HOW TO MANAGE UPSTREAM COMPLEXITY IN THE FOOD PROCESSING INDUSTRY

(1)

HOW TO MANAGE UPSTREAM COMPLEXITY IN THE

FOOD PROCESSING INDUSTRY

A multiple case study

Master thesis, MSc, specialization Supply Chain Management University of Groningen, Faculty of Economics and Business

June 22, 2015 TOM GREVERS Student number: s2585847 E-mail: t.grevers@student.rug.nl Supervisor/ university Dr. D.P. Van Donk Co-assessor/ university H. Dittfeld, MSc

(2)

ABSTRACT

Upstream complexity is a scarcely studied subject, especially in the specific context of the food processing industry. Literature lacks in addressing the upstream complexity construct uniformly, its related drivers, and strategies/coping mechanisms to accommodate and/or reduce the experienced upstream complexity. Therefore, this paper questions how focal companies in the food processing industry manage (the impact of) upstream complexity. The goal of this study is to define the upstream complexity construct, its drivers, for the food processing industry, and to come up with coping mechanisms to deal with upstream complexity. These insights are obtained through a multiple case study, since the phenomenon can be explored in-depth in its natural setting. This study found that specific upstream complexity drivers, variation in quality, supply and price, added due to the food processing industry, are mentioned as the main upstream complexity drivers. Those particular upstream complexity drivers can be dealt with by collaboration, relationship management (contracts and price incentives), and the use of multiple suppliers/a flexible supply base. This study gives insights to literature by defining the upstream complexity construct for the food processing industry, its related drivers, as well as strategies to cope with these specific drivers. These strategies can be used by managers to cope with experienced upstream complexity, however, their specific context should always be taken into account.

(3)

PREFACE

“The only simple truth is that there is nothing simple in this complex universe.

Everything relates. Everything connects”

-‐ Johnny Rich (The Human Script)

(6)

INTRODUCTION

In 2012, Nestlé presented ‘The Cocoa Plan’. In this plan, Nestlé pointed out an interesting supply chain objective: “By working closely with farming cooperatives and paying a premium for quality, we

aim to reduce the complexity of the supply chain, improve returns to farmers and improve the quality

of cocoa for Nestlé”. Nestlé aimed for a reduction of supply chain complexity, an interesting topic addressed by many supply chain-related papers. In this example, Nestlé (the focal organization) experienced complexity in the linkages with their cocoa suppliers (the farmers). This is called upstream complexity; a relevant but scarcely studied subject and hence we do not know if and how such efforts, pointed out by Nestlé, help, for themselves and for others. This paper contributes to current literature by obtaining more insights about upstream complexity in the food processing industry.

A focal organization has to manage upstream complexity; the complexity of purchasing parts, materials and services negatively affect the performance of an organization (Bozarth, Warsing, Flynn, & Flynn, 2009; Brandon-Jones, Squire, & van Rossenberg, 2014; Wagner & Bode, 2014). However, management strategies to deal with this upstream complexity in the food processing industry have been a scarcely studied subject yet.

The structure of the supply chain affects the occurrence of disruptions when focusing on upstream complexity and supply-side disruptions (Wagner & Bode, 2014). However, they did not come up with strategies to manage upstream complexity, neither did Choi & Krause (2006) in their paper about supply base complexity. In their future research directions, Bozarth et al. (2009) come up with the suggestion to investigate the strategies organizations use to moderate the impacts of supply chain complexity. Brandon-Jones et al. (2014) did come up with strategies to manage upstream complexity, but they were not industry specific and did not include possible additional upstream complexity drivers due to characteristics of a specific industry, which was also suggested to study by Bozarth et al. (2009) since “complexity drivers might vary across industries” (Bozarth et al., 2009: 90). De Leeuw, Grotenhuis, & van Goor (2013) took a specific context into consideration by depicting the relationship between supply chain complexity coping mechanisms and supply chain complexity drivers in the context of wholesalers. However, they did not take the distinction of upstream complexity, proposed by Bozarth et al. (2009), into account. De Leeuw et al. (2013) mention in their further research section that it is necessary to do though, focusing explicitly on complexity of suppliers as well as “further detailing the mechanisms of coping with supply chain complexity and the tools, which may be used to mitigate and to reduce supply chain complexity” (de Leeuw et al., 2013: 977). That is also important when looking at the Nestlé example.

(7)

mechanisms to manage and/or reduce upstream complexity. Since organizations in the processing industry aim to reduce upstream complexity in the supply chain, as highlighted in ‘The Cocoa Plan’, combined with industry-specific characteristics of the food processing industry that might cause variation in the complexity drivers (Bozarth et al., 2009), it is important to come up with strategies which mitigate the effects of these specific complexity drivers. This study focuses on dealing with upstream complexity in the food processing industry by identifying upstream complexity drivers specific for the food processing industry and comes up with identified strategies and underlying mechanisms which focal organizations use to manage the experienced upstream complexity in order to improve the organizational performance. The following research question will be addressed:

How do food processing companies manage (the impact of) upstream complexity?

In order to answer the research question, a multiple case study will be conducted to investigate strategies organizations use to manage the experienced upstream complexity. A multiple case study allows to explore the phenomenon in its natural setting. This will fill the gap in current literature by addressing the construct of upstream complexity in the food processing industry and strategies managers can use to manage upstream complexity.

(8)

THEORETICAL BACKGROUND

Upstream complexity construct

The construct of upstream complexity is not uniformly defined in literature, however, it is defined by the following dimensions in this paper, based on the merge of theory discussed in the papers of Bozarth et al. (2009), Brandon-Jones et al. (2014), Choi & Krause (2006) and Wagner & Bode (2014); scale, differentiation, delivery, geographic dispersion and position. These dimensions can be subdivided into drivers. All together, the upstream complexity construct is depicted in Table 1 and further elaborated on in this section.

UPSTREAM COMPLEXITY

Dimensions Drivers

Scale Number of direct suppliers

Differentiation Cultures, practices and capabilities among suppliers Delivery Delivery reliability and supplier lead times

Geographic dispersion Geographical spread of the supply base

Position Number of tiers

Table 1: Upstream complexity construct

Scale

The first dimension, scale, is defined by the upstream complexity driver number of direct suppliers in the supply base (Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014). The more different suppliers an organization has to manage, the more complex (Bozarth et al., 2009). This increase in upstream complexity is attributable to an increase in the number of information flows, relationships and physical flows an organization has to manage when doing business with multiple direct suppliers (Bozarth et al., 2009; Brandon-Jones et al., 2014). This is confirmed by the paper of Choi & Krause (2006) since the more suppliers an organization is doing business with, the more relationships should be maintained which causes an increase in upstream complexity. The number of direct suppliers can also be linked with upstream complexity in two different ways. First, no supplier is perfectly reliable. By increasing the number of direct suppliers, there is more change for ‘unreliability’ which causes upstream complexity. Second, the more direct suppliers, the more efforts on managing, coordinating and monitoring these suppliers and thus an increase in upstream complexity (Wagner & Bode, 2014).

Differentiation

(9)

Delivery

The third dimension, delivery, is defined by the upstream complexity drivers delivery reliability and supplier lead times (Bozarth et al., 2009; Brandon-Jones et al., 2014). Unreliable suppliers and long supplier lead times leads to an extensive use of demand data (more), extended planning horizon and engagement in supplier development and/or activities which increase upstream complexity. The need for longer planning horizons and greater detail because of unreliability and longer lead times increases the upstream complexity (Bozarth et al., 2009).

Geographic dispersion

The fourth dimension, geographic dispersion, is defined by the driver geographical spread of the supply base (Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014). The more a supply base is globalized, the geographical spread of the supply base, the more an organization is exposed to a wider range of complicated factors as import/export laws, cultural differences, and longer and uncertain lead times, the higher the level of upstream complexity (Bozarth et al., 2009). The more the supply base is globally dispersed, the longer the paths and lead times, which increases upstream complexity. Global supply is also associated with increased uncertainty and less transparency, due to trade restrictions, exchange rate fluctuations and institutional differences which will increase upstream complexity (Wagner & Bode, 2014). Brandon-Jones et al. (2014: 2) mention that “The more geographically disparate suppliers are, the more upstream complexity through having to manage suppliers with different cultural or linguistic characteristics”. In the paper of Choi & Krause (2006) they mention geographical separation as a driver of differentiation, which also determines the level of upstream complexity.

Position

The fifth dimension, position, is defined by the driver number of tiers in the supply chain and is related to the position of the organization in the supply chain (Wagner & Bode, 2014). The more downstream in the supply chain, the more upstream ‘partners’ and thus a longer upstream supply chain, which will cause an increase in upstream complexity (Wagner & Bode, 2014).

The food processing industry (FPI)

(10)

homogeneous products are involved in at least one process, and it is not labor intensive. Capacity is the main determination factor, and the organizations in the food processing industry deal with a divergent product structure. The packaging phase is labor intensive and due to the variation in supply, quality and price of raw materials which causes uncertainty, more than one recipe is available for a product. It has to be mentioned that not all of these characteristics are present in every organization, it is case-specific, but all of the mentioned characteristics have to be taken into account.

Upstream complexity in the food processing industry

Due to these distinctive food processing industry characteristics, the complexity dimension, FPI-uncertainty, as mentioned by Van Donk (2001), can be added to the construct of upstream complexity. The dimension, FPI-uncertainty, is defined by the drivers perishability, variation in quality, variation in supply, and variation in price. Perishability affects the level of uncertainty (Georgiadis, Vlachos, & Iakovou, 2005) which in turn drives complexity (Bozarth et al., 2009; Wilding, 1998). Since there has to be dealt with the supply of raw materials upstream in the food processing supply chain, it is assumed that perishability causes more uncertainty and thus upstream complexity. Also, FPI-uncertainty increases due to the variation in farmers’ yield which affects quality, supply (reliability) and price. It is also assumed that this makes it more complex to manage, the upstream supply chain, in the food processing industry. Table 2 is based on the literature of Bozarth et al. (2009), Choi & Krause (2006), Brandon-Jones et al. (2014), and Wagner & Bode (2014) combined with the distinctive characteristics of the food processing industry discussed by Van Donk (2001).

UPSTREAM COMPLEXITY IN THE FOOD PROCESSING INDUSTRY

Dimensions Drivers

Scale Number of direct suppliers.

Differentiation Cultures, practices and capabilities among suppliers. Delivery Delivery reliability and supplier lead times.

Geographic dispersion Geographical spread of the supply base.

Position Number of tiers.

FPI-uncertainty

Perishability, variation in supply, variation in

quality and variation in price.

Table 2: Upstream complexity in the food processing industry

(FPI) Upstream complexity and performance

(11)

impact on plant performance (Brandon-Jones et al., 2014) in terms of schedule attainment and unit manufacturing cost (Bozarth et al., 2009). In his master thesis, Ter Horst (2013) state that variability related complexity drivers generally have an impact on performance. The dimension, FPI-uncertainty, added because of the food processing industry, is measured by three variability drivers (variation in quality, variation in supply and variation in price). It is assumed that high levels of upstream complexity in the food processing industry (high variation in quality, supply and/or price) negatively impact the performance of an organization. For example, variation in quality as one of the upstream complexity drivers might negatively impact the productivity of an organization since the plant is not able to process 100% utilized because not all the input can be processed due to quality issues of the raw materials.

Mitigation strategies/coping mechanisms

Managers can directly deal with the upstream complexity drivers and/or they can use coping mechanisms to moderate the impact of upstream complexity on plant performance. Strategies and/or coping mechanisms to manage (the impact of) complexity is mentioned by several authors (de Leeuw et al., 2013; Hoole, 2005; Manuj & Sahin, 2011; Perona & Miragliotta, 2004; Serdarasan, 2013; Tang, 2006; Ter Horst, 2013), and are used in this paper as a source of inspiration for potential strategies/coping mechanisms to reduce or accommodate (the impact of) upstream complexity. The strategies are depicted in Table 3. Managers can adapt these strategies and/or use them as a source of inspiration, however the circumstances of the particular organization in a particular industry need to be taken into account (Chopra & Sodhi, 2004).

Strategies Description Sources

Cultural alignment Making the relationship stronger, bringing interests together (alignment of different interests).

De Leeuw et al. (2013); Gadde & Snehota (2000); Gimenez et al. (2012); Hoole (2005); Manuj & Sahin (2011); Perona & Miragliotta (2004); Serdarasan (2013); Skjott-Larsen, Schary, Mikkola, & Kotzab (2011); Stecke & Kumar (2009); Tang (2006); Ter Horst (2013) (supplier) Integration Bringing external processes together with

internal processes.

(supplier) Collaboration Interdependent relationships for the creation of mutually beneficial outcomes by working closely together. It is also about the development of partners.

Relationship management Managing a relationship on a continuum between adversarial relationship and close integration. The closer to integration, the more it is based on trust. Contractual agreements are part of this strategy.

Elimination of non-value-added steps Reduction of non-value-adding steps. Certifications are part of this strategy. Information sharing and systems The availability of accurate data by relevant

technologies and skilled staff. Monitoring and control, auditing are part of this strategy. Human cognitive abilities Abilities gained through experience, training

and/or consultancy.

(12)

Multiple suppliers Accessibility to a broader supply base, all suppliers are used at the same time.

Flexible supply base Addition of a supplier to the supply base, used only when necessary (alternative sourcing arrangement).

Economic supply incentives Stimulation by bonuses/penalties in terms of money.

Outsourcing Make-or-buy decision. Subcontracting a

party/specialized group.

Table 3: Potential strategies/coping mechanisms to deal with upstream complexity

Conceptual model

As discussed in the previous paragraphs, this research consist of the elements upstream complexity in the food processing industry, strategies/coping mechanisms to deal with (the impact of) upstream complexity and the relationship between upstream complexity and plant performance. This is combined in a conceptual model, depicted in Figure 1. Since it is known that upstream complexity has a negative impact on plant performance (Bozarth et al., 2009; Brandon-Jones et al., 2014), and variability related complexity drivers, added because of the food processing industry, generally (negatively) impact performance (Ter Horst, 2013), this study focusses on the upstream complexity drivers (in the food processing industry) and mitigation strategies/coping mechanisms which moderate the impact of these specific drivers and/or reduce the level of complexity directly. This is highlighted in Figure 1 by the bold circle, that also visualizes the research question: “How do food processing

companies manage (the impact of) upstream complexity?”

(13)

METHODOLOGY

Research design and case selection

To study the experienced upstream complexity and its related drivers in the food processing industry, as well as the strategies/coping mechanisms used to mitigate (the impact of) these upstream complexity drivers, a multiple case study is adopted. Multiple case studies are most suitable since the multiple cases allow researchers to study the phenomenon in its natural setting and generate relevant theory from the understanding gained through the observation of the phenomenon in practice. By doing it this way, researchers gain a wider range of data and in-depth insights (Karlsson, 2009). This paper uncovers areas for research and theory development since it is explorative; it explores the phenomenon in a specific context, the food processing industry. Since this study addresses upstream complexity on an organizational level in the food processing industry, the unit of analysis is the focal organization of a supply chain in the food processing industry. The scope of this paper will be the upstream complexity of the focal organizations.

In total, 11 different cases (A-K) are selected according to the characteristics of the food processing industry mentioned by Van Donk (2001). The 11 cases are depicted in Table 4. Cases are selected thought through; by selecting different types of organizations (variation in main products, main raw materials, markets) all operating in the food processing industry (See third column of Table 4). A variation in the sample is established, which is important because of the explorative character of the study.

Interviews Cases Main product(s) Turnover in terms of volume/money (annual) A1 A Flower Breading grains 180.000 Tons 20.000 Tons A2 B1 B Milk powder Butter 65 million liters C1 C Milk powder

Milk powder & baby food (packaging and adding vitamins)

52.000 Tons

130.000 Metric Tons C2

D1 D Milk powder (baby milk) and baby products (foods, toys, clothes and shampoo)

10 million cases

E1 E Native starch

Starch derivatives

600.000-650.000 Tons E2

F1 F Small chickens 78 million small

chickens F2

G1 G Fresh (chicken) meals/salads 1.35 billion Euros

G2 H1 H Frozen fries Flakes 95.000 Tons 5.000 Tons H2

I1 I Energy drinks, ginger ale, bitter lemon and tonic water 10-15 million cans

J1 J Cake, bread and pastry 50 million end products

K1 K Pet snacks (dogs and cats) 28.000 Tons

(14)

Data collection

Data are collected through semi-structured interviews taken between April 2015 and May 2015. In total, 11 different organizations operating in the food processing industry were contacted by the research group, consisting of five researchers. To validate the interviews, every organization was visited by couples, two members of the research group. At each organization, individual face-to-face interview(s) were organized (Table 4, first column ‘Interviews’). Interviews were chosen based on the perspective of the interviewee; one had a more downstream perspective and one had a more upstream/internal perspective. To improve the reliability of the study, an interview protocol and case study database were developed (Yin, 2014). All interviews began with general questions followed by four topics; organizational structure, the food processing industry, supply chain complexity and coping mechanisms (Appendix B). Also, a questionnaire was developed to measure the construct of upstream complexity (Appendix A). All included Likert scale questions were validated by using questions grounded in literature (construct validity, Appendix B). An interview took approximately one hour per interviewee. All interviews are recorded and transcribed. Internal triangulation is achieved by using literature, a questionnaire, different documents, and web sites of participating organizations.

Data organization and analysis

In total, data are gathered from 17 interviews, as depicted in Table 4. The data pool consist of data from the questionnaire (Appendix A), which measured the experienced upstream complexity drivers and data from the open questions, related to the answers on the different topics of the interview protocol (Appendix C) (by the use of open questions, strategies will be identified which are used to mitigate (the impact of) the experienced upstream complexity).

In order to analyze the data in a structured way, coding is used. Coding has been done for all the cases separately (Appendix D). First, the data was reduced by linking the quotes mentioned by the interviewees to the different dimensions of upstream complexity since these quotes were relevant (experienced upstream complexity and related drivers) to link at the end with specific strategies, according to the research question. This is called first-order coding. After that, the first-order codes are linked to the specific upstream complexity driver (descriptive second-order codes). By doing it this way, it is allowed to link specific strategies with specific complexity drivers. Those specific strategies are called third-order themes in the coding scheme (Appendix D/E). By doing it this way, this paper was able to link experienced upstream complexity drivers with specific strategies/coping mechanisms. In order to make it more measurable, a questionnaire was prepared. The answers on a scale from 1 (very low) to 5 (very high) are put into the coding scheme to give an impression of the upstream complexity level the individual cases experience.

(15)

managers, cope with specific, experienced upstream complexity drivers (in order to establish internal validity).

FINDINGS

Upstream complexity drivers

First, the findings regarding the level of upstream complexity, the extent to which specific drivers are experienced by the cases, are discussed. In Table 5 the upstream complexity drivers, explicitly mentioned by the cases (A-K) as being drivers of upstream complexity for their organization, are indicated by ‘+’. The first finding is that for most cases, except for case B and I, the added upstream complexity drivers due to the food processing industry characteristics seem to be the main upstream complexity drivers. Drivers/Cases A B C D E F G H I J K # of suppliers Cultures Capabilities Practices Reliability + + Lead times + + + Geographical spread + # of tiers Variation in supply + + + Variation in quality + + + + Variation in price + + + + + Perishability + + + +

Table 5: Upstream Complexity drivers experienced by cases (A-K)

Variation in supply drives upstream complexity at cases E, F and K. At case E they mentioned the uncertainty of the harvest in terms of amount which causes variation in supply: “The supply is

uncertain, everything is stable except for the harvest itself. The harvest varies from 500.000 – 600.000 Tons” (case E). At case F, the “rather fluctuating supplier market” (case F) drives upstream

complexity since every year they have to deal with extreme shortages for a certain period and they do not know where to get their eggs from. Also, at case K, it is mentioned that the availability of raw materials drives upstream complexity. This indirectly influences the price, another upstream complexity driver (variation in price); “I purchase materials which are not available at all time, it

affect ones security of supply and increases the price massively. Questions concerning the reliability of supply is our main challenge” (case K).

Variation in quality drives upstream complexity at cases A, F, G and H. This is supported by the following exemplary quotes: “Then we have uncertainty of the harvest, last year we got a harvest with

(16)

are a natural product. This makes it complex, as not one year is the same. It all depends on the growing season” (case H).

Variation in price drives upstream complexity at cases A, C, D, J and K. At case A, variation in price is influenced by the quality: “The price is dependent on the quality of the harvest. The price fluctuates

quite a lot between days” (case A). Case C mentioned that “The variation of the price of raw materials is very high” (case C), and at case D was indicated: “There is a large variability in price”

(case D), “The variety of prices of raw materials is high, which may increase the complexity since the

raw material price depends on the type of raw materials” (case J) and “For some raw materials, like meat products, fix prices are not common and the market are highly volatile” (case K).

Perishability drives complexity at case F, G, H and J. At case F, they mentioned ‘the need for’ inventory. However, due to perishability, “Keeping inventory is hard” (case F). The same holds at case G: “The best before date of raw materials makes the purchasing department reluctant to keep

inventories, this makes it complex” (case G). Also at case J, where they ‘complain’ about perishability: “Eggs are perishable. The shelf life is very short. Perishability influence the stock level of raw materials for products with seasonal demand” (case J). At case H, they have to deal with

perishability, caused by weather conditions: “At the supply-side company H has to deal with

perishability, in particular in the growing and 'fresh' period, after grubbing up the raw materials (potatoes), possibly with rot, in sweltering weather, rot can continue fast” (case H).

At both cases A and D, reliability of suppliers as well as supplier lead times drive upstream complexity. “Grain is always supplied by ship". The transportation by ships adds complexity. There

are many different casualties you can run in when transporting via ship” (case A). At case D, some

raw materials are imported from abroad; “lead time is nearly three months” (case D).

Particularly, lead times of suppliers drive upstream complexity at case G since “those lead times are

long. A chicken has a certain life cycle, it depends on the type of chicken. If I decide to sell blue chickens today, I have to go 40 weeks backwards since the upstream chain is that long. So the supplier lead time is very long” (case G). At case J, “Some raw materials need to be imported from abroad. For these raw materials, the lead time is nearly one month” (case J). At case K, “Speed plays a very important role for discounters. In most of the cases, raw materials are available faster than required packaging materials” (case K).

Geographical spread is mentioned at case K: “Another difficulty are foreign exchange rates, like the

current situation of EUR-US Dollar which is actually worse to purchase but good to sell goods” (case

(17)

Strategies/coping mechanisms

The experienced upstream complexity needs to be managed; strategies/coping mechanisms which deal with upstream complexity are indicated by cases. In Table 6 the strategies are linked to the cases, a ‘x’ highlight that a case uses a specific strategy to deal with upstream complexity.

Strategies/Cases A B C D E F G H I J K

Cultural alignment X

Integration X X X

Collaboration X X X X X X X X X

Relationship management X X X X X X X X X X X

Elimination of non-value-added steps X X

Information sharing and systems X X X X X X

Human cognitive abilities X X X

Buffers (inventory) X X X X X X

Buffers (extra capacity) X X

Multiple suppliers X X X X X X

Flexible supply base X X X X X X

Economic supply incentives (price incentives) X X X X X X

Outsourcing X X X

Segmentation X X X

Table 6: Coping mechanisms/strategies indicated by cases (A-K)

The first strategy to accommodate or reduce upstream complexity is managing relationships, although it is a broad term, relationship management, it is mentioned in all cases, see Table 6. Relationships can vary, however, it is more than simply “I pay and you deliver”. Somehow, it is more about the value of specific buyer-supplier relationship, “It is all about the relationship with the farmer which

has lasted for several years now” (case H).

At case E, a good distinction is made between relationships and partnerships, as it shows that there is a continuum from adversarial relationships to partnerships (Skjott-Larsen et al., 2011).

“We establish relationships, but no partnerships. It's more like a good neighbor. You want a good relationship with your neighbors, you behave nicely. However, your responsibilities are determined by yourself, without any obligations. However, it does not mean that I will always (every day) eat with them. I don't have got that obligation” (case E).

This quote is a good example of how relationships with suppliers can help to accommodate or even reduce upstream complexity. Taking certain expectations/needs into account, be valuable, however, at your own responsibility. So at the supply side, they know what you need, and behave nicely by doing their best to constantly supply what you expect, at their own initiative and responsibility.

A buyer-supplier relationship can be based on a contract. In such a contract the buyer and supplier agreed upon specifications.

(18)

contracting are possible. The farmer can also opt to not have any contract at all, then we do not have a relationship with that farmer” (case H).

These contracts could include price incentives, economic supply incentives, in order to stimulate the supplier. A good example is given by interviewee F2:

“When egg farms do their best, they receive a premium on their product. We try to stimulate our suppliers, they are the basis of the chain, and if that goes well then we all profit” (case F).

Another coping mechanism indicated is collaboration. When collaborating, you work actively together with the supplier(s) to create a win-win situation, and both partners are responsible for it. A clear example is mentioned by interviewee H2:

“It's more collaborating with a farmer than integration with a farmer”. “We are working hard together. Company H considers the growers as their business partners” (case H).

An aspect of supplier collaboration is supplier development. In such a case, the buyer is working together with the supplier by guiding and helping the supplier. This can be achieved by the use of representatives, most of the time specialists in the field they are representing.

“We deploy staff who assist our growers by providing them with advice about storing the potatoes, which pesticides to use or not to use, recommend about the barns to use, things where growers have to deal with. Those specialized field staff is called 'Agronomisten'. So basically it starts with recommendations about growing and storing the potatoes, the grow-guidance, you name it” (case H).

(Vertical) Integration is mentioned to be a mechanism to cope with upstream complexity. This is ‘a

step further’ than relationships and/or collaboration, it is about integrating upstream processes with your own processes. It is possible that upstream processes are taken in-house. By doing this, the focal company deals with upstream complexity drivers by having more control about the specific situation. The company structure of two of the cases is like this; those cases (C and E) are owned by the main upstream suppliers. An interviewee of case C mentioned:

“Since we are highly (upstream) vertically integrated, we know exactly where it comes from and that is really important. Traceability” (case C).

In order to manage and/or control (the flow of) raw materials, measures of current performance are needed. Therefore, sharing information needs to be done. To cope with upstream complexity,

information sharing and systems is used as a strategy:

(19)

you would receive a truckload of potatoes that was either good or bad, if it was bad it would be returned. But it was always uncertain, only when the raw materials arrived at the factory you would know what you were talking about. It is all about exchanging information, beforehand, during the harvest, about the length etc. Currently this is all put into the system (SAP) which is not yet 100% reliable, but we are getting closer to the truth” (case H).

A way of controlling and measuring suppliers is performing audits; measuring if suppliers still meet specifications where agreed upon in contracts. Therefore, information is needed about suppliers’ performance. At case B, a good example is given about measuring performance and related follow ups, supplier developments:

“There is a framework where the farmers should meet the requirements. I think Group X is very far with that, we have high requirements compared to competitors. At Group Y, the farmers do have workshops, which is not the case at Group X since there we only make a distinction based on measured values. Group X will do workshops in the future (case B). Multiple suppliers are also used as a strategy:

“We try to keep our supplier portfolio as big as possible to reduce risks. The more suppliers we have in our portfolio the better our position is to put a purchase order out for tender. It makes a difference whether I have 1, 3 or 10 suppliers (case K). “We have got a big network of suppliers” (case C) and “We spread our suppliers” (case C). Also interviewee A indicates that they make use of multiple

suppliers: “You buy from various regions. We do not want to be dependent on one region, so we buy

from Eastern-Germany, France/Belgium, and the USA” (case A).

Another form of sourcing, discussed as a strategy to cope with upstream complexity, is making use of a flexible supply base. Using this, there is a preferred supplier and always a backup supplier:

“We usually choose multiple suppliers and label them as type A and type B suppliers. Type A suppliers are the suppliers who provide raw materials regularly. When some crisis appears at type A suppliers, type B suppliers provide raw materials for us” (case D).

(20)

Cultural alignment is also mentioned during the interviews as a strategy. Interests can vary between

the buyer-supplier, however, in order to reduce the upstream complexity caused by these differences, cultures need to be aligned. For instance, by tours for suppliers at the customer, as indicated by interviewee C1: “We have company tours for farmers who are invited, to see what is going on here” (case C). Also, mentioned by interviewee G2: “We put a lot of effort into it. What we notice, which

makes sense, there are differences in experiences. It is also important for poultry farmers to visit our plant and to talk about issues we experience and how they can influence and help us solving those issues. At the end, those kind of things bring cultures together” (case G).

And then, there is outsourcing as a strategy to overcome upstream complexity. This is the opposite of integrating buyer-supplier processes; processes (responsibilities/control) will be ‘transferred’ to a specialist, mostly a third party. This is done at case B, H and I. Interviewee B mentioned “Group X

deals with the goat farmers, group Y deals with the cow farmers” (case B). Case H outsources a part

of the supply base, as they mention that “Other suppliers can be contacted through a third

organization, in that sense we do not buy directly from the supplier but through an agency” (case H).

And at case I, they mention that “once you finished your mixture for the production of the drink you

will hardly be in contact with the raw material supplier anymore. The co-packer needs to ensure that raw and packaging materials are available on time. The co-packer himself manages the demands of cans, raw materials and cardboard packaging which are topics of his responsibility. Our co-packer bears the entrepreneurial risk (case I).

Segmentation is also a strategy to deal with upstream complexity, since it enables an organization to

deal with raw materials which are not applicable for their own production process anymore. In that case, another market segment is used to sell the raw materials to. This has been mentioned by case A, B and C: “will be send away/sold off as cattle feed” (case A), “We deliver it to company Z as cattle

feed” (case B) and “… are sold to pig farmers, to another company of case C. We can earn money with it” (case C).

Mitigating (the impact of) upstream complexity drivers

In order to answer the research question, it is interesting to investigate and elaborate on the relationship between particular upstream complexity drivers and mitigation strategies/coping mechanisms. Since it is found that the cases mainly experience upstream complexity drivers which are context specific (added due to the food processing industry), this paper explicitly highlights the relationship between these specific upstream complexity drivers and ‘their’ related mitigation strategies, mentioned during the interviews (Appendix E and F).

Variation in quality versus collaboration

(21)

considers the growers as their business partners. By going along with suppliers, company H tries to increase the reliability” (case H). Also, “It is important that you cooperate with partners who can develop themselves with you, there are information sessions for farmers, which is going very far, it's a complete organization” (case C). “It is about understanding the quality, if you want to know that you have to be close to the supplier” (case A).

Variation in quality versus relationship management

Variation in quality is also mitigated by relationship management, especially due to the agreed upon contracts which include specifications about quality. Price incentives included in those contracts are used to stimulate suppliers to ‘produce at certain specification’, as quoted by case G: “We try to

stimulate to meet quality requirements by using price incentives” (case G). Also, case F states “We stimulate by price incentives so we can keep all very good farmers close to use” (case F) and by doing

that, they are able to cope with the variation in quality.

Variation in supply versus multiple suppliers

Variation in supply is mitigated by the use of multiple suppliers. “We have got a big network to secure

supply of raw materials” and “We spread our suppliers to not being dependent on one supplier” (case

C). Also case A indicated that they make use of multiple suppliers from multiple regions in order to secure the availability of raw materials: “You buy from various regions. You could also say: I will buy

everything from one region, but if it turns out that region had a bad summer, resulting in bad quality and a variation in supply, then you will have a problem. Therefore, we do not want to be dependent on one region, so we buy from Eastern-Germany, France/Belgium, and the USA (case A).

Variation in supply versus a flexible supply base

Variation in supply is also mitigated by the use of a flexible supply base. The use of flexible suppliers enables an organization to work with a preferred supplier, but to hedge against risks, an alternative supplier is ready to fall back on. This is highlighted by case D: “We usually choose multiple suppliers

and label them as type A and type B suppliers. Type A suppliers are the suppliers who provide raw materials regularly. When some crisis appears at type A suppliers, type B suppliers provide raw materials for us. This ensures the supply of raw materials stable” (case D). The same strategy to deal

with variation in supply is mentioned by case G: “That is a strategy to cope with variation in supply,

by having suppliers in backup” (case G).

Variation in price versus relationship management

Variation in price is mitigated by relationship management, in particular the contract both parties agreed upon. This is shown by cases G and H compared to cases A and K. At both cases, G and H, they use contracts to mitigate the variation in price: “By using contracts, we 'mute' this variation in

(22)

basis, half, many different ways of contracting are possible. The farmer can also opt to not have any contract at all, then we do not have a relationship with that farmer” (case H). Because of these

contracts, these cases do not experience variation in price. This is in contradiction with case A and K, where both cases ‘use’ the open market. It is assumed that the use of contracts (relationship management) is influenced by the overall business strategy. Both cases, A and K, are price driven. Which means that purchasing raw materials is about ‘getting the raw materials for the lowest possible price’. In that case, the open market is a feasible option, however, it brings more variation in price since the price of raw materials fluctuate a lot between days on the open market. Both cases, G and H, are not that price driven. Of course, they do not want to pay too much for their raw materials and have to keep certain budgets in mind, however, they also know that prices of raw materials fluctuate and do not want to squeeze out their suppliers; they agreed upon a constant price for their raw materials. In that case, this decreases the uncertainty caused by the open market and levels the variation in price.

Perishability versus segmentation

During the interviews, a strategy to deal with already obsoleted raw materials is mentioned: segmentation. The organizations try to manage the flow of raw materials in a way in which the raw materials do not become obsolete, however, when raw materials become obsolete, the raw materials can be used for other purposes. In most of the cases, the obsolete raw materials are sold as cattle feed, as quoted by case B: “If it becomes obsolete, we deliver it to company Z, cattle feed. We have got a

feed stream. That's not a small market, we earn money with it” (case B). Also, “Obsolete raw materials are sold to pig farmers, to another company of case C. We can earn money with it” (case C).

These examples show that there is a strategy to deal with obsolete raw materials; searching for other markets where the obsolete raw materials are still valuable and can be processed.

Perishability versus buffers

What emerges from the interviews is that due to perishability, inventory to cope with uncertainty (caused by different factors) is not applicable in all cases. In order to cope with perishability, organizations use different coping mechanisms/strategies. To deal with perishability, organizations use cooled storage facilities and/or pay for cooled storage at suppliers. Because of that, they are able to hold a certain amount of stock and which otherwise it not possible due to perishability. This is stated by case H: “Potatoes are stored at the growers, they have cooled barns. We need to store since the

dug up period causes peaks in supply but we need supplies during the whole year” (case H). At

another case, case C, “We invested in big towers to be able to continuously process (extra capacity)

all the supply of milk” (case C) and case H also mentioned close coordination with their suppliers as

well as with the transportation company: “The growers dug up the potatoes only when company H

(23)

DISCUSSION

Upstream complexity drivers

All drivers, according to the literature about upstream complexity, are discussed during the interviews to answer the research question of how focal organization in the food processing industry deal with upstream complexity. This paper took the drivers mentioned by (Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014) into account; number of direct suppliers, cultures, practices, capabilities of suppliers, geographical spread and number of tiers (position in the supply chain). However, it turns out in the findings of this paper, that those upstream complexity drivers are hardly discussed (and experienced as drivers of upstream complexity) in the specific context of the food processing industry. According to these papers (Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014), the more direct suppliers, the more upstream complexity. However, this does not necessarily count for organizations in the food processing industry since at most cases a large number of suppliers are needed to deal with the required input of raw materials. Those organizations cannot rely on a few suppliers since one supplier is not that big in size to ‘produce’ the requirements. Also, the differences in cultures, practices and capabilities between suppliers and the focal organization as well as the geographical spread of suppliers are hardly discussed as being upstream complexity drivers in the food processing industry. Those drivers are added due to the globalization of supply chains (Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014). However, it turns out that the supply side, the upstream partners (suppliers) in a supply chain in the food processing industry are not that globalized compared to suppliers in other industries and so there are not many differences in cultures, practices and capabilities as well as taxes and languages. Suppliers in the food processing industry are also bound to a specific region due to climate and grow circumstances, so there is no high dispersion. Because of this, the appropriateness of these drivers in order to determine the upstream complexity level in the food processing industry should be discussed. Also, the position in the supply chain (number of upstream tiers) is hardly discussed as being an upstream complexity driver in the food processing industry. This is due to the fact that most focal organizations in the food processing supply chain, are more close to the upstream side of the supply chain. There are not much upstream tiers, and so they are positioned more upstream than downstream. Because of that, this driver is not experienced as a complexity driver in the food processing industry.

Strategies/coping mechanisms

Multiple suppliers as a coping mechanism

(24)

2) most harvests differ between years in terms of quality and supply, and by using multiple suppliers this variation is leveled and made more constant in order to be able to get what is demanded. In contrast to the general believe that organizations do business with multiple suppliers to get a price advantage (Yu, Zeng, & Zhao, 2009) is the food processing industry it is found that multiple suppliers are used to secure supply (Zhaohui Zeng, 2000) and to deal with variation in price as well as the variation in supply.

Flexible supply base as a coping mechanism

Another coping mechanism used by organizations is a flexible supply base. During the interviews, the use of multiple suppliers and a flexible supply base as strategies are mixed up. However, there is a difference between the both strategies; when using a flexible supply base, there is a backup supplier who is able to supply when necessary if the preferred supplier is not able to (Tang, 2006). It is not clear when multiple suppliers and/or a flexible supply base is used, however, it is assumed that a flexible supply base is used as a risk strategy to hedge against a variation in supply since, in normal conditions, the preferred supplier is able to supply the demanded input of raw materials. It is also assumed that multiple suppliers are used as a strategy when it is clear that one supplier cannot supply what is needed. However, the focal organization still ‘uses’ multiple suppliers, they should be actively search for as well, which requires coordination and time, what makes it more complex (Bozarth et al., 2009). So, in literature it is argued that upstream complexity increases due to the use of multiple suppliers (number of direct suppliers) and a flexible supply base; in the food processing industry, the use of multiple suppliers and a flexible supply base is used to cope with the upstream complexity driver variation in supply.

Inventory

Due to the perishability of raw materials, most focal organizations in the food processing industry are not able to put a certain amount of raw materials on stock. However, inventory is mentioned in literature as being a coping mechanism to deal with complexity (de Leeuw et al., 2013; Manuj & Sahin, 2011). Inventory helps to overcome unreliable suppliers, as well as long supplier lead times (de Leeuw et al., 2013). This is only applicable to a certain level for organizations in the food processing industry, and depends on the perishability of the raw materials they use as a input. To avoid raw material inventory and thus the possibility for obsolete input, organizations in the food processing industry are expected to use lean-tools as JIT since the main objective of lean is to eliminate waste by reducing or minimizing for example supplier variability (Bozarth et al., 2009), upstream complexity drivers.

Outsourcing

(25)

linkages as being complex; there is dealt with upstream complexity for them. This is in contrast to what is mentioned in the literature: outsourcing is not a feasible option in the food processing industry because of the high safety and health concerns, it should be kept in-house. In-house, these issues can be controlled more and better (Mehrotra, Dawande, Gavirneni, Demirci, & Tayur, 2011). The contradiction can be explained by the use of certifications. These certifications represent strict rules and regulations and are allocated by institutions who manage and control that. In that sense, the high safety rules and health concerns are dealt with and processes can be outsourced. However, this is an assumption and should be elaborated on further in research.

CONCLUSION

To conclude, an answer on the research question should be given: how do food processing companies

manage upstream complexity? It depends on the specific drivers which drive the upstream complexity.

In the food processing industry, variation in supply, price and quality as well as perishability drive upstream complexity. In order to manage the variation in quality, an organization needs to collaborate, work together, with its suppliers as well as use price incentives in contracts. Multiple suppliers and/or a flexible supply base are needed to cope with the variation in supply and contracts need to be agreed upon to deal with variation in price. Perishability is dealt with by extra (cooled) capacity, as well as segmentation of markets were obsolete raw materials could be sold to.

Theoretical implications

(26)

Managerial implications

The findings of this paper offer managers’ guidance of how to manage upstream complexity in the food processing industry. For example, there is more than simply ‘I pay and you deliver’, a specific buyer-supplier relationship including contracts could be valuable to cope with variation in price, as it is an upstream complexity driver in the food processing industry. Although there are a variety of relationships, they can help to deal with uncertainty caused by the variation in farmers’/suppliers’ yield. Another suggested strategy is collaboration and its underlying mechanism development to overcome variation in quality. By collaborating and developing, quality could be enhanced and a win-win situation is created. To cope with variation in supply, this paper suggests to not rely on a single supplier. However, as shown in this paper, managers should always take their particular context into consideration since one strategy is more applicable than another for organizations within the food processing industry. For example, inventory versus the perishability of raw materials.

Limitations and further research

The interviews were taken by different couples of two researchers from the research group. This is a limitation, since the questions of the interview protocol are approached differently per couple/person. This has an impact on the usability of the data in the case study database.

As indicated in the theoretical section of this paper, four upstream complexity drivers are added to the current literature about upstream complexity drivers due to the specific context wherein this research took place, the food processing industry. In the discussion section it is pointed out that only the added upstream complexity drivers are experienced as upstream complexity drivers. This leads to a limitation of this research, since this paper cannot say with certainty if all the necessary, important upstream complexity drivers are used in order to define/measure the upstream complexity construct in the food processing industry. Therefore, a suggestion for further research is to explore if upstream complexity drivers are missing in this research and what all the upstream complexity drivers are in the specific context of the food processing industry.

This study investigated how the upstream complexity drivers should be managed by linking the upstream complexity drivers with specific management strategies/coping mechanisms. Concerning the previous limitation, the possible incompleteness of upstream complexity drivers, another further research project could be to determine other/more coping mechanisms to deal with those upstream complexity drivers.

(27)

(28)

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, 27: 78– 93.

Brandon-Jones, E., Squire, B., & van Rossenberg, Y. G. T. 2014. The impact of supply base complexity on disruptions and performance: the moderating effects of slack and visibility.

International Journal of Production Research, 1–17.

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: 637–652. Chopra, S., & Sodhi, M. S. 2004. Managing Risk To Avoid Supply-Chain Breakdown Supply-Chain

Breakdown. MITSloan Management Review, 46(1): 53–61.

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.

Gadde, L.-E., & Snehota, I. 2000. Making the Most of Supplier Relationships. Industrial Marketing

Management, 29(4): 305–316.

Georgiadis, P., Vlachos, D., & Iakovou, E. 2005. A system dynamics modeling framework for the strategic supply chain management of food chains. Journal of Food Engineering, 70: 351–364. Gimenez, C., Van Der Vaart, T., Pieter Van Donk, D., Van Donk, D. P., Huang, M.-C., et al. 2012.

Supply chain integration and performance: the moderating effect of supply complexity.

International Journal of Operations & Production Management, 32(5): 583–610.

Hoole, R. 2005. Five ways to simplify your supply chain. Supply Chain Management: An

International Journal, 10(1): 3–6.

Isik, F. 2010. An entropy-based approach for measuring complexity in supply chains. International

Journal of Production Research, 48(12): 3681–3696.

Karlsson, C. 2009. Researching Operations Management.

Manuj, I., & Sahin, F. 2011. A model of supply chain and supply chain decision-making complexity.

International Journal of Physical Distribution & Logistics Management, 41(5): 511–549.

Mehrotra, M., Dawande, M., Gavirneni, S., Demirci, M., & Tayur, S. 2011. Production planning with patterns: a problem from processed food manufacturing. Operations Research, 59(2): 267–282. Perona, M., & Miragliotta, G. 2004. Complexity management and supply chain performance

assessment. A field study and a conceptual framework. Int. J. Production Economics, 90: 103– 115.

Serdarasan, S. 2013. A review of supply chain complexity drivers. Computers and Industrial

(29)

Skjott-Larsen, T., Schary, P. B., Mikkola, J. H., & Kotzab, H. 2011. Managing the Global Supply

Chain.

Stecke, K. E., & Kumar, S. 2009. Sources of Supply Chain Disruptions, Factors That Breed Vulnerability, and Mitigating Strategies. Journal of Marketing Channels, 16(3): 193–226. Tang, C. 2006. Robust strategies for mitigating supply chain disruptions. International Journal of

Logistics, 9(1): 33–45.

Ter Horst, E. C. 2013. The relation between complexity and performance in the food processing

industry - a literature review (1998-2013).

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, 69: 297–306.

Wagner, S. M., & Bode, C. 2014. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. J. Operations Manage. Journal of Operations

Management, 1–14.

Wilding, R. 1998. The supply chain complexity triangle. Journal of Physical Distribution & Logistics

Management, 28(8): 599–616.

Yin, R. K. 2014. Case Study Research (5th ed.). SAGE Publications.

Yu, H., Zeng, A. Z., & Zhao, L. 2009. Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37(4): 788–800.

Zhaohui Zeng, A. 2000. A synthetic study of sourcing strategies. Industrial Management & Data

Systems, 100(2): 219–226.

(30)

APPENDICES

Appendix A, Questionnaire

Upstream Complexity

How many direct suppliers does the plant have?

Differentiation: suppliers are (1) very similar to (5) very dissimilar compared to our own organization in

Very similar Similar Neutral Dissimilar Very Dissimilar

1. Culture m m m m m 2. Practices m m m m m 3. Capabilities m m m m m

Delivery, lead times

Very short Short Neutral Long Very long

Average lead times m m m m m

Can you give a percentage of on-‐time deliveries?

Geographical dispersion

Very close Close Neutral Dispersed Very dispersed

Suppliers are located m m m m m

Position

Far

upstream

Upstream Neutral Downstream Far downstream

In the supply chain, the

organization is located m m m m m

(31)

Appendix B, Operationalization of FPI upstream complexity

Driver Variable Values/Range Source

# of suppliers Number of direct suppliers in the supply base.

The number of direct suppliers in the supply base:

How many direct suppliers does the plant have?

(Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014) # of tiers The position of the

organization in the supply chain.

The supply chain position, from far upstream (1) to far downstream (5) on a Likert scale.

(Wagner & Bode, 2014)

Deliver reliability The deliver reliability of suppliers. % of on-time delivery. Reliability from (1) very reliable to (5) very unreliable. (Bozarth et al., 2009; Brandon-Jones et al., 2014)

Lead times The lead time of

suppliers.

Average lead time from (1) very short to (5) very long.

(Bozarth et al., 2009; Brandon-Jones et al., 2014)

Cultures Degree of different

characteristics such as organizational

cultures, operational practices and technical capabilities.

Suppliers are (1) very similar to (5) very dissimilar.

(Brandon-Jones et al., 2014; Choi & Krause, 2006) Practices Capabilities Geographical dispersion Geographical spread of the supply base.

Suppliers are (1) located very close to (5) very dispersed.

(Bozarth et al., 2009; Brandon-Jones et al., 2014; Choi & Krause, 2006; Wagner & Bode, 2014)

Perishability Risk of obsolesce from

(1) very low to (5) very high. (Bozarth et al., 2009; Georgiadis et al., 2005; Van Donk, 2001; Wilding, 1998)

Variation in supply Variation in supply

from (1) very low to (5) very high.

Variation in quality Variation in quality

from (1) very low to (5) very high.

Variation in price Variation in price from

(32)

Appendix C, Interview protocol

Interview: xx-xx-2015 Place: Opening

• Introduction of interviewer and interviewee • Confidentiality assurance

• Permission to audiotape •

Overview – purpose of the study

Structure of the Interview:

• Questions over (a) general information, (b) how you experience certain dimensions of supply chain complexity, (c) strategies to handle complexity, (d) the food processing industry, and (e) organisational structure.

faculty of economics and business

department of operations

HOW TO MANAGE UPSTREAM COMPLEXITY IN THE FOOD PROCESSING INDUSTRY