COPING WITH SUPPLY CHAIN COMPLEXITY

Abstract:

There is a considerable literature on – supply chain – complexity, and on exploring its dimensions, drivers, and measures. However, mitigation strategies to cope with it and contextual factors have hardly been addressed. From literature, we derive a set of complexity dimensions to assess supply chain complexity. A multiple case study rooted within the food processing industry shows that the contextual setting, being both externalities and the processing technology, influences the set of appropriate mitigation strategies. Moreover, we find that considering the interaction of different complexity dimensions helps to better understand the performance impact of supply chain complexity.

Keywords: Supply chain complexity, Performance, Mitigation strategies

COPING WITH SUPPLY CHAIN COMPLEXITY – HOW CAN MITIGATION STRATEGIES IMPROVE PERFORMANCE IN THE FOOD PROCESSING

INDUSTRY?

Research Master thesis, Operations Management & Operations Research University of Groningen, Faculty of Economics and Business

August 24, 2014 HENDRYK DITTFELD Studentnumber: s2173816 e-mail: hendryk.dittfeld@gmail.com Supervisor/ university D.P. van Donk Co-assessor/ university

Introduction

“Managing the complexity that we encounter in our supply chain has a direct impact on our financial performance” one Supply Chain Manager argued in our pilot study. It highlights the potential impact of managing or not managing supply chain (SC) complexity. Despite its importance, often managers are unaware of what drives complexity and thus have problem dealing with it. The level of complexity is understood to be higher if more uncertainty, more changes, more variability, more diversity and/or less structure are present in the SC (De Leeuw et al., 2013). Starting with the seminal work of Simon (1962), much work has been done in defining and measuring complexity in general. However, from a theoretical point of view Isik (2010) takes a critical perspective on the relevance of defining and measuring SC complexity on its own. She argues that the level of SC complexity is irrelevant, but matching it with appropriate mitigation strategies is essential. Moreover, understanding the contextual setting seems important, as advocated in SC strategy literature (e.g. Wagner et al., 2012). So far, little has been done to empirically explore complexity and mitigation strategies in a supply chain context. This is the gap we aim to address in this paper.

hundred end products from one main raw material. Probability, these different challenges lead also to different strategies to cope with the predominant complexity in the different systems.

Therefore, our main research question is: Which mitigation strategies are used to influence the relation between SC complexity and performance and what are effective strategies?. In addition, we investigate what the role of the context is. As little is known on the subject, the empirical part of our study uses case studies. We opted for cases in the food processing industry (FPI) as specific industry related contingencies such as perishable raw materials, expensive, often single purpose capacity, divergent product structure, and the power situation between the FPI and major retailers (e.g. Van Donk, 2001; Van Wezel et al., 2006) are present.

The paper is structured as followed. First of all, we address the theoretical background of work related to SC complexity, mitigation strategies and the food processing industry and develop a research framework. Secondly, we present the methodology including a brief case description of all cases. Thirdly, the findings are shown followed by a discussion in which we shed light on the outcomes of our data analysis. Finally, we complete the paper with a conclusion section in which we summarize the main findings, state limitations of the study and provide suggestions for future research.

Theoretical background

In this section we discuss SC complexity and its link to performance, mitigation strategies, as well as characteristics of the food processing industry. Eventually, we reconcile the insights from the three subsections into a research framework at the end of the section.

Exploring Supply Chain Complexity

complexity. De Leeuw et al. (2013) recently reviewed the SC complexity literature and identified eight drivers of SC complexity: uncertainty, variability, (lack of) information synchronization, (lack of) cooperation, size, speed, diversity and structure. Two of those eight drivers are formulated in a negative way (lack of…) which indicates that the presence of those two “drivers” actually reduces complexity or its impact. Therefore, we consider information synchronization and cooperation not as complexity drivers but as mitigation strategies. As a matter of fact, we will treat De Leeuw’s drivers as dimensions of SC complexity as they make up SC complexity, rather than drive it as external factors. By taking that step we are confident to avoid further confusion around the term SC complexity and its sources, antecedents, drivers and dimensions mentioned in previous papers. We follow the definition of Perona and Miragliotta (2004) who consider complex systems to be made up by single elements which have intimate connections, counterintuitive and non-linear links. More specifically, we look at one element of a complex system at a time and explore it and its links in terms of uncertainty, variety, size, speed, structure and diversity. Previous empirical studies (De Leeuw et al, 2013: Bozarth et al. 2009; Vachon and Klassen, 2002) treat and measure complexity dimensions as separate entities and do not acknowledge the interaction between different dimensions. In complex system theory the interaction is captured by the degree of coupling, but in empirical work over SC complexity it is often missing. In this paper we deliberately consider the interdependence of the dimensions we defined above and reintegrate the interrelatedness of complexity into the concept of SC complexity.

the total cost, the scrap rate and the inventory turnover are negatively impacted by SC complexity. Interestingly, all studies relating SC complexity to performance link sources, antecedents, drivers, or dimensions of complexity to performance rather than an overall SC complexity concept. We argue that separately looking at various sources of SC complexity and relating them to performance aspects does not capture the nature of SC complexity, as implied in our definition above. Only linearly relating – as done in a linear regression model – single complexity sources to performance measures does not capture the essential interrelatedness of SC complexity dimensions nor its performance impact. Perona and Miragliotta (2004) take an alternative approach to complexity’s performance impact by including mitigation strategies, such as partnerships with suppliers, product modularization, and information systems, into their conceptual model. In their case study, Perona and Miragliotta (2004) found empirical evidence that SC complexity affects the scrap rate, average stock coverage, number of man hours in R&D and procurement, and the length of the frozen period. We will add to their work by including context and addressing SC complexity as discussed above rather than computing a complexity index for each mitigation strategy as they do in their paper.

Mitigating strategies

product modularization, or an automated production process. In his conceptual paper, Hoole (2005) discusses a number of strategies to be able to cope with complexity. Collaborative forecasting and planning, postponement, supplier collaboration and outsourcing are found to aid in dealing with complexity (Hoole, 2005). Additionally, De Leeuw et al. (2013) find rationalization, keeping inventories and holding flexible resources as practices used to cope with SC complexity and influencing its level. Manuj and Sahin (2011) identify information systems as a moderator to cope with SC complexity but also emphasize that human cognitive capabilities such as training and experience influence the ability to cope with SC complexity. An issue that has not been addressed before is how those mitigation strategies and practices can be matched to a certain level of complexity. In the context of the FPI it is furthermore interesting to study mitigation strategies because for example holding inventory and postponement are only possible to a limited extent (Van Kampen and Van Donk, 2014).

Characteristics of the food processing industry

of production batches (Soman et al., 2004). Overall, agricultural supply chains are perceived as being complex due to the perishable nature of the raw materials, high fluctuations of demand and prices, dependence on climate conditions and an increasing concern of customers regarding food safety (Shukla and Jharkharia, 2013).

Research framework

Based on the above discussion we sketch the relationships between the different parts in our theoretical framework (see Figure 1). Contextual, industry-specific factors are expected to impact the SC complex and suitable mitigation strategies. In the reviewed literature, we found a solid foundation for each variable separately, but there is a limited understanding of the relationships between the variables. The link that is studied the most extensive is the one between SC complexity and performance. However, the impact of mitigation strategies on this link received less attention. In fact, Bozarth et al. (2009) argue that next to defining mitigation strategies, the next step for further research should be to explore which mitigation strategies are effective for which level of complexity. Gerschberger et al. (2012) and Manuj an Sahin (2011) both propose that the context influences SC complexity and should get a more prominent place in future studies. The concept of supply chain complexity itself is approached through the dimensions uncertainty, variability, diversity, size, speed, structure and an interaction terms that yet must be explored.

Methodology

To get an in-depth insight into the relation between SC complexity and performance and its underlying factors, we conduct a multiple case study mainly rooted within the context of a large multinational dairy company. This offers an excellent base as all supply related characteristics from the FPI are present. We include three plants from the dairy company and complement these cases with a plant from a breadcrumb producer. A multiple case study is an adequate method to address our research question since we aim to gain a deeper understanding of how mitigation are used to manage SC complexity and its impact on performance (Yin, 2009). Currently, we have just a limited understanding of these links and few work has been done to date concerning this area of interest. An in-depth analysis of qualitative data allows us to better understand the links between SC complexity, mitigation strategies and a possible performance impact, which makes a case study a suited instrument for this paper (Voss et al., 2002) .

production steps, and coupling to other plants for receiving and delivering byproducts/ingredients. Additionally, those plants produce different products for different markets both serving B2B and B2C. The breadcrumb plant was included as a plant with a lower complexity. It is smaller in terms of production volume and number of employees. It also has fewer interdependencies with other plants. At the demand side this plant delivers to a number of different customers which use breadcrumbs to process their products. Below we provide a short case description for each plant which is summarized in Table 1.

Table 1: Overview cases

Plant Characteristics Interviewees

Plant A Products: evaporated milk; milk powder 800 employees; processing ca. 1.000.000.000 liter milk/year

Supply Chain Manager Logistic Manager

Plant B Product: infant food; 800 employees; processing ca. 600.000.000 liter milk+whey/year

Master Planner

Business Development Manager

Plant C Product: cheese; 81 employees; processing ca 350.000.000 liter milk per year

Production Manager

Plant D Products: breadcrumbs; ca 30* employees; producing a total of 12.000t* of end products (*in 2006)

Planner

Business Development Manager

Demand Internal customer of plant A,B, C Director Supply Chain (sales division)

Supply Responsible for allocation of liquid raw materials

Manager milk allocation

HQ supply allocation

(pilot)

Responsible for allocation of liquid raw materials

Manager Supply Allocation, Director Milk Valorization Manager Supply Chain, Supply Chain Manager

the dairy company reflecting on SC complexity from the demand side. By including also interviewees that are not working with the plants which are analysis we are able to better assess and triangulate the answers given in the interviews with the focal plants. The nine interviews took on average 50 minutes. During the plant visits observations have been made regarding the production process. For triangulation, further data has been obtained from documents about operational planning and company studies on a complexity index. Moreover, we had access to the milk allocation plan of plant A, B and C in which long and short term planning could be assessed. Lastly, we used earlier case studies at the three of the four plants (A, B, D) in the form of Master’s theses written the University of Groningen as input for our analysis and as a source for triangulation.

The interview protocol used (see Appendix A), contains general questions regarding the position of the interviewee, a short description of the production process, main products, suppliers and customers, and size related information regarding number of employees and turnover. Next, we asked questions related to the six dimensions of SC complexity, the possible performance impact, mitigation of specific dimensions and effects of the food context. We used open questions and used our conceptual model as a guide for the interview. Questions were based upon the sources identified in the literature section and the measures used there. More specifically, we used the six dimensions to assess complexity and asked for each dimension how it contributes to complexity, what is done to cope with the dimension, and how this dimension might impact performance. Before we started discussing the dimensions in the interviews, the definitions were read to the interviewees to make sure to share a common understanding. In each interview we first presented our conceptual framework and explained the purpose of our study.

Table 2: Definitions of SC complexity dimensions (adapted from De Leeuw et al. 2013) Uncertainty The lack of predictability and reliability of demand and supply

Diversity The number of different elements in a system (products, suppliers, customers, activities)

Variability Sudden, large and fluctuating changes in requirements impose on the system over time

Size Relative number or volume of elements (products or activities)

Structure The interconnections between multiple systems, levels, processes within and across elements

The acquired data have been analyzed by coding the answers of the interviews and finding patterns within those answers. We used codes to cover the FPI characteristics, the six dimensions of complexity, the interaction of those six dimensions, the performance impact of the complexity dimensions, and all strategies used to cope with those dimensions. Appendix B contains the coding scheme for SC complexity. The concept of SC complexity has been approached with a rather strong theoretical frame in mind and has purposely been coded using the dimensions of De Leeuw et al. (2013). However, the approach towards possible mitigation strategies and the role of FPI characteristics has been much more open allowing for the opportunity to find a better understanding of the links between complexity, performance, coping mechanisms and context. As recommended by Eisenhardt (1989), both a within case analysis and a cross-case analysis are performed and presented in the following section.

Findings

The findings are divided into a within and across case analysis. The within case analysis contains a brief case narrative and findings concerning SC complexity, performance and mitigation strategies. The cross case analysis, in turn, contains similarities and differences which emerge from the within case analysis. The relevance of the findings in the context of the food processing industry will be elaborated on in the discussion section.

Within Case Analysis

In terms of SC chain complexity this plant is considered having a high level of complexity by interviewees from the headquarters as well as by the two interviewees working at the plant. A main issue is the degree of coupling in the production process and the interdependences between the tin production, which serves at packaging material for the evaporated milk. Interestingly, there is a big difference in terms of perceived complexity between the two main products produced in plant A. In fact, the interviewees argue that almost all complexity experienced in plant A is due to the evaporated milk. Milk powder does not significantly contribute to complexity because of the following reasons. First of all, the milk powder is a generic recipe which is suitable for large bulk production and reduces the variety. Secondly, the milk powder is packed in so called big bags and does not need to pass the packaging line to make it customer specific as the evaporated milk does. Thirdly, as a generic product milk powder is traded on the world market, which implies that the product can basically always be sold. Fourthly, the milk powder is an ingredient rather than product such as evaporated milk which is eventually sold to household as end customers. The differences show that variety of recipes in line with that that customization of the product at the packaging stage contributes to the internal complexity at plant A. The external complexity, in turn, is influenced by the customer requirements and level of uncertainty induces by this requirements. However, the low level of complexity and risk is also reflected in the margin for milk powder, which is considered to be low as well.

In terms of performance impact plant A beliefs that higher complexity leads to higher costs, a lower service level and lost sales, less output, more downtime and lower efficiency and higher total cost. The performance affected by higher complexity is argued to be on an operational level even if the root causes for the performance impact come from inside as well as outside.

Plant B has a total of 800 employees as well and processes around 600,000,000 liter of milk and whey. It is considered to be a high complexity plant. The main product is infant food which is distributed worldwide by an internal sales office. Next to the products which are eventually offered to end customers, which are considered B2C as they are sold and under their own brand, the plant also produces recipes for business partners which is considered to be their B2B market. In total the plant makes 120 end products from 12 base products. Quality standards and requirements are very high and the end product is a high margin product. The packaging for the B2B products is done in the plant as well at four different packaging lines. The B2B products are sold in big bags. Of special importance for infant food production is whey which is a by-products of cheese production.

SC complexity at plant B seems to be highly related to the required quality standard needed for infant food which is highly regulated and is reflected in their production process as well as in their supplier selection process and the nature relationship with suppliers. Moreover, an important issue at plant B is that they have to combine two main raw materials which are raw milk and whey which is a by-product of cheese production. On the supply chain level this creates a dependence on farmers and on cheese production facilities which both must be planned and allocated in order to produce infant food. Again it is acknowledged that a higher product variety makes it more complex. Many problems with the production plan would not be there if plant B would solely produce one brand in just one packaging size. The quality regulations and requirements make changes in recipes and supplier base more challenging which eventually is reflected in the level of complexity as well.

Further market intelligence aids in making decisions which might be impacted by changing rules and regulations. Diversity managed by including the customer in an early stage into the product design including the recipe, in such a way that their expectation are managed and that they understand for example that preferences for a unique recipe might influence the cost they have to pay significantly. Additionally, collaborative planning is used to determine demand figures and an ERP system to cope with the perceived complexity along the supply chain.

In terms of performance impact plant B argues that higher complexity leads to more write-offs and accordingly to less output. Moreover, cleaning cost and cost related to rework may increase and delivery reliability and the service level could drop. Size as a dimension of complexity lead to higher efficiencies and allow for more economic batch sizes which help to decrease some performance indicators mentioned before such as cleaning cost.

Plant C has a total of 81 employees and processes around 350,000,000 liter of milk each year. It is considered to be a low/medium complexity plant. The main product is cheese with a production of 32,000,000 kg of cheese a year. In total they produce 38 different types of cheese which are sold worldwide. Each type can be sold under different brand names and in different packaging sizes. The plant delivers solely cheese wheels as a final product which is further processed elsewhere to eventually sell it to end customers.

In terms of mitigation strategies plant C uses training and a flexible workforce. In addition, a structured planning process is in place to allocate supply, demand and available capacity in order to cope with uncertainty. Other strategies to cope with the various dimensions of complexity are preventive maintenance, holding buffer capacity and postponement. To be more precise, plant B postpones making the product customer specific by selling only cheese wheels which must be packaged at a later stage in the supply chain.

In terms of performance, plant C focused very much on relating SC complexity to efficiency and product quality. Again size as a dimension of complexity is perceived to have positive effect on efficiency measures and also on product quality which can be explained that with bigger batches the standardization of recipes is easier. Next to efficiency and product quality a third performance measure is find to be effected by higher complexity. More specifically, in plant C working capital invested into stock that yet has to ripe is dependent on the complexity dimension speed.

Plant D is the only one that does not sell dairy products as its main product is breadcrumbs. With around 30 employees the plant produces 12,000,000 kg of end products. This plant is considered to be a low/medium complexity plant. The breadcrumbs are produced as follows: they first make dough, then they form breads and bake them in an oven, then they get chilled down and go in a dryer and where they eventually crumb them into different crumb sizes, afterwards the breadcrumbs are packed. The end products are not sold by retailers, but required by industrial customers that use it as an ingredient to produce, for example, snacks, vegetables, chicken, fish, and potato based products.

meaning that when one product is performing worse, another product might cover up for this by performing better. We see that a SC complexity dimension might influence complexity in different directions.

In terms of mitigation strategies plant D has the following instruments in place. First the work with a well-defined production planning process combined with clear contracts that defined delivery times. They argue that rules and commitment to these rules is essential to cope with the perceived complexity. Secondly, a reduction in number of recipes is aiding in cope with increased complexity encountered at the internal production planning. Other strategies are communication, training and hiring qualified employees to mitigate complexity. However, capturing business intelligence in systems such as a customer relationship management system is important to keep knowledge in-house even if experienced employees retire or change employer. As such the mitigating complexity gets more sustainable.

In terms of performance plant D argues SC complexity leads to lower service levels. Furthermore, costs are affected because of changeover and cleaning times necessary if a higher product variety is offered. Size as a dimensions of complexity is argued to have a positive impact on efficiency as economies of scale can be exploited. Lastly, flexibility might suffer under higher SC complexity.

An overview of the within case analysis is shown below in Table 3. Table 3: Findings within case analysis

Plant Dimension Performance impact on: Mitigation strategy

A

Uncertainty Lost sales, Service level Planning, holding extra capacity as buffer, monitoring

Diversity Productivity, output ERP system, rationalization of products

Size Efficiency; cost impact -

Variability Downtime Daily control system; communications, preventive maintenance, lean

Structure Cost impact Planning system, flexible resources

Speed - -

B

Uncertainty Write offs, Output, Planning, be close to the market concerning rules and regulations

Diversity Output, write offs, Cleaning cost, rework

Manage customers’ expectations, standardize recipes

Size Positive for efficiency, risk -

Variability Delivery reliability Planning; ERP

Structure Service level Organizational design; collaborative planning Speed Quality

C Uncertainty Efficiency, Product-quality Training, planning, buffer capacity

Size Positive for efficiency and quality Flexible workforce Variability Efficiency, product quality Preventive maintenance

Structure - Buffer capacity

Speed Working capita Rationalization, postponement, planning

D

Uncertainty - Planning system, Contracts, rules and commitment Diversity Cost (through) changeover and

cleaning time

Reduce number of recipes

Size Positive for efficiency Innovations, new product development Variability Flexibility Qualified employees

Structure - Planning, communication, capture business

intelligence in systems

Speed Service level Planning, changing production process

Cross Case Analysis

The four production plants included in our analysis show rather similar findings of what makes up SC complexity. Uncertainty and diversity make up a bigger part of complexity than dimensions such as speed or size. We find that often the interaction between different SC complexity dimensions need to be considered to understand the performance impact.

Supply Chain Complexity

Diversity is recognized to drive complexity on two levels. Diversity is an internal issue and can be divided into the number of recipes and the number of end products. SC complexity increases when the number of recipes and end products is higher because each possibility added means an additional possible routing in the production process, extra setups and more restrictions in the scheduling due to sequence dependent setups. All cases have a small number of suppliers and therefore suppliers turned out to be no issue. Plant B has the highest number of suppliers. However, they argue that not the number of suppliers makes it more complex but the relationship they maintain which a certain supplier. Every ingredient delivered by external suppliers is required to have a high level of quality, which is ensured by a strict audit of these suppliers. Monitoring and maintaining the quality of ingredient suppliers increases the impact of diversity on complexity. A master planner concludes that “the product mix makes it complex” after explaining that 66% of the recipes account for less than 1% of the yearly output.

Variability is mainly related to disturbances or breakdowns in the production process. It seems that plants with a tightly coupled production process perceive variability as more contributing to SC complexity than plants that have not. This dimensions has a strong relation to uncertainty as well, because variability – induced through the seasonal pattern of milk supply – is also a source for supply uncertainty. Disturbances and breakdowns of production is seen as an major issue if combined with high utilization of the available machines.

Size is found to have no direct impact on SC complexity. Contrarily, it seems to be helping to be able to efficiently produce a diverse product portfolio. Size can amplify the SC complexity when it is considered together with uncertainty and variability. Interviewees argued, for example, that disturbances and breakdowns in the production process lead to more complexity and combined with a large volume the impact of these variability related factors increases. One Supply Chain Manager stated: “Being bigger gives you choices which can help you to decrease complexity. It helps you to be able to offer a diverse product portfolio and still remain to achieve high efficiencies.”.

depend on the same main raw material and mutual deliveries and receipts of byproducts/ingredients. One example is whey, a by-product in cheese production but at the same time an essential ingredient for infant food production. Hence, the internal planning from the production facilities is dependent on the overall allocation plan at the company level and has to consider raw milk as a primary stream and all derivatives of milk as secondary streams. The breadcrumb producer did not experience such an issue as they do not share their raw material and they also do not produce by-products which are delivered to other plants.

Speed seems to drive complexity as the raw materials from the dairy processing plants have a short shelf-life. We learned that once the milk is conserved the complexity impact through speed decreases. The breadcrumb producer perceives speed as a driving force for complexity and experiences that short lead times required by customers increase the perceived complexity. Plant A and B acknowledge that the lead time also impacts their operations and lead to higher SC complexity.

The interaction of dimensions has partly been discussed above. We find that uncertainty drives SC complexity but combined with diversity the impact can decrease as for example the demand uncertainty of different products levels out and the average demand uncertainty is lower. Size can be as a dimensions of complexity in interaction with variability and also as a strategy mitigating the impact of diversity. A Supply Chain Director even argued that size can be used to mitigate demand uncertainty related to rules and regulation, as “Size becomes power and power becomes leverage which we use to influence decisions regarding regulations on a political level”.

Performance

variability turn out to have a negative impact on the service level. Size is argued to have a positive impact on efficiency as economies of scale can be realized. In the FPI this may be even be more important than in other industries as, next to setup times which often are sequence dependent, cleaning times impact efficiency and large batches are needed to produce efficiently. Combined with high uncertainty and variability, size also comes with risk as we will discuss in the next section. Other performance measures are output, flexibility, delivery reliability and quality.

Mitigation strategies

aids in short term decision making and helps keeping the overview in complex supply chains. Buffer capacity decreases the perceived complexity as it mitigates both uncertainty and variability. As extra capacity requires investments is not clear whether the lower complexity levels compensate for the higher cost for extra capacity.

Discussion and Contribution

product customer specific is already determined by producing a certain recipe. Therefore, related to our main research question it seems that postponement is no effective mitigation strategy, whereas forecasting and planning, modularization and ERP systems are effective in the FPI. These findings contribute to the current body of literature by providing an overview of which mitigation strategies are used in the FPI and explain how differences to former findings can be explained using industry specific characteristics. However, we could not find major differences between the plants in term of mitigation strategies. This holds for differences between low and high complexity plants and also for differences which were related to different FPI characteristics. It seems that the call for context specific perspective on SC complexity and its mitigation strategies (Gerschberger et al., 2012; Manuj and Sahin, 2011; De Leeuw et al., 2013) should be study on the industry level rather than looking at different plants within one industry as we did in this study. By comparing the food processing industry with the manufacturing industries, we might be able to gain better insights into the role of the context than we were able to do in this study.

A surprising finding in our cases is that - related to diversity - the number of suppliers or end products do not necessarily drive SC complexity. For suppliers the nature of the relationship and importance of the purchased item for the end product is more likely to influence complexity. For the number of end products, in turn, an impact is only there when the recipe for the new end products cannot be produced from the old base products. These findings might be closely related to the food context.

In sum our findings help us better understand the proposed links from our conceptual model. More specifically, we have better insights into what makes up SC complexity in the food sector and which strategies are used to cope with different levels of complexity. Furthermore, we take a first step towards a better understanding of how the context influences the applicability of mitigation strategies.

Conclusion

This paper investigates mitigation strategies to cope with SC complexity and how those strategies reduce the performance impact of complexity in a food processing context. We find that mitigation strategies discussed in the reviewed literature are also applied in the FPI. However, we contribute to the current knowledge by being able to explain how the mitigation strategies relate to complexity. Moreover, we approach SC complexity using an adaption from the framework of De Leeuw at al. (2013) and added interdependencies between the dimensions to better understand the nature of SC complexity and its performance impact. We find that looking at different dimensions separately does not capture the complexity as perceived by practitioners. Future research is encouraged to further explore this research avenue treating SC complexity as a construct in which multiple dimensions are interwoven. During the interviews we also learned that complexity is more than we can capture with our structural way of approaching it. In this paper we did not further explore SC complexity related to relationships or human behavior, but future research might pick it up and study how SC complexity is perceived by human actors working within supply chains.

develop a conceptual foundation for future research in this area. Additionally, our cases had less variation than expected which made it difficult to understand how differences in characteristics influence the level of complexity. We will investigate in future research additional cases with more variety in demand, supply and plant characteristics to better understand relationships, interactions and underlying mechanisms. It seems that looking for contextual differences within one industry is not very useful to explore contextual influences on SC complexity and its mitigation strategies. Therefore, we advise to compare different industries instead in future studies.

For managers these findings imply that is worthwhile to reflect on the contextual setting when deciding which mitigation strategies are used to cope with increasing complexity. Moreover, this paper provides an overview over mitigation strategies that are currently in use at the plant level in the food processing industry, which gives managers the opportunity to reflect on their own approach towards facing supply chain complexity and compare it with the findings of this paper.

References

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, Vol. 27, No.1, pp.78-93.

Choi, T.Y., Dooley, K.J., & Rungtusanatham, M. (2001), “Supply networks and complex adaptive systems: control versus emergence”, Journal of Operations Management, Vol. 19, No. 3, pp. 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, Vol. 24, No. 5, pp. 637-652.

Eisenhardt, K. M. (1989), “Building theories from case study research”, Academy of Management Review, Vol. 14, No. 4, pp. 532-550.

Frizelle, G., & Woodcock, E. (1995), “Measuring complexity as an aid to developing operational strategy”, International Journal of Operations and Production Management, Vol. 15 No. 5, pp. 26-39.

Funk, J.L. (1995), “Just-in-time manufacturing and logistical complexity: a contingency model”, International Journal of Operations & Production Management, Vol. 15, No. 5, pp. 60-71.

Gailbraith, J. (1974), “Organization design: an information processing view”, Interfaces, Vol. 4, No. 3, pp, 28-36.

Gerschberger, M., Engelhardt-Nowitzki, C., Kummer, S., & Staberhofer, F. (2012), “A model to determine complexity in supply networks”, Journal of Manufacturing Technology Management, Vol. 23, No. 8, pp. 1015 – 1037.

Giménez, C., Van der Vaart, T., & Van Donk, D. P. (2012), “Supply chain integration and performance: the moderating effect of supply complexity”, International Journal of Operations & Production Management, Vol. 32, No. 5, pp. 583-610.

Hoole, R. (2005), “Five ways to simplify your supply chain”, Supply Chain Management: An International Journal, Vol. 10. No. 1, pp. 3-6.

Isik, F. (2010), “An entropy-based approach for measuring complexity in supply chains”, International Journal of Production Research, Vol. 48, No. 12, pp. 3681–3696.

Manuj, I., & Sahin, F. (2011), “A model of supply chain and supply chain decision-making complexity”, International Journal of Physical Distribution & Logistics Management, Vol. 41, No. 5, pp. 511 – 549.

Meepetchdee, Y. and Shah, N. (2007), “Logistical network design with robustness and complexity considerations”, International Journal of Physical Distribution & Logistics Management, Vol. 37, No. 3, pp. 201-222.

Perona, M. & Miragliotta, G. (2004), “Complexity management and supply chain performance assessment. A field study and a conceptual framework”, International Journal of Production Economics, Vol. 90, No. 1, pp. 103-115.

Shah, R., & Ward, P. T. (2007), “Defining and developing measures of lean production”, Journal of Operations Management, Vol. 25. No. 4, pp. 785-805.

Shannon, C.E. (1948), “A mathematical theory of communication”, The Bell System Technical Journal, Vol. 27, pp. 379-423.

Shukla, M., & Jharkharia, S. (2013), “Agri-fresh produce supply chain management: a state-of-the-art literature review”, International Journal of Operations & Production Management, Vol. 33, No. 2, pp. 114-158.

Simon, H. A.(1962), “The architecture of complexity”, Proceedings of the American Philosophical Society, Vol. 106,No. 6, pp. 467-482.

Sivadasan, S., Efstathiou, J., Frizelle, G., Shirazi, R., & Calinescu, A. (2002), “An information-theoretic methodology for measuring the operational complexity of supplier–customer systems”, International Journal of Operations and Production Management, Vol. 22 , No.1, pp. 80–102.

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, Vol. 171, No.1, pp. 208-26.

Soman, C. A., Van Donk, D. P., & Gaalman, G. (2004), “Combined make-to-order and make-to-stock in a food production system”, International Journal of Production Economics, Vol. 90, No. 2, pp. 223-235.

Vachon, S., & Klassen, R.D. (2002), “An exploratory investigation of the effects of supply chain complexity on delivery performance”, IEEE Transactions on Engineering Management, Vol. 49, No. 3, pp. 218-230.

Van Donk, D. P. (2001), “Make to stock or make to order: The decoupling point in the food processing industries”, International Journal of Production Economics, Vol. 69, No. 3, pp. 297-306.

Van Dorp, K. J. (2002), “Tracking and tracing: a structure for development and contemporary practices”, Logistics Information Management, Vol. 15, No. 1, pp. 24-33.

Van Wezel, W., Van Donk, D.P., & Gaalman, G. (2006), “The planning flexibility bottleneck in food processing industries”, Journal of Operations Management, Vol. 24, No. 3, pp. 287-300.

Voss, C., Tsikriktsis, N., & Frohlich, M. (2002), “Case research in operations management”, International Journal of Operations & Production Management, Vol. 22, No. 2, pp. 195-219.

Wagner, S.M., Grosse-Ruyken, P.T. & Erhun, F. (2012), “The link between supply chain fit and financial performance of the firm”, Journal of Operations Management, Vol. 30, No. 4, pp. 340–353.

APPENDIX A: Interview Protokol

Opening

 Introduction of interviewer and interviewee

 Confidentiality assurance

 Permission to audiotape Overview – purpose of the study

Questions concerning general information:

 Job-title of participant

 Main products

 Short description of production process

 Number of employees  Turnover plant  Main customers  Main suppliers

Food

processing

context

Complexity Performance

Supply Chain Complexity Dimensions

Questions

 How do these dimensions of complexity relate to performance? Could you explain how performance is influenced?

 How do you manage/try to influence the impact of the various dimensions?

 Could you try to link the different complexity dimensions and explain how they possibly interact?

 How do industry specific characteristics might influence the level of complexity?

APPENDIX B: Coding scheme used to asses Supply Chain Complexity

Dimension Measurement Source

Uncertainty - average reliability of forecast (per Stock Keeping Unit)

- average delivery reliability performance from supplier

<all supply chain complexity literature mentioned below>

Variability - average absolute deviate (highest – lowest) of utilization in capacity - average absolute deviate (highest – lowest) of utilization in purchase volume

- % use of temporary employees

(Bozarth et al. 2009) (Sivadasan et al. 2002)

Size - sales and purchase volumes - batch sizes used in purchasing

(Perona and Miragliotta 2004), (Funk 1995), (Bozarth et al. 2009)

Speed - frequency of releasing sales orders and purchase orders for processing

- order throughput time

(Bozarth et al. 2009), (Sivadasan et al. 2006)

Diversity - number of Stock Keeping Units in purchasing

- number of different customers and suppliers used

(Frizelle and Woodcock 1995), (Sivadasan et al. 2006), (Perona and Miragliotta 2004), (Funk 1995) Structure - level of vertical integration

- number of different processes