Capacity Coordination in Combined MTO-MTS Systems: A Multiple Case Study in Food Processing

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Capacity Coordination in Combined MTO-MTS

Systems: A Multiple Case Study in Food Processing

MSc. Technology and Operations Management

Master’s Thesis

Rick Cramer

S4156439

First supervisor : Dr. O.A. Kilic

Second supervisor : Prof. Dr. D.P. van Donk

Word count : 11701

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Abstract

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Preface

This thesis is the final work of my Master’s degree in Technology and Operations Management. First of all, I would like to thank my supervisors Prof. Dr. van Donk and Dr. Kilic for all their assistance during the process of writing the thesis. They provided valuable feedback during the process and were always prepared to answer questions and organize regular meetings to improve the quality of my work. This hold especially in the beginning where we had meetings every week to make sure we had a sound and justified research proposal. Their help was indispensable in obtaining the academic level of this Master’s thesis. I would also like to thank my fellow students Jacob Berger and Mathijs Zandberg for providing feedback on my thesis and assisting with analysing the interviews and results.

Furthermore, I would like to express my gratitude to all companies who were willing to cooperate with the research and devoted their time and resources to it. Without their input, it would not be possible to conduct this research and finish my Master’s program. Their input allowed me to see how companies take decisions which provided valuable insights from practice in addition to the theory. This together gave me a good impression of how the industry works which I found very interesting to learn about.

Lastly, I would like to thank all my family and friends who supported me during the process of writing my thesis and helping me to improve my thesis.

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1. Introduction

Companies in the food processing industry increasingly make use of a combination of Make-to-stock (MTS) and Make-to-order (MTO) (Soman, et al., 2002; Wang, et al., 2018; Cao, et al., 2018; Nagib, et al., 2016). MTO and MTS products have different planning approaches. Moreover, the food processing industry has specific characteristics (compared to discrete production companies) that further complicates capacity coordination between MTO and MTS (van Donk, 2001). For instance, long and costly set-ups necessitate larger batch sizes, which in turn reduce flexibility in capacity coordination. On the other hand, perishability calls for lower batch sizes as there is increased risk of obsolescence.

Capacity coordination between MTO and MTS products entails deciding on how much capacity is devoted to MTO and MTS products. It focuses on the mid-term planning which ranges from a few weeks to a month and will be defined as the process of decision making that results in setting inventory target levels for MTS products and order acceptance and due date for MTO products (Soman, et al., 2002; Youssef, et al., 2017; Wang, et al., 2018; Bortolini, et al., 2019). However, we need to understand how to arrive at a final decision and which factors affect the capacity coordination.

The majority of the literature states that MTO products should always be prioritized over MTS products, since MTO products by definition have a higher priority than MTS products (Youssef, et al., 2004; Gharehgozli, et al., 2008; Chang & Lu, 2010; Rafiei & Rabbani, 2012; Fernandes, et al., 2015). On the contrary, other literature suggests a policy to prioritize MTS products over MTO products (Carr & Duenyas, 2000; Iravani, et al., 2012). However, more recent literature state that both MTO and MTS should be included without merely prioritizing one over the other as it provides benefits such as lower costs and shorter delivery times (Romsdal, et al., 2013; Beemsterboer, et al., 2017a; Beemsterboer, et al., 2017b). This contradicts with the aforementioned literature. Besides, while it is stated that MTO and MTS both should be included in the capacity coordination decision, how to do so is not clearly established.

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7 models) and based on restrictive assumptions and simplifications. This is why empirical research is critical to increase our understanding. The main research question is: How do food processing companies make decisions in capacity coordination in an MTO-MTS system? By answering this question, we want to gain insights in the mechanism and factors influencing capacity coordination and (whether and if so) why there are differences between companies. We aim to find out how food processing companies deal with capacity constraints. That information indicates what factors they deem important when coordinating capacity. The main contribution of this paper is thus increasing our understanding on capacity coordination in the food processing industry that use a hybrid MTO-MTS system. Furthermore, we aim to resolve the contradictions of previous literature via an empirical study to see how companies make this decision in practice. Managers benefit from this study by understanding how to optimize capacity coordination between MTO and MTS products based on the factors that characterise their company. Since the findings are based on practice, managers can implement these findings without serious difficulties.

The paper is structured as follows. The MTO-MTS system is discussed in more detail. Furthermore, a literature review is conducted in which factors are discussed from other studies that influence capacity coordination decisions. In the third section, an overview of the methodology and data collection method used for this research is outlined. In the fourth section the results of the data collection will be presented and discussed. In the fifth section we will relate the findings back to the theory and in chapter six we will provide the overall conclusions. Lastly, we will state the limitations of this research and provide suggestions for further research.

2. Theoretical background

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2.1 Combined MTO-MTS systems

First, MTO and MTS is discussed after which we will delve into the combined MTO-MTS system. MTO products are only released to production after a customer places an order and its planning is based on the due date when the customer wants it to be delivered. MTS production is based on anticipated future demand and the planning is based on the stock levels (Youssef, et al., 2004; Motomiya & Arima, 2018; Bortolini, et al., 2019; Wang, et al., 2018; Youssef, et al., 2017; Hachicha, et al., 2010). This means that the MTS production planning’s main aim is to keep all stock levels above a pre-specified minimum in order to fulfil demand. The goal of MTO is to shorten the manufacturing time to improve the competitive advantage in the market (Tsubone, et al., 2002).

To reap the benefits of both the MTO and the MTS system, food processing companies implemented a hybrid MTO-MTS system. Products that are customer specific will be produced make-to-order whereas standard products with low variety are produced make-to-stock. Using a hybrid system allows food processing companies to exploit the advantages of both product types. Furthermore, a hybrid system enables production systems to better respond to changes in consumer and market demand (Romsdal, et al., 2013). To that end, it is essential to coordinate capacity in such a way that the MTS products meet the pre-specified inventory target levels and the MTO products are finished on time (before due date).

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9 Figure 1: Hierarchical approach to MTO–MTS problem (Soman et al., Combined make-to-order and make-to-stock in a food production system, 2002, p. 232)

2.2 Capacity coordination

The term “capacity coordination” has been used to reflect on production decisions in different contexts. In some studies it includes operational planning issues (Beemsterboer, et al., 2017a; Wang, et al., 2018; Vahdani, et al., 2017; Chang & Lu, 2010), whereas in some others it only includes tactical planning (Youssef, et al., 2017; Beemsterboer, et al., 2016; Beemsterboer, et al., 2017b; Iravani, et al., 2012). There are also studies where it captures a combination of these (Rafiei, et al., 2013; Tsubone, et al., 2002). It is yet clear that it does not include scheduling. In general terms, operational planning focusses on a shorter planning horizon ranging from days to a week. It aims at achieving the goals set by tactical planning which focusses on a few weeks to a month.

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2.3 Factors influencing capacity coordination in combined MTO-MTS

systems

In this section, we review the literature for factors that influence capacity coordination in hybrid MTO-MTS systems. We first discuss the factors that are influential for MTS and MTO products separately. The factors we discuss are mostly based on quantitative studies as there is not much empirical research on the subject. Hence, they are not based on empirical findings, but they represent what other researchers considered in their studies. Then, we focus on food processing industry specific characteristics, as they have direct ramifications on the capacity coordination.

2.3.1 MTS-related factors

Factors that influence how much capacity is devoted to different MTS products are all based on the product value, required service level and costs. The factors are presented in Table 1.

Factor Definition Paper

Holding costs

Cost of holding inventory during a certain period

(Liu, et al., 2006; Rafiei, et al., 2014; Beemsterboer, et al., 2016;

Beemsterboer, et al., 2017b; Youssef, et al., 2017; Vahdani, et al., 2017)

Profit contribution

The amount of profit that is earned on a single item or product group

(Huiskonen, et al., 2003; Ebadian, et al., 2009; Rafiei & Rabbani, 2012)

Potential future sales

The demand that is forecasted for coming the coming period

Service level The demand that is satisfied from finished

stock compared to total demand

(Rafiei, et al., 2013)

Backlog costs

Per unit time penalty of not being able to satisfy demand because workload exceeds capacity

(Wang, et al., 2012)

Table 1 MTS-related factors

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11 is, the more stock is needed to maintain this service level. Thus, a higher service level means more capacity will be devoted to these products. The fifth factor is the backlog cost. Backlog occurs when the workload is higher than the production capacity. This results in demand that cannot be satisfied and thus backlog occurs.

2.3.2 MTO-related factors

The factors that influence capacity coordination among MTO products are based on order characteristics. These factors are presented in Table 2.

Factor Definition Paper

Profit contribution

The amount of profit that is earned on a single item or product group

(Huiskonen, et al., 2003; Ebadian, et al., 2009; Rafiei & Rabbani, 2012; Rafiei, et al., 2013)

Future potential demand of a customer

The likelihood that a customer will purchase more products in the future

Order lot size Quantity of an item that is ordered (Huiskonen, et al., 2003; Ebadian, et al., 2009; Rafiei & Rabbani, 2012)

Customers’ order range

Number of different products ordered by the customer

Lead time Time until the product needs to be delivered to the customer

(Rafiei, et al., 2014; Youssef, et al., 2017)

Lateness costs Costs of delivering products to late (Beemsterboer, et al., 2016; Beemsterboer, et al., 2017a;

Beemsterboer, et al., 2017b; Liu, et al., 2006)

Value of product not produced in previous period

Product that should have been produced in the previous planning period, but which are not produced yet

(Rafiei, et al., 2014)

Table 2 MTO-related factors

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12 fourth factor is the customer’s order range. The wider the range, the less capacity is allocated to the order since it is more feasible to wait until other orders for that product are placed which means less set-ups are needed. The fifth factor is the lead time. The shorter the lead time of the product is, the more capacity needs to be allocated to finish it on time. This relates to the service level since the time it takes for the customer to get the product represents the service level. The sixth factor is the lateness cost. The higher the lateness costs are, the more capacity needs to be allocated to avoid these costs from incurring. It is possible that not all orders from previous periods are finished on time. The last factor is the value of the product that is not produced in the previous planning period. This refers to products that should have been finished in the previous planning period, but which have not been produced and are thus delayed. The higher the value of that product is, the more attractive it is to finish it as soon as possible, thus implying that capacity should first be allocated to that product.

2.3.3 Food processing specific characteristics

In his study, van Donk (2001) listed all characteristics specific to the food processing industry. For this research, we excluded that volumes and weights are used; some processes are homogeneous; some processes are labour intensive; there is a divergent product structure; and production rate is determined by capacity. These are excluded as they are not relevant for this research. Hence, we arrived at the factors presented in Table 3.

Plant characteristics

• Expensive and single purpose capacity • Long set-ups between product types Product characteristics

• Perishability • Variable quality

Production process characteristics • Variable processing times

• Several recipes are available to cope with uncertainty in pricing, quality and supply Table 3 Food processing characteristics

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13 directly influence how you coordinate capacity among MTO and MTS products, so it is not included in this research. The divergent product structure, as for the packaging stage, also does not impose a trade off in capacity coordination that is different than in other industries. Production rate is determined by capacity, but this is a result of how capacity is used, so it does not influence capacity coordination.

Expensive and single purpose capacity asks for long runs of the same product. For popular MTS products, lot sizes are bigger which results in longer runs. There is small product variety and high volume which makes it suitable for producing long runs. However, customers increasingly ask for product variety and shorter delivery times. Hence, the MTO products are customer specific and are produced in smaller quantities. Besides, quantity and timing of demand is uncertain (Soman, et al., 2007). This means that long runs are not suitable for MTO products. To maintain low costs, capacity should be coordinated well to use the expensive and single purpose capacity optimally.

Furthermore, long and costly set-ups limit the freedom that food processing companies have in coordinating capacity. Companies use product group set-ups and minimum batch sizes to reduce costs. Products that share similar production characteristics are grouped into product groups that only need one set-up. This allows the food processing companies to process different products without the need for a set-up after every different product. The batch size determines how many products are made during one production run. This reduces the flexibility food processing companies have in coordinating capacity. When they choose a certain product to be produced, they need to produce the minimum batch size or produce the complete product group to maintain low costs.

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14 medium-term, then short-term scheduling will run into problems when they need more time since there is no extra capacity for it.

Perishability calls for low stock levels as they otherwise might become obsolete. In the best case a customer places an order when the product is finished. MTS products should thus be produced at a very specific level. This especially holds when the best before period is very short as there is a bigger risk of stock running obsolete.

2.4 Coordinating capacity between MTO and MTS

In this section we will review the models used in previous studies to investigate how trade-offs are made between MTO and MTS products. We present the approaches that are used and the results based on the study.

Rafiei et al. (2013) state that additional capacity should be assigned based on forecasts for (and placed) MTO orders to make sure high priority MTO orders can be produced. However, when the planning is made for MTO and MTS, MTO orders that cannot be produced should be rejected instead of adjusting the planning at the expense of MTS products. Capacity should only be increased if this is profitable. So, when coordinating between MTO and MTS, companies should first devote capacity to high-priority MTO orders (based on the criteria from Table 2) and after that to MTS orders. Priority is not only determined by profit, but also by lot sizes and product order range. The capacity assigned also includes the high priority orders that are expected to be placed by a customer in the coming period.

Beemsterboer et al. (2016) focus on minimizing costs. For MTS, holding costs per period are used and for MTO lateness costs are used. Lateness costs occur when an order is delivered too late. It should still be delivered, but a cost penalty is incurred. Findings of the study are that for high total demand MTO products should be prioritized to minimize costs. Low demand periods are less interesting since there is no trade-off on capacity coordination as there is sufficient capacity. What is interesting is that in periods where there is high demand and thus limited capacity, capacity should first be assigned to MTO products.

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15 Based on interviews conducted in previous years, whether companies produce private label or not influenced their decision making on capacity allocation. Sometimes, these products were considered to be less important than the brands. Possibly because a private label is not directly associated with the company who produces it. This could mean that capacity is first allocated to the brand products (which can be MTO and MTS) and only after that to private label products, implying that the own brand comes first.

For this research, we will place the above approaches within the context of the food processing industries to see whether these also apply there or other approaches are used. As discussed before, it is likely that approaches in the food processing industry differ because of the different characteristics of the food processing industry. To summarize the previous sections and place it in a more understandable context. A framework is provided in which the factors influencing capacity coordination are stated including the relationships among them. The framework can be found in Figure 2.

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16 The framework shows the factors influencing capacity coordination. The factors within MTO and MTS that are bold represent factors that are even more important in the food processing to consider as discussed. The bold lines represent relationships that are already discussed in this paper. However, we do not fully understand yet how capacity is coordinated between MTO and MTS in a hybrid system. The dotted line from the trade-off factors to capacity coordination represents this unknown relationship. Since we perform this research within the context of food processing, we also aim to find out how food processing characteristics affect what factors are used for coordinating capacity between MTO and MTS. This relationship is represented by the dotted line from food processing to the trade-off factors.

3. Methodology

The question this paper aims to answer is: How do food processing companies allocate available capacity in a hybrid MTO-MTS system? More importantly, we do not merely want to know how they prioritize within MTO and MTS products, but how they prioritize between MTO and MTS when coordinating capacity. We conducted a multiple case study which is suitable for asking how-questions and it provides an in-depth understanding of the topic studied. Furthermore, it allows us to find similarities and differences between companies and why these occur. This is important for understanding why companies make different decisions. Furthermore, it gives us insights into how decisions are taken in practice without assumptions and simplifications as in the models that are used in previous research. Via this we can check whether practice corresponds with literature or what gap can be observed between literature and what companies actually do.

3.1 Case selection

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17 via the internet and contacted them via e-mail or telephone. So, we used literal replication since all cases that are selected share the same characteristics, namely, that they all operate in the food processing industry. However, the companies themselves differ on other things such as size, MTO-MTS ratio and product variety (number of SKU’s). For more representative results, well known and established companies within the sector are included as well as somewhat smaller less known companies. Also, the food processing characteristics differ between companies. For instance, we chose companies who have products which have high perishability (less than a week) and low perishability (more than a year). Lastly, we included companies with varying number of SKU’s to see how this impacts capacity coordination. We tried to find as much information as possible on these criteria beforehand to have a representative sample. For convenience in terms of contact and traveling, all companies that are contacted have a production plant in the Netherlands. In Table 4 an overview of the selected cases can be found. The criteria used to determine whether something is low or high can be found in Table 5. Cases A until E are contacted for this research specific. All other cases are interviews performed by students in previous years which means that the case selection criteria differ for those cases.

Companies Industry %MTO/MTS Size Perishability SKU’s

Case A Meat

replacements

60%/40% Medium/large High Low

Case B Brewing company 33%/66% Large Medium Low/medium

Case C Candy

manufacturer

15%/85% Small/medium Low High

Case D Gingerbread

manufacturer

10%/90% Medium Medium/high Low

Case E Rye bread

manufacturer

0%/100% Small Medium Low

Case F Meat processing 1%/99% Medium Medium/high Medium

Case G Bread

replacements

10%/90% Large Medium/high Medium/high

Case H Dairy 20%/80% Large High Medium

Case I Cheese and Tapas 0%/100% Medium/large Medium/high High

Case J Dairy 40%/60% Medium/large Medium/high Medium/high

Case K Mustard

manufacturer

60%/40% Small Low Low/medium

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3.2 Data collection

For the data collection method, we used semi-structured interviews (the interview protocol can be found in Appendix A). Interviews allow for follow up and in-depth questions that increases the understanding of how companies make the decisions. However, data is also used from interviews conducted by MSc students from the University of Groningen in previous years who also focussed on capacity coordination in hybrid MTO-MTS systems in the food processing industry. The scores in Table 5 are based on their scores. Furthermore, the scores are determined in collaboration with two other MSc students to adjust for personal bias. The interviews are conducted with employees who are responsible for the planning. These can be planners but also operations, supply chain or production managers. These are the employees who decide on capacity coordination, so they are most suited to answer questions on the topic.

Size employees Perishability Product variability (SKU) Required delivery reliability Required delivery time Process flexibility

1 0-99 small 1 year or more 0-99 <95% >96 hours low

2 100 -199 small/medium 6 months - 1year 100-199 95%-96% 72h-96h medium-low 3 200-299 medium 2-6 months 200-299 96%-97% 48h-72h medium 4 300-399 medium/large 7 days- 2 months 300-399 97%-98% 24h-48h medium-high 5 400 or more large

1-7 days 400 or more >98% <24 h high

Table 5 Allocation of scores

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19 the questions in the interview protocol we could determine what criteria the companies use when allocating capacity and see whether it fits with the factors that are found in the literature or whether they differ. Furthermore, questions are asked about food characteristics to see whether differences in criteria used can be explained by the food processing industry characteristics. Table 4 provides an overview of the selected cases and their industry. Table 6 contains information on the interviews that are conducted.

Companies Number of interviewees

Position of interviewee Sources of information

Length of interview

Case A 1 Continuous improvement

manager

Interview 100 minutes

Case B 1 Tactical production planner Interview 90 minutes

Case C 1 Supply chain manager Interview 70 minutes

Case D 2 Manager production &

Production planner

Case E 1 Managing director Interview 45 minutes

Case F 1 Production planner Interview 50 minutes

Case G 1 Trainee operations Interview 60 minutes

Case H 1 Supply chain manager Interview 70 minutes

Case I 1 Manager planning & logistics and team lead planning

Case J 1 Supply chain network

planning manager

Case K 1 Planner / company director Interview 60 minutes

Table 6 Information on interviews conducted

3.3 Data analysis

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20 detailed topics. These topics are then converted to more general categories. However, new categories (for the trade-off between MTO and MTS) emerged during the interview, so we used inductive coding as well. The interview data is divided into company characteristics and decision-making characteristics. A list of all codes can be found in Appendix B. An example of the code tree can be found in Table 7. The most codes refer to the MTS or trade-off factors since only 2 cases assigned capacity to MTO first.

Case Code Quote from interview

A (MTS) Holding

costs of inventory

Yes, of course they are important, every pallet is one. But those are not used that much for the decision since other factors have a higher weight than holding costs

A (Trade-off)

Shortest due date first

If I have a product that needs to be delivered in 30 days, but production takes 2 days, I will not produce that product already. I will start production only a few days before it needs to be delivered.

J (MTS) Profit

contribution of the product

Yes, the customers where we have the highest margins are sometimes given preference when it is very busy

F (MTS) Backlog

costs

In the beginning of the week I look at what products are lowest on stock, those need to be produced first (otherwise backlog costs occur)

A (MTS) Capacity

allocation first to customers with highest SLA

We distinguish between clients based on their service levels. In general we deliver all, but if we need to make choices then we will do that based on the service levels. Customers with the highest service levels are getting the products delivered first and customers with a low service level later if possible. The service level of a client is a leading criteria for where capacity is assigned to first.

A (Trade-off)

Minimal costs

But the customer’s service level is always leading, not the flow of the production process or minimal costs of production

A (MTS) Capacity

allocation first to MTS, penalty for non-delivery

The chain is oriented on supermarkets who give high penalties if products are delivered too late. The products with these strict contracts are mainly MTS products. With MTO products you are more flexible. We have standard delivery times but if it will take longer we will consult with the customer

D (MTS) Service

level

The delivery performance should stay high and is currently 99%-99.5%

K (MTO)

Required lead time

If the customer wants is faster than we consult with the planners, often it is possible because we create capacity for that order at the expense of other products

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4. Results

In this section we will provide an overview of all cases and the corresponding characteristics which can be found in Table 8. An overview of the scores and how they are allocated can be found in Table 5. Furthermore, the overview contains decisions companies make in terms of planning and capacity allocation. Bases on the results, different clusters emerged. The clusters are organized based on the most important criteria that is used when allocating capacity. The clusters are named after this leading criteria. If companies take the SLA of the customers as a leading criteria and allocate capacity first to the products which are tied to customers with the highest SLA, the cluster is named accordingly. Four clusters emerged based on how capacity is allocated. These are Highest SLA first, Lowest stock first, Highest profit margin first and MTO first. In the next subsections we will discuss the clusters in more detail. For every cluster we will discuss how they coordinate capacity within MTS and MTO, and how they coordinate capacity between MTO-MTS.

4.1 Cluster 1: Highest SLA first

The first cluster that we identified consists of cases A, B, C, G and H. These companies first allocate capacity to products that are tied to customers with high Service Level Agreements (SLA’s). This can also be seen by the high required delivery reliability and the low required delivery time that they have to uphold.

4.1.1 Capacity coordination within MTS products

Service level is de most important criteria when capacity is allocated within the MTS products. Case C groups customers based on how important they are to them, “The customers

are grouped into three categories, gold, platina and silver. The highest category always has priority. Then we will increase production for those customers at the expense of customers from the lowest category.” Products that are tied to these customers are always MTS in order to fulfil

demand fast. In the SLA it is stated that these products should be delivered within 48 hours. This makes it impossible to classify the product as MTO since the production rate is often not high enough to deliver in time.

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Selected cases Case A Case B Case C Case D Case E Case F Case G Case H Case I Case J Case K %MTO/MTS 60%/40% 33%/66% 15%/85% 10%/90% 0%/100% 1%/99% 10%/90% 20%/80% 0%/100% 40%/60% 60%/40%

Size (employees) Medium/large Large Small/medium Medium Small Medium Large Large Medium/large Medium/large Small

Perishability High Medium Low Medium/high Medium Medium/high Medium/high High Medium/high Medium/high Low

Product

variability Low Low/medium High Low Low Medium Medium/high Medium High Medium/high Low/medium

Required delivery

reliability High High High High Low High High High High Low High

Required

delivery time Medium/high High Medium/high Medium Low Medium/high Medium/high Medium/high Low High Low

Process

flexibility High Low/medium Medium Low/medium Low Low/medium Medium/high Low/medium Medium/high Medium Low/medium

Private label Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes

Planning

timeline Short Long Medium/long Medium/long - Medium Medium/long Medium Long Medium Short/medium

Product groups based on Product/ process Product/ process Product/ process Product/ process - Product/ process Product/ process Product/ process Product/ process Product/ process Product/ process Specificity of

lines Flexible Specific

Some are

flexible Specific Specific Specific Flexible Specific Flexible

Some are flexible Specific Bottleneck Packing/ employees Production/ employees Packing/ machines Packing/ machines Production/ machines Production/ machines Production/ machines Production/ employees Production/ machines Production/ machines Production/ employees Capacity limitations Buffers, extra shifts, outsourcing Buffers, extra shifts Extra shifts, outsourcing Buffers, extra shifts Buffers Buffers, extra shifts, outsourcing Buffers, extra shifts Buffers, extra shifts Buffers, extra shifts Buffers Buffers, extra shifts Capacity allocation Highest SLA first Highest SLA first Highest SLA first First MTO orders Highest profit margin/ Highest SLA first Products with lowest stock first Highest SLA first Highest SLA first Products with lowest stock first Highest profit margin First MTO orders Capacity allocation first

MTO-MTS MTS MTS MTS MTO MTS MTS MTS MTS MTS MTS MTO

Most important characteristics for capacity allocation Market/ customer Market/ customer Market/ customer Market/

customer Product/market Product

Market/ customer

Market/

customer Product/market Product

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Some supermarkets even give their own forecast to assist in planning as mentioned in case B

“Every week we get a new forecast from the customers which we process in our planning.”

Sometimes the financial liability of not buying according to the forecast the customer gave is for the customer “If they give us an forecast and we already made commitments to our

suppliers, they (the customers) are responsible for it.” 4.1.2 Capacity coordination within MTO products

Within the MTO products, the most important criteria is the required lead time. Often, the companies have standard lead times for MTO products as in Case A “For MTO products, we

have standard delivery times per product.” This means that the products that are for the order

with the shortest due date are getting assigned capacity first. If orders cannot be fulfilled on time, the companies consult with the customers if it is possible to deliver the order later as Case A mentioned “If we cannot deliver the order in time we consult with the customers to come to

a solution.” Since these products are MTO there are no buffers to hedge against the risk of late

delivery. If a customer already had delayed orders in the past, capacity will first be allocated to the products for that customers to make sure that the next order is delivered on time.

4.1.3 Capacity coordination between MTO-MTS products

The most important criteria that is used when coordinating capacity between MTO and MTS products is the strictness of the SLA as mentioned in Case C “The customers with the highest

service level always have priority.” The companies in this cluster are well known in their

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24 company (Case B) operates in is very stable in terms of demand. The more stable markets have longer planning timelines as opposed to the less stable markets. These differences however seem to be not of influence on the way they allocate capacity. Furthermore, they all use buffers/safety stock for MTS products to make sure that they can still uphold the high SLA if production problems occur which lower yield. In case that this is not sufficient, they make use of extra shifts to increase the output and fulfil the demand. Case A deviates from this as it has a high percentage of MTO products. It compensates for this by having flexible lines which makes it possible to more easily change the planning to make sure the MTO products can also be delivered on time. between the long- and short-term planners is thus limited.

4.2 Cluster 2: Lowest stock first

The second cluster consists of cases F and I. These companies first allocate capacity to the products that are the lowest in stock or have the fastest run-out time. It is similar to Cluster 1. The similarity is that they allocate capacity to the products that are lowest on stock and are tied to customers with high SLA’s. The difference between these clusters is that cases F and I do not prioritize between the customers with high SLA’s.

The most important factor for the MTS products is the backlog costs that occur when products are out of stock. Companies in this cluster allocate capacity based on the stock level of all products rather than on the stock level of the products for the customers with the highest SLA. Case I said “At the beginning of the week I look at what products are lowest on stock,

those are going to be produced first.” However, they have a high delivery reliability, but since

customers are equally important, they allocate capacity based on stock levels to satisfy all customers. Since there is less focus on specific customers the capacity allocation decision is based on product characteristics, namely, the stock level of products.

4.2.2 Capacity coordination within MTO products

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The most important criteria for coordinating capacity between MTO and MTS is the stock level of the products. The only products that have stock are MTS which means that these companies allocate capacity first to MTS products. These companies have a low percentage of MTO products (Case I actually does not produce MTS in general). Since the MTO products represent a low percentage, it is not needed to take this into account in planning. Since they use buffers and extra shifts when needed, they are able to fulfil the low MTO demand when orders come in. Because of this stronger focus on MTS products they can forecast better which allows them to keep all stocks above zero rather than those for the most important customers.

4.3 Cluster 3: Highest profit margin first

The third cluster consists of cases E and J. The companies allocate capacity first to the products which have the highest profit margin. The companies differ more on the characteristics as presented in Table 8 as in other clusters which makes the results less generalizable.

Within MTS products, capacity is first allocated to the products with the highest profit margin. Case E said, “Capacity goes first to the products which have the highest profit margin.” These companies have less strict SLA’s which means that their capacity allocation is not based on market characteristics as seen in other clusters. Because of this they can fully focus on maximizing profit and based on that they allocate capacity. Products on which the highest profit margin is earned are getting assigned capacity first.

Only company J uses a hybrid MTO-MTS system. However, they forecast the MTO orders that will come in based on historical data. This forecast is often accurate which allows them to threat MTO products as MTS (in the case referred to as Make-to-Forecast). “You want to follow

the forecast as much as possible and sometimes this is a point of discussion because orders deviate from the forecast. But, in general the forecasts are accurate.” They reserve space for

the forecasted incoming order to make sure it if finished on time. The main criteria they thus use is required delivery time.

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26 products are classified as MTS. Capacity is thus first allocated to MTS products. this can be explained by the fact that they have less strict SLA’s and thus the consequences of non-delivery are smaller. Since they do not have to uphold strict SLA’s they can focus on the profit that is earned on products.

4.4 Cluster 4: MTO first

The fourth cluster consists of cases D and K which allocate capacity to MTO products first. This is striking since it contradicts with all other cases who first allocate capacity to MTS products. However, this can be explained by the low flexibility which characterises their production.

Within the MTS products capacity is first allocated to the products which are tied to the customers with the highest SLA as in Cluster 1. As Case D stated, ”The delivery performance

should stay high, currently it is 99% to 99.5%.” What case D and K have in common is that

they have a relatively high stock of MTS products, more than other cases. They thus make use of large buffers/safety stock. The reason they do this is that they also face penalties in case of non or late delivery for MTS products which means that the backlog costs is also an important criteria when allocating capacity.

Since they want to deliver all orders on time, they allocate capacity to the products which have the shortest delivery time. They have to take into account the variability in yield since this has implications on how much you produce. Case D mentioned “We work with agricultural

products, so you never know how many (end)products you get exactly.” This means that they

have to reserve some capacity if more production is needed because of lower yield.

Because they have such high safety stock of MTS products, they can allocate capacity first to MTO products. However, they did state that it is more important to deliver MTS products on time than MTO products. For cases D and K, they first have to allocate capacity to MTO products to fulfil total demand as opposed to other clusters where MTS products are produced first. The reason they do this becomes clear from the following quote. “If we deliver 100 boxes

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27 against non or late delivery with a higher safety stock. This can be explained by the low flexibility that characterises cases D and K. Other cases could almost always make room in their planning for MTO products when orders come in. However, since case D and K do not have this flexibility, they need to produce the MTO products first to prevent non or late delivery. The high MTS safety stock allows them to work this way. For Case D, the amount of safety stock is so high that within 2-3 more weeks, the product cannot be sold anymore because of perishability. Companies often have agreements about the best before date. One third of the total best before date is for the manufacturer and two third for the supermarket and consumer. The fact that they have a variable yield further explains their reason for allocating capacity first to MTO. If not enough output is realised, less MTS products are made to complete the MTO order. If MTS products would be produced first, there is a chance that no room is left to compensate for smaller outputs which results in non-delivery.

4.5 Discussion of clusters

The different clusters all have their own criteria for allocating capacity. In this subsection we will focus on the underlying reasons companies choose for a certain strategy. All companies aim to fulfil total demand, MTO and MTS, but all use different capacity allocation criteria to achieve this goal. The discussion is an interpretation of the results.

The first cluster is composed of various companies with diverse characteristics. One thing that they have in common is that they are relatively large and well known players in their markets. They all stated that quality is very important for them. Their biggest customers are all supermarkets which require strict SLA’s with penalties in case of non-delivery. When we asked why all supermarkets are so strict and if they always accept such SLA’s they stated that “late

delivery in the food industry is just not done”. The companies accept this since they want to

become or stay a big supplier for that supermarket. The bigger and well known companies have to comply with this as it is an industry standard. Supermarkets are so strict since non-delivery results in empty shelfs for consumers which they want to ultimately prevent. On the other hand, the food processing companies can expect regular orders from supermarkets which is beneficial for their business. Moreover, it was mentioned that if a company wanted to increase market share by selling more to a certain supermarket, they would focus fully on 100% delivery reliability for that supermarket “It can also be strategic, if you want to expand at a certain

customer then you always give the 100% delivery performance”. Companies who are or want

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28 deliver fast as well as supermarkets often require short delivery times. Companies who are characterised by this tend to allocate capacity solely based on a customer’s SLA. In practice this will result in first allocating capacity to high priority MTS products. Their policy is to produce the most important MTS products first regardless of extra production costs or efficiency losses.

The second cluster is composed of companies that focus less on specific customers (such as supermarkets) and more on all customers together. These companies often almost only produce MTS, so they keep stock of almost all products. The low percentage of MTO products means that their production planning does not often have to be altered a lot which would affect the production of MTS products. This allows them to keep all stocks on a sufficient level without many problems. If there is not enough capacity to keep stocks above their minimum level, expanding their total capacity is a good option since they know that the demand is rather stable. Demand is not influenced much by irregular orders. MTS products are ordered regularly with similar volumes which gives these companies the ability to make better and more accurate forecasts. Because they can determine future demand far ahead, problems concerning stock levels can be identified on time, so they have enough time to prevent stock-outs from occurring. Companies that have a relatively low percentage of MTO products thus tend to allocate capacity based on MTS stock levels to prevent stock-out. The difference with the first cluster is that they can forecast further into the future and more accurate. Because of that, they can serve all customers in time instead of having to prioritize certain customers (with high SLA’s) over others. Capacity allocation in these companies thus focusses on maintaining the minimum stock levels for all MTS products.

The third cluster is composed of companies who focus more on their products and process rather than the customers or the market. They do not have strict contracts and have relatively low delivery reliability. Because of the lower market pressure, they can shift the focus from the customers to themselves. Since they want to achieve the highest profit, they allocate capacity first to products which have the highest profit margin. The reason they can do this is that they do not risk severe penalties. Companies in this cluster are thus more concerned with their own processes instead of adapting production a lot to the customer. They do this is because they are relatively small companies who do not make a lot of different products. Another reason is that they have a very high utilization as Case J stated, “We are producing 24/7, so actually we are

on our maximum”. Since they have no extra capacity left, they rather allocate capacity to the

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29 The fourth cluster is composed of companies who are similar to the companies in Cluster 1. The difference is that these companies are characterized by less flexibility. They allocate capacity first to MTO products whereas MTS products are more important. Although it seems contradicting there is a logic behind this. Companies in this cluster have specific production lines, in the theory referred to single purpose capacity. This means that they cannot change their planning that much without serious consequences. The specific lines limit the option to produce MTO products when orders arrive without hindering the MTS products. Since lines are specific, one may be fully utilized and some may be idle. If the MTO product needs to be made on the line which is already fully running, you either have to delay the order or be satisfied with lower MTS levels. It seems that this food processing industry characteristic is a very important determinant of how companies allocate capacity between MTO and MTS. Companies who are more flexible do not have this problem since they can divide capacity more and better. Hence, companies in this cluster have an inventory policy which favours a relatively high safety stock level. This buffer allows them to first focus on the MTO products without risking out of stock for MTS products. If a problem occurs in production on a busy line that lowers yield, they can still produce MTO products and deliver all MTS products. Via inventory buffering they are able to fulfil total demand. Companies that are characterised by low flexibility but who do have a combined MTO-MTS system thus allocate capacity to MTO first and adapt their inventory policy to high safety stock levels for MTS products.

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30 For all clusters, especially those whose capacity allocation is influenced a lot by customer/market characteristics, there was not much cooperation with scheduling. All companies use product groups that can be produced in sequence without set-ups and changeovers to improve efficiency. However, at the capacity allocation level, they do not use minimum set-ups and changeovers as a criteria. So, the long term planners do not consider the criteria that the short term planners use, which is why cooperation between them is lacking. Companies stated that optimal flow or lowest costs are not a direct goal of capacity coordination. An explanation for this is that costs that are saved when flow and efficiency is optimized, could incur later in terms of a penalty. Therefore, scheduling has to deal with the decisions that are taken on the capacity coordination level and optimize production flow and sequences as much as possible.

5. Discussion

The results of this empirical research differ from what is stated in previous literature as discussed in section 2.4. The focus of the models that previous researchers used was mostly on the costs of production and due dates. Models are very suitable to capture this kind of (optimization) issues. However, what the models fail to incorporate are the decisions that are taken in reality which are not based on costs or due dates. For many companies, strategic partnerships with (large) customers and expanding market share were important criteria and not the costs of production and storage. The criteria they deem important are directly translated to the way they coordinate capacity. Deciding on how you can increase business with customers or how you can get a strategic partnership are decisions which cannot be incorporated in the models that are used in previous research. Minimizing costs and optimizing flow are also important for the companies. However, these criteria are of less importance when capacity is coordinated. The models place too much focus on the companies themselves rather than on the market/customer whereas many companies made clear that market characteristics are important for them when they decide on capacity coordination. Therefore, the focus of the models is too narrow to resemble how capacity is actually coordinated in practice.

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31 the most extreme case where MTS stock levels will drop below zero, capacity will first be allocated to MTS products to prevent penalties. In essence, they thus first look if MTS stock levels are in danger and after that they will assign capacity. Because of the high MTS safety stock this rarely occurs in practice. This means that for this cluster MTS stock-ups should be avoided (if they are sufficient) and capacity should thus first be allocated to MTO products as is also stated by Beemsterboer et al. (2017a).

The trade-off factors in capacity coordination that this researched found out are different for the clusters as mentioned before. The findings of this research are in line with some of the MTS factors that are identified in the theoretical background. Namely, factors that influence capacity coordination are service level (Rafiei, et al., 2013), profit contribution (Huiskonen, et al., 2003; Ebadian, et al., 2009; Rafiei & Rabbani, 2012) and backlog costs (Wang, et al., 2012). These factors seem to hold for food processing companies as well. The MTO factors seem to be of less influence in the capacity coordination decision. Only for Cluster 4 these are present. All other clusters base the decision on the MTS factors. MTS products have a better flow since the planning can be made in advance, this allows the planner to better create product groups to improve efficiency. If orders arrive, they always look if there is space in production somewhere that fits within the same product group to maintain efficiency. The order delivery time for MTO products is often long enough that this can almost always be achieved, apart from companies in Cluster 4, who are less flexible. MTO products seem to be of less importance than MTS products when capacity has to be coordinated and companies have to choose between the two product types

6. Conclusions

This research focused on capacity coordination between MTO and MTS products in the food processing industry. The aim was to determine what factors are most influential for the capacity coordination decision and whether it would result in allocating capacity to MTO or MTS first. Based on the results, four clusters were identified who coordinated capacity based on different criteria. For every cluster, we identified what criteria they use and what the underlying reasons are. Furthermore, we showed what product type (MTS or MTO) has a higher importance when capacity is coordinated in a hybrid MTO-MTS system.

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32 products were most important for the capacity allocation decision. Especially service level, as also stated by Rafiei et al. (2013), was found to be of high importance. Related to this are the backlog costs (Wang, et al., 2012), which were also of high importance. If the required service level was not met, some customers imposed high penalties on the companies which is why service level is so important. For the companies who have less strict service levels the focus is more on profit contribution (Huiskonen, et al., 2003; Ebadian, et al., 2009; Rafiei & Rabbani, 2012). We could not find evidence for the importance of the MTO factors in a hybrid MTO-MTS system within the food processing industry. All companies stated the MTO-MTS products are always given preference when capacity has to be allocated, even for the companies who initially allocate capacity first to MTO products. However, within MTO products the most important criteria for allocating capacity was the required lead time (Rafiei, et al., 2014; Youssef, et al., 2017). Furthermore, we could identify four clusters for which we discussed what different criteria they used when allocating capacity and what were the underlying reasons that different companies do this whereas their goal is the same, fulfilling total demand, MTO and MTS.

Second, we contributed by literature by conducting empirical research. Previous literature focussed on quantitative research with mathematical models. These were often simplified and made under assumptions and simplifications. Empirical researched focussing on understanding how companies within the food processing industries coordinate capacity was lacking. This research thus focussed on understanding how companies coordinate capacity between MTO and MTS and why differences between companies occur. This allowed us to understand what criteria food processing companies use for making this decision. This research increased our understanding in capacity coordination in hybrid MTO-MTS systems in the food processing industry and showed what characteristics lead to the use of certain criteria.

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33 allocation decision. The decisions that are described in this research are directly taken from practice which makes it easier for managers to implement them or translate them to their own situation.

7. Limitations and future research

The limitations that are discussed in this section all refer to the factors that limit the generalizability of the results as we deem these limitations to be the most important.

This research was conducted with companies who all have their production plant in the Netherlands. It became clear that food processing companies here often have strict contracts with supermarkets which influences the capacity coordination decision a lot. Other countries may require less strict SLA’s which shifts the focus more to the companies themselves. The capacity coordination decision in foreign companies may thus be influenced by different criteria. Including companies from multiple countries or cultures in the sample may uncover interesting results which increase the generalizability of the results or lead to more representative results. It is likely that companies operating in other countries or cultures use different criteria that they deem important when coordinating capacity.

Furthermore, the clusters (except for Cluster 1) consist of only two companies. It is therefore hard to generalise these findings. A larger sample may uncover common characteristics among the companies within a cluster or relationships that could not be discovered within the sample used in this research. More companies should be analysed in order to back up the conclusion stated in this research or to elaborate or even contradict with these findings. This increases the generalizability of the clusters that are discussed.

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Appendix A Interview protocol

Product groups/families

1. Do you subdivide in product families/groups? a. Based on what product characteristics?

i. Perishability ii. Profit margin

iii. Variability in quality (raw materials and finished goods) b. On what specific market characteristics?

i. Actual customer demand/orders ii. Forecastibility of orders

iii. Raw material availability

c. On what specific process characteristics i. Processing time

ii. Dedicated process steps iii. Variability in yield

2. What are the main performance measures of your customers? a. Based on which factor do you win or lose customers? 3. Do you produce private labels and/or brands?

4. Are there lines specific for certain products or is this flexible?

Production planning

5. Which steps do you take to create your production planning per product group? a. What is the timeline? At what point is it fixed?

b. Who are involved?

c. How do you deal with capacity constraints? Do you use/have buffers? d. What are your process constraints?

e. How do you deal with these constraints? f. What is limiting the output of the factory?

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Appendix B Detailed open coding

○

Bottleneck - employees

Case A

De verpakkingsafdeling is erg afhankelijk van de grilligheden in de markt. Dus voor de bottleneck als je kijkt naar hinder in het proces moet je denken aan de verpakkingsafdeling

Case B

Dat is in principe de duurste factor wat bij ons werknemers zijn. Er is op dit moment geen productielijn die een constraint vormt, uitgaande van dat die beschikbaar is. Dus dat we 24/7 kunnen produceren. Er is geen enkele lijn met een probleem in die zin

Case D

De beperkende factor zijn niet de machines maar zullen eerder het aantal shifts of werknemers zijn

Case H

Je ziet de XXline, die zit helemaal vol. Bijvoorbeeld wanneer je acties hebt bij XX dan heb je zulke volumes, dat kan je niet aan door de week. Dan vraag je bij productie of ze ook op zaterdag of zondag kunnen werken. Dan moeten ze overwerken en extra diensten draaien. Meestal lukt dat wel.

Case I

Ja dat wordt wel steeds meer een ding.

Case K

Nee, we kunnen eigenlijk nog wel meer krijgen. En dat is nou, ik geloof sinds kort. Normaliter onze hoofdlijn, lijn 1, is altijd in 3 ploegen. Maar ik heb nu al een week dat dit in 2 ploegen is

○

Bottleneck - machines

Case C

De bottlenecks zijn meestal de machines en niet de werknemers. Dat kan een gietlijn zijn of een inpaklijn. Voor XX is dat op dit moment een inpaklijn

Case D

Mochten we moeten opschalen dan zijn de inpaklijnen wel de bottleneck vanwege de snelheid van de lijn. De ovens kunnen sneller bakken dan dat de lijnen kunnen inpakken

Case E

Nee dat doen wij niet, wij draaien elke dag volop. Als de oven vol is, meer kan niet

Case E

Capacity Coordination in Combined MTO-MTS Systems: A Multiple Case Study in Food Processing