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Flexibility, supplier collaboration and supply chain resilience:
the moderating effect of supply chain complexity
Master Thesis
MSc. Supply Chain Management Faculty of Economics and Business
University of Groningen
Author: František Staněk Student number: s2958961 E-mail: f.stanek@student.rug.nl
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Abstract
As the importance of supply chain resilience grows in business environment, a question which factors contribute to supply chain resilience is crucial. This thesis aims to answer the research question how flexibility and supplier collaboration affect supply chain resilience and what the effect of supply chain complexity on these relationships is. Previous research mostly focuses on certain elements of supply chain complexity, supply chain resilience, flexibility and supplier collaboration and especially survey-based research is lacking. Based on questionnaire responses from 128 Czech, Chinese and Dutch automotive suppliers, direct positive relationships are found between supplier collaboration and supply chain resilience as well as flexibility and supply chain resilience. While supply chain complexity is found to positively moderate the relationship between supplier collaboration and supply chain resilience, the moderating effect is not significant for the relationship between flexibility and supply chain resilience. This paper provides insights to managers and contributes to the literature by providing new evidence that supplier collaboration and flexibility increase supply chain resilience, while being strongly dependent on the environmental factor of supply chain complexity.
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Table of contents
Abstract ... 2
Introduction ... 4
Theoretical background ... 6
Supply chain resilience ... 6
Flexibility ... 6
Supplier collaboration ... 7
Supply chain complexity ... 8
Hypothesis development ... 9 Methodology ... 10 Data collection ... 10 Measurement development ... 12 Data analysis... 12 Results ... 13 Discussion ... 17 Conclusions ... 19
Managerial and Theoretical implications ... 20
Limitations ... 21
Directions for future research ... 21
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Introduction
"Expansion means complexity and complexity, decay" says the Parkinson's Third Law (p.233, 1962) written by Parkinson, a public administration and management scholar. Whether this statement is correct or not, a question of how to deal with increasing complexity of supply chains (Blackhurst et al. 2005) is now more important than ever. Traditionally, risk management has been regarded as a tool to deal with the uncertainty embedded in supply chains, however not all the possible negative events can be foreseen (Scholten and Schilder, 2015) and thus cannot be managed using this method. Hence the importance of building resilient supply chains, which are able to absorb these unforeseeable negative effects has been highlighted (Chopra and Sodhi, 2004). As to large extent this topic has not been thoroughly studied yet, it is an ambition of this thesis to improve the understanding of how supply chain resilience can be enhanced under different levels of supply chain complexity.
The question of how supply chain resilience can be enhanced has been approached in many ways. Jüttner and Maklan (2011) proposed, that there are four formative resilience capabilities: Visibility, velocity, flexibility and collaboration. While generally this approach is accepted in recent literature (e.g. Scholten and Schilder, 2015), empirical research on this topic is still scarce. For example, visibility was identified as a direct enhancer of supply chain resilience in empirical research (Brandon-Jones et al., 2014), however conclusive evidence regarding the effects of flexibility and collaboration, and more specifically collaboration directed towards suppliers, on supply chain resilience is to a large extent missing.
There is an evidence available that connects some of the elements of flexibility and supplier collaboration to some elements of supply chain resilience, however this thesis is motivated by a need for a more holistic approach to truly understand, how can the supply chain resilience be enhanced instead of focusing on rather narrow elements. Brandon-Jones et al. (2015) showed that flexibility positively moderates the negative effect of supply chain disruptions frequency on company performance, it was also shown that flexibility through postponement increases supply chain resilience (Tang, 2006). Similarly, the positive effect of supplier collaboration on supply chain resilience has been broadly examined (Hohenstein et al., 2015; Tukamuhabwa et al., 2015). However, most of the research viewed supplier collaboration mainly as a strategy to decrease cost and gain competitive advantage (Soosay and Hyland, 2015). Only a small part of the research focused on the relationship between collaboration and resilience (Scholten and Schilder, 2015).
It can be seen that there are clues that the flexibility and supplier collaboration enhance supply chain resilience, however these relationships have not yet been thoroughly investigated on the capability level. Therefore the first ambition of this thesis is to investigate the direct effects of flexibility and supplier collaboration on supply chain resilience. This research gap is supported by recent literature: Bhamra et al. (2011) argued that more empirical research regarding supply chain resilience is needed and more specifically, Brandon-Jones et al. (2014) argued that there is a need for examining the effects of resources and capabilities including flexibility and collaboration on the supply chain resilience. There seems to be a research gap that needs to be filled. Based on this, the first research question is formed:
What is the influence of flexibility and supplier collaboration on supply chain resilience?
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level of risk embedded in their activities. To approach this phenomena in a holistic way, concept of complexity was introduced to the supply chain science. Resulting concept of supply chain complexity generally describes the level of variability and uncertainty experienced by a focal company (Bode and Wagner, 2015; Vachon and Klassen, 2002). While the recent development in global market lead many companies to expand scope of their activities in their supply chains to overcome global competition, the possible negative effects of such activities leading to increased complexity of supply chains were only recently highlighted (Hoole, 2006).
Supply chain complexity was classified according to its source to upstream, downstream and internal manufacturing complexity (Bozarth et al.,2009). While there is substantial empirical evidence that upstream supply chain complexity increases the frequency of disruptions experienced by a focal company (Bode and Wagner, 2015, Brandon-Jones et al., 2015), the connection of the other two dimensions to the supply chain resilience is yet to be investigated. The contingent resource-based view (Brush and Artz, 1999) suggests that companies are achieving competitive advantage by combining resources to create capabilities (Barney, 1991), however the effect of these capabilities can be strongly dependent on the environment (Sirmon et al., 2007). While upstream supply chain complexity was identified to change the effect of supply chain visibility on the supply chain resilience (Brandon-Jones et al., 2014), similar research on the effects flexibility and supplier collaboration on the supply chain resilience is missing.
Galbraith (1977) proposed that companies can deal with environmental uncertainty in two ways: (1) absorb the effects of uncertainty or (2) enhance the ability to manage uncertainty. Bozarth et al. (2009) argued that the question of how effective these strategies are has not yet been properly answered in connection with complexity. Therefore there is a research gap that can be filled by this thesis as resilience can be seen as a mechanism to deal with environmental uncertainty caused by supply chain complexity. Therefore a second research question arises:
What is the influence of supply chain complexity on the relationships between flexibility, supplier collaboration and supply chain resilience?
This thesis aims to enrich the theoretical knowledge of this subject in two ways: Firstly, the effects of flexibility and supplier collaboration on supply chain resilience at the capability level will be clarified and possibly supported by quantitative evidence. Supply chain resilience can still be seen as a relatively new concept that has not been yet thoroughly investigated, especially concerning empirical research. Therefore, additional research to support exploratory research with empirical evidence is crucial in order to further develop the understanding of supply chain resilience. Secondly, the effect of supply chain complexity as a single construct determining uncertainty of the environment on these relationships will be studied, leading to better understanding of the consequences of supply chain complexity. While most of the existing research attempts to uncover the effects of a specific component of the supply chain complexity, this thesis aims to take a more holistic point of view by studying supply chain complexity as a single construct. This approach provides more applicable findings as it is a point of view of this thesis that the elements, determining supply chain complexity cannot be easily separated because they affect focal company at each point in time and therefore a studying them separately can omit their possible inter-relationships.
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insights into how to deal with supply chain complexity. While there is substantial literature on how to reduce supply chain complexity, it might not be always possible to do so and therefore the question of how to deal with high levels of supply chain complexity is of upmost importance. The research will be done by hypotheses testing based on an internet-based survey research in automotive industry. Using survey tool should enable gathering of comprehensive data from multiple sources through random sampling which should enhance the validity and generalizability of the research.
This thesis is structured as follows: The second chapter will focus on the theoretical background, conceptual model and hypotheses. In the third chapter, the methodology will be described. The results will be reported in the fourth chapter. Discussion of the results will be the goal of the fifth chapter followed by the sixth and final chapter which will depict conclusions, limitations and directions for future research.
Theoretical background
Supply chain resilience
The concept of supply chain resilience relates to the ability of supply chains to deal with the negative effects of unforeseeable disruptions (Pettit et al., 2013) and return to original operations state or even move to a better one after a disruption occurs (Christopher and Peck, 2004). Even though the concept of supply chain resilience is a relatively new one, many different conceptualizations and definitions already emerged. The aim of this thesis is to investigate, how the supply chain resilience can be enhanced and, for this purpose, the definition proposed by Brandon-Jones et al. (2014, p.58) is the most suitable one. They defined supply chain resilience as: "the ability of a system to return to its original state, within an acceptable period of time, after being disturbed".
There are many perspectives on which elements actually constitute supply chain resilience and in which way. As this thesis aims to improve the understanding of how supply chain resilience can be created, both of these questions are of a high importance. While Christopher and Peck (2004) proposed that there are certain formative elements which together constitute resilience, others proposed that these elements should be considered as more of antecedents of supply chain resilience (e.g. Ponomarov and Holcomb, 2009; Jüttner and Maklan, 2011). This thesis adopts the approach of Brandon-Jones et al. (2014) based on resource-based perspective. According to the resource-based perspective, by bundling resources of various types, companies create certain capabilities to gain competitive advantage (Grant, 1991; Wu et al., 2006, Sirmon et al., 2008). In line with the recent research (Brandon-Jones et al., 2014), supply chain resilience is therefore seen as an output measure of certain capabilities.
Regarding the question of which capabilities enhance supply chain resilience, this thesis adopts the commonly used approach of Jüttner and Maklan (2011), who claimed that there are four capabilities that capture the conceptual essence of supply chain resilience: velocity, visibility, flexibility and collaboration. The focus of this thesis is on the last two, therefore in the next parts the focus is on defining them precisely and establishing the mechanisms of how these two capabilities enhance supply chain resilience.
Flexibility
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Hanna, 2009). Flexibility requires to be designed in the supply chain in the form of strategies and inter-organizational processes (Tang and Tomlin, 2008). Overall, it can be said that supply chain flexibility describes, how well a given company is able to deal with uncertainties and changes and represents reactive, rather than proactive approach towards risks (Hohenstein et al., 2015).
In line with the resource-based perspective, it is a point of view of this thesis that flexibility is a capability created by certain dedicated resources and abilities. The ability to accommodate changes in product mix has been emphasized as a factor enhancing flexibility (Christiansen et al., 2003). Indeed it seems quite reasonable to argue that as product mix can be changed due to several reasons such as competition or changes in the market demand, companies that are able to accommodate these changes smoothly are more flexible. Redundancy is another important element often associated with flexibility (e.g. Christopher and Peck, 2004; Pettit et al., 2013). Specifically, redundancy in the form of extra capacity allows companies to continue their operations following failure (Rice and Canioato, 2003) and aids a supply chain's rapid response and recovery (Tukamuhabwa et al., 2015). Excess capacity also allows companies to reduce throughput times based on customer demand and change production volume capacity and thus be more flexible in a time of crisis. Furthermore Pettit et al. (2013) indentified flexibility in order fulfilment to be important enhancer of flexibility. In line with that Jüttner and Maklan (2011) argued that flexibility in order fulfilment allows the supply chain to absorb the risk event by responding effectively.
To sum up, flexibility is a capability determined mainly by three company abilities: ability to accommodate changes in the product mix, availability of capacity buffers and flexibility in order fulfilment. While at first sight there might seem to be an overlap in definitions of supply chain resilience and flexibility, there is a distinct difference in these two concepts. While supply chain resilience considers the whole process of readiness, responsiveness and recovery (Sheffi and Rice, 2005) to the disruption and therefore an output measure of how well the companies can deal with disruptions, flexibility is focused on the process of adapting to the disruptions in their initial phase.
Supplier collaboration
Supplier collaboration is one of the most frequently studied elements of supply chain collaboration (Soosay and Hyland, 2015). While there is no unanimously accepted definition, in this thesis an adapted definition of Pettit et al. (2010) is utilized: collaboration is the ability to work effectively with suppliers for mutual benefit. Based on literature review, there are two important elements that together constitute the capability of supplier collaboration: the long-term nature of the buyer-supplier relationship and the level of information-sharing.
The role of building a long-term relationship to increase collaboration with suppliers has been emphasized (Wang et al., 2016). This attitude prevents the threat of opportunistic behaviour (Li et al., 2010; Nyaga et al., 2013). Moreover long-term relationships enhance jointly created knowledge in case a disruption occurs (Scholten and Schilder, 2015). Long-term nature of a buyer-supplier relationship also enhances mutual development (Lambert and Schwieterman, 2012).
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appropriately with future disruptions (Sheffi, 2001). Christopher and Peck (2004) argued that lack of information sharing leads to additional costs and is a source of vulnerability to a supply chain.
To sum up, supplier collaboration is a capability enhanced by information-sharing and building a long-term relationship. Together these dimensions improve coordination in a supply chain, help to prevent opportunistic behaviour and therefore improve the ability of a supply chain to deal with uncertainty.
Supply chain complexity
In this thesis, supply chain complexity is considered to be a context variable and therefore changes the environment in which companies operate (Gimenez et al., 2012) and increases the difficulty of managing supply chains (Choi et al., 2001). Complexity is a term used in many scientific fields and contexts, ehich led to many different definitions, measurements and conceptualizations. However, for the sake of this thesis, a definition by Simon (1991, p.468) is influential. He proposed that a socio-technical system is complex if it is "made up of a large number of parts that interact in a non-simple way." Similarly, supply chain complexity is determined by the number of stakeholders, their variations and interactions (Choi and Krause, 2006).
The question what the sources of supply chain complexity are has been approached in many different ways. Some researchers focused on upstream supply chain complexity (Bode and Wagner, 2015; Brandon-Jones et al., 2015; Choi and Krause, 2006), a two stage supply chain (Sivadasan et al., 2010) or solely manufacturing plant (Martinez-Olivera, 2008). However, for the sake of this thesis, the approach of Bozarth et al. (2009) is adopted as it is the most comprehensive one regarding the sources of supply chain complexity. They proposed that supply chain complexity consists of three elements: upstream complexity, downstream complexity and internal manufacturing complexity. The importance of interrelations of the stakeholders in a supply chain has been highlighted (Chen et al., 2001; Choi et al., 2001) and therefore considering all of these three sources of supply chain complexity at once should provide the most complete perspective on how supply chain complexity affects the focal companies.
Bozarth et al. (2009, p.80) defined internal manufacturing complexity as "complexity found within the manufacturing facility's products, processes, and planning and control systems". Unstable production schedules were identified as a driver of internal manufacturing complexity (Bozarth et al., 2009) as they enhance unpredictability caused by not meeting the production and material plans (Vollmann et al., 2005). Furthermore, Sivadasan et al. (2006) proposed that increasing levels of uncertainty and variety lead to increased level of operational complexity.
Upstream complexity is defined as "the level of complexity originating in a manufacturing facility's supply base" (Bozarth et al., 2009, p.81). Choi and Krause (2006) argued that the higher the number of direct suppliers, the more effort needs to be invested into coordinating, managing and monitoring them. In line with this, Bozarth et al. (2009) noted that increased number of suppliers necessarily increases the complexity of the system due to the higher amount of information, physical flows and relationships that need to be managed. Furthermore, number of suppliers increase the frequency of disruptions in supply chains that need to be managed by the focal company (Bode and Wagner, 2015; Brandon-Jones et al., 2015).
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(Forrester, 1961; Lee et al., 1997): Wide fluctuations in the ordering patterns upstream caused by only slight fluctuations in downstream demand are caused by lack of coordination and alignment of ordering policies of different agents in the supply chain.
To sum up, supply chain complexity is considered to be an aggregate construct consisting of internal, upstream and downstream complexities and is defined in line with Bozarth et al. (2009, p.80) as "complexity exhibited by the products, process and relationships that make up a supply chain".
Hypothesis development
Flexibility has been identified as one of the four normative capabilities constituting supply chain resilience (Jüttner and Maklan, 2011). Sheffi and Rice (2005) suggested that flexibility can support sensing disruptions and therefore increases readiness and hence resilience. In line with this, Manuj and Mentzer (2008) argued that flexibility improves coordination of processes and improves the ability of organizations to cope with uncertainty. Moreover, Brandon-Jones et al. (2015) suggested that increased capacity buffers, which are an important element of flexibility, improve the ability of companies to deal with disruptions by diverting products and manufacturing from the parts of supply chain that are currently impacted by disruptions. Christopher and Peck (2004) proposed that keeping extra capacity improves resilience more efficiently than a buffer of inventory because of lower risk of obsolescence. Based on these mechanisms, the hypothesis 1 is formed:
H1: Flexibility has a positive influence on supply chain resilience
Supply chain collaboration improves the ability of companies to respond and recover from supply chain disruptions and reduce their impact (Scholten and Schilder, 2015). One of the important mechanisms to achieve this is an improved coordination among the supply chain partners by increased communication and information exchange (Costantino et al., 2015). The underlying argument is that as supply chains are interconnected networks, uncertainty and resulting supply chain disruptions are negatively affecting not only the focal company, but also its suppliers. Joint coordinated effort to combat these risks should enhance the ability to deal with risks in a fast and effective manner and, therefore, improve supply chain resilience. Moreover, collaboration prevents opportunistic behaviour (Jüttner and Maklan, 2011), which should further enhance this mechanism. Collaborative communication and information exchange was also identified to mitigate the bullwhip effect (Skjøtt-Larsen et al., 2007) which frequently occurs in various industries (Costantino et al., 2015). Based on these clues, the hypothesis 2 is formed:
H2: Supplier collaboration has a positive influence on supply chain resilience
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H3: Supply chain complexity has a positive influence on the relationship between flexibility and supply chain resilience
De Leeuw et al. (2013) identified information exchange, communication and cooperation as key mechanisms to deal with complexity. These components are considered to be the main elements of supplier collaboration. Therefore following similar logic as in the hypothesis 3, it is proposed that the higher the complexity, the more opportunities for supplier collaboration to reduce its negative consequences. Moreover lack of cooperation between partners was identified to be a driver of complexity (Perona and Miragliotta, 2004; Sivadasan et al., 1999), and, therefore, increasing supplier collaboration should reduce the negative effects of complexity. Based on this, it is argued that the higher the complexity, the stronger the positive effect of supplier collaboration on resilience. Therefore the hypothesis 4 will be tested:
H4: Supply chain complexity has a positive influence on the relationship between supplier collaboration and supply chain resilience
Based on the discussion above, the conceptual framework is formed (see figure 1).
Figure 1: Conceptual model
Methodology
The aim of this thesis is to test, whether there are relationships among the variables that were presented earlier. Therefore the theory-testing survey research design is an appropriate approach (Karlsson, 2009). Furthermore, even though the topic of this thesis is developing in the sense that while some of the important areas have not been studied yet, there is a basis of exploratory and explanatory research available on which the hypotheses can be based. Moreover, survey-based research ensures high generalization because of the number of respondents, and allows to draw empirical conclusions. Because of the high rates of utilization of computers within the target population, a web-based survey approach was employed.
Data collection
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directly contacted through the automotive companies in case of China. In order to improve generalizability of the research, companies within the two databases were contacted randomly, based on fitting within the following criteria. Only companies with at least 50 employees were contacted as most of the research dedicated to supply chain complexity and resilience is concerned with companies of size over this threshold. Moreover, including both small and large companies could bias the results as the policies and circumstances differ to a great extent between these two categories. The preferred population consisted mostly of managers in the fields of logistics, operations, purchasing or connected fields to ensure that they posses knowledge of supply chain processes, are directly involved in decision-making and have at least a general idea of the whole supply chain in which their companies are involved. This method was chosen purposefully to achieve external validity and generalizability of the results (Cook et al., 1979). The data collection took place from November to mid-December 2016. The initial population of the Czech part of the data collection consisted of 617 companies, which were retrieved from the database of automotive suppliers created by Czech government agency CzechInvest. 132 individuals working in the companies as a purchaser or logistics manager/operations manager were targeted to be a key informants and were contacted.
To approach companies within the population, key informants in the subsample were contacted by phone. During the phone call it was initially checked, whether the contacted company is indeed a supplier of automotive industry. Afterwards they were invited to take part in the survey. In case of an agreement to participate in this research, a link to the electronic survey was sent via e-mail. To increase the response rate, reminders were sent via e-mail to non-respondents after 7 days. Moreover, the participants were offered the option to receive a management report summing up the main findings of this research to improve the response rate. Subsequently 31 valid responses were collected, which results in a response rate of 24%. In the same way, the data collection was conducted in The Netherlands using the database of the Dutch chamber of commerce, where 94 questionnaires were sent out, out of those 12 valid responses were collected for response rate of 13%. In China, 464 surveys were sent out to suppliers of two major automotive manufacturers which led to 90 completed valid questionnaires for response rate of 19%. Therefore, to sum up, 133 responses were collected out of 690 individuals contacted for a total response rate of 19%. For the sake of analysis, 5 responses were removed as they were too incomplete. Table 1 describes the working positions of participants of the survey.
Harman's single factor test was conducted in order to check the possibility of common method bias and the level of explained variance was acceptable to conduct further analysis. In order to determine, whether there were differences in early and late responses, a non-response bias test was conducted. Using one-way analysis of variance (ANOVA) (p<0.01), no significant difference was found on the size of company and therefore non-response bias is not considered to be a problem (Karlsson, 2009).
Position Number Percent
Purchasing manager 31 24,2%
General manager 13 10,2%
Sales manager 11 8,6%
Logistics manager 18 14,1%
Operations manager 24 18,8%
Supply chain specialist 10 7,8%
Not indicated 21 16,4%
Total 128 100,0%
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Measurement development
The constructs that were used in this research were a part of a large questionnaire concerned with supply chain integration, complexity and resilience. However, only constructs concerned with flexibility, supplier collaboration, supply chain resilience and supply chain complexity were utilized. These constructs were derived from scientific literature concerned with supply chain management and integration.
The first construct of supply chain resilience is based on the article by Brandon-Jones et al. (2014). To capture the essence of supply chain resilience as defined in the theoretical part, four items were utilised. The first two items investigated, how fast can the material flows be restored after a disruption and how fast can the normal operating performance be recovered. The other two items measured recovery of supply chain to its original state and general fastness of dealing with disruptions.
The second construct of supplier collaboration was based on the article by Prajogo and Olhager (2012). Based on this literature review regarding supplier collaboration, items that are focusing on information sharing, long-term relationship and incentives alignment were selected. Namely, the level of information sharing regarding both sensitive information and helpful information, regarding events or changes that affect another party and of relationships with suppliers are regarded as long-term alliance.
The third construct, supply chain flexibility was measured by three items based on Pettit et al.(2013). This construct measured an ability of a company to change production volume capacity, ability to accommodate changes in product mix and ability to reduce manufacturing throughput times to satisfy buyer demand.
The last construct of supply chain complexity was measured by a four items based on the work by Bozarth et al. (2009). The four items from upstream, downstream and manufacturing complexity were chosen to reflect the entire concept of complexity as discussed in the theoretical background section. To be more specific, one item for upstream supply chain complexity was selected measuring the number of direct suppliers. Downstream complexity is determined by two items, which measure the level of stability of customer and manufacturing demand. Lastly, internal manufacturing complexity is measured by a single item concerned with level-loading of master schedules. For further analysis the items related to manufacturing and downstream complexity were reversed as suggested by Bozarth et al. (2009).
All of the 15 items can be seen in table 2 below. All items except number of suppliers and firm size were measured on a 5-point Likert scale. The number of suppliers was measured by a 5-point scale with values of 1, 2-5, 6-25, 26-50, and more than 50, respectively. A firm size, measured by the number of employees working in the company, was chosen as a control variable to account for external effects. This variable was measured on a 5-point scale (less than 50 employees, 50-100 employees, 100-250 employees, 250-500 employees, and more than 500 employees, respectively).
Data analysis
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be seen in the table 2, some of the items measuring supply chain complexity were recoded so that low and high supply chain complexity correspond to low and high score, respectively.
To test the hypotheses regarding both direct and moderating effects, hierarchical regression analysis was performed using SPSS. To perform the analysis regarding moderating effects, standardized z-values of the main effects were utilized to calculate the interaction effects. In line with standard approach regarding moderating effects, supply chain complexity significantly moderates the direct relationships if the interaction effect is significant for a direct relationship (Baron and Kenny, 1986; Wu and Zambo, 2008).
Factor
Items 1 2 3 4
F1: Supply chain resilience Cronbach’s α = .84
Material flow would be quickly restored 0.81
It would not take long to recover normal operating performance 0.82 The supply chain would easily recover to its original state 0.72
Disruptions would be dealt with quickly 0.64 0.49
F2: Supplier collaboration Cronbach’s α = .66
We share sensitive information (financial, production, design, research,
and/or competition) 0.58
Key suppliers are provided with any information that might help them 0.60 We keep each other informed about events or changes that may affect
the other party 0.79
The key suppliers see our relationship as a long-term alliance 0.76
F3: Flexibility Cronbach’s α = .73
Ability to change production volume capacity 0.64
Ability to accommodate changes in product mix 0.85
Ability to reduce manufacturing throughput times to satisfy buyer
delivery 0.68
F4: Supply chain complexity Cronbach’s α = .65
Our total demand, across all products is relatively stablea 0.80
Manufacturing demands are stable in our planta 0.68
The master schedule is level-loaded in our plant, from day to daya 0.48
How many suppliers does the plant have 0.39
Eigenvalue 4.81 1.63 1.41 1.17
Percentage of variance explained (%) 32.56 42.92 52.31 60.09
a: reverse scored
Table 2 Results of EFA
Results
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are above the minimal recommended value (0.60) (Nunnally, 1978). Therefore, it can be concluded that the constructs show good internal consistency and can be considered reliable.
In order to investigate the direct relationships between flexibility, supplier collaboration and supply chain resilience, linear regression is used. Because all the participants were contacted separately and every participant worked in a unique company, all of the participants can be seen as independent from each other. Moreover from the histograms of all the used variables it can be observed that the data is approximately normally distributed. Table 3 displays the means, standard deviations and correlations of the variables. The absolute values of Pearson correlations were found to be lower than 0.5 which indicates low level of correlation and hence indicate discriminant validity.
Variable Mean SD 1 2 3 4 5 1 Firm size 3.22 1.07 2 Flexibility 3.84 0.66 -0.11 3 Supplier collaboration 3.84 0.54 -0.09 0.33** 4 Complexity 2.69 0.67 0.11 -0.46** -0.32** 5 Resilience 3.83 0.63 -0.28 0.45** 0.37** -0.36**
** Correlation is significant at the 0.01 level (2-tailed)
Table 3 Descriptive statistics and Pearson Correlations
The results of the linear regression analysis are presented in tables 4, 5 and 6. Firm size is included as a control variable. In line with the hypotheses 1 and 2, supply chain resilience is used as a dependent variable in both direct models presented in table 4. Step 1 indicates that the control variable does not have a significant effect on supply chain resilience. In step 2(1) it is found that flexibility has a significant positive impact on supply chain resilience (β = 0.48, p < 0.001). In the second model represented by step 2(2), supplier collaboration is used as an independent variable and resilience as the dependent one. Supplier collaboration is found to have a significant positive impact of on supply chain resilience (β = 0.39, p < 0.001). Adjusted values for R2
of 0.22 and 0.14 show that models 2(1) and 2(2) account for 22% and 14% of variation. In addition, the F-values for both models are significant at p<0.001, which implies that both models are significant. Based on these results, hypotheses 1 and 2 can be accepted.
Steps
Supply chain resilience
Variables 1 2 (1) 2 (2) 1 Firm Size -0.03 0.01 0.01 2 (1) Flexibility 0.48*** 2 (2) Supplier collaboration 0.39*** Intercept 3.92 1.95 2.11 R2 0.00 0.22 0.15 Adj R2 -0.01 0.21 0.14 F 0.09 15.63*** 9.58*** Change in R2 0.22 0.15 Change in F 31.15*** 19.07***
Standardized regression coefficients are reported Notes: *p<.05; **p<.01; ***p<.001
Table 4 Results of Regression Analysis: Direct Model
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5 and 6 present the results. In line with the common procedure, in step 1 the linear regression is conducted for the control variable, which is not significant in either of the two models. Steps 2 and 3 add the main effects into the models and therefore the models start to differ significantly in the step 3. In this step, the effect of supply chain complexity differs as in the flexibility-related model, complexity is not significantly related to supply chain resilience (β = -0.18, n.s.), whereas in the supplier collaboration-related model, complexity is significantly and negatively related to supply chain resilience (β = -0.26, p < 0.01).
Step 4 adds the moderator variable to the regression analysis. Table 5 shows that the interaction effect between flexibility and supply chain complexity with supply chain resilience is not significant (β = -0.06, n.s.) and therefore hypothesis 3 is not supported. Finally, as Table 6 shows, the interaction between supply chain complexity and supplier collaboration is significant (β = 0.18, p < 0.05), and therefore hypothesis 4 is supported. As these two models are the most relevant for this thesis, it is important to note that they show acceptable level of adjusted R2 =0.23 for the case of flexibility-related model and adjusted R2=0.21 for the supplier collaboration-related model, which are acceptable values. Moreover, F-values for both models are significant at p<.001, which implies significance of both models.
Steps
Supply chain resilience
Variables 1 2 3 4 1 Firm Size -0.03 0.00 0.02 0.02 2 SC Complexity -0.34*** -0.18 -0.18 3 Flexibility 0.41*** 0.42*** 4 Complexity x Flexibility -0.06 Intercept 3,92 3,84 3,78 3,77 R2 0.00 0.11 0.25 0.25 Adj R2 -0.01 0.1 0.23 0.23 F 0.09 6.83** 11.92*** 9.01*** Change in R2 0.11 0.14 0.00 Change in F 13.57*** 19.73*** 0.45
Standardized regression coefficients are reported
Notes: *p<.05; **p<.01; ***p<.001
16 Steps
Supply chain resilience
Variables 1 2 3 4
1 Firm Size -0.03 0.01 0.02 0.03
2 SC Complexity -0.35*** -0.26** -0.23*
3 Supplier collaboration (Col) 0.31** 0.28**
4 SC Complexity x Col 0.18* Intercept 3.91 3.82 3.79 3.81 R2 0.00 0.12 0.21 0.24 Adj R2 -0.01 0.11 0.19 0.21 F 0.08 7.55** 9.45*** 8.35*** Change in R2 0.12 0.09 0.03 Change in F 15.01*** 11.73** 4.19*
Standardized regression coefficients are reported
Notes: *p<.05; **p<.01; ***p<.001
Table 6 Results of regression analysis: Complexity as Moderator on the relationship between Supplier collaboration and Supply Chain Resilience
To graphically depict this moderating effect of complexity on the relationship between supplier collaboration and supply chain resilience (β = 0.18, p < 0.05), the plot of two-way interaction effects for standardized variables was used as shown in figure 2. This figure demonstrates that the effect of supplier collaboration on supply chain resilience is stronger in the environment with high supply chain complexity compared to the environment with low supply chain complexity, as can it be observed from the differences in the two slopes.
Figure 2 Visualization of the moderating effect of supply chain complexity on the direct relationship between supplier collaboration and supply chain resilience
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Figure 3 summarizes the results of hypotheses testing in a structural model. In summary, hypotheses 1 and 2 concerning the positive direct relationships between flexibility and supply chain resilience and the positive direct relationship between supplier collaboration and supply chain resilience were accepted. However, there is a difference between the results concerning hypotheses 3 and 4. While the hypothesis 3 regarding the moderating effect of supply chain complexity on the relationship between flexibility and supply chain resilience cannot be accepted, the hypothesis 4 proposing that supply chain complexity positively moderates the relationship between supplier collaboration and supply chain resilience is accepted.
Figure 3 Structural model
Discussion
The aim of this thesis is to investigate the role of flexibility and supplier collaboration in building supply chain resilience. More specifically, the goal is to unveil, how these two capabilities contribute to supply chain resilience under different levels of supply chain complexity. The evidence is provided on how both flexibility and supplier collaboration are crucial for building supply chain resilience, however the effect of supply chain complexity on these two relationships differs substantially. While the relationship between supplier collaboration and supply chain resilience is positively and significantly moderated by supply chain complexity, this is not the case for the relationship between flexibility and supply chain resilience. To put it differently, higher supply chain complexity provides conditions under which supplier collaboration is more effective in improving supply chain resilience. Therefore, this thesis provides three main findings that add to the current body of supply chain resilience literature: Significant positive direct effects of both flexibility and supplier collaboration on supply chain resilience and the fact that these two capabilities behave differently under different levels of supply chain complexity.
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the framework of Jüttner and Maklan (2011), possibly leading to deeper alignment of conceptualizations regarding supply chain resilience.
The finding that supplier collaboration positively affects supply chain resilience is in line with recent research (Jüttner and Maklan, 2011). However, collaboration has been mostly seen as a capability enhancing visibility, velocity and flexibility which consequently increase supply chain resilience (Scholten and Schilder, 2015). This approach suggests that supplier collaboration should mainly positively influence these capabilities, however the results of this thesis show that there is a significant direct positive effect of supplier collaboration on supply chain resilience present. In fact, even though the interrelations of these resilience-related capabilities are out of scope of this thesis, a linear regression model using the same procedure as in the previous section was run to briefly uncover the moderating effect of supplier collaboration on the direct relationship between flexibility and supply chain resilience. While the approach of Scholten and Schilder (2015) suggests that collaboration is an enhancer of flexibility and consequently there should be a positive moderating effect of supplier collaboration on the relationship between flexibility and supply chain resilience, the effect of supplier collaboration was found to be significant and negative (β = -0.21, p < 0.05). These findings suggest that supplier collaboration should perhaps be considered a direct enhancer of supply chain resilience rather than enhancer of other capabilities. This direct positive effect can be attributed to both the increased coordination between supply chain partners which positively affects recovering from a disruption and experience-sharing resulting in an increased ability to deal with disruptions (Jüttner and Maklan, 2011; Sheffi and Rice, 2005).
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Figure 4 Visualization of the moderating effect of supply chain complexity on the direct relationship between supply chain flexibility and supply chain resilience
There are several possible explanations of why these two capabilities behave differently. Firstly, supplier collaboration improves coordination between supply chain partners (Costantino et al., 2015) and, therefore, it should help in solving the disruptions effectively, while flexibility is essentially individual company capability that leaves the companies to deal with the disruptions individually. The results of the regression analysis regarding the moderating effects of supply chain complexity therefore support the notion that the trend in business environment switches from competition of individual companies to competition of whole supply chains (Wilding, 1998). Companies that acknowledge this new trend and therefore invest resources into collaboration with supply chain partners gain advantage in the form of increased resilience in the environment with high supply chain complexity. Secondly, there seems to be a difference between flexibility and supplier collaboration in how fast they can be adjusted. The nature of supplier collaboration lies in information-sharing, long-term relationship and following common goals in a form of strategic alliance. While it seems reasonable to assume that the nature, extent and specificity of the information shared can be adjusted easily a time of crisis, flexibility presents a more reactive approach towards resilience (Hohenstein et al., 2015). Thirdly, collaboration can also be seen as a means to access complementary skills which improve the ability to meet competitive challenges (Soosay et al., 2008; Simatupang and Sridharan, 2008) and therefore provides additional opportunities in dealing with uncertainty embedded in a supply chain. Galbraith (1977) proposed that one of the ways to accommodate environmental uncertainty is by putting in place mechanisms that absorb its effects. As supplier collaboration was found to contribute more to supply chain resilience in a highly complex environment, it can be seen as such a mechanism.
Conclusions
The dynamic changes in global economy will continue to endanger supply chains in terms of increasing uncertainty and vulnerability (Hohenstein et al., 2015). In these circumstances, it is
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increasingly important to study, how resilient supply chains that can cope with disruptions can be created. As the concept of supply chain resilience is a relatively new one, there are still areas related to this cocept that have not been fully discovered. This thesis clarifies the relationships between supply chain resilience and two previously identified resilience capabilities: flexibility and supplier collaboration. Although these two capabilities were previously identified as antecedents of supply chain resilience in an exploratory research, this thesis provides statistical evidence that they indeed directly significantly enhance supply chain resilience. Secondly, while the concepts of supply chain complexity and supply chain resilience were partially connected in some of the recent literature, this thesis provides insights of their inter-relations. Building on a holistic approach regarding supply chain complexity based on the work of Bozarth et al. (2009) a positive moderating effect of supply chain complexity on the relationship between supplier collaboration and supply chain resilience was found. On the other hand, no significant moderating effect of supply chain complexity on the relationship between flexibility and supply chain resilience was found. These findings not only show the inter-connections of supply chain complexity and supply chain resilience, but they also suggest that supply chain complexity affects different supply chain resilience capabilities in various ways.
Managerial and Theoretical implications
From a theoretical point of view, this thesis provides statistical evidence on the connection between flexibility and supplier collaboration to supply chain resilience. Furthermore as this thesis proposed to take a more holistic view on supply chain complexity by including downstream, upstream and manufacturing complexity, the findings can be seen as more justifiable because possible interconnections among these three dimensions are taken into account, compared to the research focusing only on a part of supply chain (e.g. Bode and Wagner, 2015). Additionally, this thesis provides additional insights into the role of supplier collaboration in improving supply chain resilience. While the role of collaboration in building supply chain resilience has been previously identified (e.g. Jüttner and Maklan, 2011), the role of collaboration has been mostly highlighted as enhancer of the other resilience-related capabilities. This thesis shows that there is also a strong direct positive effect of collaboration, which should be taken into consideration in future studies of supply chain resilience. Additionally, it is shown that the concepts of supply chain complexity and supply chain resilience are closely connected, while the research on this topic has been scarce so far. Namely, this thesis contributes by showing that supply resilience-related capabilities are affected by supply chain complexity in different ways.
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however compared to supplier collaboration, its effectiveness is not that high in highly complex environment.
Limitations
There are several limitations of this thesis. Firstly, the small sample size is an issue, especially considering that the data collection took place in three countries with different cultural and economic background. The small sample size can be seen as a consequence of both limited time for data collection and also overall reluctance to respond the questionnaire based on policies of automotive suppliers concerned with confidentiality. Moreover, the length of the questionnaire, as it was used for multiple theses on multiple topics, was very high and resulted in lower response rate. This is supported by the fact that a number of survey participants decided to abandon the questionnaire after partially filling it out.
Directions for future research
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References:
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173-1182.
Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375-5393.
Blackhurst, J., Wu, T., & O’Grady, P. (2005). PCDM: a decision support modeling methodology for supply chain, product and process design decisions. Journal of Operations Management, 23(3–4), 325– 343.
Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215-228.
Bozarth, C. C., Warsing, D. P., Flynn, B. B., & Flynn, E. J. (2009). The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1), 78-93. Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
Brandon-Jones, E., Squire, B., & Van Rossenberg, Y. G. (2015). The impact of supply base complexity on disruptions and performance: the moderating effects of slack and visibility. International Journal of Production Research, 53(22), 6903-6918.
Brush, T., & Artz, K. (1999). Toward a Contingent Resource-Based Theory: The Impact of Information Asymmetry on the Value of Capabilities in Veterinary Medicine. Strategic Management Journal, 20(3), 223-250
Cao, M., Vonderembse, M. A., Zhang, Q., & Ragu-Nathan, T. S. (2010). Supply chain collaboration: conceptualisation and instrument development. International Journal of Production Research, 48(22), 6613-6635.
Choi, T. Y., Dooley, K. J., & Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: control versus emergence. Journal of operations management, 19(3), 351-366.
Choi, T. Y., & Krause, D. R. (2006). The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation. Journal of Operations Management, 24(5), 637-652. Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan management review, 46(1), 53-61.
23
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The international journal of logistics management, 15(2), 1-14.
Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings (Vol. 351). Boston: Houghton Mifflin.
Costantino, F., Di Gravio, G., Shaban, A., & Tronci, M. (2015). The impact of information sharing on ordering policies to improve supply chain performances. Computers & Industrial Engineering, 82, 127-142.
Daugherty, P. J., Richey, R. G., Roath, A. S., Min, S., Chen, H., Arndt, A. D., & Genchev, S. E. (2006). Is collaboration paying off for firms?. Business Horizons, 49(1), 61-70.
Erol, O., Sauser, B. J., & Mansouri, M. (2010). A framework for investigation into extended enterprise resilience. Enterprise Information Systems, 4(2), 111-136.
Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10(2), 87–99.
Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10), 1037-1041.
Galbraith, J. R. (1977). Organization design. Addison Wesley Publishing Company.
Gimenez, C., van der Vaart, T., & Pieter van Donk, D. (2012). Supply chain integration and performance: the moderating effect of supply complexity. International Journal of Operations & Production Management, 32(5), 583-610.
Hoole, R. (2006). Drive complexity out of your supply chain. Harvard Business School Newsletter, 3. Hohenstein, N.-O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90–117.
Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259.
Karlsson, C. (2009). Researching operations management. New York: Routledge.
Lambert, D. M., & Schwieterman, M. a. (2012). Supplier relationship management as a macro business process. Supply Chain Management: An International Journal, 17(3), 337–352
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558.
de Leeuw, S., Grotenhuis, R., & van Goor, A. R. (2013). Assessing complexity of supply chains: Evidence from wholesalers. International Journal of Operations & Production Management, 33(8), 960-980.
24
Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192–223.
Martínez-Olvera, C. (2008). Entropy as an assessment tool of supply chain information sharing. European journal of operational research, 185(1), 405-417.
Nunnally, J. (1978). Psychometric theory. McGraw-Hill.
Nyaga, G. N., Whipple, J. M., & Lynch, D. F. (2010). Examining supply chain relationships: do buyer and supplier perspectives on collaborative relationships differ?. Journal of Operations Management, 28(2), 101-114.
Nyaga, G. N., Lynch, D. F., Marshall, D., & Ambrose, E. (2013). Power asymmetry, adaptation and collaboration in dyadic relationships involving a powerful partner. Journal of Supply Chain Management, 49(3), 42–65.
Parkinson C.N., (1961) In-Laws and Outlaws and Parkinson's Third Law. Campridge. Riverside press Perona, M., & Miragliotta, G. (2004). Complexity management and supply chain performance assessment. A field study and a conceptual framework. International journal of production economics, 90(1), 103-115.
Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of business logistics, 31(1), 1-21.
Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46-76.
Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143.
Prajogo, D., & Olhager, J. (2012). Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. International Journal of Production Economics, 135(1), 514–522.
Rice, J. B., & Caniato, F. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7(5), 22-30.
Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471-484.
Sheffi, Y. (2001). Supply chain management under the threat of international terrorism. The International Journal of logistics management, 12(2), 1-11.
Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan management review, 47(1), 41-48.
Simatupang, T. M., & Sridharan, R. (2008). Design for supply chain collaboration. Business Process Management Journal, 14(3), 401-418.
25
Sirmon, D. G., Hitt, M. A., & Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of management review, 32(1), 273-292.
Sirmon, D. G., Gove, S., & Hitt, M. A. (2008). Resource management in dyadic competitive rivalry: The effects of resource bundling and deployment. Academy of Management Journal, 51(5), 919-935. Sivadasan, S., Efstathiou, J., Shirazi, R., Alves, J., Frizelle, G., & Calinescu, A. (1999, December). Information complexity as a determining factor in the evolution of supply chains. In Proceedings of the International Workshop on Emergent Synthesis, 237-242.
Sivadasan, S., Efstathiou, J., Calinescu, A., & Huatuco, L. H. (2006). Advances on measuring the operational complexity of supplier–customer systems. European Journal of Operational Research, 171(1), 208-226.
Sivadasan, S., Smart, J., Huatuco, L. H., & Calinescu, A. (2010). Operational complexity and supplier–customer integration: case study insights and complexity rebound. Journal of the Operational Research Society, 61(12), 1709-1718.
Skipper, J. B., & Hanna, J. B. (2009). Minimizing supply chain disruption risk through enhanced flexibility. International Journal of Physical Distribution & Logistics Management, 39(5), 404-427 Skjøtt-Larsen, T., Mikkola, J. H., & Kotzab, H. (2007). Managing the global supply chain. Copenhagen Business School Press
Soosay, C. A., Hyland, P. W., & Ferrer, M. (2008). Supply chain collaboration: capabilities for continuous innovation. Supply Chain Management: An International Journal, 13(2), 160-169.
Soosay, C. A., & Hyland, P. (2015). A decade of supply chain collaboration and directions for future research. Supply Chain Management: An International Journal, 20(6), 613–630
Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications, 9(1), 33–45.
Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27.
Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623.
Vachon, S., & Klassen, R. D. (2002). An exploratory investigation of the effects of supply chain complexity on delivery performance. IEEE Transactions on engineering management, 49(3), 218-230. Vollmann, T. E., Berry, W. L., & Whybark, D. C. (2005). Manufacturing planning and control systems.
26
Wilding, R. (1998). The supply chain complexity triangle: uncertainty generation in the supply chain. International Journal of Physical Distribution & Logistics Management, 28(8), 599-616.
Wu, L., Yue, X., & Sim, T. (2006). Supply clusters: a key to China's cost advantage. Supply chain management review, 10 (2), 46-51
