“Accurate forecasting information for Wavin’s procurement, a matter of control”

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Specialization Organizational & Management Control

“Accurate forecasting information for Wavin’s procurement,

a matter of control”

April 2013

R.E. Heeres

Address:

Kromme Elleboog 19

9682 XH, Oostwold

E-Mail:

reneheeres@gmail.com

Student number:

1768727

University of Groningen

Faculty of Economics and Business

Supervisor:

prof. dr. ir. P.M.G. van Veen-Dirks

Co Assessor:

M. Paping, MSc

Supervisor Wavin:

G. Luna

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Dear reader,

My time as a student ends with this master’s thesis. It feels not very long ago that I started my bachelor Business Economics, which indicates that time past very quickly. The master Business Administration with specialization in Organizational & Management Control (O&MC) helped me to gain insights into various control issues, along with knowledge from other courses. This makes that I will look back at my time at the University of Groningen with good memories.

This thesis is written in combination with a challenging research internship I did at the procurement department at the company Wavin. I had a very good time at the head office in Zwolle thanks to the friendly and motivated people that work there. Lots of interesting and rapid developments took place in the six months that I was part of the organization, meaning that management usually did not have as much time to guide me through all issues as they would liked to have. Nevertheless, I would like to thank Peter for the opportunity he has given to me to do an internship at a multinational company. Furthermore, I thank Gabriele for being my supervisor at Wavin and for his help and suggestions when I needed directions. Special thanks to Christel for her support and advice, to Carolien for keeping me company at the office and to the people of the Procurement and SCOPEX teams that were interested in my research.

I would also like to thank my supervisor at the university, Paula van Veen-Dirks for her advice and recommendations during the meetings we had. Translating practical experiences into academic research was not one of the easiest tasks. I think that this thesis has benefitted in multiple ways from her supervision. Furthermore, I would like to thank my family for their support and expressing their interest.

I hope you will enjoy reading this thesis,

Oostwold, April 2013

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Abstract

This thesis is focused on a problem experienced by Wavin. The procurement department has the feeling that the company is not able to accurately forecast the need for raw materials. As this forecast is needed for decisions on strategic supplier selection, it is of great importance to make this process work well. In order to come up with suggestions on how to improve this process, it is investigated in what way this forecast influences decision making and control. A literature review is conducted to come to a framework to incorporates forecasting characteristics and control literature in the context of purchasing activities. In this framework it becomes clear that the quality of a (raw material) forecast depends for a large extent on the role it plays in the organization. Based on this finding, several recommendations are made to Wavin to improve the control for the procurement department.

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Summary

The research for this thesis was carried out at the company Wavin, who faced problems with their raw material forecast. The information from this forecast is needed by the procurement department to make decisions with suppliers on how much to order. A regularly incorrect forecast can lead to problems such as last minute negotiations with suppliers in order to arrange extra orders. The raw material forecasting process at Wavin is organized in such a way that four regions communicate with the head office on how much materials they need for the following month, after which the

procurement team at the head office decides on the allocation over the suppliers and the ordering process.

Because the head office is unhappy about the forecasting performance of the regions (measured in forecast accuracy) and it does not know well what can be done to handle the problem, further research is needed. This thesis therefore investigates how to improve the raw material forecast accuracy and its role within an organization. Furthermore, it is investigated in what way this forecast influences decision making and control, especially because Wavin would like to centralize more purchasing decisions. This leads to the formulation of the following research question:

“How does the

forecasting accuracy of the need of raw materials play a role within

an organization, how can it be improved and how does it influence decision making

and control within a firm?”

A literature review is conducted to come to a framework that incorporates forecasting

characteristics and control literature in the context of purchasing activities. In this framework it becomes clear that the quality of a forecast depends in lesser extent on whether a company makes more use of judgment or depends more statistical techniques, but more on the role of the forecast in the organization. Especially for a procurement department it is important to keep in mind the focus of a forecast on its use for decision making and on collaboration between business functions.

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will be easier if the forecasting performance can be measured in a clear and understandable method without individual forecasts being canceled out.

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1. Introduction

1.1 Introduction

Planning is an important topic in the management control literature. Planning and control are different concepts, but interact with each other (Cassar and Gibson 2008). The information that is provided by the planning function can help the organization to strengthen the control of the firm. Forecasting is part of the planning function which influences corporate decisions and therefore ultimately also the profitability of the corporation (Cassar and Gibson 2008). Based on an accurate sales and revenue prediction one can better plan finance decisions, scheduling and the purchasing of inputs. Especially this last factor is important for the purchasing department of an organization. An inaccurate forecast can cause the company to have large stocks and costly changes in scheduling (Wacker and Sprague 1995). To be more precise, the raw material forecast is of particular interest for the purchasing function. And the role of raw materials has increasingly become more and more a critical factor for the success of organizations. At the end of 2011, European executives indicated that the raw material prices are expected to rise in the future, impacting the companies’ performance (www.businessweek.com). Business Week discusses a report that states that the raw material costs even have more influence on the performance of a firm than economic pressure or market competition. Therefore, it seems that the corporate purchasing department at an organization can have a primary role in adding value to the business. In that perspective, it is not strange that authors argue that there might be a strategic role for the purchasing function in the organization (Cammish and Keough 1991; Giunipero and Pearcy 2000; Crespo Marquez and Blanchar 2004; González-Benito 2007; Baier, Hartmann and Moser 2008).

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materials the company needs. Based on the information it receives from the firm and others it can plan how much raw materials to produce. This shows the link between planning and forecasting on the one hand and purchasing on the other. Especially with the high costs involved for the firm, it wants to know how good (or bad) their raw material forecasts actually are. When it can achieve a raw material forecast as accurate as possible, chances increase that the organization will be in a better position to negotiate with the supplier. A benefit of a better negotiating position for the firm is that it can save costs on the purchase of raw materials. The raw material forecast can therefore take a prominent place in an organization, at least in the purchasing department.

In addition to the benefits of a stable raw materials forecast for the purchasing function, it is interesting to see what consequences the raw material forecast has on the control of the organization. The question becomes if and how the raw material forecast has effect on the way management wants to attain the objectives of the firm. Does it use the data from the forecast as an input to control behavior, for example through the use of target setting or measuring performance?

1.2 Research

Therefore, given the vital role of planning the raw materials for the purchasing function and its possible effect on the control and profitability of the organization, it seems right to further investigate the role of the raw material forecasting process in a company and how it can be improved. The management of the organization then knows what factors are to be taken into account in order to make better decisions. For example on how the organization can improve its negotiations with suppliers as a result of a better forecast accuracy. What makes this thesis interesting is that it combines forecasting, purchasing and control into one research, something that has not been done before as far as known. The combination of these research topics at the same time could provide for insights and interrelations.

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Besides stressing out the importance of the raw material forecast, this thesis will also focus on giving advice to Wavin. This Advice and suggestions for procurement at Wavin has the purpose to help implement changes that can help to reduce raw material forecast error. The ultimate goal is that the procurement department can focus more on other activities instead of wasting valuable time by correcting forecast errors. In this way, cost savings can be achieved because the company has a better negotiation position with suppliers which has the potential to produce more lucrative contracts. Especially the short-term raw material forecast of one month should be stable with no large last-minute changes necessary. Should Wavin be able to achieve a short-term forecast that does not need adjustments, it will appear more reliable to its suppliers.

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2. Research Questions

2.1 Objective of research

The main objective of this research is to investigate how the forecasting process of raw materials can contribute to the improvement of the control of a corporation. The research is related to a practical problem experienced by the firm Wavin. Most of the literature has focused on techniques and methods to improve forecast accuracy (Dalrymple 1987; Fildes and Kingsman 2011), but Lawrence et al. (2000) show that aside from technical solutions, managerial judgment must also be considered. It is of importance to say these papers have mainly focused on the accuracy of forecasting demand and/or sales data. This paper therefore tries to find out the role of an accurate raw material forecast in a purchasing organization. As the purchasing and procurement function in an organization fulfills an ever increasing important and even strategic role (Cammish and Keough 1991; Giunipero and Pearcy 2000; Crespo Marquez and Blanchar 2004; González-Benito 2007; Baier, Hartmann and Moser 2008), it is wise to consider how the forecast accuracy for purchasing raw materials can be improved and what its consequences are for the control of the organization. This is especially important because of the high costs of the purchase of raw materials as a fraction of the total production costs in the manufacturing industry (Ghao and Tang 2003). Furthermore, the purchasing function is a part of the supply chain of an organization (Giunipero and Brand 1996) which implies that management needs to take into account other organizational functions in order to introduce points of improvements. Therefore it is essential to research to what extent organizational control is affected by the accuracy of the raw material forecast and the other way around. Based on the planning process of management, the main research question of this paper is as follows:

“How does the

forecasting accuracy of the need of raw materials play a role within

an organization, how can it be improved and how does it influence decision making

and control within a firm?”

2.2 Sub questions

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the supplier depends on the company for a considerable part of his sales. For these negotiations to run smoothly, the purchasing department needs to know within acceptable boundaries the amount of materials the organization plans to purchase. Furthermore, whenever there is some deviation of the forecast, it should not be of a scale that would complicate the relationship with the supplier. An organization cannot face the risk that its supplier is not able to deliver additional materials on a very short notice. This illustrates the necessity of an accurate forecast for the purchasing function. However, improving the accuracy of forecasts is not an easy task: there have been several papers that discuss how one can refine the prediction of future data with the help of statistical models (Ho and Ireland 2012) or a combination of statistical and judgmental models (Bunn and Wright 1991; Caniato, Kalchschmidt and Ronchi 2011). But besides these models, there may be other factors that can be worth to take a look at. For example, depending on the industry in which the company is competing, it will have more or less difficulty in determining the amount of raw materials. Furthermore, the type of material may play a role as well. Motivated by the expectation that there might be more to it than statistical models alone, the following sub question is presented:

1. “Why is it important for a firm to achieve an accurate raw material forecast?”

The purchasing department plays a significant role in most organizations, but it is not the sole department that takes part in the process of determining the need for raw materials. An organization can only know how much materials are needed for the coming period when it knows how much and which products it will sell. Therefore the raw materials forecast is dependent on the sales forecast, direct or indirect. For this reason the purchasing department is forced to work together with the sales staff among others. It is suggested by Allal-Chérif and Maira (2011) that the procurement function can improve operational activities by internal collaboration. Hence, it might be of interest to research the role of the departments that could affect the accuracy of the raw materials forecast. These departments are for example sales, marketing and production because they can have influence over the different types of forecast in an organization. Sales wants to know how much and which products it will sell, marketing could propose product promotions which would change the demand for certain products and production is likely to want to have an influence on the inventory levels. Therefore the following sub question is presented:

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The reason for researching the accuracy of raw materials is, as previously mentioned, the growing role of importance for the purchasing function in the organization. To understand why an accurate raw material forecast can benefit the organization it is sensible to find out how the raw material forecast impacts the decisions made in the organization. Schoenherr et al. (2011) put forward that research on the decision making process is still not much widespread in the purchasing literature. Based on this it can be of interest to see for what decisions the information from the raw material forecast is used in the organization. The importance of the accuracy of these forecasts in relation to the stakes of the decision is also taken into account.

3. “How can the users and managers of the raw material forecasting system use it to improve control over the organization and thereby decision making?”

2.3 Management problem

The research questions can be very interesting from a theoretical point of view. However, in order to investigate whether these questions can be answered a case study approach is followed in this thesis. Thanks to the company Wavin an opportunity was offered to follow the procurement department over a period of six months. The research questions can therefore be also practically relevant. This is because the findings that are obtained at the company are expected to help solving the business problem. As other companies (e.g. operating in the same industry) might face similar problems like Wavin, any practical insights that evolve from the investigation of the research questions could encourage those companies to reconsider their current practices. Furthermore, they may better understand the implications of the role of the raw materials forecast in the organization.

Therefore the company Wavin plays a central role in this thesis. In Europe, the firm is the market leader for products as plastic pipe systems that can provide for the distribution of drinking water. Furthermore, Wavin provides solutions for telecom activities and cooling and heating of buildings.

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materials based upon the forecasts of various operating companies (OC) in the four main regions in Europe. However, the current quality of these forecasts is not optimal. The quality of the forecasts differs per month as well as per OC. According to Wavin, the main reason for the differing quality of the forecasts can be attributed to the use of local forecasting systems and local forecasting processes by the OC’s.

The result of this inaccuracy is that Wavin orders too little or too much materials with its suppliers. This leads to ad hoc decisions to make corrections regarding the missing or excess volumes. Also, management cannot focus on its usual activities as it needs to put time and effort in solving the issues with the suppliers. It is imaginable that the correcting of supplier issues will cost money to Wavin. Furthermore, these issues could be harmful to the reputation of business relations Wavin has with its suppliers.

Therefore, Wavin plans to revise the current forecasting system for raw materials in order to achieve a better forecast accuracy. In the current situation the forecasts are made by the various OCs using forecasting systems that are not similar. Instead, each OC makes use of an independent system. The idea is that once the OC’s all use the same forecasting program, the Enterprise Resource Planning (ERP), the forecasting process will become more transparent and reliable. At the moment of this writing, there is one OC that operates the new ERP system for raw materials.

Solving the business problem of Wavin can assist in answering the research questions because it gives a practical insight of the factors that cause problems in the forecasting process. These practical findings can therefore serve as a source of information for comparison with theoretical expectations and can add value as not yet explored research areas are investigated. The results and possible solutions from the business problem might therefore be of importance in answering the research question.

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3. Literature Review

3.1 Forecasts in an organization

To uncover how a raw material forecast can be improved, it is wise to research how the raw material forecast can influence an organization. Therefore an overview is presented of current literature regarding the role of the forecasting process in corporations. First, forecasting is viewed as part of the planning function in general. After that, some literature that shows the present view on forecasting is presented. But as most of the literature concerning forecasts focus on the sales forecast, it is of importance to also acquire more knowledge about the role of the purchasing function in an organization. Based on the literature, attention is paid to the linkage between the forecasting process and the purchasing function. Because planning and control interact (Merchant and van der Stede 2007, Cassar and Gibson 2008), appropriate control literature is considered for this research. The control aspect also is part of the research question stated in the previous section. The combination of the planning, forecasting, purchasing and control literature can serve as a framework which shows how the raw material forecast plays a role in the organization and how it can be improved.

3.2 Forecasting as a planning function

A forecast is defined as “A statement about the future value of a variable such as demand” (Stevenson 2011). Forecasts are an important part of an organization as they fulfill a planning

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certain that there will be no large deviation from the plan. As a consequence the firm can make decisions more comfortably. According to Stevenson (2011) forecasting serves two planning functions in organizations. The first is the long-term planning which includes among others the type of products to offer and the equipment needed, or in the case of purchasing which materials specifications to use and with which preferred suppliers negotiations must be started. The second part is the short-term planning which concerns inventory, short-term purchasing of materials and budgeting. Except for demand and sales, forecasting is also used as a planning mechanism for raw materials (which is the main topic of this thesis), profits, revenues, stock prices and interest rates. The focus of this thesis lies mainly on the short-term planning of raw materials. The procurement organization wants to know how the forecast for the coming months can be improved. However, the long-term objective of a purchasing organization must not be forgotten. Plans in a company can take the form of a budget which often states what financial targets must be achieved in a specified time period. Cooper, Crowther and Carter (2001) say that one of primary functions of accounting information is to serve as a predictive planning instrument. Budgets are however not necessarily seen as a forecasting instrument, but also serve as a communication tool for describing objectives and motivating employees (Cassar and Gibson 2008).

3.3 Forecasting characteristics

Forecasts in an organization can take various forms. Not all firms are alike and operate in exact the same way and one can imagine the same is true for the use of the forecasts. Based on the literature describing forecasting practices, certain forecast characteristics are brought forward that could explain differences in forecasting practices in organizations. These include for example the use of a specific forecast technique, the role the forecast plays in an organization and whether collaboration exists between organizational units in determining and using the forecast. The literature consulted discusses mostly sales and demand forecasting, but as those forecasts are related to the raw material forecast it may provide for usable insights in answering the research question(s). Therefore a

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Table 3.1

Characteristic

Type

Techniques Statistical Judgmental

Role Focus on decision making No focus on decision making Collaboration Between business functions Between business and

supplier

The first characteristic that is widely discussed in forecasting literature is related to the technique used for the forecast. There can be two major different (sales) forecast techniques identified: those that are made by management based on experience and judgment and forecasts that are based on statistical computer systems. However, as Caniato et al. 2011 show, these two approaches can also complement each other. Lawrence et al. (2000) investigate sales forecasting in manufacturing companies. They compare forecasts that are judgmental with forecasts that are made with the help of computer software. Contrary to what one might be expect, the accuracy of forecasts does not widely differ between the methods. Results show that simple forecasts are not uniformly inferior to complicated, sophisticated systems. The conclusion of Lawrence et al. (2000) points out that forecast accuracy is not the sole objective of a forecast in an organization. This is also supported by Mentzer and Bienstock 1998). The forecasting process is also influenced by the motivation of staff and business meetings where information is exchanged. Based on these findings, it appears that the forecasting process is not only a matter of technical systems, but also a matter of people and control. Wright et al. (1996) highlight the important role of human reasoning in forecasting processes. Even when companies make use of statistical techniques, judgment of humans is essential for the functioning of the forecasting process.

A special note in the perspective of the raw material forecast deserves the concept Materials Requirement Planning (MRP) as this serves as the link between the material forecast and other forecasts in an organization. It can be seen as a logistic concept that reveals how much materials are needed for the coming period. Therefore, this can also be seen as a forecasting technique. Yet, also these outcomes are related to the demand- and sales forecasts that are generated in the

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(2012) research the effects of (demand) forecasting error in the context of an Enterprise Resource Planning (ERP) system, of which the MRP system is part of. They find that forecast error can have a remarkable impact on the performance of the ERP system and therefore also on the MRP. But the investigation of such statistical methods is beyond the scope of this thesis. Nevertheless, Ho and Ireland (2012) make clear that the demand forecasting error is closely related to the performance of determining the amount of raw materials and therefore raw material forecast accuracy. But is it also interesting to know that demand forecasting errors can be mitigated.

A second characteristic of forecasting that can be distinguished is which role the forecasts play in an organization. The forecasting process can be of great importance for operational decisions as these are based on proper estimations of future demand. It is argued that situations in which parts of the organization do not base decisions on the same forecast, inefficiencies can occur because of a lack of alignment between business functions (Triantis 2002). In this perspective, Danese and Kalchschmidt (2011) research the role of the forecasting process on the accuracy of the forecasts and on

operational performance. Furthermore, forecasting can have an influence on decision-making processes in the business. Users of the forecasting system need to know how to use the outcomes of the system for the benefit of the firm. Therefore it is important not only to consider the forecast techniques, but also to take into account how the forecasting process is organized and managed. Danese and Kalchschmidt (2011) find that forecast accuracy could increase significantly when a company focuses on the use of forecasting for decision making. Another finding is that the forecasting process variables directly impact the performance of the company. The information provided by the forecasting process can be exploited by the users of the system to optimize their business decisions and therefore the costs and delivery performance of the business. However, the researchers point out that this finding relates to the forecasting process and not forecasting accuracy. That is, one cannot say that by increasing the forecast accuracy the cost and delivery performance will also perform better.

Another study by Wacker and Sprague (1995) focuses on potential factors that can affect forecast accuracy in a company. The article focuses on manufacturing managers because they are an

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functions of the organization in the forecasting process does not help to produce more accurate forecasts. Another factor that hurts the accuracy of forecasts is the involvement of top management in the process. Furthermore, from the perspective of executives, the use of quantitative methods to produce forecasts does not improve the accuracy of the forecasts. Wacker and Sprague (1995) have an interesting finding that involvement of various business functions in the forecasting process is not recommendable. They find that forecast accuracy does not improve when the number of functions involved increases. It requires a great amount of skills to combine forecasts that come from various functions. It is also hard to determine how much influence each function has, making it more a political game which does not enhance the forecast accuracy (Wacker and Sprague 1995). However, there is also another view which is made clear by Triantis (2002). He places added value to

collaborative forecasting. An important benefit that collaboration offers is that the forecaster has more information and is therefore better able to find the causal factors that affect the accuracy. In addition, collaboration among functions can make the forecast more acceptable because of the involvement of the different business members. It is interesting to see how a raw material forecast fits into these perspectives.

The three characteristics that are selected from the literature show that the forecasting process is of importance for a business to make their operations perform well. There have been several studies that investigate forecasting, but there are still some significant areas of interest left unexplored. The combination of forecasting with the purchasing function and control perspective could contribute to new insights.

3.4 Purchasing and Procurement Function

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from which the whole company can benefit. The raw material forecast is of great importance for the purchasing function as many decisions depend on it.

A common objective of a purchasing and procurement department is to save costs and at the same time ensure the quality of the acquired materials and goods. One aspect of purchasing these raw materials by an organization is the communication with its suppliers. Preferred suppliers are chosen by the purchasing function to achieve long-term relationships with the intention to create value for the company. To achieve these benefits, the purchasing function needs to exploit a wide range of information available (Chen 2009).

3.5 Material forecast as part of the control of an organization

As said earlier, the forecasting function is closely related with the control function. To be more specific, the focus of this research lies on management control. According to Merchant and van der Stede (2007) the central function of management control is to influence the behavior of the employees in such a way that the probability increases that the objectives of the organization are achieved. The systems that are in place to guide management control are referred to as

Management Control Systems (MSCs). A forecast can become part of the targets that are set in the organization. The motivation of managers can also be affected as their evaluation is sometimes dependent on these targets. In this way the forecast can serve as an instrument of control for a firm.

The raw material forecast is a forecast that shows how much raw materials are needed for the coming period. A forecast is built to give a specific as possible expectation for the future. An organization thinks it can achieve this forecast and therefore it may become a target. However, whereas forecasts are built to show an expectation, targets are introduced to stimulate behavior in the desired direction. Therefore, not all forecasts can be classified as targets. Jones (1973) wonders to what extent an organization must try to achieve a (sales) forecast as a target. For example, he argues that it is good for individual production lines of the organization that the target is set a little higher than the forecast. As current performance increases, the targets should move with it.

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deviate as little as possible from the amount of materials that were forecasted, transforming the forecast into a target.

Otley (1999) developed a framework that can be used as a tool to analyze management control in an organization. Among others, target-setting, measurement and information feedback are issues on which the framework is build. Questions regarding these topics can be addressed not only for accounting measures, but also for managerial business processes (Otley 1999). The raw material forecasting process which is elaborated in this thesis will therefore be the process that is to be addressed by the framework. This method allows to see what questions may be relevant in the investigation of how the material forecast can have an impact on the control of an organization. As also said by Merchant and van der Stede (2007) in their definition of the function, objectives take a very prominent role in management control. One of the objectives for a procurement department can be to select suppliers (de Boer and van der Wegen 2003) and to maintain good relations with those suppliers (Ounnar et al. 2007). However, in order to fulfill these both objectives a good indication of the materials to be purchased is necessary.

Furthermore, Otley (1999) stresses the importance of defining goals and measuring of those goals. Although the two goals mentioned are only a fraction of the goals an organization or procurement department can pursue as part of its strategy, the purpose of this paper is not to evaluate the entire control system of the organization, but rather to see how the material forecast would fit into the management control system. For example, the goal of maintaining good relations with suppliers could be measured with surveys that indicate how suppliers rate the organization on various aspects that might include the reliability of forecasts and how an organization handles problems as a

consequence of those inaccurate forecasts. The raw material forecast accuracy can therefore serve as a measure that indicates how well an organization performs in a particular area. Through the effect on the supplier relations, an inaccurate raw material forecast can also have financial consequences for a company such as inventory costs and it can have influence on contract

negotiations. Therefore the material forecast may take a prominent role and a target concerning the forecast may be formulated in the procurement department because of the decisions that depend on it.

In the framework of Otley (1999) it becomes clear that after the formulation of goals and

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forecast means that the maximum performance that could be targeted is an error-free forecast. However, because of external factors that cannot be controlled by an organization (e.g. supplier delays in deliveries or suddenly changed market circumstances) it is not reasonable to assume an error-free forecast can be achieved. It is interesting though to see what performance level is targeted by an organization when the above mentioned factors are taken into account. In addition, the

forecasting process itself can also be of influence on the performance of the forecast (Danese and Kalchschmidt 2011).

In evaluating the control of an organization one must not forget to focus attention on the role of information. According to Otley (1999) information is a crucial element to finalize the ‘control loop’. In this perspective, actual performance is compared with planned performance (in the form of targets) in order to see the deviation. This deviation then serves as a sign which can indicate the need to correct actions. Information can even be used to correct actions as to actually prevent bad consequences from happening. The material forecast can be a source of information that is used as an indicator through the use of the forecast accuracy. The function of the material forecast accuracy is to compare the predicted amount of materials with the materials actually used. One can wonder whether an organization indeed uses the forecast as a signal for identifying problems to correct these. The information feedback loop from Otley (1999) discusses learning processes in which an organization learns from decisions that are made based on feedback it receives from various signals. A procurement department and perhaps other parts of a company may learn from experience with older forecasts and adapt their processes and working procedures as a result. In this way, informal working practices can exist that are actually based on comparison analyses.

In the remaining part of this thesis, research at Wavin can give insights to what extent forecasting and control theories are applicable to raw material forecast practices that are in place in the

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4. Research Methodology

4.1 Research Design

It is important for a researcher to consider which research design and methodology is most appropriate for the research problem. For this thesis, a choice has been made to do a case study. According to Yin (1989), a case study is “an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used”. Based on this definition, it becomes clear that the focus of a case study lies in exploring the phenomenon in its real-life situation. This is different from an experiment, which sees a strict distinction between the context and the phenomenon investigated. This isolation is made deliberately to research the effect of the variables without the environment interfering with the results. In contrary, case studies accentuate the context in which the research takes place. Cooper and Schindler (2005) argue that in answering ‘how’ and ‘why’ questions, case studies offer a useful approach. These questions require the investigation of a large number of multiple variables. But as the number of variables increases, so does the need for more observations. A case study is then more practical in providing insight into the variables. Yet, both case studies and other research approaches can sometimes be appropriate as a research design. In situations where both designs are suitable, it is dependent on the researcher which research method he prefers. In this thesis, a case study is chosen because of the opportunity to closely monitor the actions of a multinational company that experiences problems with raw materials. As the main research question is a ‘how’ question and is centered on the role of the raw material accuracy in a company, the case study can offer more understanding by illustrating the practices within the firm. In addition, most sub questions are focused on qualitative output. That is, the purpose of these questions is not to discover quantitative evidence like measuring by how much the forecast accuracy can be improved as a consequence of certain actions. However, there can be indications on whether there are factors which might be of influence in improving the planning of raw materials. Therefore a case study seems most appropriate for this investigation as a research design.

4.2 Data Sources

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provide for the richness of a case study (Rowley 2002). Qualitative data is necessary to find out what factors according to management are important. In all likeliness, they have their own view and opinion on the business problem. Based on the findings of management and based on the literature review conducted, several important factors are selected to be investigated more in depth. A benefit of discussions with management is that they can point to other sources of information which might be helpful. The first discussion took place at the beginning of the internship. In the form of an informal conversation, the procurement director and the category lead buyer made clear what according to them the main business problem was.

The second scheduled meeting was scheduled as a semi-structured interview with the category lead buyer. On the basis of open questions, information from this relevant person is obtained. A semi-structured interview offers opportunities to collect information relevant to the business problem. Not all questions that were asked during the interview were predefined as information revealed by the interviewees offered the opportunity to ask new questions. Also, the interview may not necessarily follow exactly the predefined structure, which is also a characteristic of semi-structured interviews (Bryman and Bell 2003). In this way it is tried to capture the perspective and opinion of the interviewee on the issue. The questions that were asked during the semi-structured interviews are provided in the appendix. Besides the interview with the category lead buyer, also the purchasing manager raw material was questioned in an informal conversation. To avoid the danger of too much reliance on the upper management at the head office, another interview was arranged with a fulfillment planner from the production site in the Netherlands. Using multiple interviewees with different background has as benefit that statements can be checked on their validity (Glick et al. 1990). The interviews can be found in the appendix. Furthermore, not only Dutch persons were involved in the research. Contact with the regions abroad took place several times to ask for information about their planning and forecasting processes. In this way it has been tried to reduce the chances of biases of key persons in this research as much as possible.

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forecasting system. These can be very useful and may be relevant for the case. Nevertheless, one must bear in mind that documents and records are created for a special purpose (Cooper and Schindler 2005). Therefore, it is taken into account that the documents are made by the procurement department and that it may therefore represent their viewpoint rather than that of the entire organization. Besides the use of the intranet, access was given to the exclusive purchasing database program, called Planning Tool. This database forms a central part of this case research as it contains most of the data necessary to calculate accuracy levels. In the next section, more information is given on the Planning Tool and its purpose in the organization.

The last source of information for this case study is observation. Observation is an important method to conduct research. A benefit of observation can be that the researcher is part of the organization, and he is there when actions happen and therefore does not have to rely on the perceptions of employees (Woodside 2010). Attending meetings, making conversations and observing daily activities provides valuable information on practices within Wavin. An important notion must be paid to the type of observation. As said, this case study observation is done as a participant, which means that the researcher is part of the organization. In contrast to direct observation, participant observation offers the benefit of more information. However, because of the involvement with the organization, there may be concerns for the loss of objectivity (Cooper and Schindler 2005). As this research is conducted with the full awareness of these potential downsides, it has been tried to maintain an independent view. For example, the literature study that includes the fields of forecasting, purchasing and control from authors that are well known in their field, form a basis to reduce the bias of the researcher.

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Table 4.1

Type of information

Person(s)

Date

Duration

Meeting Procurement Director & Category Lead Buyer

3rd of September, 2012 0,75 hour

Interview Category Lead Buyer 24th of September, 2012 1 hour Conversation Purchase Manager Raw

Materials 4th of October, 2012 0,5 hour Meeting Procurement Department and Regional Purchasing Managers 9th of October, 2012 7 hours

Interview Fulfillment Planner 11th of October, 2012 1,5 hour Observation of meeting Category Lead Buyer 1 &

2

22nd of October, 2012 1,5 hour

Meeting Category Lead Buyer 1, Supply Chain Optimization (SCO) Manager, Business Process Manager

9th of November, 2012 + 1 hour

Interview Category Lead Buyer 16th of January, 2013 0,75 hour Presentation and

discussion of findings

Procurement director, Lead buyers raw

materials

26th of February, 2013 1 hour

Mails and telephone calls

Regional Purchasing Organizations

No fixed dates

Talk with colleagues Procurement Organization and other

departments at Head Office

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4.3 Limitations and points of attention

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5. Practice at Wavin

In this chapter current raw material forecasting practices are described to determine what are the main reasons for the problems experienced by the procurement department.

5.1 Introduction of the Company

As said, the research for this thesis takes place at the head office of the Wavin Group in Zwolle. Wavin is a Dutch multinational that was founded in 1955 and was taken over by the Mexican chemical company Mexichem in 2012. As a consequence, Wavin was delisted from the Dutch stock exchange that same year. The main activity of the company is the production of plastic tubes. The organization mainly produces in Europe, where most of their factories are located. Furthermore, there are countries in which Wavin is active and where they sell products, but not produce products. These activities outside Europe are the responsibility of Wavin Overseas, which is located in

Dedemsvaart until the end of 2012, after which it joins the departments at the head office. To keep an overview of the organization, Wavin is divided into four regions which all report to the head office in Zwolle, the Netherlands. These four regions consist out of operating companies (OCs) that each have one or more plants under their authorization. For almost every country there is one OC allocated, except for Poland, where there are three OCs. These three OCs are called Wavin Poland, MPC and Arot. An overview of the regions and their corresponding OCs can be found in table 5.1.

Table 5.1

Region

North West

Europe (NWE)

South East

Europe (SEE)

Central East

Europe (CEE)

South West

Europe (SWE)

OC Netherlands Italy Poland United Kingdom

“” Belgium Hungary MPC (Poland) Ireland

“” Germany Turkey Arot (Poland) France

“” Denmark Lithuania

“” Norway Russia

“” Sweden Czech Republic

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To produce these tubes it needs to purchase several raw materials from suppliers as input for the production process. The three most important materials are Polyvinyl chloride (PVC), Polyethylene (PE) and Polypropylene (PP). Plants produce tubes from all three materials, two or only one of them. For example, PVC products are produced in the Netherlands but not in Germany. In the latter country production is focused around products from PE which in turn are not produced in the Netherlands. However, both countries are responsible for the production of PP products. In addition, the importance of each OC differs based on the volumes produced. For 2011, the main producers that make up the most volume of the three types of materials can be seen in table 5.2. From this table it becomes clear that the production of PE products mainly take place in Germany, as the operating company in Germany produces 45 % of the total volume of PE. The distribution for the other materials is more equally divided.

Table 5.2

Material

PVC

PE

PP

1 Netherlands (21,1%) Germany (45,1%) Czech Republic (18,9%) 2 Poland (17,1%) Sweden (9,4%) Poland (15,9%) 3 United Kingdom (15,1%) Poland (8,2%) Germany (13,6%)

Furthermore a distinction can be made on the volumes per material. The largest material that is purchased in terms of volume is PVC, followed by PE and PP. For 2010, the latest year available, the material PVC made up more than 50 percent of the total volume of raw materials. In table 5.3, the percentages of the three materials as part of the total volume are shown for the last five years.

Table 5.3

Material

PVC

PE

PP

2008 48,6% 30,5% 20,8% 2009 52,6% 28,4% 19,0% 2010 53,4% 27,7% 19,0%

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budgets and the related possible bonuses play an important role in deciding the allocation of raw materials. Furthermore, the head office sometimes decides on a correction of the volume suggested by the regions/OCs.

The head office in Zwolle is home to various departments. However, the focus of this research will be on the procurement department. In the words of Wavin, the task of this department is to make use of the combined purchasing power of the Wavin Group in their negotiations with suppliers, to create close relationships with a small number of preferred suppliers and work towards the achievement of a world class leading purchasing organization.

5.2 Problems

According to the management of the procurement department, the current situation is not ideal. From their perspective the raw material forecasts they receive from the regions/OCs are not accurate enough. The head office relies on the accuracy of the regions’ forecasts in the allocation process. Sometimes however, it appears that more or fewer materials are needed than initially was forecasted. Furthermore, the head office wishes to see with what materials are needed for the coming months with an acceptable accuracy level.

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consequence of an inaccurate raw material forecast, either an over-forecast as well as an under-forecast, are listed below in table 5.4.

Table 5.4

Over-Forecast

Under-Forecast

Unhappy supplier Possibility of production halt Loss of bonuses Last minute negotiations with supplier No Accuracy Premium Unhappy supplier

Costs of inventory Loss of bonuses No Accuracy Premium

Unhappy customer

There are several advantages that Wavin can get from high raw material forecast accuracy. According to the category manager, there are four major benefits that can be achieved as a result from high forecast accuracy. The most important benefit is the avoidance of risk of supply. Mentioned as a problem, the company cannot meet the demand as a consequence of a shortage of raw materials. A high accuracy makes it likely that this will not happen too often. A second benefit from a better accuracy is cost reduction. At the beginning of the year, budgets for the full year are known. Meeting this budget can have a positive outcome on the price the company has to pay to the supplier. Certain agreements show that if Wavin orders a predefined amount of materials, it will receive a bonus in the form of a cash reduction. The third benefit is the accuracy premium. Some suppliers (not all) value a good accuracy so much, that they offer companies a discount on the ordered materials if the forecast they receive from the company meets a certain accuracy level. Benefit four is the supplier preference: when the supplier receives a good quality forecast over a longer period of time, it can lead to the creation of trust. The supplier is then keen on working with the reliable company. This gives the company a strong position in negotiations, especially when there are more satisfied suppliers.

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under-forecast receives somewhat more attention than an over-forecast. Nevertheless, both situations need to be avoided as much as possible. An accurate raw material forecast from the regions can help the procurement department at the head office, but also Wavin as a whole because of relations with the suppliers and customers.

5.3 Current Performance

Before one can tackle the problem of the organization it is necessary in the first place to find out the source of the business problem. With the help of a basic database that keeps track of forecast, planning and realization figures of the years 2011 and 2012 it becomes possible to view how well the various raw materials are forecasted. This database, called Planning Tool at Wavin, can be both accessed by the head office and the OCs. In fact, this program is used to communicate the

forecasting figures for the coming three months from the OCs to the head office. It shows for each OC and plant how much and which raw materials they expect to use for the production process. The raw materials are divided into several subcategories at five levels in which each level represents a further level of detail. The OCs are also expected to fill in at which supplier the specific raw materials are supposed to be ordered. Then the head office turns the forecast into a planning based on the current rebate and price agreements with the suppliers. This mainly affects the allocation so a material might be ordered at a different supplier than was initially chosen by the OC. The head office puts this revised allocation into the Planning Tool manually after which it can be seen by every user of the program. The OCs then know what materials they need to order with the suppliers. However, before they order their materials with the supplier, a conference meeting is scheduled between the head office and the OCs to obtain additional information and to finalize the planning. Aside from the forecast and planning, one can consult the Planning Tool for numbers on the realized amount of materials. The realized amount shows how much raw materials are actually supplied by the supplier. These numbers are put in the Planning Tool automatically through the reading of a file from the supplier. The planning tool calculates the “accuracy” of the forecast by dividing the realized amount by the forecasted amount. There are three color schemes that indicate the quality of the forecast: green for forecasts that deviate less than 5 %, yellow for those between 5 % and 10 % deviation and red for the forecasts that are more than 10 % off the realization. At the beginning of the month the numbers of last month in combination with the color schemes are communicated to the

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As to gain further insight into the origin of the forecast accuracy, a selection was made on the basis of the type of material, level of material, OC, plant and supplier. First of all, the forecast numbers are separated on the basis of type of material. Furthermore, for this research a separation is made between the years 2011 and 2012 to decrease the possibility of year-specific incidences distorting the aggregated picture.

In table 5.5 one can see the aggregated number of materials of an OC that consists out of the total realized number of materials divided by the total of forecasts for the various regions, split up by material and year. This means that a number above 100 means it forecasted too little and vice versa. Overall the accuracy looks acceptable, except for certain cases where there exists a very large deviation. Following the classification of an acceptable forecast in Planning Tool, there are seven cases that have a deviation of more than ten percent, for a total of twenty-four cases. It is remarkable that the region SWE consistently delivers a bad forecast for PVC and PP: respectively from an under forecast to an over forecast and vice versa. But as regions consist out of OCs, it is wise to evaluate at a level deeper. It could even be that regions that perform well on an aggregated basis consist out of OCs that offset each other on accuracy. Where one OC may under forecast the need for raw materials, another OC might overestimate this need with the consequence of an over forecast. Table 5.5

Material

& Year

PVC 2011 PVC 2012 PE 2011

PE 2012

PP 2011

PP 2012

Region

NWE

94 95 93 98 94 98

SEE

103 153 100 149 94 96

CEE

147 91 97 93 92 100

SWE

115 81 98 91 87 115

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considerably. While some OCs produce relatively reliable forecasts for the two years, others are less consistent. This illustrates the problem of finding the source of the problem with the Planning Tool program. While the forecasting quality of an OC may look acceptable, it can be that it over forecasted one of its materials while under forecasted the other material. The result is the same as that of the region: the overall picture looks good while in reality there might be a serious problem of forecasting the individual materials. This is especially true for a region that consists out of an OC that is very large in volume: when this OC offsets the over- and under forecasts of its materials it determines for a large part the seemingly good accuracy of the region. So based on this method there is no reliable way of assessing the forecasting performance of a region or OC.

Table 5.6

Material

and

Year

PVC 2011 PVC 2012 PE 2011

PE 2012

PP 2011

PP 2012

OC

NL

89 91 72 64

BE

96 96

D

108 104 91 93 105 113

DK

109 88 91 91

N

136 175 109 99

S

91 109 92 107

FI

138 0

5.4 Definition of “Forecast Accuracy”

Clearly it does not suffice to aggregate the numbers of the Planning Tool to investigate the forecast accuracy performance of the regions and OCs. What is needed is a definition of forecast accuracy. Wavin uses a dictionary for its Key Performance Indicators (KPI’s) of which one of them is the

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Formula 5.1. 1 − (( 1 − ( − 1) / ( − 1)) ∗ 100 %

For example, with the help of this formula one can calculate in November the forecast accuracy of the month October. For this it uses the forecast as made in September for the following month and the actual sales as made in October. Note however that for the investigation of raw materials it is not sales that is used as an input, but realizations. In case the one wants to calculate the accuracy for aggregate data, the formula becomes:

Formula 5.2. 1 – [ ( ) / ] ∗ 100%

The main benefit from using this formula instead of dividing the realization by the forecast (as is used in the program Planning Tool) is that it prevents leveling the accuracy. This can be illustrated by the previous example where the aggregated data looked fine but displayed a different picture a level below. Positive and negative deviations for each OC, product and supplier combination leveled each other out in this case. Therefore the Planning Tool seems only usable for information on the lowest level where no aggregation takes place. The aggregation of these data on this level is necessary because of the organizational structure. As mentioned earlier, Wavin consists out of four regions. Those regions are controlled by regional purchasing managers that are accountable for what happens to their regions. Therefore the procurement department at the head office would like to know how each region performs in terms of the forecast accuracy as to acquire knowledge on the source of the problem.

Based on the above formula data from the Planning Tool is used to calculate the forecast accuracy of raw materials. The results for the regions are summarized in table 5.7. As one can see, a fair amount of changes can be seen in comparison with the previous calculation. A good example is the

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forecast was larger than the entire forecast. However, because the accuracy can never be smaller than zero and not larger than one-hundred, the negative number is not used.

Table 5.7

Material

and

Year

PVC 2011 PVC 2012 PE 2011

PE 2012

PP 2011

PP 2012

Region

NWE

89 74 0 47 36 51

SEE

57 60 74 31 85 67

CEE

40 85 86 63 40 69

SWE

69 78 49 62 49 0 (-71)

5.5 Origin of the Raw Material forecast

The raw material forecasts that the head office of Wavin receives from its regions do not exist on their own. Instead, they are dependent on figures from the marketing, sales and production departments. It all starts with the statistical demand forecast that is based on historical sales data calculated by computer programs. These demand forecasts are made for a couple of years in advance and then regularly adjusted as time passes by. Then, one month in advance a Sales & Operations Planning (S&OP) meeting takes place in which a definitive sales forecast is prepared for the month ahead. In addition, a production plan is made based on the results from the S&OP meeting. This production plan then states how much and which products are to be produced. The demand for the raw materials can be calculated when the production plan is known. This is because when the organization knows which products are needed for the next month, a calculation can be made based on the raw material mix. Furthermore, the desired stock level needs to be taken into account to come to the amount that will be ordered with the supplier. In the end this is the number that the regions put into the Planning Tool program and which is used by the head office to

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suppliers. This is when the forecast is transformed in a planning. Figure 5.1 shows a summary of the raw material processes and activities between the OCs and the head office.

Figure 5.1

According to the procurement management of Wavin, the main problem for the unsatisfying raw material accuracy lies in the differences of the forecasting process of the various regions. The procurement department at the head office does not know exactly when the regions possess information on how much materials they need for the next month. Therefore the next step consists out of informing the regional purchasing organizations on their specific forecasting process. The points of interest are the sales forecast for the coming month, the S&OP meeting, the production plan and finally the raw material forecast. As pointed out in the methodology section, the

communication with the regions takes the form of e-mail and telephone conversations. It was

interesting to understand that the forecasting process within the regions was quite similar, except for the region SWE. However, between the regions large discrepancies exist. It is not the way of

Final production plan & raw material requirements

Type in raw material forecasts

Raw material forecast in Planning Tool

Adjustment by HO, based on contracts

Raw materials meeting Raw Materials Planning

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forecasting that differs much though, it is the time at which each activity takes place that is different. The consequence of these differences is that there is no fit between the activities of the regions and those of the head office. It therefore appears that the local organizations and the head office are working side by side instead of working together. But it is not clear if this could have an influence on the accuracy of the raw material forecast that the head office receives.

For the region NWE there was an opportunity to interview the fulfillment planner. He is part of the Wavin Netherlands organization and is stationed at the production site in Hardenberg. His task consists out of managing the process from the moment that orders are placed up to the moment that the products are delivered. It is mainly a supply chain function in which inventory levels and production capacities are taken into account. The main purpose of the interview is to understand how the program Planning Tool is used, where its information comes from and how the production site communicates to the head office. As with the other regions, the practices within the NWE region are largely the same. The fulfillment planner receives the first input in the Planning Tool program from the demand manager. The sales forecast that is made for the Netherlands, is also made for Belgium and Germany by the same demand manager. He makes use of a statistical program that includes historical data from the past 24 months. The outcome is then adjusted by the marketing department based on market intelligence. Furthermore, the demand manager can adjust the sales forecast upwards or downwards based on short term market developments. Then a calculation on how much raw materials are needed for the coming period can be made based on this information. The fulfillment planner looks to the inventory of raw materials to see whether more or less materials are needed. These matters are then discussed in the S&OP meeting. After this process is completed, he puts the number of needed raw materials in the Planning Tool after which the head office knows how much to order with the suppliers.

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the release of the raw materials figures to the head office takes sometimes place after the raw materials are communicated to the head office. When this happens, the fulfillment planner tells that the region sends the figures that were known last month. The consequence is that the head office works with numbers that are one month old. When asked about a comparison of the accuracy of the old and the new numbers, he said: “the old numbers are probably not as accurate as the new ones”. This is therefore an important organizational matter that can have an influence on the raw material accuracy of a company. With regard to the accuracy, the fulfillment planner says the primary factor that affects the accuracy is the market development. Short term developments can cause changes for the need of products, and ultimately, the need for raw materials. An example would be price- or other actions of competitors that lead to a lower demand for Wavin products. Also, demand for industry-specific products in general can slow down unexpectedly according to the fulfillment planner.

So according to the fulfillment planner, the most important factor influencing the sales accuracy, and therefore also the raw material accuracy is the movement in the market. However, this is not

something the procurement department can influence. To uncover the quality of the input data (i.e. demand/sales), and to see whether there is space to improve the raw material forecast accuracy through the improvement of processes, the supply chain optimization (SCO) manager was asked to show how well the company can forecast the sales of products. According to him there should not be a very large last minute change to the amount of raw materials for the next month as one can forecast the demand quite accurately based on the statistical forecast. Nevertheless, it does not alter the fact that the procurement department faces problems with its raw material forecast.

With this information in mind, another direction is needed in order to uncover the source of the business problem. As the demand and sales forecast is fairly accurate according to people from various parts of the organization, the focus will turn to the technique of the raw material forecast.

5.6 Determining the raw materials quantity

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demand planning from which the quantity of raw materials is estimated. However, because the focus is on the mid-term period (one to three months), there is no very detailed schedule and this makes it difficult to calculate the need for raw materials accurately. The region can forecast the need for these materials for one month in advance with a reasonable accuracy level, but looking further into the future would be a much more difficult task. The reason for this is because the raw material forecast is based on a sales plan, not the production plan. This means that the raw material forecast does not take into account specific production information that is necessary to come the needed amount of materials. So the organization knows with a large degree of certainty how much and which products it will sell, but not what it is going to produce for the coming period. As a consequence, it cannot accurately determine the amount of materials needed to produce the products.

In order to improve the raw material planning process and to know better how much materials are needed, the OC in France has abandoned the above process for a new method. In this new situation, the planning of materials takes place within an Enterprise Resource Planning (ERP) system. The determining of the amount of materials is focused on detailed scheduling, which is a weekly plan. Orders are directly placed into the system and based on a Bill of Materials (BOM) the company is able to directly calculate the need for materials for the future. Advantages of this method are that the calculation is based on the ‘ingredients formula’ of a product and should therefore be more accurate than estimation. Furthermore, more recent and detailed information is used for the calculation instead of a mid-term plan. In addition, the ERP system offers an integrated connection between demand planning and production. That means that one can look further into the future than was possible under the other method. Note however that the accuracy of the forecast will decrease as the time horizon increases, just as often is the case (Stevenson 2011). This is because long-term forecasts must take into account more uncertain factors than short-term forecasts. Nevertheless, this new method should in theory improve the accuracy of the raw materials forecast.

Therefore a comparison between the old and the new system is made to unveil if the potential benefits from theory also hold in practice. As the method in the OC in France has not been in

“Accurate forecasting information for Wavin’s procurement, a matter of control”

Specialization Organizational & Management Control