrieved to find out the articles on lean supply chain related practices in automobile like industries. Related keywords such as lean manufacturing, lean supply chain management, lean supply chain practices, lean tools and automobile industry have been used in the search of the articles electronically. Research articles from different journals have been identified with lean supply chain practices as an emphasis. After retrieval of the articles studies has been classified in conceptual, empirical and literature review. After a careful review of all categorized articles, eight practices were identified mentioned in Table 1 with reference to lean supply chain of automotive and related sectors. While doing the literature review the following interrogations were kept in mind such as: (a) what are the prevalent practices in the firms like automobile and related sector? (b) What type of approach is adopted while studying the lean practices along the supply chain? (c) How did supply chain practices help the firms over the period of time?

Once the literature review was piloted, then experts of the automotive supply chain were interviewed to finalize and rank the dimensions of lean supply chain. Therefore, author(s) approached local automobile firms from Pune, India, to understand the working of their supply chain. The selected panel members were having minimum 15 years of experience and handled supply chain related profiles at least for 5 years in the automotive supply chain. Author(s) collected primary data by conducting the interviews with experts from the automotive firms. Experts provided their opinion on the relationships between identified supply chain components. The panel performed pair-wise comparisons to determine the degree of interrelations. Further to that the total relation matrix was formed along with the interrelation map/cause and effect diagram as described in Figure 2. DEMATEL was developed using information collected which serve as an input to fuzzy-VIKOR to assess and evaluate the effective practices and their role in lean supply chain. This study is one of the very few to identify the influencing lean supply chain practices with the help of DEMATEL and fuzzy-VIKOR in Indian automotive context. In a value chain, it is desirable to keep minimum non-value-added activities. High concentration of non-value-adding operations were the major issues identified with respect to automotive supply chain. Referring to the issues recognized, the general approach for achieving lean supply chain includes reduction of non-value-adding operations at every node of it. Elimination of non-value added includes reduction in waste and rework along with continuous improvement of value-adding operations. Figure 1 describes the steps followed in this study.

Application of DEMATEL and VIKOR in Real-world Problems

DEMATEL and VIKOR methodologies are being widely used by researchers across management disciplines to solve the complex issues and to understand/establish the causal relations among variables. Table 2 lists the few studies from different management domains where DEMATEL and VIKOR were applied successfully to solve real-world issue.

Table 2.

Application DEMATEL and VIKOR in Real-World Problems

Author (year)	Title of Study	Purpose of Study
Application of DEMATEL
Chang, Liu, and Chen (2017)	Applying DEMATEL to assess the theory of inventive problem solving (TRIZ's) inventive principles for resolving contradictions in the long-term care cloud system	To explore key innovative principles for evaluating the long-term care cloud system by exploring contradictory and complex points in its development
Bai, Sarkis, and Dou (2017)	Constructing a process model for low-carbon supply chain cooperation practices based on the DEMATEL and the NK model	DEMATEL was used to develop a process model for introducing and implementing relational supply chain practices for low-carbon supply chains
Govindan, Khodaverdi, and Vafadarnikjoo (2016)	A grey DEMATEL approach to develop third-party logistics provider selection criteria	To provide the framework for selection of 3PL service providers,*
Sharma, Dixit, and Qadri (2016)	Empirical assessment of the causal relationships among lean criteria using DEMATEL method	DEMATEL was used to establish causal relation and to divide lean criteria’s in two groups: cause and effect
Chen (2016)	Applying the DEMATEL approach to identify the focus of library service quality: A case study of a Taiwanese academic library	Author used DEMATEL methodology to validate the service factors of an academic library
Application of VIKOR
Soto, Liern, and Gladish (2018)	A VIKOR-based approach for the ranking of mathematical instructional videos	To assess and rank the educational quality of free-online instructional video.
Wang and Cai (2017)	A group decision-making model based on distance-based VIKOR with incomplete heterogeneous information and its application to emergency supplier selection	To develop a framework for emergency supplier selection where a compromise solution is more acceptable and suitable
Sahu, Datta, and Mahapatra (2016)	Evaluation and selection of resilient suppliers in fuzzy environment: Exploration of fuzzy-VIKOR	Evaluation and selection of resilient suppliers
Vinodh, Nagaraj, and Girubha (2014)	Application of fuzzy-VIKOR for selection of rapid prototyping technologies in an agile environment	To select the appropriate rapid prototyping (RP) technologies in an agile environment.

Source: The authors.

Analysis Using DEMATEL and Fuzzy-VIKOR

The analysis conducted in this study is divided into two parts. The next section describes the DEMATEL technique followed by fuzzy-VIKOR elaborated later.

DEMATEL

DEMATEL is considered as an operational technique for the recognition of cause and effect relationship between factors. It helps in defining the important factors through graphical model. The impact of the relationship between factors can be derived for making decisions on real-time basis. DEMATEL is MCDM for identification of interrelations between different criteria’s. The technique is used previously by several researchers to map the causal relations among variables. Sharma et al (2016) used DEMATEL to analyse the causal relationship among dimensions of lean manufacturing. Singh and Acharya (2014) used to evaluate interrelationship of flexibility dimensions in supply chain. DEMATEL method is used to perceive intricate relationships and build a network relation map between the decision criteria. Ranjan et al. (2016) used DEMATEL and VIKOR to evaluate performance of Indian Railways. Barrios et al. (2018) used combination of AHP–DEMATEL–VIKOR to evaluate hospital services and proposed a framework to assess the risk of adverse events in the hospital sector.

The steps followed in this study for DEMATEL are:

Step 1: Evaluation criterion for automotive lean supply chain is done with the help of an expert panel. The panel include executives from automotive firms and their perception values of the lean criterion were considered. The practices in automotive lean supply chain have been recognized from evaluation criteria (Table 3).

Step 2: The panel of 21 experts has compared the practices in automotive lean supply chain pair-wise and evaluate the cause and effect of one to another on a scale of 0–4. Tables 4 and 5 display the profile and domain of those experts who have helped in the extraction of lean supply chain practices. The mean of all interrelationship scores were considered to form the initial direct relation matrix (Appendix A1). Appendix A2 represents the normalized initial direct matrix.

Table 3.

Criterion for Automotive Lean Supply Chain Practices

P1	Procurement practices
P2	Design and engineering practices
P3	Quality management practices
P4	Inventory management practices
P5	Information management practices
P6	Marketing management practices
P7	Distribution management practices
P8	Customers management practices

Source: The authors.

Table 4.

Profile of Experts

Age Group	Graduate	Post-graduate	PhD	Total
31–40	2	3	—	5
41–50	1	5	1	7
51–60	—	5	3	8
60+	1	—		1
Total	4	13	4	21

Source: The authors.

Table 5.

Domain of Experts

Domain of Work	AVP/VP/EVP	Consultant	Director/CXO/Founder	Manager/Sr. Manager	Total
Consulting	—	1	1	—	2
Manufacturing	5	1	3	6	15
Procurement	1	—	—	—	1
Quality	1	—	—	1	2
Supply chain planning	—	—	1	—	1
Total	7	2	5	7	21

Source: The authors.

Step 3: Construct the normalized initial direct relation matrix (D) by using the following formula:

M = \min [\frac{1}{\max \sum_{j-1}^{n} dij}, \frac{1}{\max \sum_{i-1}^{n} dij}]

(1)

D = M \times Z

(2)

where Z is the initial direct relation matrix.

Step 4: Using normalized direct-relationship matrix (D), the total relation matrix T is calculated as presented in the following equation:

T=D \times {(I-D)}^{- 1}

(3)

where I is the identity matrix.

Total relation matrix (Appendix A4) or final influence matrix is presented as:

T = [t_{ij}] n \times n

(4)

Threshold value = 0.744

Step 5: After constructing the total relation matrix, calculate the sum of rows and the sum of columns. The summation of rows and columns of the total relation matrix T are calculated. R and C are presented in Table 6 through the following equations:

[R_{i}] n \times 1 = [\sum_{j = 1 t}^{n} i j] n \times 1

(5)

[C_{i}] n \times 1 = [\sum_{j = 1 t}^{n} i j] 1 \times n

(6)

After this the criterion are ranked as per the R + C values:

Step 6: Draw the cause and effect diagram by mapping the values of R + C and R − C and referring to the total relation matrix as shown in Figure 2.

Step 7: Select the top two ranks. In this case, we have customer management and information management as first and second. With these two criteria’s author(s) further applied the fuzzy-VIKOR to validate the results.

Appendix A2 and A3 represent the calculations of total relation matrix T, whereas Table 6 represents the prioritization of practices along with their influence. Figure 2 shows the cause and effect influence of all eight practices towards each other.

Table 6.

Calculation of R and C and Ranking of Practices

	R	C	R + C	R – C	Rank
P1	5.979	5.777	11.756	0.201	6
P2	4.727	5.402	10.129	–0.675	8
P3	5.935	6.419	12.354	–0.483	3
P4	6.076	5.654	11.729	0.422	7
P5	6.291	6.154	12.445	0.137	2
P6	6.570	5.664	12.235	0.906	4
P7	6.184	5.701	11.884	0.483	5
P8	5.867	6.858	12.725	–0.991	1

Source: The authors.

Fuzzy-VIKOR

Fuzzy-VIKOR was employed to further check the significance of criteria as the appropriate methodology. VIKOR is being used by researchers widely for prioritization as well as to identify most significant dimension among many. The VIKOR method is an effective MCDM tool, specifically applicable to those situations when the decision-maker is not able, or does not know to express his/her preference at the beginning of the decision-making process (Ranjan et al. 2016). Sahu et al. (2016) used fuzzy-VIKOR to assess and evaluate the suppliers on basis of their agility. Ranjan et al. (2016) used combined DEMATEL and VIKOR to evaluate the performance of Indian Railways. Therefore, in the present study, authors made an attempt to assess the lean criteria using VIKOR technique.

A fuzzy number is defined as a fuzzy set M.

M= \{(X), μ_{M (X),} XεR\}

where µM(x) denotes continuous mapping in closed interval [0,1] and R denotes the interval. Fuzzy-VIKOR include the 5 steps described below.

Step 1: Identification and definition of linguistic terms. The panel of four experts and decision-makers used linguistic terms to assign ratings of supply chain practices. The linguistic terms and their corresponding fuzzy numbers are used in the study are indicated in Table 7.

The following variables are framed in the fuzzy-VIKOR methodology.

A set of m possible criterion called A = {A1, A2 . . ., Am}; (i = 1, 2 . . ., m);

A set of n decision criteria called C = {C1, C2 . . ., Cn}; (j = 1, 2 . . ., n);

A set of K experts called D = {D1, D2 . . ., Dk}; and

A set of ratings used to define the lean supply chain practices:

X_ijk = {X_ijk1, X_ijk2, X_ijk3, X_ijk4 } where i = 1, 2 [. . .], m; j = 1, 2 [. . .], n and k = 1, 2 [. . .], k}

It represents the ratings given by the decision-maker k for the effect of criteria j on criteria i.

The initial direct matrix formed by recording expert opinion as shown in Appendix A5. The ratings of criterion are shown in Appendix A6. The linguistic values of criterion are then converted into fuzzy numbers as shown in Appendix A7.

Table 7.

Linguistic Terms and their Corresponding Fuzzy Numbers

Importance	Abbreviation	Fuzzy No.	Score
Very low	VL	0.0, 0.1, 0.2	0
Low	L	0.1, 0.2, 0.2, 0.3	0
Medium low	ML	0.2, 0.3, 0.4, 0.5	1
Medium	M	0.4, 0.5, 0.5, 0.6	1
Medium high	MH	0.5, 0.6, 0.7, 0.8	2
High	H	0.7, 0.8, 0.8, 0.9	3
Very high	VH	0.8, 0.9, 1.0, 1.0	4

Source: The authors.

Aggregations of the values are carried out using the equations:

\begin{matrix} X_{ij} = \{X_{ij1} {, X}_{ij2} {, X}_{ij3} {, X}_{i}_{j4}\} \\ X_{ij1} {= \min}_{k} \{X_{ijkl}\}, X_{ij2} = 1 / 4 \{\sum_{K = 1}\} \\ Xij2= 1 / 4 \sum_{k = 1}^{K} xijk2, Xij3= 1 / 4 \sum_{k = 1}^{K} xijk4 \\ X_{ij4} {= mix}_{k} \{X_{ijk4}\} \end{matrix}

Step 2: The decision matrix was formulated. The different views of the experts pertaining to the scores of supply chain practices were aggregated into a single set of value. The aggregated fuzzy score with respect to criterion Cj was calculated. The aggregated fuzzy numbers of the criterion score of supply chain practices are shown in Appendix A8.

Step 3: The normalization of decision matrix was done by considering two types of criterion: the cost and quality. While cost criterion includes measures dealing with cost of operations and the quality criterion comprised of parameters that helps in achieving the best product performance and delight the customer. According to the criterion type, normalized decision matrix values are calculated as presented in Appendix A9.

As the quality maximization and cost minimization was expected, the quality criteria were assigned the maximum value from the aggregated set and cost criteria were assigned the minimum value.

\begin{matrix} U_{ij} = [X_{ij1} / X_{i j4}^{+}, X_{ij2} / X_{ij4}^{+}, X_{ij3} / X_{ij4}^{+}, X_{ij4} / X_{ij4}^{+}] \\ U_{ij} = [X_{ij1} / X_{i j4}^{-}, X_{ij2} / X_{ij4}^{-}, X_{ij3} / X_{ij4}^{-}, Xij4 / X_{ij4}^{-}] \\ X_{ij4}^{+} {= \min}_{1} \{X_{ij4}\} \\ X_{ij4}^{-} {= mix}_{1} \{X_{ij1}\} \end{matrix}

Step 4: After the normalization, de-fuzzification was done. The criteria scores, which were in the form of fuzzy numbers, were converted into normal values. The normal values thus obtained are shown in Appendix A10.

\begin{matrix} f_{ij} = 1 / 4 [x_{ij1} / x_{ij4}^{+} + x_{ij2} / x_{ij4}^{+} + x_{ij3} / x_{ij4}^{+} + xij1 4 / x_{ij4}^{+}] \\ f_{ij} = 1 / 4 [x_{ij1} / x_{ij4}^{-} + x_{ij2} / x_{ij4}^{-} + x_{ij3} / x_{ij4}^{-} + xij1 4 / x_{ij4}^{-}] \end{matrix}

Step 5: After calculating the normal values, the best and worst values were calculated for every criterion against supply chain practices and the results obtained are shown in Table 8.

\begin{matrix} f_{i}^{+} {= mix}_{1} \{f_{ij}\} \\ f_{i}^{-} {= \min}_{1} \{f_{ij}\} \end{matrix}

Step 6: Finally, the indices utility (Si), regret (Ri) and VIKOR (Qi) were computed. The utility index indicates the relative benefits of one practice over the other. The regret index describes the relative disadvantage of a practice with other, and the VIKOR index gives an overall performance score of the lean supply chain as described in Table 9.

Si= \sum_{j - 1}^{n} = Wj (f_{i}^{*} - f_{ij}^{*}) S^{-} = \max \{S_{i}\}, S^{*} = \min \{S_{i}\}

\begin{array}{l} R_{i} = m axi wj (f_{i}^{*} - f_{ij}^{*}) / (f_{i}^{*} - f_{ij}^{-}) \\ Qi=v* [(S_{i} - S^{*}) / (S^{-} - S^{*})] + (1 - v) (R_{i} - R^{*}) / (R^{-} - R^{*}) R^{-} = \max {R_{i}}, R^{*} = \min {R_{i}} \end{array}

Table 8.

Best and Worst Values

	P1	P2	P3	P4	P5	P6	P7	P8
(fi+) – (fi–)	5.25	0.1875	0.65	0.625	2.25	0.075	0.375	2.45

Source: The authors.

Table 9.

Calculation of Si, Ri, Qi

Si		Qi
P5	3.50	P5	0.71
P8	2.10	P8	0.00
Ri
P5	0.85
P8	0.70

Source: The authors.

Table 10.

Rank of Supply Chain Practices

Ranks	1	2
Si	P8	P5
Ri	P8	P5
Qi	P8	P5

Source: The authors.

Results and Discussion

Lean supply chain management starts from the focal firm’s shop floor. Lean supply chain results are dependent on the technology a firm is using in operating and the structure of the supply chain. The environmental conscious practices mediate the relationship between lean management and supplier management. Employee’s commitment and strategies of a firm are impacted positively by lean supply chain practices. Lean supply chain ensures the minimum waste in the process and adding value. This is quite possible with the efficient use of resources which is one of the fundamentals of lean supply chain. By application of DEMATEL the priority matrix was developed wherein the supply chain practices were ranked according to their effect on leanness of a supply chain. Customer management and information management were found to be vital criterion followed by quality management and marketing management in automotive lean supply chain. With these ranks the fuzzy-VIKOR analysis performed to check the significance of criteria in the setting of automotive supply chain. The panel of experts used linguistic terms for assigning scores of supply chain practices. Following this, the utility (Si), regret (Ri) and VIKOR (Qi) indices were computed. The criterion were arranged in ranks as per the Si, Ri and Qi values (Table 10) in ascending order as presented in Table 9.

The practice with least VIKOR value was selected as the most significant practice in the supply chain. As VIKOR analysis is a compromise-based ranking technique, it is likely to get more than one solution. Based on acceptable benefits and stable conditions a compromise needs to be derived. To measure the significance of various supply chain practices in automotive industry with an Indian perspective, DEMATEL and fuzzy-VIKOR were applied and the results of each approach were computed and presented in the above case study. Through this study selection of appropriate supply chain practices in a lean environment was evaluated. The previous studies have exhibited the contrasting results. Marodin et al. (2017a, b) study concludes that procurement practices have direct impact on shop-floor related performance whereas customer management related practices moderately affect the firm shop-floor supply chain practices. On the other hand, leanness and agility is required in the customer management practice to address the fast changing needs of customers (Gunasekaran, Lai, & Cheng, 2008; Yusuf, Gunasekaran, Adeleye, & Sivayoganathan, 2004). Information across the supply chain encourages the suppliers and other stakeholders to be involved in the supply chain and reduce the cost, which is ultimate objective of the lean supply chain management (Malhotra & Mackelprang, 2012; Mehrotra, 2010; Qrunfleh & Tarafdar, 2013). Acceptable benefits based on expert opinion were considered for decision-making. The best practices in the study were found to be quality management, information management and customer management practices. The supply chain performance is majorly affected by customer management being the top most priority followed by information management. Lean supply chain management can impact the firms positively from economic sustainability point of view. Lean supply chain management serves as a methodology for customer–supplier problem-solving. Critical elements in manufacturing plant-level lean supply chain management includes: materials, personnel, schedules and equipment.

Past researchers have used the similar approach including DEMATEL and VIKOR (Ranjan et al., 2016). In their study they only considered DEMATEL–VIKOR rather than DEMATEL and fuzzy-VIKOR. In a recent study Chakraborty, Chatterjee, and Prasad (2018) had applied DEMATEL–VIKOR methodology for the cotton fibre selection and evaluation. Use of simple DEMATEL–VIKOR may present some uncertainty in the decision-making. In another study DEMATEL, ANP and VIKOR methodology is applied for the evaluation of airline service quality (Percin, 2018). In another study it is found that supply chain integration mediate the relationship between information management and systems infrastructure that impact the manufacturing performance (Sundram, Bahrin, Abdul Munir, & Zolait, 2018). The information required by each stakeholder in the supply chain is diverse but common in nature that originates from customer (Nakandala, Samaranayake, Lau, & Ramanathan, 2017). The fuzzy approach is appropriate to handle the case of partial truth. The values of variables can range from completely true to completely false. Hence, fuzzy-VIKOR has been applied in this study.

The material management, information flow in the firm and co-operation between different stakeholders is very critical for any lean and sustainable supply chain (Seuring & Muller, 2008; So & Sun, 2010). Lean practices in the supply chain lead the firm towards sustainability and improve their competitiveness (Hajmohammad, Vachon, Klassen, & Gacronski, 2013; Martinez-Jurado & Moyano-Fuentes, 2014). There are few studies that lean practices in supply chain can help the manufacturing sustainability, but may not impact positively the delivery and logistics practices (Jakhar, Rathore, & Mangla, 2018; Martinez-Jurado & Moyano-Fuentes, 2014). Customers play a critical role in the designing and creation of supply chains and making it lean up to an extent (Li et al., 2006; Rexhausen, Pibernik, & Kaiser, 2012). Customer management is one of the important aspects of any supply chain management. Customer requirements and opinions will affect the firm decisions including suppliers and process modification, purchasing practices, inventory practices, information sharing platforms, etc. (Lapide, 2008; Mercier, Sirkin, & Bratton, 2010). Overall alignment among the business partners plays a significant role in the firm performance. Therefore, information management practices need to be very strong in the firm and with their both sides of the supply chain. Customer relationship plays a crucial role in aligning supply chain after understanding continuously changing customer needs and requirements.

Concluding Remarks

The present study displays the importance of different lean supply chain practices to achieve the desired performance level. Literature and experts have been consulted to device the important criterion and at the end these were limited to eight practices. After detailed interviews with the experts and calculations through the scores provided, customer management, information management and quality management were top practices those have a significant impact in the performance of a supply chain. This has been further verified through fuzzy-VIKOR. Lean supply chain focusses on eliminating wastes and creates value for customers through reduction of inventories and total lean time. Lean supply chain also moves the operations towards pull from the customer, instead of push orientation. First-time quality output is another important characteristic that prevents errors in the eco-system. The design of systems and flow of information is used to achieve common goals. With least total cost. In addition to this, the strategic alliance with stakeholders adds to the strength of lean supply chain. The value stream map helps in drawing the current state map to visualize the entire operations and planning for the future state of the supply chain with improvements. Future state of supply chain is helpful in confirming the degree of visibility in supply chain, levelling the flow from inbound and outbound and total cost incurred in the operations.

Today visibility in any supply chain is a major challenge. The information management practices can help the firms to manage their end-to-end operations in a more sustainable and leaner way. Firms today are moving towards new technologies like blockchain and Internet of Things (IoT) that help in sequencing, recording and creating the audit trail with other parameters such as pressure, temperature and humidity into a system of ledger. Nowadays, every customer requires tracking facility for their orders, and it is equally important for companies and other stakeholder also. The information management in this way provides the precise, audited, seamless, safe and lean supply chain to the company. Furthermore, a supply chain can be segmented on the basis of its application, criticality, end user and region-wise. The aspects like risk and agreement management, traceability and payment can be considered to make supply chain lean and sustainable.

Managerial Implications

DEMATEL and fuzzy-VIKOR can be applied to managerial decision-making in automotive industry. The methodology can be applied in diverse industrial sectors for making reliable and participatory decisions based on expert opinions. The practices filtered through literature review and verified by experts can help the practitioners to identify and prioritize the implementation of lean supply chain step by step. Practitioners from other industries may have varied practices those are crucial for their supply chain to become lean. With reference to the present example managing customer and information systems management helps the supply chain to be faster and responsive while ensuring the minimum cost and waste in the value chain. This study encourages the practitioners to infuse the best practices in the value chain for achieving the desired level of leanness. Once a focal firm is sure about the benefits of the implementation of lean practices, then firm may expand to the other tier of customers and suppliers.

Working executives have to ensure the smooth working of online platforms and their responsiveness to achieve the objective of lean supply chain. Additionally, firms need to set the appropriate workflow to ensure good customer experience through their processes. In customer management, the cross-training of employees plays an important role to ensure same rate of service and in same style. Customers are the source of bringing the desired changes in the company in the form of products, processes and hierarchy. The practicing managers also have to put millstones in place from time to time to ensure customer satisfaction and continuous improvement. This all is possible through right approach of information management. The current study can serve as a ready template for improving the supply chain performance for practitioners.

Limitations and Future Scope

The present study has few limitations due to the type of samples used in the study. Survey was conducted with a set of experts who have fair working knowledge about automobile lean supply chain. The respondents or experts were from the firms located in Pune, India. Their answers to the asked questions may be associated with domestic issues. The experts were from an automobile cluster in Pune, India, so there are chances that cluster have some practices those are very much applicable to these companies and some are not. Future study can be conducted to validate the results empirically by having large sample of working executives from different automobile firms from India. From literature, only eight practices were identified. Future study may include the practices from upstream to downstream with their items. Future studies can also investigate if firm size, time of firm established, firm position in supply chain have different scenario when it comes to choose the lean supply chain practices and their impact on supply chain performance. The upcoming studies may consider the adoption of lean supply chain practices in high performance and compare them with low performance firms. Future studies may include the outbound logistics to have a broader perspective of supply chain network.

Alone DEMATEL cannot address the uncertainty and vagueness along with decision-making process. In practical scenario the decision-makers comes from diverse background with different skills, experience and personality. Some of the decision-makers may give high inclination to their preferred elements and low inclination towards offensive elements. Furthermore, these methods are subjective and time-consuming (Li & Tzeng, 2009). VIKOR method is more complex and incurs more cost of computation then simple additive weighting (SAW) and TOPSIS methods (Sayadi, Heydari, & Shahanaghi, 2009). Future research may evaluate and compare the advantages and disadvantages of different DEMATEL methods, so that one appropriate can be selected for decision-making. The complex interrelationship between the factors need to be analysed accurately can be considered in future research. Further fuzzy and grey set theories can be applied to address the ambiguity and uncertainty in the decision-making process. A Fuzzy-AHP–DEMATEL can be applied for the assessment of lean supply chain practices.

List of Abbreviations

AHP: Analytical hierarchy process

ANP: Analytic network process

CAGR: Compounded annual growth

DEMATEL: Decision-making trial and evaluation laboratory

IBEF: India Brand Equity Foundation

MCDM: Multi-criteria decision-Making

PROMETHEE: Preference ranking organization method for enrichment of evaluation

SAW: Simple additive weighting

TOPSIS: Technique for order preference by similarity to an ideal solution

VIKOR: Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multicriteria optimization and compromise solution, with pronunciation)

Appendix A1.

Average Initial Direct Matrix

	P1	P2	P3	P4	P5	P6	P7	P8	Sum
P1	0	1.5	3	3.5	2.5	1.75	2.25	1.5	16
P2	1	0	3.5	1.75	1.25	1.75	1	2.25	12.5
P3	2.75	3.25	0	1.5	1.75	2	2	3	16.25
P4	3.75	1.25	2	0	3	1.5	2.75	2	16.25
P5	2.25	2	2	2.25	0	2.75	2.25	3.5	17
P6	2.25	2.75	2.5	1.25	2.75	0	2.75	3.75	18
P7	1.75	1.25	1.75	3.25	3.5	2.25	0	2.75	16.5
P8	1.75	2.25	2.75	1.75	2	3	2.25	0	15.75
Sum	15.5	14.25	17.5	15.25	16.75	15	15.25	18.75

Source: The authors.

Appendix A2.

Normalized Initial Direct Matrix (D)

	P1	P2	P3	P4	P5	P6	P7	P8
P1	0.000	0.080	0.160	0.187	0.133	0.093	0.120	0.080
P2	0.053	0.000	0.187	0.093	0.067	0.093	0.053	0.120
P3	0.147	0.173	0.000	0.080	0.093	0.107	0.107	0.160
P4	0.200	0.067	0.107	0.000	0.160	0.080	0.147	0.107
P5	0.120	0.107	0.107	0.120	0.000	0.147	0.120	0.187
P6	0.120	0.147	0.133	0.067	0.147	0.000	0.147	0.200
P7	0.093	0.067	0.093	0.173	0.187	0.120	0.000	0.147
P8	0.093	0.120	0.147	0.093	0.107	0.160	0.120	0.000

Source: The authors.

Appendix A3.

(I − D)−1 First Part of Inverse Matrix

	P1	P2	P3	P4	P5	P6	P7	P8
P1	1.640	0.664	0.838	0.781	0.794	0.703	0.734	0.825
P2	0.554	1.475	0.718	0.568	0.591	0.576	0.545	0.702
P3	0.751	0.739	1.702	0.688	0.750	0.712	0.712	0.881
P4	0.816	0.660	0.808	1.637	0.828	0.705	0.765	0.856
P5	0.772	0.719	0.836	0.757	1.709	0.783	0.765	0.950
P6	0.796	0.781	0.891	0.743	0.865	1.685	0.812	0.997
P7	0.744	0.673	0.808	0.790	0.861	0.751	1.650	0.907
P8	0.705	0.691	0.818	0.689	0.756	0.748	0.719	1.740

Source: The authors.

Appendix A4.

D × (I − D) Second Part of Inverse Matrix

	P1	P2	P3	P4	P5	P6	P7	P8	R
P1	0.640	0.664	0.838	0.781	0.794	0.703	0.734	0.825	5.979
P2	0.554	0.475	0.718	0.568	0.591	0.576	0.545	0.702	4.727
P3	0.751	0.739	0.702	0.688	0.750	0.712	0.712	0.881	5.935
P4	0.816	0.660	0.808	0.637	0.828	0.705	0.765	0.856	6.076
P5	0.772	0.719	0.836	0.757	0.709	0.783	0.765	0.950	6.291
P6	0.796	0.781	0.891	0.743	0.865	0.685	0.812	0.997	6.570
P7	0.744	0.673	0.808	0.790	0.861	0.751	0.650	0.907	6.184
P8	0.705	0.691	0.818	0.689	0.756	0.748	0.719	0.740	5.867
C	5.777	5.402	6.419	5.654	6.154	5.664	5.701	6.858

Source: The authors.

Appendix A5.

Initial Direct Matrix Formed

		P1	P2	P3	P4	P5	P6	P7	P8
Expert 1	P5	3	3	2	3	0	3	2	4
Expert 1	P8	2	1	2	1	2	3	2	0
Expert 2	P5	2	3	2	2	0	2	3	4
Expert 2	P8	3	3	3	2	2	2	3	0
Expert 3	P5	1	1	2	1	0	2	0	2
Expert 3	P8	0	3	3	2	2	4	2	0
Expert 4	P5	3	1	2	3	0	4	4	4
Expert 4	P8	2	2	3	2	2	3	2	0

Source: The authors.

Appendix A6.

Criterion Ratings

		P1	P2	P3	P4	P5	P6	P7	P8
Expert 1	P5	H	H	MH	H	L	H	MH	VH
Expert 1	P8	MH	M	MH	M	MH	H	MH	L
Expert 2	P5	MH	H	MH	MH	L	MH	H	VH
Expert 2	P8	H	H	H	MH	MH	MH	H	L
Expert 3	P5	M	M	MH	M	L	MH	L	MH
Expert 3	P8	L	H	H	MH	MH	VH	MH	L
Expert 4	P5	H	M	MH	H	L	VH	VH	VH
Expert 4	P8	MH	MH	H	MH	MH	H	MH	L

Source: The authors.

Appendix A7.

Linguistic Values of Criterion

		P1	P2	P3	P4	P5	P6	P7	P8
Expert 1	P5	0.7, 0.8, 0.8, 0.9	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.1, 0.2, 0.2, 0.3	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.8, 0.9, 1.0, 1.0
Expert 1	P8	0.5, 0.6, 0.7, 0.8	0.4, 0.5, 0.5, 0.6	0.5, 0.6, 0.7, 0.8	0.4, 0.5, 0.5, 0.6	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.1, 0.2, 0.2, 0.3
Expert 2	P5	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.1, 0.2, 0.2, 0.3	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.8, 0.9, 1.0, 1.0
Expert 2	P8	0.7, 0.8, 0.8, 0.9	0.7, 0.8, 0.8, 0.9	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.1, 0.2, 0.2, 0.3
Expert 3	P5	0.4, 0.5, 0.5, 0.6	0.4, 0.5, 0.5, 0.6	0.5, 0.6, 0.7, 0.8	0.4, 0.5, 0.5, 0.6	0.1, 0.2, 0.2, 0.3	0.5, 0.6, 0.7, 0.8	0.1, 0.2, 0.2, 0.3	0.5, 0.6, 0.7, 0.8
Expert 3	P8	0.1, 0.2, 0.2, 0.3	0.7, 0.8, 0.8, 0.9	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.8, 0.9, 1.0, 1.0	0.5, 0.6, 0.7, 0.8	0.1, 0.2, 0.2, 0.3
Expert 4	P5	0.7, 0.8, 0.8, 0.9	0.4, 0.5, 0.5, 0.6	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.1, 0.2, 0.2, 0.3	0.8, 0.9, 1.0, 1.0	0.8, 0.9, 1.0, 1.0	0.8, 0.9, 1.0, 1.0
Expert 4	P8	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.5, 0.6, 0.7, 0.8	0.7, 0.8, 0.8, 0.9	0.5, 0.6, 0.7, 0.8	0.1, 0.2, 0.2, 0.3

Source: The authors.

Appendix A8.

Aggregated Fuzzy Numbers of the Criterion Score

	P1	P2	P3	P4	P5	P6	P7	P8
P5	0.4, 0.67, 0.7, 0.9	0.4, 0.65, 0.65, 0.9	0.5, 0.6, 0.7, 0.8	0.4, 0.675, 0.7, 0.9	0.1, 0.2, 0.2, 0.3	0.5, 0.73, 0.8, 1	0.1, 0.625, 0.675, 1	0.5, 0.83, 0.925, 1
P8	0.1, 0.55, 0.6, 0.9	0.4, 0.675, 0.7, 0.9	0.5, 0.75, 0.775, 0.9	0.4, 0.575, 0.65, 0.8	0.5, 0.6, 0.7, 0.8	0.5, 0.78, 0.825, 1	0.5, 0.65, 0.725, 0.9	0.1, 0.2, 0.2, 0.3

Source: The authors.

Appendix A9.

Normalized Decision Matrix

	P1	P2	P3	P4	P5	P6	P7	P8
P5	4, 6.75,7, 9	1, 1.625, 1.625, 2.25	1, 1.2, 1.4, 1.6	1, 1.6875, 1.75, 2.25	0.125, 0.25, 0.25, 0.375	0.5, 0.725, 0.8, 1	0.1, 0.625, 0.675, 1	0.5, 0.825, 0.925, 1
P8	1, 5.5, 6, 9	1, 1.6875, 1.75, 2.25	1, 1.5, 1.55, 1.8	1, 1.4375, 1.625, 2	0.625, 0.75, 0.875, 1	0.5, 0.775, 0.825, 1	0.5, 0.65, 0.725, 0.9	0.1, 0.2, 0.2, 0.3

Source: The authors.

Appendix A10.

Normalization De-Fuzzification

	P1	P2	P3	P4	P5	P6	P7	P8
P5	26.75	6.5	5.2	6.6875	1	3.025	2.4	3.25
P8	21.5	6.6875	5.85	6.0625	3.25	3.1	2.775	0.8

Source: The authors.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Rohit Kumar Singh

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