Accepted Manuscript Lean Supply Chain Management: Empirical research on practices, contexts and performance Guilherme Luz Tortorella, Rogério Miorando, Giuliano Marodin PII:
S0925-5273(17)30223-2
DOI:
10.1016/j.ijpe.2017.07.006
Reference:
PROECO 6762
To appear in:
International Journal of Production Economics
Received Date: 26 October 2016 Revised Date:
5 July 2017
Accepted Date: 6 July 2017
Please cite this article as: Tortorella, G.L., Miorando, Rogé., Marodin, G., Lean Supply Chain Management: Empirical research on practices, contexts and performance, International Journal of Production Economics (2017), doi: 10.1016/j.ijpe.2017.07.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Lean Supply Chain Management: empirical research on practices, contexts and performance Guilherme Luz Tortorella (
[email protected]) Federal University of Santa Catarina
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Campus Trindade, Mailbox 476, Florianópolis, Brazil
Rogério Miorando (
[email protected]) Federal University of Santa Catarina
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Campus Trindade, Mailbox 476, Florianópolis, Brazil
Giuliano Marodin (
[email protected] ) University of South Carolina
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Columbia, South Carolina, USA
Abstract
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This paper has two major objectives. First, we aim to provide a framework in order to define the exact practices and bundles that should be considered as pertained to Lean Supply Chain Management (LSCM). Although several authors have proposed definitions of LSCM, there is neither systemic nor comprehensive definitions of the bundles and organizational practices that encompass LSCM. Second, we aim at testing empirically the positive association between LSCM bundles and supply chain performance. There is little empirical evidence about the relationship and consequences of a systemic implementation of LSCM and its effect in the supply chain performance. Thus, understanding how the LSCM bundles affect supply chain performance becomes critical. The aforementioned relationships were determined and validated through a survey carried out with 89 Brazilian companies and their supply chain. Hence, it is provided an empirically validated instrument for assessing LSCM bundles and their impact on key supply chain performance indicators with no parallel in the existing literature. With regards to supply chain context, our study focuses on four variables: tier level, plant size, company’s experience with lean implementation and onshore supply. Two major findings are suggested. First, the identification and empirical validation of bundles of LSCM practices provides means to focus on the most popular and elementary practices among different industry sectors and supply chains. Further, the concurrent application of these bundles appears to make a significant contribution to supply chain performance. Second, supply chain context matters with regard to implementation of LSCM practices, although not all aspects matter to the same extent and effect. Besides its theoretical contribution to the body of knowledge on LSCM, this research provides managerial implications that may support leaders and practitioners to better comprehend the need and the advantages of a systemic lean implementation across organizations, helping to anticipate occasional difficulties and setting the proper expectations along the lean implementation.
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Keywords: Lean production, SCM performance, SCM practices
ACCEPTED MANUSCRIPT 1. Introduction Lean Manufacturing (LM) is widely deemed as one of the most disseminated production systems. Evidence in literature indicates a positive association between
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implementing LM and improving operational performance (Marodin and Saurin, 2013; Rezende et al., 2016). In a general approach, LM practices and principles aim at reducing waste and variability in the processes, adding more value to customers and providing operational performance improvement (Shah and Ward, 2003). Nevertheless,
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most successful companies are the ones that are expanding and linking their internal
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improvement processes with external customers and suppliers (Frohlich and Westbrook, 2001). Thus, supplier and customer integration emerges as an important element to improve competitiveness beyond the organizational boundaries (Flynn et al., 2010; Frazzon et al., 2015). This concept is perfectly aligned with classical Supply Chain Management (SCM) definitions, since it comprises the flow of goods from supplier
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through manufacturing and distribution chains until the end user (Power, 2005). In this sense, the focus of SCM practices must shift from functional and independent to general
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and integrative initiatives (Theagarajan and Manohar, 2015). Hines et al. (2004) affirm that the understanding about LM evolved from the application
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of simple practices at the workstation level to a supply chain or value systems across multiple organizations as a natural organizational learning process expanding the knowledge boundaries. Further, some recent research on LM (Moyano-Fuentes and Sacristán-Díaz, 2012; Bhamu and Singh Sangwan, 2014; Jasti and Kodali, 2015) indicates that the value system, which involves understanding the customer and suppliers, is considered one of the new frontiers of research on lean. Therefore, due to an increasing competitive pressure for shorter lead times, lower costs and better quality,
ACCEPTED MANUSCRIPT the principles of LM have been incorporated into the supply chain integrative approaches (Cudney and Elrod, 2010). Thus, lean supply chain management (LSCM) can be defined as a set of organizations directly linked by upstream and downstream flows of products, services, information
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and funds that collaboratively work to reduce cost and waste by efficiently pulling what is needed to meet the needs of individual customers (Vitasek et al., 2005). The adoption of LSCM entails a different business model, in which improved profits arise from the
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cooperation rather than bargaining or imposing power over supply chain partners (Alves Filho et al., 2004; Naim and Gosling, 2011; Chiromo et al., 2015). In line with this
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integrated LSCM vision, the entire flow from raw materials to final consumer is regarded as an integrated whole in lean supply chains, in which the interfaces between companies are seen as a result of economic arrangements of assets ruled by several contextual factors, such as tier level, geographical location of raw materials, and size of
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the companies (Wu, 2002; Goldsby et al., 2006; Boonsthonsatit and Jungthawan, 2015). Previous studies (e.g., Lewis, 2006; Cagliano et al., 2006; Blanchard, 2010) have
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implemented LM principles and practices in SCM activities and reported improved organizational outputs. However, although widely discussed, the integration of LM
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principles and practices into SCM still has much to evolve in order to better comprehend the adaptation of lean approach (Shamah, 2013; Marodin et al., 2016). The adaptation of LM principles to SCM activities is not a simple process (Hines et al., 2004) due to several reasons, such as: (i) waste is easier to be identified and quantified in shop floor environment rather than supply chain; and (ii) manufacturing processes can be controlled through top management, while SCM requires attention for the entire chain, from suppliers to customers (Anand and Kodali, 2008; Soni and Kodali, 2012). Thus, based on these arguments, two research questions can be raised:
ACCEPTED MANUSCRIPT 1) What are the fundamental LSCM practices and bundles? 2) What is the association between the LSCM bundles, supply chain context, and performance? In this context, this paper has two major objectives. First, we aim to provide a
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framework in order to define the exact practices and bundles that should be considered as pertained to LSCM. Although several authors have proposed definitions of LSCM, there are neither systemic nor comprehensive definitions of the bundles and
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organizational practices that encompass LSCM. It is imperative to achieve that consensus in order to expand the knowledge of the subject, as Shah and Ward (2007)
(Marodin and Saurin, 2015).
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provided a definition of the bundles of LM that are widely used in the literature
The second objective of this paper is to test empirically the positive association between LSCM bundles and supply chain performance. Since Womack and Jones (2003; 2005)
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argue that true lean enterprises must implement lean principles throughout the information and production flows considering both customers and suppliers, there is
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little empirical evidence about the relationship and consequences of a systemic implementation of LSCM and its effect in the supply chain performance. The literature
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about LSCM is scarce and only suggests a positive association between LSCM and supply chain performance, but without testing empirically (e.g. McIvor, 2001; Wee and Wu, 2009; Perez et al., 2010; Kisperska-Moron and Haan, 2011). Thus, understanding how the LSCM bundles affects supply chain performance becomes critical. The aforementioned relationships were determined and validated through a survey carried out with 89 Brazilian companies and their supply chain. Hence, it is provided an empirically validated instrument for assessing LSCM bundles and their impact on key supply chain performance indicators with no parallel in the existing literature.
ACCEPTED MANUSCRIPT Besides its theoretical contribution, our research provides managerial implications that may support leaders and practitioners to better comprehend the need and the advantages of a systemic lean implementation across organizations. Since LM is initially implemented on the shop floor area and further deployed across the supply chain,
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LSCM practices tend to be less adopted and senior managers’ awareness regarding its benefits is still limited. Furthermore, the understanding of the relationship between the implementation of LSCM practices and their effect on supply chain performance helps
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to anticipate occasional difficulties and sets the proper expectations along the lean implementation, providing improvement guidelines that might support certain supply
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chain objectives.
From the seven pillars of the LSCM framework proposed by Jasti and Kodali (2015), we have empirically validated four bundles of inter-related and internally consistent LSCM practices, these were: customer-supplier relationship management (CSRM),
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logistics management (LOM), elimination of waste and continuous improvement (EWCI), and top management commitment (TMC). Further investigation pointed out that CSRM, LOM and TMC have a significant and positive effect on supply chain
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performance. With regards to supply chain context, our results suggest that some LSCM
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practices are associated with the contextual variables of tier level, plant size, company’s experience with LM implementation and onshore supply. It is worth noting that this survey was carried out in Brazilian companies and their supply chain, whose specific contexts might influence differently if compared to other countries. In fact, our results show that the explanation for a few counter-intuitive findings can be associated with geographic issues and characteristics which may not be replicated in other countries or economies.
ACCEPTED MANUSCRIPT This rest of this paper is structured as follows. Section 2 presents the theoretical background and propositions developed to answer our research question. Section 3 describes how we performed the research method by surveying Brazilian companies and their supply chain, construct validation procedures and testing our propositions using
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Ordinary Least Square regression. We present the results of our research in Section 4 and discussions in Section 5. Finally, section 6 includes the conclusions and final remarks.
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2. Literature and propositions
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2.1. LSCM practices and bundles
A lean supply chain should allow a flow of goods, services and technology from suppliers to customers without waste (Goldsby et al., 2006; Wee and Wu, 2009). The LSCM approach moves away from the current “trading mentality”, in which profit
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targets are short term and highly dependent on market prices and the ability to negotiate strongly with suppliers or customers, to a strategy based on a long term commitment to supply chain partners, with a cooperative and systematic waste elimination along the
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chain (Yusuf et al., 2004; Agarwal et al., 2006). However, many organizations have struggled to implement LSCM practices due to lack of awareness and improper
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implementation approach. Further, several studies have focused only on individual aspects of LSCM and very few researchers have approached both upstream and downstream activities of the organization (Anand and Kodali, 2008; Jasti and Kodali, 2015; Riet et al., 2015). Moreover, despite the fact that the stable and unidirectional theory and concepts of LSCM are not fully developed yet (Anand and Kodali, 2008), most of the studies have been restricted to a particular sector instead of a generalization of the LSCM framework (Perez et al., 2010; Petra and Marek, 2015).
ACCEPTED MANUSCRIPT Further, many studies have approached a few LSCM practices and bundles in an isolated manner, providing a narrow perspective of their application and benefits. For instance, Miwa and Ramseyer (2002) and Kim et al. (2004) studied the effect of power dependence on performance in keiretsu member firms, indicating that the benefits of
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such LSCM practice may not equally occur among the agents of a supply chain. Dong et al. (2001) and Green et al. (2014) investigated how the use of JIT purchasing, production, selling and information reduces logistics costs for suppliers and customers,
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suggesting that the adoption of this bundle of practice entails benefits for both of them. Other evidence of such isolated approach is found in Jean et al. (2014) and Kohtamäki
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and Partanen (2016), which examined the influence of customer–supplier relationships for enhancing knowledge and learning along the supply chain for product innovation and services development. In a general way, these studies raise arguments to support a positive impact of certain LSCM practices on performance, although they usually
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investigate them from a limited point of view.
A lean implementation across multi-levels of a supply chain is extremely difficult to achieve (Bruce et al., 2004; Taylor, 2006). Additionally, at the level of the whole supply
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chain it may not be feasible to achieve such ideal perfection. However, from the
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perspective of a specific supply chain echelon, it becomes easier to identify whether or not current practices are lean as well as their level of adoption (Levy, 1997; McCullen and Towill, 2001; Yusuf et al., 2004; Wong et al., 2014). Further, most literature evidence has concentrated on outlining practices and benefits, assuming that once companies are aware of a set of lean practices, a lean implementation would automatically be initiated (Stratton and Warburton, 2003; Cagliano et al., 2006; Naim and Gosling, 2011). A review of this literature reveals a number of practices that are commonly associated with LSCM, as summarized in Table 1.
ACCEPTED MANUSCRIPT Table 1 lists the key LSCM practices most cited in the studied literature. It shows that there is a varying degree of frequency that each of the practices is considered in the studies reviewed. Practices LSCM1 (pull system), LSCM2 (close relationship between customer, supplier and relevant parties) and LSCM3 (leveled scheduling) appear to be
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most frequently included. Particularly for practices LSCM1 and LSCM3, almost all studies that quote these practices have mentioned them simultaneously. Since these practices are closed linked to materials management and scheduling, it is quite
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reasonable that researchers usually apply them together (e.g. Chiromo et al., 2015; Riet et al., 2015). Practice LSCM2 encompasses the way customers and suppliers relate and
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manage their partnership. Initial studies on lean supply chain (e.g. Lamming, 1996; Levy, 1997; Naylor et al., 1999) have widely explored and emphasized this practice, claiming that a collaborative environment is mandatory for achieving a truly lean supply chain. On the other hand, the practices LSCM20 (establishment of distribution centers),
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LSCM21 (consignment stock) and LSCM22 (functional packaging design) seem to be more recently associated to the LSCM literature and, hence, studies that reference those practices are scarcer. One reason that may justify a later association with LSCM is that
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the adoption of these practices may not be widely deemed among different sectors and supply chains, although their practical benefits are quite intuitive (Cagliano et al., 2006;
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Petra and Marek, 2015). Overall, the 22 practices emerge from an extensive literature review and provide a representative view of the main practices adopted in a LSCM. There are many ways to combine individual practices to represent the multi-dimensional nature of a lean system (Shah and Ward, 2003). Previous studies in operations management literature have used exploratory or confirmatory factor analysis to combine individual practices into a multiplicative function to form orthogonal and unidimensional factors (e.g. Shah and Ward, 2007; Tortorella et al. 2016; Marodin et
ACCEPTED MANUSCRIPT al., 2016). Specifically within the LSCM literature, few researchers have tried to establish the main components that characterize a LSCM. For instance, Blos et al. (2015) suggest a framework based on eight SCM operational constructs, whose purpose is to keep the supply chain more resilient for both internal and external risks: (i)
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customer service, (ii) inventory management, (iii) flexibility, (iv) time to market, (v) finance, (vi) ordering cycle time, (vii) quality, and (viii) market. Perez et al. (2010) indicate a seven-dimension model for lean SCM implementation. Each dimension has
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been categorized in terms of the five lean principles (Womack and Jones, 2003; Womack and Jones, 2005): value, value stream, flow, pull, and perfection. Soni and
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Kodali (2012) and, later, Jasti and Kodali (2015), based on an extensive literature review of the past thirty years with regards to LSCM, found at least 30 lean SCM frameworks proposed in the literature. Moreover, the authors identified about 129 unique LSCM elements/practices, from which eight practices were defined as pillars of
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lean SCM implementation: (i) information technology management, (ii) supplier management, (iii) elimination of waste, (iv) JIT production, (v) customer relationship management, (vi) logistics management, (vii) top management commitment, and (viii)
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continuous improvement.
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Table 1 – LSCM practices and their appearance in key references 2.2. Contextual variables and LSCM implementation 2.2.1. Overview
Overall, a successful implementation of any specific management practice depends upon a set of organizational characteristics, therefore implying that not all organizations should implement the same set of tools (Pettersen, 2009). In this sense, there is not a fixed approach for success since organizations present different contextual variables and constraints (Bhasin, 2012). The understanding of the company’s current context is
ACCEPTED MANUSCRIPT fundamental for the appropriate lean implementation (Achanga et al., 2006). Therefore, organizations should adopt the practices that are effective in their context (Anvari et al. 2011). The contingency approach assumes that it is the contextual variables that determine the organizational responses in the lean implementation (Rota et al., 2002;
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Pavnascar et al., 2003). Contextual variables represent situational characteristics usually exogenous to the focal organization or manager (Tortorella et al., 2015). Thus, the manipulation of these variables tends to be limited and only possible in the long run
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with significant effort (Azadegan et al., 2013).
There is a trend to think that a lean supply chain brings together the best practices, but
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in fact the extension of lean to different sectors is challenging and contingent. Research related to the implementation of LSCM practices usually neglects the supply chain contexts, or narrowly approach it focusing on a specific industry sector (e.g. Taylor, 2006; Theagarajan and Manohar, 2015). Due to this investigation gap, the evidence on
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the impact of LSCM on supply chain performance does not allow a holistic perspective of the problem, hindering the establishment of generalizable assumptions or further deepening. Besides the lack of empirical studies concerning the contextual variables,
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there is a paucity of theories to guide both practitioners' and researchers’ expectations
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about the possible effects on LSCM implementation. In a literature review of 546 papers on lean from 1988 to 2011, Jasti and Kodali (2014) concluded that 87% of these papers reported implementation in companies from developed countries, mostly from the USA, Japan and Europe. Eroglu and Hofer (2011), Wan and Chen (2009), Shah and Ward (2003) are some examples of studies in American companies, Angelis et al. (2011) and Bhasin (2011) worked with English companies and Bonavia and Marin-Garcia (2011) with Spanish companies. Therefore, the extant knowledge about lean implementation from emerging markets is still
ACCEPTED MANUSCRIPT superficial and substantially lower than from developed countries (Panizzolo et al., 2012). Indeed, previous studies have shown that there are differences when comparing the lean implementation in developed countries and in emerging countries. Consequently, difficulties around lean implementation in companies and their supply
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chain located in emerging markets may be potentiated due to the new market orientation and the insecurity in the organizational climate inherent to such change (Saurin and Ferreira, 2009).
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In this study, we examine four contextual variables – tier level, plant size, years of company’s experience with LM implementation, and percentage of onshore supply –
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and their influence on the implementation of LSCM practices. Marodin et al. (2016) recently found a significant impact of the first three (tier level, plant size, years of company’s experience with LM) in the degree of adoption of LM practices in a study at the automotive industry. Thus, there are reasons to believe that those contextual
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variables also impact the use of LSCM. The degree of onshore supply was added because it can significantly difficult the use of some LSCM practice, more specifically in terms of production flow and information integration. Because of the exploratory
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nature of this research, it is developed a set of propositions rather than formal
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hypotheses, and those are presented as follow. 2.2.2. Tier level
Companies are increasingly looking for competitive success not only through the integration of internal business process, but also through the alignment of intercompany processes (Cagliano et al., 2006). This can be achieved by considering the network as whole, integrating all key process from end-customer to original suppliers and ensuring that it is done in the context of a true understanding of demand and quality requisites (Perez et al., 2010). In this sense, companies that supply directly to the end-
ACCEPTED MANUSCRIPT costumer may be more aware of their requirements regarding design, quantities, technical specifications and cost of the components that they will purchase (Marodin et al., 2016). In other words, first tier companies are more likely to better understand end-
conditions for eliminating waste along its value stream.
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customers’ perception about value-added activities, and, hence, establish the proper
A LSCM must produce according to customers’ needs, encouraging all agents of the supply chain to produce under a pull system rather than pushing products or services
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(Morris et al., 2004; Jasti and Kodali, 2015). Thus, an efficient information flow from the end-costumer to all tiers of the chain becomes essential so that each agent converts
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this data into valuable information to schedule production and to minimize inventory levels (Vitasek et al., 2005). However, as companies are positioned farther from the end-costumers, this information may be amplified, entailing noise effects that can cause overproduction effects.
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Further, first tier companies usually produce modules of higher price, adding more value than those produced by third and fourth tier suppliers (Doran, 2004;
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Boonsthonsatit and Jungthawan, 2015). Therefore, to achieve similar revenue, suppliers from higher tier levels have to produce a broader range of different products, which
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increases their mix of products and reduces production volumes; while undermining the implementation of some lean practices and principles (Doolen and Hacker, 2005; Kisperska-Moron and De Haan, 2011). Some studies have focused at the lean implementation in the Brazilian automotive industry (Walter and Tubino, 2013). Among these studies, it is worthy to notice a few in particular. Carpinetti et al. (2005) evaluated the steps taken by a first level supplier to implement lean practices, and pointed out several guidelines for a more effective and systemic deployment. Miyake et al. (2010) perform an in-depth investigation of the lean initiatives in three firms (two
ACCEPTED MANUSCRIPT automakers and one first level supplier) to describe and compare the differences and similarities of those initiatives in terms of the launching and deploying phases, and respective outcomes. However, although the existing literature presented above indeed investigate lean implementation through different tier levels at the automotive industry
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in Brazil, these studies were based on in-depth case studies in only one industrial sector, which limits its generalization. Given the aforementioned context, it may seem intuitive that first tier suppliers are more likely to adopt LSCM practices, although empirical
we have formulated the following proposition:
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evidence to support that assumption is still scarce. Therefore, to better investigate that,
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Proposition 1: Companies that are closer to the end-customer are more likely to implement LSCM practices than companies that are farther.
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2.2.3. Plant size
One of the features of a LSCM is that the promotion of an integrated and collaborative relationship between the agents of the chain is fundamental for achieving its full
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benefits (Cox et al., 2007). Thus, companies involved in a lean supply chain may invest time, personnel and financial resources, accepting all the potential opportunities, costs
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and risks arising from this lateral cooperation (Tapping and Shuker, 2002). However, some researchers identified a further issue that may harm such level of relationship in the long term. For instance, Rota et al. (2002) and Cox (2004) state that high levels of buyer dominance over low power suppliers may become a barrier for a LSCM. Perez et al. (2010) affirm that existing adversarial, power-based relationships undermine the successful implementation of a LSCM. Furthermore, such power-based relationships become more evident when there is a significant difference on size and financial capability of the companies involved (Achanga et al., 2006).
ACCEPTED MANUSCRIPT In this sense, the classification of the variable size may differ within the literature, although most of the inferences related to this variable converge and present coherent results (Saurin and Ferreira, 2008; Calarge et al., 2012). There is a common belief among a few authors (Shah and Ward, 2003; Dora et al., 2013) that large-sized
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companies present more availability of both capital and technological resources that may imply higher bargaining power within the respective supply chain. It is also worth noticing that specifically in Brazil, lean implementation is often imposed by top
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management or overseas corporate offices, which is typically observed in large organizations, instead of bottom or medium-up implementations, as happens in other
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countries (Bianco and Salerno, 2001). Moreover, Blos et al. (2013) concluded that Brazilian managers from smaller companies often focus on short-term results and little visibility is given on how management actions on operations processes can positively affect business performance. More recently, Brazilian companies have begun to care
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about sustaining business performance in the long-term, but managers from smaller companies usually lack training for this situation (Rocha, 2011). Thus, empirical evidence supports that large companies may have a more structured and systematic lean
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implementation while are less sensitive to power-based relationships; and, in cases where such relationships exist, these companies are more likely to rule them according
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to their own interests. So, we have proposed the following with regard to plant size: Proposition 2: Large-sized companies are more likely to implement LSCM practices than small-sized companies.
2.2.4. Experience with LM implementation A large number of studies are now focusing on how companies should integrate their processes with customers and suppliers, and how LSCM practices should be aligned
ACCEPTED MANUSCRIPT with companies’ strategy (e.g. Setijono et al., 2010; Carmignani, 2015; Marodin et al., 2016). These studies argue that the identification of joint patterns for lean implementation on both internal manufacturing and supply chain would allow a better
performance (Cudney and Elrod, 2010; Shamah, 2013).
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understanding of the mutual implications and reinforce synergic benefits on
According to Hines et al. (2004), lean thinking was primarily developed on shop floor activities, and its deployment to other business processes and the expansion to suppliers
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and customers were lately undertaken. However, most companies are still putting a lot of effort into improving their internal operations by exclusively adopting lean practices
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into their manufacturing process (Cagliano et al., 2006). This fact is particularly noticed in companies that are on the early stages of LM implementation, whose focus is mainly on reducing process variability and stabilizing production outcomes (Lewis, 2006; Duggan, 2012).
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Particularly in the scenario of emerging economies such as Brazil, some companies have been implementing a few sets of lean practices in manufacturing processes and
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only few companies have expanded to other areas, such as product development, finance, supply chain and sales (Dal Forno et al., 2011). However, since lean principles
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go beyond the implementation of practices, Brazilian companies still need to improve the understanding around their implementation (Saurin et al., 2010; Prudentino, 2013). Such a need was emphasized in a systematic literature review and assessment conducted by Walter and Tubino (2013). The authors stated that the research dearth regarding methods that evaluate lean implementation might justify the misunderstanding and failure in implementing the practices. In this sense, the implementation of lean thinking can be seen as part of an evolutionary process in which, once a minimum level of process improvement and stability is achieved, the continuous improvement initiatives
ACCEPTED MANUSCRIPT must also focus on issues outside the manufacturing boundaries, such as the flows of material and information with customers and suppliers (Perez et al., 2010; Riet et al., 2015). So, we propose the following with regard to company’s experience with LM implementation:
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Proposition 3: Companies that have been implementing LM for a longer time are more
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likely to implement LSCM practices than less experienced companies.
2.2.5. Onshore suppliers
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Globally distributed supply chains have been the approach that several companies decided to follow. Due to budgetary limitations, outsourcing manufacture to low cost overseas suppliers appears to be an appealing maneuver, but often performed without proper concern to the market needs and the corresponding demands on the associated
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delivery systems (Mason-Jones et al., 2000; Stratton and Warburton, 2003). Offshore supply offers attractive cost benefits, but the trade-off is usually an increase in inventory levels to support longer material replenishment lead times, and a complex and inertial
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information flow with suppliers (Frohlich and Westbrook, 2001; Alves Filho et al., 2004). If combined with an environment that presents high demand variability, this
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trade-off becomes even more significant, entailing problems such as material shortages or obsolescence which jeopardizes organizational performance (Theagarajan and Manohar, 2015). Therefore, those companies that rely heavily on offshore suppliers instead of purchasing raw material and components from onshore suppliers are less likely to respond quickly to market demands and its fluctuations. Liker (2004) states that companies that indiscriminately search for lower cost suppliers lose the opportunity to develop partnerships and to share knowledge among the supply
ACCEPTED MANUSCRIPT chain, which may allow the obtainment of larger benefits in the long term. Cudney and Elrod (2010) and Duggan (2012) affirm that the increase of offshore suppliers implies significant delays in resolving technical issues that may appear during the productive process, and managers systematically underestimate these costs. In addition, Levy
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(1997) suggests that a geographically dispersed supply chain must be seen as a complex dynamic system in which disruptions due to quality problems, delivery delays, engineering change orders interact with long lead times to create substantial costs that
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go beyond the prices negotiated. An additional challenge relies on companies from emerging markets that are often struggling to become lean and global at the same time.
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Hence, both changes require a better comprehension of local culture and value added maximization (Paiva et al., 2009). For example, Ghosh (2012) concluded that the way that lean practices, such as pull production and total productive maintenance, were implemented in Indian companies is different from the American companies. Therefore,
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the level of onshore suppliers may influence the adoption level of LSCM practices, and to investigate that association, we have formulated the fourth proposition as follows: Proposition 4: Companies that rely more on onshore suppliers are more likely to
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implement LSCM practices.
2.3. Bundles of LSCM practices and supply chain performance Several studies relate the implementation of LSCM practices to improvements on supply chain performance (e.g. Wu, 2002; Blanchard, 2010). Overall, research on LSCM indicates that the implementation of these practices is frequently associated with improvement on measures such as inventory levels (Bruce et al., 2004; Chiromo et al., 2015), quality (Wee and Wu, 2009; Perez et al., 2010), supply lead time (Agarwal et al., 2006; Naim and Gosling, 2011), delivery service level (Savino and Mazza, 2015; Petra
ACCEPTED MANUSCRIPT and Marek, 2015) and cost (Stratton and Warbusrton, 2003; Theagarajan and Manohar, 2015). However, most studies are constrained to a few facets of LSCM, often including deliveries in small lot sizes, synchronization and leveling of scheduling and production, and close relationship between suppliers and customers in order to provide win-win
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situations. Cagliano et al. (2006) state that LSCM practices can be recognized according to two distinct objectives: (i) integration of the forward physical flows; and (ii) coordination and integration of backward information and data flows from customers to
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suppliers.
Despite evidence regarding performance improvements related to these practices,
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relatively little research exists to gauge the existence of supply chain performance improvements. This gap is particularly increased if we add the simultaneous use of several LSCM practices to the empirical investigation. We aim to examine the implementation of LSCM practices and their implications on supply chain performance
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after controlling for the effects assigned to contextual variables. Previous research argues that a truly lean system may obtain benefits from the mutual application of several complementary practices, whose adoption intensities may vary according to the
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existing problems within the organization (Womack and Jones, 2005; Kisperska-Moron
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and De Haan, 2011; Wong et al., 2014). Thus, we posit that the simultaneous implementation of various LSCM practices combined into bundles of practices will have a positive impact on supply chain performance. To represent that, proposition five is stated as follows:
Proposition 5: Companies that have a higher degree of adoption of LSCM practices are more likely to have higher supply chain performance.
3. Method
ACCEPTED MANUSCRIPT 3.1. Sample selection The criteria for selecting the sample of companies were the following: (a) to include companies located in a specific region of the country, in this case, southern Brazil, as to reduce the effects of the external environment (e.g. regional culture, and socio-
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economic development), since this would be relatively homogeneous within the sample; (b) to include companies from different industrial sectors because lean has been expanding over many kinds of companies in recent years; and (c) respondents should
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have experience in lean and a role whose function was directly related to SCM in the company. The non-random choice of companies for surveys and the search for
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companies that are already known to the researchers is a commonly used strategy in other studies on LM (Boyle et al., 2011; Netland and Ferdows, 2014; Tortorella et al., 2016). For example, Shah and Ward (2007) used a sample with participants drawn from courses and training events when they conducted a survey on LM, since it was
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necessary that the respondents had experience in the subject. Kull et al. (2014) suggest that national culture could influence the implementation of lean practices. Therefore, a single geographic location also increases the homogeneity of the sample. Additionally,
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although implementing lean is usually associated primarily with high volume and
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discrete part manufacturers, pervasiveness of practices across the industrial spectrum is unknown (Tortorella et al., 2015). Questionnaires were sent by e-mail to 497 former students of executive education courses on lean offered by a large Brazilian University since 2008. The institution is the only one in its region offering short courses on lean that are open to the general public. A first e-mail message containing the questionnaires was sent in January 2016, and two follow-ups were sent on the following weeks. The final sample was comprised of 89
ACCEPTED MANUSCRIPT valid responses (representing a response rate of 17.91%); respondents’ demographics are presented in Table 2. The sample presents a relatively balanced amount of companies for each contextual variable. Most respondents were from large companies (64%); the majority of
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companies belonged to the automotive supply chain (27%). Most respondents (63%) belonged to companies that have been implementing LM for less than 5 years, and were positioned at the first and second tiers of their supply chains (57%). Finally, regarding
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more than 30% of their suppliers being onshore.
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the amount of onshore suppliers, there was a predominance of companies (69%) with
Table 2 – Sample characteristics (n=89 companies)
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3.2. Questionnaire development and data collection
The questionnaire has three parts. The first part aims at assessing the level of change over the last five years of the supply chain performance indicators (dependent
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variables): (i) supply lead time, (ii) costs with supply and raw material, (iii) inventory
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level, (iv) delivery service level and (v) quality. Shah and Ward (2003) proposed a fiveyear period evaluation for operational performance variation. A five-year time period should be enough for implementing lean tools systemically, and for them to impact operational performance outcomes. A 5-point scale ranging from 1 (worsened significantly) to 5 (improved significantly) is used in the questionnaire. The second part aims to collect information regarding the contextual variables of the companies. All contextual variables were re-coded into two categories each. The first category for tier level comprised companies from the first and second tiers, while the
ACCEPTED MANUSCRIPT second category consisted of companies from tiers three and four. For plant size, largesized companies were determined as the ones that presented more than 500 employees, and small-sized were characterized by companies with less than 500 employees, as suggested by SEBRAE (2013). LM experience was coded into (i) more than five years
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and (ii) less than five years of LM experience; according to Marodin et al. (2016), who suggest that companies with more than five years of LM implementation might achieve a stage where lean initiatives are being applied to suppliers and customers and the
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transition from top-down to bottom-up improvement would be completed. Finally, the variable onshore suppliers were categorized according to the amount of companies’
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suppliers that are located onshore. As suggested by Canuto et al. (2013), an index of 70% of onshore suppliers would be a reasonable threshold value specifically within the Brazilian industry scenario.
Finally, the third part of the questionnaire comprises the assessment of the
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implementation level of the 22 LSCM practices listed in Table 1 (independent variables). The approach of measuring the maturity of lean implementation based on the assessment of the adoption level of pre-defined practices has been extensively used in
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previous studies (Shah and Ward, 2007; Netland and Ferdows, 2014; Marodin et al.,
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2015) and seems to be quite effective to comprehend companies’ maturity. For that, we developed a questionnaire using a five-point Likert scale (1=low implementation to 5=high implementation). We used an adapted header of Shah and Ward (2003), which stated “Please indicate the extent of implementation of each of the following practice in your supply chain?”. The instrument was pre-tested by three academics and three supply chain managers working for companies that were in the process of implementing lean in order to ensure face and content validity. The instrument was improved with the necessary
ACCEPTED MANUSCRIPT modifications according to suggestions made (see Appendix). Two major changes were provided based on this pre-test. First, we included a question with respect to company’s stock value that was actually imported. The idea was to verify the existing differences between the amount of offshore suppliers and their stock value within the same
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company. No significant association was found between these variables, disregarding the responses related to the imported stock value. Second, academics suggested to better specify the cost with supply and raw material, so respondents could be more assertive
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when answering this performance indicator.
Surveys that use data from single respondents may be affected by respondent’s bias; to
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minimize that we followed some directions given by Podsakoff et al. (2003). Regarding question formulation, supply chain performance (dependent variable) was placed first and physically far from LSCM practices (independent variables) in the questionnaire. Also, pre-test was used to reduce the risks of using ambiguous, unfamiliar and/or vague
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terms. For preventing respondent bias, a statement clarifying that the respondents were going to be kept anonymous and that there was no right or wrong answer was included at the invitation e-mail. Also, the respondents were appropriate key informants for the
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data collected, as 68.53% of them had middle and upper management roles in
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production departments, and 56.17% had more than 5 years of experience with LM. Regarding statistical remedies, Harman’s single-factor test with an exploratory factor analysis is one of the most widely used for researchers to address common method bias (Podsakoff et al., 2003). The Harman’s test with all independent and dependent variables resulted into a first factor that included 37.74% of the variance. Thus, we concluded that there was no major factor that accounts for the majority of the covariance among measures. We therefore assumed that any level of common method bias that may exist in our data is not a significant concern.
ACCEPTED MANUSCRIPT Further, regarding the assessment of the implementation level of LM practices, we tested for non-response bias as proposed by Armstrong and Overton’s (1977) using Levene's test for equality of variances and a t test for the equality of means between early (respondents of the first e-mail sent) and late (respondents of the two follow-ups)
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respondents. Results indicate no differences in means and variation in the two groups, with 95% significance. Thus, there is no statistical evidence that our sample is significantly different from the rest of the population. A correlation matrix for each of
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the contextual variables and LSCM practices included in the analysis is shown in Table
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3.
Table 3 – Sperman’s correlations between LM experience, tier level, offshore suppliers,
3.3. Data analysis
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plant size and LSCM practices*
The 22 LSCM practices were combined into four LSCM bundles. For instance, all
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practices related to lateral cooperation and collaboration were combined to form CSRM.
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The underlying rationale is that CSRM plays a key role on the achievement of a healthy relationship and integration among the agents of a certain supply chain. The LOM bundle comprises practices that can be identified by their focus on optimizing business activities (e.g. efficient and continuous replenishment, outbound transportation, establishment of distribution centers, and functional packaging design) related to the efficient flow and storage of inventory, goods, and services throughout the supply chain (CSCMP, 2008). Practices related to continuous improvement and waste elimination were combined to form EWCI bundle. It includes practices such as pull system, leveled
ACCEPTED MANUSCRIPT scheduling, win-win problem solving methodology, value stream mapping and consignment stock. The fourth bundle, TMC, consists of initiatives involving top level management to enhance supply chain performance (Soni and Kodali, 2013) and ensures mutual organizational commitment throughout the lean implementation. Thus, the most
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commonly cited practices aligned to this objective are two-way feedback assessment, value chain management team, intervention strategy, among others (Soni and Kodali, 2012).
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Each of the bundles was formed by adding the scores for each of the practices included in the bundle for each responding company. All 22 LSCM practices were entered for
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PCA (principal component analysis) and varimax rotation was used to extract orthogonal components, and four components were extracted. Thus, the bundles were empirically validated using PCA with varimax rotation and reliability analysis (Cronbach’s alfa), as shown in Table 4. The data was analyzed using SPSS version 23.
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The results were replicated using oblique rotation as a check for orthogonality and the extracted components were similar. Additionally, unidimensionality of each component
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was verified and confirmed by applying PCA at the component level. A reliability assessment was performed determining the Cronbach’s alpha values for each
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component, which depends upon the number of items in the scale and the average interitem correlation (Meyers et al., 2006). All components displayed high reliability, with alpha values above 0.804. Finally, the response value for each bundle was obtained through the average of the corresponding practices included in the bundle weighed by their respective factor loadings from the PCA. Regarding performance, a scale was constructed for supply chain performance measure based on PCA of the five indicators aforementioned, and the factor scores were used as the dependent variable. Table 5 shows that all five indicators load on one factor, with an
ACCEPTED MANUSCRIPT eigenvalue of 2.857 explaining almost 60% of the variation. Further, the obtained Cronbach’s alpha value is 0.812. Thus, we assume that all five indicators are
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empirically related and represent a single dimension of supply chain performance.
Table 4 – PCA to validate LSCM bundles–rotated component matrix
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Table 5 – Supply chain performance: factor loading, means, standard deviation and
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Spearman correlation
4. Results
4.1. Contextual variables and implementation of LSCM practices
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We use likelihood ratio χ2-test statistic to evaluate the relationship of association between contextual variables and implementation of LSCM practices. The results
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4.1.1. Tier level
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presented in Table 6 are discussed below for each of the contextual variables.
Of the 22 LSCM practices, tier level seems to present a significant association with 15. From all the four contextual variables, tier level appears to be one that is most associated with LSCM practices. However, contrary to popular belief, the results show that tier level has a positive association with the implementation of LSCM practices. More specifically, companies from third and fourth tiers seem to be adopting LSCM practices more extensively than companies from first and second tiers. One argument for this unexpected effect is that the respondents are all from Brazilian companies. In
ACCEPTED MANUSCRIPT Brazil, the companies from third and fourth tiers usually belong to an oligopoly, which provides them bargaining power to significantly influence and drive several aspects of their supply chains. Additionally, these are usually large-sized companies that supply to different customers and productive chains, whose comprehension of strategic
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approaches such as lean may substantially vary.
Studies from Saurin et al. (2010) and Godinho Filho et al. (2016) affirm that lean implementation in Brazilian companies still lacks some better understanding, since
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theoretical and practical evidence is relatively recent, especially when considering industrial sectors others than automotive. The findings from Panizzolo et al. (2012)
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expand this discussion by stating that lean implementation in developing economy countries should be approached differently than in developed countries, as socioeconomic variables may influence managerial decisions in a whole different level. Therefore, while our results show a significant association between tier level and the
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implementation of LSCM practices, the direction of the effect is not always as the one
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predicted in the literature.
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Table 6 - Pearson’s χ2 to test for association between contextual variables and implementation of LSCM practices a
4.1.2. Plant size
The likelihood ratio χ2 indicates that 15 LSCM practices are significantly influenced by plant size. As predicted, all the effected practices have a significant positive association with this contextual variable. This result suggests that larger plants are likely to implement these 15 LSCM practices more extensively than smaller ones. The main
ACCEPTED MANUSCRIPT expected effect was that larger companies would be more likely to present buyer dominance, and, hence, the underlying supply chain relationships would be mostly lead towards their interests facilitating the implementation of LSCM practices. Therefore, the results with respect to plant size support proposition 2 (except for practices LSCM1,
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LSCM8, LSCM11, LSCM14, LSCM15, LSCM18 and LSCM22) and somewhat corroborate to the findings of Marodin et al. (2016), Tortorella et al. (2015) and Shah and Ward (2003), which indicate that this contextual variable must be carefully taken into
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consideration by managers and researchers when planning the lean implementation on
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both manufacturing processes and along the supply chain.
4.1.3. Experience with LM implementation
Results show that only 3 LSCM practices present a significant positive association with
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company’s experience on LM implementation. They are: LSCM1 (pull system), LSCM7 (win-win problem solving methodology) and LSCM11 (intervention strategy). This result is somewhat surprising in light of conventional wisdom about the difficulty of
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implementing any management practices without a minimum level of knowledge within the organization. Further, none of the practices included in the bundles of CSRM and
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LOM seem to be influenced by LM experience. This finding suggests that companies undergoing a lean implementation have been approaching the manufacturing improvements regardless of SCM, and little managerial association has been given to the synergic benefits of both approaches. This result converges to the findings of Bhasin (2012) and Saurin et al. (2011), which indicate that one of the main barriers for the implementation of a truly lean system is the inherent systemic improvement approach that it requires. Literature evidence also suggests that a narrow lean implementation may be one of the main causes why
ACCEPTED MANUSCRIPT companies struggle so much on sustaining the lean principles and practices on a long term (Taylor, 2006; Azadegan et al., 2013; Tortorella and Fogliatto, 2014). Therefore, the effect of LM experience on the implementation of GSCM practices seems to be less
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persuasive than expected on proposition 3.
4.1.4. Onshore suppliers
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Although the literature suggests that the level of onshore suppliers has influence on the LSCM implementation, this influence was not as apparent for LSCM practices, except
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for practices LSCM5 (two-way feedback assessment), LSCM11 (intervention strategy), LSCM12 (material handling systems), LSCM13 (standardized work procedures) and LSCM17 (hoshin kanri). Many authors from lean literature reinforce the importance of keeping the proximity with suppliers in order to allow a more extensive interaction on
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both material and information flows, corroborating to the establishment of a cooperative environment. Thus, the strategy of outsourcing manufacture to overseas suppliers would characterize a barrier that discourages such interaction. However, particularly for the
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case of the studied companies embedded in the Brazilian industrial scenario, this fact does not seem to be much significant for the implementation of LSCM practices.
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One of the reasons that might explain such finding is that Brazil is denoted as a developing economy country, where manufacturing costs are usually low if compared to developed economy countries (Nicita et al., 2013). Therefore, the search for offshore low cost suppliers may be significantly less intense in this scenario than in developed economies, mitigating its effect on the implementation of LSCM practices. This fact can be noticed by the indication that 70% of onshore suppliers is a relevant threshold value for the studied sample; while previous studies in large manufacturers from developed economy countries (e.g. Gereffi, 1999; Doh, 2005) suggest that this index may vary
ACCEPTED MANUSCRIPT from 30 to 50%. Thus, based on this study sample, despite evidence suggests a positive association, proposition 4 is poorly supported and further studies need to be developed to deepen the comprehension about differences on influence of this contextual variable
4.2. Contextual variables, LSCM bundles and performance
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under both scenarios of developed and developing economies.
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With regards to the effect of the LSCM practices on supply chain performance, a hierarchical regression model was undertaken. Regression analyses are a set of
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statistical techniques that allow one to assess the relationship between on dependent variable and several independent variables (Tabachnick and Fidell, 2013). Thus, an Ordinary Least Squares (OLS) regression analysis method was used to describe how the four LSCM bundles (independent variables) are associated with the factor score for
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supply chain performance (dependent variable). The regression was performed in two stages. In the first stage, it was included only the contextual variables (tier level, plant size, LM experience, and onshore suppliers) as control variables; while the second stage
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added the influence of the independent variables. Therefore, it allows us to evaluate the incremental effect of LSCM bundles regardless of contextual variables.
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Table 7 shows the results from the hierarchical regression analysis. Model 1 indicates that contextual variables as a group (control variables) account for a small but significant amount of variance (adjusted R2=0.177, p 5 years
37%
1 and 2
57%
3 and 4
43%
< 70%
31%
> 70%
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Table 2 – Sample characteristics (n=89 companies)
LM experience Metallurgical
21%
Food & Beverage
19% Tier level
Furniture
15%
Agroindustry
8% Onshore suplliers
Electronic
5%
Chemical
2% Size of the company 3%
Less than 500 employees
36%
More than 500 employees
64%
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EP
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Others
69%
ACCEPTED MANUSCRIPT
-
LSCM2
0.05
-0.27
-0.09
0.11
LSCM3
0.16
-0.25
0.00
LSCM4
0.11
-0.35
LSCM5
0.00
-0.16
LSCM6
0.07
LSCM7 LSCM8 LSCM9
0.00
0.04
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.05
0.00
0.01
0.00
0.01
0.03
0.00
0.00
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.28
-
0.02
0.00
0.00
0.02
0.00
0.00
0.00
0.29
0.55
0.26
-
0.00
0.06
0.00
0.00
0.08
0.28
0.55
0.43
0.32
-
0.00
0.00
0.00
0.17
0.24
0.37
0.32
0.20
0.44
-
0.00
0.03
-0.34
0.02
0.37
0.41
0.26
0.42
0.38
0.46
-
0.00
0.25
-0.04
0.23
0.50
0.50
0.13
0.44
0.33
0.24
0.49
-
0.21
-0.38
0.09
0.25
0.47
0.31
0.45
0.27
0.29
0.63
0.50
0.03
-0.38
-0.02
0.38
0.54
0.32
0.39
0.48
0.46
0.61
0.44
LSCM10
-0.04
-0.29
0.01
0.35
0.44
0.39
0.37
0.49
0.54
LSCM11
-0.01
-0.05
0.02
0.18
0.24
0.39
0.14
0.26
0.50
LSCM12
0.05
-0.30
0.06
0.28
0.50
0.31
0.25
0.57
0.32
LSCM13
0.17
-0.41
-0.08
0.21
0.16
0.49
0.17
0.51
0.25
LSCM14
0.05
-0.30
0.02
0.20
0.19
0.47
0.16
0.47
0.41
0.35
0.37
0.39
0.32
0.34
0.48
-
0.01
0.01
0.00
0.00
0.00
0.00
0.01
0.00
LSCM15
-0.01
-0.23
0.02
0.18
0.49
0.25
0.22
0.45
0.31
0.37
0.34
0.21
0.35
0.29
0.20
0.40
0.15
0.27
-
0.00
0.00
0.00
0.01
0.05
0.01
0.06
LSCM16
0.17
-0.32
0.17
0.23
0.51
0.30
0.18
0.61
0.28
0.35
0.36
0.35
0.34
0.30
0.19
0.55
0.42
0.28
0.40
-
0.00
0.00
0.00
0.00
LSCM17
0.12
-0.22
0.17
0.30
0.55
0.33
0.35
0.52
0.43
0.40
0.45
0.29
0.37
0.30
0.26
0.55
0.32
0.37
0.60
0.60
-
0.00
0.00
0.00
0.03
0.00
LSCM18
0.01
-0.17
0.01
0.22
0.44
0.38
0.23
0.42
0.48
0.34
0.42
0.37
0.46
0.41
0.32
0.46
0.28
0.34
0.43
0.48
0.59
-
0.00
0.00
0.00
0.00
LSCM19
0.13
-0.20
0.16
0.34
0.39
0.38
0.27
0.54
0.34
0.34
0.40
0.15
0.26
0.42
0.33
0.44
0.40
0.45
0.29
0.52
0.47
0.41
-
0.00
0.05
0.00
LSCM20
0.02
-0.30
-0.05
0.26
0.28
0.36
0.20
0.46
0.27
0.33
0.28
0.31
0.32
0.33
0.32
0.38
0.48
0.36
0.21
0.52
0.40
0.53
0.51
-
0.00
0.00
LSCM21
-0.02
-0.28
0.05
0.22
0.38
0.24
0.37
0.28
0.09
0.32
0.45
0.45
0.39
0.20
0.12
0.23
0.10
0.27
0.29
0.17
0.23
0.34
0.21
0.35
-
0.00
LSCM22
0.02
-0.36
0.07
0.12
0.39
0.33
0.25
0.45
0.22
0.26
0.27
0.44
0.38
0.27
0.40
0.40
0.45
0.32
0.20
0.51
0.36
0.40
0.41
0.61
0.31
-
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0.01
0.00
0.06
0.01
0.00
0.06
0.09
0.03
0.00
0.04
0.00
0.01
0.04
0.07
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.03
0.01
0.06
0.00
0.02
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.01
-
0.00
0.00
0.00
0.00
0.00
0.00
0.05
0.00
0.01
0.00
0.00
0.00
0.00
0.62
-
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.06
0.01
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0.02
0.04
0.00
0.31
0.34
0.65
-
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.28
0.17
0.33
0.36
0.44
-
0.02
0.03
0.00
0.06
0.07
0.02
0.00
0.00
0.00
0.00
0.35
0.36
0.30
0.47
0.33
0.24
-
0.00
0.00
0.35
0.14
0.34
0.34
0.35
0.24
0.42
-
0.00
0.36
0.23
0.00
0.00
0.47
EP
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0.10
0.02
LSCM18
LSCM11
0.03
LSCM22
0.22
0.00
LSCM21
0.23
0.00
LSCM20
-0.36
0.00
LSCM19
0.13
LSCM17
LSCM1
LSCM16
0.04
LSCM15
0.03
-
LSCM14
0.02
0.26
LSCM13
-
0.04
0.01
0.05 0.00
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0.01
0.40
LSCM12
0.29
4
0.00
SC
3
LSCM10
0.02 0.00
LSCM9
0.00
-
LSCM8
0.02
0.00
LSCM7
0.01
-
2
LSCM6
LSCM4
0.00
1
LSCM5
LSCM3
0.00
LSCM2
4
0.01
LSCM1
3
2
1
Table 3 – Sperman’s correlations between LM experience, tier level, offshore suppliers, plant size and LSCM practices*
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
(1) LM experience, (2) Tier level, (3) Onshore suppliers, (4) Plant size. * Values below diagonal are Spearman’s correlations and values above diagonal are P-values. Only significant P-values ≤ 0.10 are shown
0.04
0.00 0.03
0.00 0.00
0.00
ACCEPTED MANUSCRIPT Table 4 – PCA to validate LSCM bundles–rotated component matrix Factor loadings LSCM practices CSRM
LOM
EWCI
TMC
0.072
0.131
0.308
0.728
-0.052
0.166
0.230
LSCM17
0.795
0.223
0.172
0.135
LSCM18
0.516
0.314
0.220
0.276
LSCM4
0.460
0.588
0.157
0.228
LSCM12
0.353
0.655
0.183
0.090
LSCM13
0.127
0.757
0.040
0.169
LSCM14
0.122
0.582
0.140
0.373
LSCM16
0.450
0.709
0.073
-0.029
LSCM19
0.457
0.572
0.079
0.151
LSCM20
0.241
0.721
0.205
0.090
LSCM22
0.204
0.684
0.264
0.047
LSCM1
0.525
0.017
0.617
0.208
LSCM7 LSCM8 LSCM21 LSCM5 LSCM6 LSCM9
0.170
0.081
0.693
0.084
0.415
0.058
0.650
0.034
0.004
0.250
0.762
0.271
0.103
0.213
0.707
-0.004
0.301
0.112
0.031
0.785
0.230
0.162
0.407
0.581
0.212
0.160
0.529
0.596
0.178
0.202
0.257
0.712
0.310
0.073
0.673
TE D
LSCM10
0.021
8.556
1.897
1.700
1.293
Initial percent of variance explained
38.890
8.625
7.729
5.876
Rotation sum of squared loadings (total)
3.863
3.584
3.273
2.726
Percent of variance explained
17.560
16.290
14.877
12.393
0.804
0.865
0.805
0.817
EP
Eigenvalues
SC
M AN U
LSCM3
LSCM11
RI PT
0.591
LSCM15
LSCM2
Cronbach α (sample n=89)
AC C
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization.
Table 5 – Supply chain performance: factor loading, means, standard deviation and Spearman correlation Loading on first component
Mean
Std. Supply dev. lead time
Supply lead time
0.81
3.12
1.06
Costs with supply and raw material
0.70
3.21
Inventory
0.78
Delivery service level
0.75
Quality
0.73
Eigenvalue
2.857
Five-year change in
(variance explained) Cronbach α
Costs with supply and raw material
Inventory
Delivery service level
Quality
-
0.47
0.49
0.52
0.52
0.97
0.47
-
0.43
0.40
0.34
3.03
1.11
0.49
0.43
-
0.55
0.52
3.32
0.97
0.52
0.40
0.55
-
0.42
3.57
0.93
0.52
0.34
0.52
0.42
-
57.14% 0.812 Pairwise deletion method used. All correlations are significant at 0.01 level (two-tailed)
ACCEPTED MANUSCRIPT Table 6 - Pearson’s χ2 to test for association between contextual variables and implementation of LSCM practices a LSCM1
Tier level 14.31 ***
LSCM2
6.85
LSCM3
11.56 **
LSCM4
11.13 **
LSCM5
6.93
LSCM6
15.09 ***
LSCM7
6.38
Plant size 7.57
LM experience 10.20 ** 5.61
3.40
11.90 **
2.58
1.73
9.86 **
7.42
13.28 **
5.79
17.57 ***
3.32
25.57 ***
10.08 **
17.78 ***
LSCM9
16.65 ***
13.03 **
5.56
LSCM10
10.14 **
11.70 **
1.77
3.79
5.81
LSCM12
9.70 **
8.12 *
LSCM13
16.89 ***
11.00 **
LSCM14
10.91 **
LSCM15
9.94 **
LSCM16
14.49 ***
7.69
7.20
4.81 4.84 0.59
8.85 *
4.98
18.26 ***
3.51
8.85 *
5.85
3.96
3.95
1.44
10.65 **
4.24
4.30
11.71 ***
2.69
6.98 *
7.49 6.51
7.40
6.33
19.38 ***
7.75
4.73
13.73 ***
6.83
7.10
9.14 *
4.13
2.11
4.29
4.32
LSCM20
17.23 ***
LSCM21
9.06 *
LSCM22
13.33 **
M AN U
4.85
LSCM19
4.79
TE D
5.33
4.63
3.18 15.22 ***
SC
7.25
LSCM11
LSCM18
6.28
8.57 *
LSCM8
LSCM17
Onshore suppliers
RI PT
LSCM practices
14.33 ***
6.87
3.81
a
All significant associations are positive except for those in parentheses, which indicate negative association.
∗
p
