The role of supply network characteristics on lean
implementation and responsiveness:
a configuration approach
Master thesis Nadine Weizel S3474119 Supervisor Dr ir. T. Bortolotti Co-assessor Dr. K. Peters Date: 22.06.2018 University of Groningen Faculty of Economics and BusinessThe role of supply network characteristics on lean implementation and responsiveness: a configuration approach
Abstract
Table of Contents
1. Introduction 1
2. Theoretical Background 3
2.1. Lean Manufacturing 3
2.2. Lean Supply Chain Management 8
2.3. Lean programs, contingencies and configurational aspects 12
2.4. Lean management and the performance link 14
2.5. Supply Network Relationships 16
2.6. Supply Chain Complexity 19
2.7. Research hypotheses 21
3. Methodology 26
3.1. Data collection and sample 26
3.2. Variables and scales 28
3.3. Construct validity and reliability 30
3.4. Forming a-priori groups 31
List of Tables
Table 2.1: Internal (LM) and external (LSCM) lean practices and lean terms used 7
Table 3.1: Overview sample distribution (n = 316). 27
Table 3.2: Construct reliability 31
Table 3.3: Coding of SCC 32
Table 3.4: Number of plants according to the a-priori groups 32 Table 4.1: Regression analysis for complete data set (n = 316) 34 Table 4.2: F-ratios and significance values from ANOVA analysis 35 Table 4.3: ANOVA means per practice and performance measure for each group 36 Table 4.4: Hochberg’s GT2 post hoc test of pairwise comparison 37 Table 4.5: Regression analysis for group 0: low SCC and low SNR (n = 54) 39 Table 4.6: Regression analysis for group 1: low SCC and high SNR (n = 120) 39 Table 4.7: Regression analysis for group 2: high SCC and low SNR (n = 101) _40 Table 4.8: Regression analysis for group 3: high SCC and high SNR (n = 41) _40
List of Figures
Figure 2.1: Research model related to Hypothesis H1 and H2 23
Figure 4.1: Overview ANOVA means per group 37
Abbreviations
ANOVA Analysis of variance
BSR Buyer-supplier relationship
CFA Confirmatory factor analysis
CSF Critical success factor
HRM Human resource management
JIT Just-in-time
LM Lean manufacturing
LSCM Lean supply chain management
SC Supply chain
SCI Supply chain integration
SCM Supply chain management
SMED Single-minute exchange of die
TPM Total preventive maintenance
TPS Toyota Production System
1. INTRODUCTION
The economic environment is rapidly changing, supply chains are nowadays extended to a global scale and products compete globally. To stay competitive companies need to constantly assess and improve their processes. Lean is a very popular tool for this and is linked with operational performance enhancements (Hallgren & Olhager, 2009; Jasti & Kodali, 2015; Marodin & Saurin, 2013; Moyano-Fuentes & Sacristán-Díaz, 2012; Netland, 2016; Shah & Ward, 2007). However, external lean practices are a rather recently added topic in academic literature (Flynn et al., 2010; Shah & Ward, 2007; Marodin et al., 2017).
relationships are certainly factors which influence the effectiveness of several practices (Bozarth et al., 2009; Bortolotti et al., 2016; Gimenez et al., 2012; Qrunfleh & Tarafdar, 2013; Simpson & Power, 2005). Thus combining both dimensions might increase the understanding of configurations which benefit from lean implementation.
This study operationalizes Lean Manufacturing (LM) will as internal lean practices and Lean Supply Chain Management (LSCM) as external lean practices. Furthermore supply chain complexity and supply network relationships will be introduced as two dimensions for a configurational approach to research the effectiveness of lean practices in different configurations.
This study will add to current literature by applying a configurational approach to examine the relationship between internal, external lean practices and performance; aiming to increase the understanding of: (i) Relationship between internal and external lean practices and their effects on performance. (ii) What is the influence of the intensity of supply network relationship and supply chain complexity on the implementation of lean practices.
Data from 316 manufacturing plants will be analyzed which gives insight into the application and levels of LM, LSCM, the performance influence and the lean performance according to a firms supply network relationship and supply chain complexity configuration.
2. THEORETICAL BACKGROUND
In this section the main concepts included in the study are reviewed, first Lean Manufacturing (LM) and Lean Supply Chain Management (LSCM). After reviewing both a section concerning configurational approaches and contingency research, and the link between lean and performance outcomes, will follow. Thereafter, the two dimensions, supply network relationships (SNR) and supply chain complexity (SCC), and their links to lean programs will be introduced. Finally, research hypotheses will be developed.
2.1. Lean Manufacturing
production, in 1990 Womack et al. presented the term LM as a synonym for the until then known (and mostly internal related lean) practices belonging to the TPS.
According to the recent understanding lean production is a philosophy as well as a tool consisting of several management practices (Shabeena Begam et al., 2013; Shah & Ward, 2003; Shah & Ward, 2007). The lean philosophy comprises values and principles which are hardly ever easy to measure (Schonberger, 2007; Shah & Ward, 2003). The second perspective on lean production is more practical, referring to lean as a multi-dimensional concept consisting of a broad selection of tools and techniques (Danese et al., 2012; Shah & Ward, 2003, 2007). Common consensus of the objectives of lean production prevails; the purpose of implementing lean is to eliminate waste and reduce inefficiencies in order to:
◦ achieve faster production processes,
◦ improve delivery performance and overall operational performance,
◦ to increase the company’s competitive advantage by means of standardizing and streamlining the production
(Chauhan & Singh, 2012; Danese et al., 2012; Lewis, 2000; Mokadem, 2017; Narasimhan et al., 2006; Shah & Ward, 2003; Shah & Ward, 2007).
and Ward (2003) also titled their research of internal lean bundles with this term, this it seems appropriate. The term lean supply chain management (LSCM) will be applied for external practices and introduced in the next section. It is to be noted that several different terms exist (e.g. just-in-time manufacturing, lean manufacturing, internal lean practices, lean production, lean management) and that there is no common definition of lean manufacturing; neither in academia nor in practice (McLachlin, R., 1997; Shah & Ward, 2003; Shah & Ward, 2007). This could be one reason why it can be difficult for firms to:
◦ maintain an overview over the most relevant lean practices, ◦ achieve an understanding of the scope of LM,
◦ implement lean programs as well as
◦ understand the relationship and mutual effects internal and external lean practices can have on each other.
The main point of LM definitions is the existence of multiple interdependent practices which contribute to an integrated system altogether (Yang et al., 2011; Chavez et al., 2015; Shah & Ward, 2003).
Table 2.1
Internal (Lean Manufacturing) and external (Lean Supply Chain Management) lean practices and lean terms used
Practice Scope LM literature Lean terms used
Flow oriented layout internal Shah & Ward (2007), Yang et al. (2011), Galeazzo & Furlan (2018), Bortolotti et al. (2014), Bortolotti et al. (2015)
LP, LM, LMg Kanban internal Cua et al. (2001), Galeazzo & Furlan (2018), Marodin et al. (2017),
Shah & Ward (2003), Shah & Ward (2007), Yang et al. (2011) , Chavez et al. (2015), Bortolotti et al. (2014), Bortolotti et al. (2015)
LM, LP, ILP
Setup time reduction internal Cua et al. (2001), Galeazzo & Furlan (2018), Marodin et al. (2017), Shah & Ward (2003), Shah & Ward (2007), Yang et al. (2011) , Chavez et al. (2015), Bortolotti et al. (2015)
LM, LP, ILP
Statistical process control
internal Cua et al. (2001), Marodin et al. (2017), Shah & Ward (2003),
Shah & Ward (2007), Bevilacqua et al. (2017), Bortolotti et al. (2014)
LM, LP Total preventive
maintenance
internal Galeazzo & Furlan (2018), Marodin et al. (2017), Shah & Ward (2003)
LM, LP Continuous improvement internal Shah & Ward (2003), Bortolotti et al. (2015) LM Human resource
management
internal Galeazzo & Furlan (2018), Shah & Ward (2003), Bevilacqua et al. (2017), Yang et al. (2011)
LM Top management
leadership for quality
internal Cua et al. (2001), Bortolotti et al. (2014), Bortolotti et al. (2015) LMg JIT delivery by supplier external Cua et al. (2001), Danese et al. (2012), Marodin et al. (2017),
Shah & Ward (2007) , Bevilacqua et al. (2017), Bortolotti et al. (2014), Bortolotti et al. (2015)
LM, LP, LMg
Supplier partnership External Cua et al. (2001), Marodin et al. (2017), Shah & Ward (2007),
Bevilacqua et al. (2017), Chavez et al. (2015), Bortolotti et al. (2014), Bortolotti et al. (2015)
LP, LM
The more recent definition states: “Lean production is an integrated socio-technical system whose main objective is to eliminate waste by concurrently reducing or minimizing
supplier, customer, and internal variability.” (Shah & Ward, 2007, p. 791).
Ten components were identified in this study, six of which are internally-related (LM practices): pull production, continuous flow, low setup times, statistical process control, total productive maintenance and employee involvement, and the remaining four practices are externally related (Shah & Ward, 2007). Those six practices comprise a more recent list of bundles which are defined as internally related lean practices.
2.2. Lean Supply Chain Management
Taking up on the last argument from the previous section, the remaining four practices are externally related; three of these concern suppliers and the last one customer. These practices are namely: supplier feedback (SUPPFEED), JIT delivery by suppliers (SUPPJIT), supplier development (SUPPDEVT) and customer involvement (CUSTINV) (Shah & Ward, 2007).
(Frohlich & Westbrook, 2001; Jasti & Kodali, 2015; Marodin et al., 2017; Simpson & Power, 2005; Wiengarten et al., 2013). A reason for that can be found in the shift of competition. It is no longer firms or solely their products competing with each other; but rather their SCs which compete against each other (Tan et al., 2002).
The extension of lean production practices beyond the focal firm’s boundaries acknowledges also the relatively young field of supply chain management (SCM). The Council of Supply Chain Management Professionals committee (CSMP committee) proposes the following definition for SCM: “Supply Chain Management encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all Logistics Management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, Supply Chain Management integrates supply and demand management within and across companies.” (Gibson et al., 2005, p. 22).
These days, companies cannot operate without having some sort of supply chain management, applying lean principles to it equals the striving for more efficiency and competitiveness. The definition of SCM highlights the collaboration and coordination aspect in a supply chain, which are frequently mentioned aspects in the LSCM literature as well; also known as buyer-supplier relationships (BSR) or supply network relationships (SNR) (Bortolotti et al., 2016; Lewis et al., 2010; Marodin et al., 2017; Narasimhan et al., 2006; Shah & Ward, 2007; Simpson & Power, 2005; Srinivasan et al., 2011; Stonebraker & Afifi, 2004; Wu, 2003).
There are several newer models and concept operationalization of lean which – similarly to Shah and Wards (2007) change to lean production - now include company links and as such related practices beyond the firm’s boundaries (Bevilacqua et al., 2017; Bortolotti et al., 2015; Bortolotti et al., 2016; Lewis et al., 2010; Marodin et al., 2017; Mokadem, 2017; Shah & Ward, 2007; Srinivasan et al., 2011; Wee & Wu, 2003). However, extending the lean scope beyond the firm’s boundaries is also what makes implementation so difficult.
In their work, MARODIN, TORTORELLA, FRANK and FILHO (2017) first state and establish the link between supply chain integration (SCI) and SCM to conclude that it is still a rather new research field. Then they continue to study the link between internal and external lean practices; finding a moderation effect of external LSCM on the relationship between internal lean shop floor practices and performance. Srinivasan et al. (2011) investigate the relationship between buyer-supplier partnership quality and supply chain performance (and moderating effects of risk and environmental uncertainty). This shows that there is a rather new stream connecting externally related (lean) practices with SCM and ultimately SC performance.
enhancing characteristics as well as oftentimes researched within the lean production context (Bevilacqua et al, 2017; Danese et al., 2012; Marodin et al., 2017). JIT deliveries by suppliers are characteristically indicated by frequent deliveries of small batch sizes and a huge contributor to experience the all possible benefits from applying a JIT manufacturing system (Danese et al., 2012). Thus, Marodin et al. (2017) describe that LSCM “should allow the flow of goods, services and technology from suppliers to customers, minimizing waste and maximizing value added to all agents of the supply chain (Goldsby et al., 2006; Wee and Wu, 2009).” (Marodin et al., 2017, p. 473f). Furthermore, it is stated that an emphasis on LSCM is still rather limited in practice and needs to be more focused on (Marodin et al., 2017).
Marodin et al. (2017) find that lean supplier relationships (related to LSCM practices) positively moderate the effect of lean shop floor practices on inventory turnover as well as quality. As mentioned at the beginning of this section, collaboration and cooperation are immensely important aspects of LSCM. One indicator is NAIM and GOSLING (2011) stating a positive shift towards aiming for cooperative business relationships as opposed to executing bargaining power over suppliers. Another perspective is presented in the research of BORTOLOTTI, DANESE, FLYNN and ROMANO (2015) who argue that in order for a firm to reach utmost performance the company should establish a core fitness level first, on which it subsequently can build on further capabilities relatively easy. In this case, this core fitness is comprised of the following four components: HRM practice, TPM practice, manufacturing strategy and supplier relationship bundle (Bortolotti et al., 2015). This shows that there are various approaches in literature how lean bundles should be configured or in which sequentiality they should be best implemented in order to be utmost successful under the lean approach.
Bortolotti et al. (2015) clearly offer a recommended order for the implementation of lean practice bundles. Hence, there is the trend of LSCM moving towards long-term commitments to partners in the SC which could be further improved and applied with the help of lean practices (Marodin et al., 2017). Therefore, more empirical research is needed with bigger sample sizes than case study research offers in order to investigate the relationship, configuration and most preferable sequentiality of LM and LSCM practices.
2.3. Lean programs, contingencies and configurational aspects
configurational aspects when it comes to lean practices, their implementation and applicability (Bevilacqua et al., 2017; Galeazzo & Furlan, 2018; Netland, 2016; Sousa & Voss, 2008; Srinivasan et al., 2011). Therefore, various aspects lead to a call for more research concerning the boundaries of lean and the influence of the different practices on each other and on performance:
◦ The increased interest in and importance of LSCM, while in practice it is still less implemented compared to LM.
◦ The increasing necessity to implement and maintain efficient SC and SCM due to the emerging global markets.
◦ Recent studies investigating contingent factors like technological turbulence, organizational culture or SC integration (Chavez et al., 2015; Bortolotti et al., 2015a; Flynn et al., 2010).
Already in 2000 Lewis (2000) stated that contextual factors exist which influence successful LM. The author proposed market type, dominant technology as well as SC structure as such contextual factors (Lewis, 2000). In the past, authors highlighted that in terms of LM a internal practices alone might not be sufficient enough to achieve the ultimate goals of lean production but that there’s a need for a combination of internal and external practices as well as links with customers and suppliers (Mokadem, 2017; Shah & Ward, 2007; Simpson & Power, 2005). GALEAZZO and FURLAN (2018) studied different configurations of lean bundles in order to find out if there are differences among them and if they vary in terms of successfulness. Their research resulted in the finding that indeed different configurations relate to different financial performance outcomes. The results support the understanding that lean practices are interrelated, complementary but also applicable in varying configurations according to the firm’s situation (Galeazzo & Furlan, 2018). Yet, these findings concern only internal lean practices and their configuration as well as their impact on financial performance, which is only one of the four common lean measurements.
2.4. Lean management and the performance link
universal applicability of lean it is worth mentioning another side to it as well. For example, Gimenez et al (2012) found that there are contexts in which integrating SCs intensively is more cost intensive than it would have positive performance impacts. Their research was related to SCI indeed but it can be argued that especially external lean practices at least to a certain extent aim for this as well. Nonetheless, lean programs and the implementation of lean practices also require financial investments in order to be executed.
In terms of LSCM it was also found that it contributes to LM, its positive impacts on the manufacturing process as well as the firm’s overall performance (Marodin et al., 2017). However, it is very difficult to measure the performance of supply chains or, in consequence, a change in performance after applying LSCM for the reason that those measures would need to be observed along the supply chain – beyond firm’s boundaries, which makes it very difficult (Gopal & Thakkar, 2012). Regarding performance measurements, lean is associated with good quality and delivery performance (Hallgren & Olhager, 2009). Based on constant reports of the measures/indicators - cost, quality, delivery, flexibility performance - companies can develop competitive advantages, thus it is advised to monitor those (Narasimhan et al., 2006; Schmenner & Swink, 1998). They are commonly used indicators to measure lean performances (Carr & Kaynak, 2007; Danese et al., 2012; Hallgren & Olhager, 2009; Marodin et al., 2017).
rarely mentioned are the relationships those practices have towards each other. At times, there are arguments found which state first insights related to the relationship between practices (Bortolotti et al., 2015; Danese et al., 2012). Furlan et al. (2010) argue for complementary effects of lean internal and external practices. Danese et al. (2012) found that a situation where JIT production is in place, however, JIT supply is not applicable, can be less beneficial and possibly cause delivery delays. The authors specifically looked into the relationship of two practices with each other, JIT production and JIT delivery by suppliers. Other studies combined different practices and researched their influence on each other. As mentioned before Bortolotti et al. (2015) argued that four bundles, TPM, HRM, manufacturing strategy and supplier relationship bundle, together should be implemented first due to their fundamental contribution to a firm’s ability to build further practices on the previously mentioned ones.
2.5. Supply network relationships
One of two dimensions this study is applying to further explore the efficiency/applicability of lean practices is the scope of chosen supply network relationships (SNR) of involved companies. Relationships in a SC, subsequently in a supply network are inevitable. The moment two parties in a supply chain conduct business there is a relationship. Those relationships are indeed a popular topic and frequently researched (Ambrose et al., 2010; Chen & Deng, 2015; Johnston & Kristal, 2008; Srinivasan et al., 2011). The design and intensity of buyer-supplier-relationships are subject to the involved parties and can vary a lot. According to PAULRAJ and CHEN (2007) supply chain relationships affect :
◦ interdependencies between firms,
◦ by all means can have a positive effect on predictability, and demand and supply stability. These relationships can be distinguished by different classifications (Roseira et al., 2010). One of the major classifications refers to the degree of collaboration between the involved parties. The lower end of the scale is described as arm’s length relationship and the upper end oftentimes mentioned as a closely-tied or collaborative relationship (Carr & Pearson, 1999; O’Brien, 2014). Hence, arm’s length relationships refer to relationships characterized by only the minimum and limited amount of interaction in regards to the order (O’Brien, 2014). Thus, associated with low levels of SNR: Frequently it may occur that with transactional relationships, which are linked to arm’s length relationships, suppliers are less motivated to excel in their service delivery (Srinivasan et al., 2011). On the contrary, there are collaborative relationships which extend to interactions beyond the execution of order fulfilments and are in general long-term oriented (O’Brien, 2014) as well as they are known to influence performance positively (Srinivasan et al., 2011). However, the interplay between the number of key suppliers and the type of relationships focal companies have with their suppliers can also imply certain downsides or risks. If a company has only one key supplier for a product there is the risk that the supplier abuses the relationship and starts to take advantage of his position; meaning that the buying company finds itself in a locked-in position because there are no alternative supplier relationships and supplies to go to (Narasimhan et al., 2009).
2.6. Supply chain complexity
Almost two decades ago, LEWIS (2000) findings supported the – what at the time was a – suggestion that in order to implement lean production successfully, contingencies, as well as complexity, have to be considered during the approach. Hereby, complexity was already linked to interactions, internally as well as externally, which aimed at generating diverse information to enable the organization to derive to decisions which maintain the companies’ competitiveness (Lewis, 2000). SCC will be the second dimension which will be applied to the study. Managing SCs requires the full use of information as well as being extremely cautious and aware of changes to the existing plan and potential consequences. Therefore, SCs are rather complicated systems (Bozarth et al., 2009). Most of the literature about SCC, mention both concepts ‘uncertainty’ and ‘complexity’ as relevant concepts since they differ in several ways. Uncertainty is more like a risk, it is unknown if or when events will occur (Srinivasan et al., 2011); in some definitions, uncertainty is included in the complexity concept though (Vachon & Klassen, 2002).
supply complexity (which amongst other factors also includes the number of suppliers) influences the extent of effort which is useful to put in in terms of integrating SCs (Gimenez et al., 2012). This finding may possibly be linked with external lean practices as they also aim to create a better and ‘leaner’ interface between supply chain parties. Additionally, Marodin et al. (2017) also link supply chain integration to their research regarding LSCM practices.
There is evidence supporting the statement that close proximity in a supply chain is beneficial for lean practice applications (Danese et al., 2012; Narasimhan & Nair, 2005). Bortolotti et al. (2016) study factors which influence the extension of lean practices to supply networks. Those factors are described as supply network characteristics and one of the introduced three characteristics is supply network structure which is comprised of the number of suppliers as well as the number of suppliers per item, the selection and evaluation process of suppliers and lastly the supplier location in relation to the involved counterparts (Bortolotti et al., 2016). While Bortolotti et al. (2016) are not referring to it as SCC; some congruent factors can be noted to the common definitions and measures of SCC, especially considering the upstream complexity in literature; such as the number of suppliers, the distance between involved counterparts and commitment of suppliers to deliver their best products and services.
2.7. Research hypotheses
(Cua et al., 2001; Hallgren & Olhager, 2009), while others applied only a subset of the four performance measures. Chavez et al. (2015) combined: cost, delivery and flexibility performance and found support for the relationship. Recently Marodin et al. (2017) researched the relationship from internal lean practices on inventory turnover and quality performance arguing that inventories can cause major inefficiencies in manufacturing plants and are one type of waste. One reason for the choice of quality as performance measure was the rather small number of empirical evidence testing specifically for the link to quality. Danese et al. (2012) investigated the relationship between LM and efficiency as well as delivery performance. Both performance links were supported. Other authors even investigated the relationship of LM with a firm’s financial performance (Galeazzo & Furlan, 2018). Therefore, the following hypothesis is stated:
H1: Lean Manufacturing is positively related to delivery and flexibility performance.
successfully implemented on top of the prior ones as well as to influence performance positively. Thus, the following hypothesis states:
H2: LM positively moderates the relationship between LM and delivery and flexibility performance.
Figure 2.1 shows the research model and indicates Hypotheses 1 and 2.
There is abundant support for the performance enhancing characteristic of lean (Hallgren & Olhager, 2009; Marodin & Saurin, 2013; Moyano-Fuentes & Sacristán-Díaz, 2012; Netland, 2016; Shah & Ward, 2007). Still, implementing lean programs is neither simple nor always successful, neither is the maintenance of lean practices once implemented. In fact, the majority of lean projects fail (Lucey et al., 2005). Independent from the outcome of a lean project, the implementation of lean practices costs financial resources. Even though lots of research has been conducted and many lists of critical success factors for lean implementation projects exist, this does not seem to be enough to improve the success-rate of those projects. What is more, recently authors found circumstances, e.g. different configurations of lean practices or contingent factors, which do not enhance the performance. This leads researchers to wonder about the universal applicability of lean (Chavez et al., 2015; Galeazzo & Furlan, 2018; Marodin et al., 2017; Netland, 2016; Sousa & Voss, 2008).
Additionally, in light of recent findings concerning SNR and SCC unanswered questions arise. On the one hand there is the genuine shift towards making long-term commitments between supply chain partners and the emphasis on collaborative relationships (Marodin et al., 2017) as well as the observation that partners in an arm’s length relationship may not be inclined to deliver their best services (Srinivasan et al., 2011). All this supports the shift and provides arguments for investing in collaborative relationships and applying lean practices to enhance those outcomes even more. On the other hand, there might be a possible risk of getting into a locked-in situation in a relationship and then falling at the mercy of the business partner (Narasimhan et al., 2009). The second dimension concerns the fact that SCs are rather complicated systems (Bozarth et al., 2009). Recently in related fields there are indications that the level of complexity influences the effectiveness of lean approaches. Bozarth et al. (2009) stated that increasing complexity decreases performance; the study applied cost and schedule attainment performance. As lean aims to reduce waste, in return increase efficiency, perhaps implementing lean practices might compensate for increasing SCC. Somewhat contrary, Gimenez et al. (2012) found evidence that the integration of supply chains in a less complex context is not enhancing the performance and at worst can lead to spending more financial resources on the practice itself than can be saved through the implementation of such.
Both dimensions have an impact on firms and their supply chain performances, and are frequently researched. Recently, contrary or at least more detailed insights have been found towards their effects on companies and supply chains. To the best of the author’s knowledge no other study considered combining these two dimensions yet; additionally distinguishing between internal and external lean practices. This leads to the third hypothesis:
Moreover, the implementation of not all but only a few lean practices receive attention in research (Bortolotti et al., 2015; Cua et al., 2001; Shah and Ward, 2007). Shah and Ward (2007) while updating their definition of lean stated that applying only internal lean practices may not be sufficient to achieve the best possible lean performance. For one there is the distinction in internal and external lean practices and their influence on each other. On the other hand there is the stream that investigates best possible sequences for lean practice implementations (Bortolotti et al., 2015; Marodin et al., 2017; Galeazzo & Furlan, 2018). Furthermore, there are studies examining the relationship of internal lean practices on performances with possible moderating effects from external practices. This is the case for Marodin et al. (2017); they concluded that the relationship of lean shop floor practices (internal lean practice) on inventory turnover as well as quality performance is positively moderated by LSCM. Danese et al. (2012) examined the possible effect JIT delivery by suppliers has on the relationship of JIT production to delivery performance as well as efficiency. They found that the relationship of JIT production to delivery performance is indeed significantly influenced by the external lean practice (JIT delivery by suppliers), but that there was no significant impact on efficiency (Danese et al., 2012). Given the previously stated hypothesis and its derivation, the level of SNR and SCC may also influence the under H1 and H2 investigated relationships, the fourth hypothesis is stated as following:
3. METHODOLOGY 3.1. Data collection and sample
To test the hypothesis, data from the dataset of the third round of the High Performance Manufacturing (HPM) project will be used (Schroeder and Flynn, 2001). The HPM project is an international research project that aims at examining the phenomena of “high performance manufacturing” and its relationship to performance. Data was gathered in plants from different countries using survey questionnaires. The selected plants are located in ten countries: Finland, the USA, Japan, Germany, Sweden, South Korea, Italy, Austria, Spain and P.R. China. Those plants operate in mechanical, electronics or transportation equipment sectors (SI Codes: 35, 36 and 37). The selection of the countries was based on their mix of high performing and traditional plants as well as their diverse national cultures and economics.
The plants on the other hand were selected randomly from a master list of manufacturing plants based on local sources (i.e., Dun’s Industrial Guide, the JETRO database, etc.). In order to obtain an approximately equally represented sample set in regards to sectors, countries and performance (traditional versus high performing plants) the researchers in each country had to stratify the sample set. However, two prerequisites were a plant size of at least 100 employees as well as amongst the sample plants there was no plant with the same parent corporation allowed. Originally the questionnaire was developed in English, but for the surveys in each country they were translated by the local HPM research teams into the countries mother tongue (Appendix: A lists all items contained in each scale.).
site in the distribution of the questionnaires and collection of such. Furthermore, the research coordinator was the contact person for the HPM research team when further questions appeared or clarifications of answers were needed. In order to reach high internal consistency the questionnaires were, when possible and applicable, distributed to different respondents from the considered plants. In total 317 complete responses from plants were received which represents approximately 65 percent of the contacted plants. Due to incomplete answers one plant was excluded, thus this study uses a sample of 316 manufacturing plants for hypothesis testing and analyzing. Table 3.1 shows the sample distribution.
For this studies questionnaire a subset of the HPM survey items will be used. In order to analyze the data on plant-level, the individual responses from each plant are aggregated into an average within-plant response and the average plant response is analyzed.
Table 3.1
Overview sample distribution (n = 316).
Country Industry Total plants per
Electronics Machinery Transportation country
Austria 10 7 4 21 China 21 16 13 50 Finland 14 6 10 30 Germany 9 13 19 41 Italy 10 10 7 27 Japan 10 12 13 35 South Korea 10 10 11 31 Spain 9 9 10 28 Sweden 7 10 7 24 United Stated 9 11 9 29
Total plants per industry
3.2. Variables and scales
In this study first-order constructs and second-order constructs are used. The first-order constructs consist of the most prominent practices of a scale, which contributes to content validity (Nunally, 1978). The second-order constructs are comprised of the first-order constructs, which are mostly multi-item scales. In this context the second-order constructs are Lean Manufacturing and Lean Supply Chain Management as well as the two dimensions SCC and SNR; measured through the first-order constructs. The majority of the items are perceptual; one of them is reverse coded in order to reduce possible common method bias. The perceptual items were to be answered using a seven-point Likert scale, where 1 relates to “strongly disagree” and 7 to “strongly agree”.
LM is a multi-dimensional concept, thus firstly, practices which are commonly used in literature were identified. The practices were distinguished according to their scope (internal or external). In chapter 2.1 Table 2.1 provides an overview over literature und applied practices and the scope. In this study LM will encompass eight multi-item perceptual scales for the LM construct. These are flow oriented layout (FL), setup time reduction (ST), Kanban (KA), statistical process control (SPC), top management leadership for quality (TML), continuous improvement (CI), total preventive maintenance (TPM), human resource management (HRM). For LSCM the external practice and multi-item scale just-in-time delivery by suppliers (JITsup) will be applied, similarly to Danese et al. (2012). In Appendix A all items that were used to measure each scale are listed.
3.3. Construct validity and reliability
Regarding content validity as mentioned in the previous section the scales and measures of this study are developed according to the review of literature and the application of scales and measures which have been assessed and validated in prior studies.
Table 3.2
Construct reliability Second-order construct
First-order construct Cronbach’s
Alpha
LM 0.777
1) Flow oriented layout (FL) 0.828
2) Setup time reduction (ST) 0.821
3) Kanban (KA) 0.830
4) Statistical process control (SPC) 0.905
5) Top management leadership for quality (TML) 0.851
6) Continuous improvement (CI) 0.794
7) Total preventive maintenance (TPM) 0.771
8) Human resource management (HRM) 0.826
LSCM JIT delivery by suppliers (JITsup) 0.724
Performance Responsiveness 0.769
SNR 0.719
1) Trust-based relationship with suppliers (TBRsup) 0.785
2) Supply chain planning (SCPL) 0.811
3.4. Forming a priori groups
Bozarth et al. (2009) who state that dynamic complexity has a greater impact than detail complexity. Gimenez et al. (2012) include LT in their SCC research approach as well. Hence, the weight for short LT is greater. This division of the two dimensions, SNR and SCC, leads to the four groups, as shown in Table 3.4.
Table 3.3 Coding of SCC Indicator Number of suppliers per plant Short lead times (LT) Percentage of purchases from plants home country Score SCC level weight of indicator 0.15 0.75 0.10 > 500 ≤ -0,6820 0% ≤ . ≤ 33% -2 Lowest
200 < . < 500 ≤ 0,1392 not specified -1 Low
missing value Missing value missing value 0 no value give not specified ≤ 0,5923 33% ≤ . ≤ 66% 1 High
≤ 200 > 0,5923 > 66% 2 highest
Table 3.4
Number of plants according to the a-priori groups Group SNR level SCC level No. of plants
0 low low 54
1 high low 120
2 low high 101
3 high high 41
3.5. Data analyses
differences between the population means of more than two groups. The analysis of a multiple linear regression model is supposed to reveal the single effect of each variable on the dependent variable. Before performing statistical tests, the underlying assumptions need to be checked to ensure the reliability and applicability of the linear regression model (see section 3.3). The basic assumptions of linear regression are that the sample is based on independent observations, a linear relationship between the two variables as well as normal distributed residuals. The assumptions of an ANOVA test are that each group is an independently selected random sample, each group contains sample data drawn from a normal population and that the data in each group have been drawn from populations that have equal variance (Field, 2005).
4. RESULTS
The results of the regression analyses for the complete data set (n = 316) is shown in Table 4.1. Model 0 represents the test of the effect of control variables on the delivery and flexibility performance. The control variables are country and industry. As reported, neither of the control variables has a significant effect on the delivery and flexibility performance. Adding the independent variables, LM and LSCM to the regression model (model 1) results in a significant and positive relationship of LM as well as LSCM on delivery and flexibility performance. These effects remain stable when adding the interaction term to the model (model 2). Thus, Hypothesis 1 is supported.
Table 4.1
Regression analysis for complete data set (n = 316) Control variables Model 0 Main effects Model 1 Interaction effect Model 2 Constant -0.004 -0.042 -0.029 Country 0.026 -0.014 -0.013 Industry -0.016 0.032 0.030 LM 0.261 * 0.263 * LSCM 0.133 * 0.136 * LM*LSCM -0.035 R2 0.001 0.122 0.124 R2 Adjusted -0.006 0.111 0.109 ∆R2 0.001 0.121 0.001
Note: Significant at *p < 0.05 and ** p < 0.01 levels; the values reported are standardized regression coefficients
external lean practices do not enhance the relationship between internal lean practices and delivery and flexibility performance.
To test Hypothesis 3, a one-way ANOVA was performed applying the beforehand introduced groups. Considering the ANOVA results the following can be stated: Between the four differently configured groups, LM results in different delivery and flexibility performances levels, F (3, 312) = 42.99, p < 0.05. Additionally, between the four differently configured groups, LSCM results in different delivery and flexibility performances levels, F (3, 312) = 9.20, p < 0.05. Table 4.2 shows the F-ratios as well as the significance levels of the latent variables. These results indicate that there are significantly different performance levels related to internal and external practices in the different groups.
Table 4.2
F-ratios and significance values from ANOVA analysis
Practices / performances F Significance
Lean Manufacturing_LM 43.00 0.00 *
Lean Supply Chain Management_LSCM 9.20 0.00 *
performance level. These results support Hypothesis 3. Table 4.3 provides a summarized overview over the results.
Table 4.3
ANOVA means per practice and performance measure for each group
Practices / performances Group 0 Group 1 Group 2 Group 3 low SCC low SNR n = 54 low SCC high SNR n = 120 high SCC low SNR n = 101 high SCC high SNR n = 41 LM -0.13 0.30 -0.34 0.17 LSCM -0.11 0.24 -0.25 0.05
Delivery and flexibility performance 0.17 0.16 -0.30 0.06
Cost Performance -0.10 0.11 -0.12 0.13
Quality performance -0.19 0.27 -0.28 0.15
LM High
LSCM High
Del. & flex. perf. Medium Cost performance Medium Quality performance High
LM Medium
LSCM Medium
Del. & flex. perf. Medium Cost performance High Quality performance Medium
Group 1 n = 120 Group 3 n = 41 n = 54 Group 0 n = 101 Group 2 LM Medium LSCM Medium
Del. & flex. perf. High Cost performance Medium Quality performance Medium
LM Low
LSCM Low
Del. & flex. perf. Low Cost performance Low Quality performance Low
Figure: 4.1: Overview ANOVA means per group
Table 4.4
Hochberg’s GT2 post hoc test results of pairwise comparisons
Latent variable Group I Group J Mean difference (I-J)
LM 0 1 -0.422 * 2 0.213 * 3 -0.291 * 1 2 0.635 * 2 3 -0.504 * LSCM 0 1 -0.332 * 1 2 0.482 *
Delivery and flexibility performance 0 2 0.477 *
1 2 0.462 *
2 3 -0.365 *
Quality performance 0 1 -0.467 *
To test Hypothesis 4 a regression analysis was performed for each of the groups. Results are reported in the Tables 4.5, 4.6, 4.7 and 4.8. The models are the same as in the regression analysis for the complete data set. The control variables are not significant in any of the groups or models.
Table 4.5
Regression analysis for group 0: low SCC and low SNR (n = 54) Control variables Model 0 Main effects Model 1 Interaction effect Model 2 Constant -0.112 -0.131 -0.163 Country 0.132 0.014 0.010 Industry 0.047 0.178 0.180 LM 0.031 0.055 LSCM 0.132 0.161 LM*LSCM 0.121 R2 0.020 0.040 0.052 R2 Adjusted -0.018 -0.039 -0.046 ∆R2 0.020 0.019 0.013
Note: Significant at *p < 0.05 and ** p < 0.01 levels; the values reported are standardized regression coefficients
Table 4.6
Regression analysis for group 1: low SCC and high SNR (n = 120) Control variables Model 0 Main effects Model 1 Interaction effect Model 2 Constant 0.070 -0.061 -0.069 Country 0.172 0.130 0.141 Industry -0.056 -0.030 -0.028 LM 0.166 0.232 * LSCM 0.162 0.242 * LM*LSCM -0.188 R2 0.031 0.110 0.128 R2 Adjusted 0.014 0.079 0.090 ∆R2 0.031 0.079 0.019
Table 4.7
Regression analysis for group 2: high SCC and low SNR (n = 101) Control variables Model 0 Main effects Model 1 Interaction effect Model 2 Constant -0.283 0.097 0.116 Country -0.047 -0.127 -0.126 Industry 0.029 0.004 0.000 LM 0.317 ** 0.284 * LSCM 0.154 0.108 LM*LSCM -0.103 R2 0.003 0.168 0.174 R2 Adjusted -0.017 0.133 0.130 ∆R2 0.003 0.165 0.006
Note: Significant at *p < 0.05 and ** p < 0.01 levels; the values reported are standardized regression coefficients
Table 4.8
Regression analysis for group 3: high SCC and high SNR (n = 41) Control variables Model 0 Main effects Model 1 Interaction effect Model 2 Constant 0.437 0.331 0.207 Country -0.144 -0.192 -0.197 Industry -0.076 -0.010 -0.006 LM 0.104 0.066 LSCM 0.144 -0.080 LM*LSCM 0.428 * R2 0.030 0.075 0.195 R2 Adjusted -0.021 -0.028 0.080 ∆R2 0.030 0.045 0.120
* LSCM * Group 1 n = 120 Group 3 n = 41 n = 54 Group 0 n = 101 Group 2
Figure 4.2: Summary of regression analysis per group - model 1
5. DISCUSSION
The main points of this study are: To investigating the performance links of different lean practice sets (internal or external) and their relationships on each other. Additionally, to examine the practice levels in different SNR/SCC groups to find out more in terms of the lean practice implementation levels and to investigate if relationships change depending on the SNR/SCC groups.
The results show support for Hypothesis 1, showing that LM (and also LSCM) has a positive direct effect on responsiveness in both tested regression models (Table 4.1). This mainly confirms what several studies reported (Chavez et al., 2015; Marodin et al., 2017; Danese et al., 2012). Yet the coefficient levels appear interesting. The LM coefficient is in both models almost twice as high as the LSCM coefficient. It implies that there is a difference between the implementation levels, which might be partially explained by the increased complexity of LSCM practices (due to the expansion of practices over the firm’s boundaries) but not solely.
By comparing the means within the high SCC level configurations with their counterparts in the low SCC level configuration, it can be said that in general the lean practice levels in high SCC configurations are not as high as in the counterpart. Similarly the performance outcomes are not as good. A configuration with high SCC and low SNR seems to be especially bad in terms of the lean implementation levels and the performance outcome. This is in line with previous findings from Vachon & Klassen (2002) and Bozarth et al. (2009). Who found that with higher SCC there is an increasingly negative influence on delivery performance. This study contributes to the theory by further enhancing the understanding of the two dimensions of SNR and SCC together on the relationship of lean practices and performance. The result that LSCM has no significant influence on in either of the high SCC configurations, combined with the lower means for LSCM, additionally indicates that in more complex supply chains lean practices, especially LSCM appear to be less established or in reverse more difficult to implement, which is proposed by Bortolotti et al. (2016)
implementation levels, but already slightest implementation, results in at least a not negative performance level. Since LSCM aims for improved SC processes over the course of the implementation it is likely that also the BSR shift, thus creating a situation where the implementation of LSCM would enhance the relationship between LM and responsiveness.
From the comparison of group combinations according to the SNR intensity, low vs high, results that higher SNR configurations (meaning more collaborative relationships as well as more cooperative planning along the SC) results in higher mean values of LSCM practices and furthermore the performance outcomes are comparably better, which is supported by the results of the regression analysis stating that a low SCC and high SNR configuration shows a direct positive relationship between LSCM and responsiveness as well as indicating a positively moderating role in group 3 on the relationship between LM and performance. This provides supporting empirical evidence for the expressed shift towards collaborative relationships in general (Marodin et al., 2017; Moeller et al., 2006; Prahinski & Benton, 2004). Not only do the results support but also extend the findings from Marodin et al. (2017) regarding the moderating role of LSCM.
There are several studies indicating the relationships between SNR and lean practices or SCC and lean practices but the prior stated result indicates that further research is needed when considering both dimensions combined and their influence on lean practice choices.
Two practical implications can be drawn from the reported results. First, if a company needs to decide and allocate resources over the implementation of lean programs or the extent of the practices (internal or external) it is beneficial to strategically observe the firms position related to the two dimensions of SCC and SNR first. Then according to the configuration come to a decision. If a company finds itself in a low SNR and low SCC configuration the decision for lean practices can depend on the performance enhancement which a company is aiming at. If it is quality, implementing lean practices seems to be more beneficial if the efforts concern internal as well as external lean practices.
6. CONCLUSION
This study intends to contribute to the rather newly kindled debate of configurational aspects of lean. This is done by applying two different dimensions which have been researched on their own in the lean context and combining it with recent findings in terms of the relationship of internal and external lean practices as well as performance. The results found support the stream that argues for contingent factors to lean implementations and indicates a configuration concerning SNR and SCC where the decision to implement lean should be made carefully, analyzing all circumstances of the firm, especially in terms of SNR and SCC. Bottom line, this study found that different configurations of SNR/SCC in the supply chain of a company can influence the implementation of lean as well as the desired outcome
Practical contributions are as such, that this result provides a decision guideline in regards to lean implementation projects. It implies the strong recommendation that before making a decision concerning a lean project, a firm needs to first assess it’s own strategic position in regards to the supply network and the supplier relationship intensity and in regards to the supply chain complexity; critically assessing those levels first to conclude the appropriate decision regarding lean projects. Furthermore it does provide evidence for the configuration of low SNR and low SCC to not invest in lean projects. Of course this depends on the performance that a company wants to improve. This result is especially interesting for firms in case of scarce financial resources for projects; it may help with the allocation of latter to projects.
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APPENDIX Appendix A
Table A.1
Contructs: all items contained in each scale
Construct Practice Items Indicator
Lean
manufacturing >> LM
1. Flow oriented layout
1 We have laid the shop floor so that processes and machines are in close proximity to each other.
LM.FL.1 2 Our processes are located close together, so that material handling and part
storage are minimized.
LM.FL.2
3 We have located our machines to support JIT production flow LM.FL.3
4 The layout to four shop floor facilitates low inventories and fast throughput LM.FL.4 2. Setup Time
Reduction
1 We are aggressively working to lower setup times in our plant. LM.ST.1
2 Our workers are trained to reduce setup time. LM.ST.2
3 Our crews practice setups, in order to reduce the time required LM.ST.3 3. Kanban 1 We use a kanban pull system for production control. LM.KA.1 2 We use kanban squares, containers or signals for production control. LM.KA.2 3 Our suppliers deliver to us in Kanban containers, without the use of separate packaging LM.KA.3 4. Statistical Process
Control
1 We use charts to determine whether our manufacturing processes are in control.
LM.SPC.1
2 We monitor our processes using statistical process control. LM.SPC.2
