Thesis Ilse Bosklopper Final s1968645
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Ilse Bosklopper Student Number: 1968645 MSc. Program: Technology & Operations Management Institution: University of Groningen Company: Trelleborg Supervisor: Dr. J. Riezebos Co-assesor: Dr. N.D. van Foreest Company supervisor: Eric-Jan Dregmans
Transcript of Thesis Ilse Bosklopper Final s1968645
Ilse Bosklopper
Student Number: 1968645 MSc. Program: Technology & Operations Management Institution: University of Groningen Company: Trelleborg Supervisor: Dr. J. Riezebos Co-assesor: Dr. N.D. van Foreest Company supervisor: Eric-Jan Dregmans
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ABSTRACT
Purpose – The goal of study is to examine whether the planners choice of aggregation level,
within an MRP system, influences the relationship between routing uncertainty and lead times.
Design/methodology/approach – The data supporting this case study is obtained from
interviews, observations and simulation experiments.
Findings – According to this study high-level MRP is better able to cope with routing
uncertainty, and should be the preferred method of planning in an MTO/ETO environment.
Furthermore, the participant of the experiment appreciated the reduced complexity of his tasks in
the high-level MRP.
Practical implications – Especially in MTO/ETO environments planning is important yet
extremely difficult. The variability, which is inevitable in these environments, can obstruct the
usability of MRP systems. Increasing the aggregation level can enhance the implementation
ability of MRP systems in MTO/ETO environments without having to develop more complex
planning algorithms.
Originality/value – This paper uses experiments to evaluate the performance results of different
aggregation levels. However, this study also observed and interviewed the planners during the
experiments to assess the impact of aggregation level on their tasks.
Keywords – MRP, ETO/MTO environments, Lead times, Planning Aggregation level,
simulation
Paper type – Research paper
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PREFACE
As the closing chapter of my master in Technology and Operations Management at the
University of Groningen, writing this thesis has challenged me more than any other part of the
master. However, I am satisfied with the results and overexcited to hand in the final results of my
Master Thesis.
This results would never been accomplished without the help of others. First, I would like to
thank Jan Riezebos for giving me the chance to do this research. His feedback, positivity and
especially his patience have helped me a lot to keep challenging myself.
The work presented in this thesis was carried out at Trelleborg Ridderkerk. I am very grateful for
being able to perform my research project there. It gave me the chance to connect theory with
practice. I would very much like to thank my supervisor at Trelleborg, Eric-Jan Dregmans for his
enthusiasm, the in-depth discussions and his eagerness to help me. Moreover, I would like to
thank all my colleagues at Trelleborg. Without their time and information contributions it would
not have been possible to finish this paper.
Finally, I would like to thank my family and friends for heir support, feedback but especially for
their encouraging words.
I hope you will enjoy reading it,
Ilse Bosklopper
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INDEX Abstract 1 Preface 2 Index 3 1 Introduction 4 2 Theoretical background 7 2.1 MRP Systems 7 2.2 Routing uncertainty in MTO/ETO environments 9 2.3 MRP adjustments to cope with routing uncertainty 12 2.4 Human influence 14 2.5 Research questions 15 3 Methodology 16 3.1 Research design 16 3.2 Measurements 16 3.3 Case organization selection and description 17 4 Simulation design 19 4.1 General design 19 4.2 MRP Tool 19 4.3 Production simulation 20 4.4 Experimental Design 22 4.5 Verification and validation 23 4.6 Experimental settings: 24 5 Findings 26 5.1 Framework for specifying planned lead-‐times 26 5.2 Influence of uncertainty on lead-‐time 29 5.3 Influence of aggregation level on lead-‐time performance 31 5.4 Human-‐system interaction 32 6 Discussion 34 6.1 Planned lead-‐times 34 6.2 Lead-‐time performance 34 6.3 Implications for human scheduler 35 7 Conclusion 36 7.1 Theoretical implication 36 7.2 Practical implications 36 8 Limitations and Further research 37 9 References 38 Appendix A: Production simulation model 42 Appendix B: Workstation settings 43 Appendix C: Welch’s Method 49 Appendix D: Confidence interval method 49 Appendix E: Normallity tests 50 Appendix F: Influence of PLT method 51 Appendix G: Influence of uncertainty on lead-‐times 51 Appendix H: Average difference in Lead-‐time 52 Appendix I: Influence of aggregation on lead-‐times 53
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1 INTRODUCTION
The current market demand for customised products is argued to be greater than ever before. This has
led to a large growth in the number of MTO and ETO companies, which produce non-‐repetitive, high-‐
variety and bespoke products. Resulting in an increase in competition among them (Aslan et al. 2012;
Van Nieuwenhuyse et al. 2011; Stevenson, L. C. Hendry, et al. 2005). With this increased competition
between MTO/ETO companies, the response time to customer orders has become more important for
obtaining competitive advantages. In MTO/ETO environments the response time consists of order
processing time (i.e. engineering, designing) and lead-‐time. Lead-‐time is defined as the time between
authorization of production to the completion of processing, at which point the material is ready to fill a
customer order (Yücesan & de Groote 2000). For MTO/ETO companies, shorter lead-‐times means faster
customer response, less cost due to work-‐in-‐process (WIP) and higher efficiency (Pahl et al. 2007; Wedel
& Lumsden 1995; Suri 2010).
Not only the physical flow influences the lead-‐time of an order, but also the planning plays an important
role (Wedel & Lumsden 1995). Organizations often use an MRP system to support the scheduler in
making the production planning, and determining when orders are released to the shop floor (Jonsson &
Mattsson 2006; Mabert 2007; Pahl et al. 2007). MRP assumes infinite capacity and static bill-‐of-‐materials
(BOM) with known product routings. It treats lead-‐times as static input data, called planned lead-‐times
(PLTs), representing the amount of time allowed for orders to flow through the task/facility (Ioannou &
Dimitriou 2012; Jodlbauer & Reitner 2012; Ioannou & Dimitriou; Ho & Chang 2001a; Bertrand &
Muntslag 1993). PLTs play an important role in the actual lead-‐time performance of the system, as they
influence the order release moment. Setting PLTs too high causes orders to be released too early, which
increases the level of WIP and results in a self-‐fulfilling extension of lead-‐time (Karmarkar 1989; Pahl et
al. 2007; Selçuk et al. 2006; Wedel & Lumsden 1995). On the contrary, if PLTs are too tight and the
orders are released to the shop floor too late, it is not possible to meet the due date (Ho & Chang
2001b). These relationships show the importance of having accurate PLTs for attaining short actual lead-‐
times.
The assumptions of MRP are hard to align with the characteristics of an MTO/ETO environment, which
complicates specifying accurate PLTs (Ioannou & Dimitriou; Aslan et al. 2012; Stevenson, L. Hendry, et al.
2005). Most attention of scholars has been directed towards relaxing the assumption of infinite capacity,
and implementing variable PLTs based on capacity loading (Ioannou & Dimitriou; Van Nieuwenhuyse et
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al. 2011). However, actual lead-‐times are not purely the effect of capacity loading, but also of several
other factors often highly present in MTO/ETO companies. While MRP assumes a static-‐BOM with known
product routing, MTO/ETO companies are often characterized by high flexibility and variety in product
routing. This routing uncertainty can result in a gap between how the MRP system models the
production processing of the product, and how the product is processed in reality. MRP systems are
vulnerable to uncertainty, and research indicates that uncertainty has a damaging effect on the accuracy
of PLTs. Driven by the still increasing power of computers, research focused on adjusting MRP systems
towards MTO/ETO environments has been concentrated on extending the MRP logic with complex
algorithms to capture the processes and its variables more precisely and correctly. However, as actual
lead-‐times are influenced by many factors, including all of these factors in an algorithm will be a very
complex task and the presence of uncertainty will make it imposable to capture the actual process
perfectly. Moreover, complex algorithms are often not well understood by the scheduler (Pandey et al.
2000), which makes it hard for the planner to keep overview and to have meaningful interaction with the
system. Therefore, in this paper we propose a different method for adjusting MRP towards MTO/ETO,
and preserve one of the most important strengths of MRP logic; its simplicity.
MRP systems are often designed to model the operations at the highest level of detail, i.e. at the task
level (Suri 2010). It is very hard for an automated scheduling system to handle an MTO/ETO shop floor at
the detailed dispatching level due to high level of variety and uncertainty (McKay, 2000). This often
results in a gap between the actual business processes and the way they are being modelled in the MRP
system (Powell et al. 2013). Research indicates that when uncertainty is high, planning too precisely
could in fact be counterproductive (Robinson & Moses 2006). Taal & Wortmann (1997) further notice
that planning too precisely can be detrimental and that using precise plans will most often result in a
nervous system. Using a more aggregated MRP system in which resources and tasks are grouped, can
positively influence the robustness of the system (Taal & Wortmann 1997). Instead of modelling at the
task level, tasks can be grouped and PLTs can be specified for the aggregated group. If resources are
planned in a more aggregated way, uncertainty in product routing can be reduced at the planning level.
While in an MTO/ETO shop floor there is often uncertainty about which machine will process the
product, it is often quite certain through which kind of operations the product has to flow through.
In this paper the view is adopted that the main problem with the usage of MRP systems in MTO/ETO
companies is the eagerness to capture every detail. Therefore, we examine a more aggregated MRP
system in which resources are grouped together. We simplify the task of adopting MRP to MTO/ETO
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companies by limiting our attention on the aggregation level of an MRP system, and do not revise any of
the other elements of MRP calculations. While several researchers have suggested a more aggregated
MRP system, little research is done about the effects it has on specifying lead-‐time and lead-‐time
performance. Therefore, we have chosen to perform an exploratory case study at a company producing
a mix of MTO and ETO products. The goal of this exploratory case study is to create more insight in the
ability of aggregation to cope with routing uncertainty, and the reflection this has on lead-‐times. The
research question guiding this case study is:
How does the planner’s choice of the aggregation level of MRP influence the
relationship between routing uncertainty and lead-‐times?
To support our analysis we will start by building a theoretical framework in which the sub-‐questions of
this research are presented. Then, we will outline the research methodology and describe the
background of our case. Because the effects of the MRP aggregation level are measured with the use of
two simulation models, the next chapter describes these models and how they validated. After this, we
will present and discuss the results followed by a conclusion, limitations and suggestions for further
research.
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2 THEORETICAL BACKGROUND
This theoretical framework will give an overview of applicable literature regarding MRP production
planning. We will start with describing how MRP works. Then, we will discuss the routing uncertainty
that is present in MTO/ETO companies and how this influences both the planning and the performance.
Then, we will discuss how scholars have tried to cope with the characteristics of MTO/ETO companies
and we will discuss an alternative solution, we will look at how these methods have affected the human
scheduler. In the last section the research questions we will answer in this paper are presented.
2.1 MRP Systems
MRP is developed as a solution to the problem of how the right component parts can be received in the
right quantity, at the right time (Ioannou & Dimitriou 2012; Murthy & Ma 1991; Ho & Chang 2001b). The
scope and usage of MRP systems has grown since the 1970’s (Orlicky 1975; Wight 1984; AMR 1995), but
in this research MRP refers to the content and processes in software programs used to make a
production planning. MRP systems assume a known BOM, predetermined fixed product routings, and
infinite capacity (Jodlbauer & Reitner 2012; Ioannou & Dimitriou; Ho & Chang 2001a; Bertrand &
Muntslag 1993).
The BOM shows the relationship between end items and their constituent parts (Hopp & Spearman
2011). In MRP systems, PLTs are specified for each level of the BOM. PLTs represent the amount of time
allowed for orders to flow through the specific task(s). PLTs determine when an order is released to the
shop floor, by subtracting the total PLT of the due date of the product, after which the material is pushed
through all subsequent work centres. An MRP system is often complemented by dispatching rules, which
arrange the queues in front of the workstations (Vandaele et al. 2008). Examples of these dispatching
rules are: First-‐in-‐First-‐out (FIFO), Last-‐in-‐First-‐out (LIFO) and Earliest-‐Due-‐Date (EDD). The order release
policy of an MRP system can be seen as an input control mechanism, as it releases jobs to the shop floor
without taking into account the system status (Fernandes & do Carmo-‐Silva 2006).
In theory, the logic of MRP would seem to preclude the use of any buffering mechanism. However, as in
realistic operating environments uncertainty exists and it is thus it necessary to implement a form of
buffer, which protects against degradation of performance due to this uncertainty. There are several
approaches to buffer against uncertainty, the most frequently described and used buffers are: safety
stock, safety lead times and safety capacity. According to Whybak & Williams (1976), safety stock should
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be used as protection against demand quantity uncertainty, and safety lead times should be used to
cover completion time uncertainty. Their research does not mention safety capacity, which also applies
to most research about buffering in MRP systems. On the contrary, extensive research has been done
about safety stock, and a modest amount of research has focused on safety lead-‐times (Dolgui &
Prodhon 2007).
PLTs are fixed input parameters that need to be specified by the planning department (Suri 2010).
Although it is long known that actual lead-‐times are heavily influence by PLTs, prescriptive ways of
setting either have not been adequately developed (Enns 2001). According to Enns (2001) PLTs should be
based on actual lead-‐times, yet he recognizes the complexity of doing this due to the stochastic and
dynamic capacity constrained production characteristics. Hoyt (1978) argues that planned lead-‐times
should be set on the basis of the average flow times being observed. This method seems not appropriate
for the stochastic real world. It can lead to a high deviation between the due date and the production
completion date, as processing requirements can vastly differ between products. This results in both a
high amount of products waiting for shipment, and a low service level. If, for example the lead-‐times are
normally distributed, half of the products will be too late and the other half will be too early. This is
made visible in figure 2.1.
FIGURE 2.1 NORMALLY DISTRIBUTED LEAD-TIME
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MTO/ETO job shops are complex dynamic systems, for which future conditions cannot be anticipated by
analysing only current performance (Fabrycky & Onur 1987). Another approach is used by Dolgiu &
Prodhon (2007), who state that the PLT is the sum of the theoretical lead-‐time and the safety lead-‐times.
They refer to Melnyk & Piper (1981), who have proposed that the safety lead-‐time should be determined
by k times the standard deviation of the lead-‐times. There are a lot of different opinions about how PLTs
should be set, and no consensus has been reached on the best method. However, it is known that the
PLT should incorporate the estimated processing time, the waiting time and an appropriate buffer.
Even though it is clear that a deep understanding on the effects of PLTs on lead-‐time performance is
needed, literature is lacking in a clear guidance on to how to specify accurate PLTs. A good system must
result in acceptable due date performance, without incurring excessive inventory overall (Enns 2001).
Important relations are:
• Increasing planned lead times result in higher WIP inventory due to queues (Enns 2001)
• Lead-‐times increase non-‐linear long before resource utilization reaches 100% (Pahl et al. 2007;
Ioannou & Dimitriou 2012)
• Several amplifiers (variability, uncertainty, capacity and demand dynamics, heterogeneity of
product mix) negatively influence lead-‐times (Pahl et al. 2007; Ioannou & Dimitriou 2012).
• Lot sizing is about balancing the desire to reduce inventory (by using smaller lots) and increasing
capacity (by using larger lots to avoid setups) and can have severe effects on lead-‐time
performance (Enns 2001; Hopp & Spearman 2011).
In MTO/ETO companies the product mix is very dynamic, this results in high variation in machine
utilization, regularly updating of PLTs is thus necessary. In the next section we will more specifically
address uncertainty within MTO/ETO environments, and how to buffer against it. Moreover, the
particular challenges for implementing and using an MRP system are discussed.
2.2 Routing uncertainty in MTO/ETO environments
Many authors have suggested that MTO/ETO companies present particular challenges for using an
appropriate planning and control system (Aslan et al. 2012; Stevenson, L. Hendry, et al. 2005; Bertrand &
Muntslag 1993; Ioannou & Dimitriou). Several articles have criticized about the applicability of MRP in
MTO/ETO environments and report about the low implementation success rate of MRP systems
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(Bertrand & Muntslag 1993; Hong & Kim 2002). To understand the problems associated with using an
MRP system in an MTO/ETO environment, it is necessary to explore the characteristics of these
environments.
In MTO/ETO companies the goods flow consists of both a non-‐physical (order processing) and a physical
stage (Bertrand & Muntslag 1993). However, in this research we are only concerned with the
characteristics of the physical flow and in factors influencing this physical flow. This is appropriate since
this paper is concerned with the planning and lead-‐times of the physical stage of the flow. Important
characteristics of MTO/ETO companies are: the important role of the customer order, the customer
specific product specifications, and product and production variability and uncertainty (Bertrand &
Muntslag 1993; Ioannou & Dimitriou 2012). These characteristics have their reflection on routing
uncertainty, but also on the methods that can be used to buffer against this uncertainty.
While the important role of the customer order has a more obvious effect on order processing, it does
effect the planning of the physical flow and therefore also the physical flow. The high level of
customization together with the relatively long lead-‐times often forces the production plan to be defined
before all information on item customization and details on the manufacturing activities are completely
disclosed (Alfieri et al. 2012). During engineering, design and process planning activities, the work
content and material content of a project becomes gradually known (Bertrand & Muntslag 1993). The
wishes of the customer can also change during the project; this can lead to design changes that affect
the product routing. Ou-‐Yang & Pei (1999) examined the effects of design changes during the planning
phase that influenced the processing of the product and they concluded that early engineering changes,
could be easily anticipated on. Nonetheless, they did not consider the effect of changes that occurred
later in the planning process nor changes that occurred while the product was already in production. Koh
& Saad (2003; 2002) looked at the diagnosis and effects of various sources of uncertainty. Change in
customer design that resulted in additional routing steps was one of the sources, and they concluded
that this has a significant negative influence on the performance of the manufacturing plant. However,
they ignored that customer design changes can also result in a change of one or more of the routing
steps, or the elimination of production steps. Moreover, while these papers identified the negative effect
of uncertainty in routing on performance, they did not specify how one should react effectively to this
uncertainty. Furthermore, the important role of the customer order and the high level of customization
of products in MTO/ETO companies influence the way one can buffer against this. Given that for
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ETO/MTO products it is impossible to keep an end-‐item inventory, as the specifications are unknown
prior to the order.
Not only does the role of the customer orders effect the uncertainty in product routing, the shop floor
configuration adds up to that routing uncertainty. Many MTO/ETO environments can still be classified as
a job shop due to the flexible nature of this configuration (Hendry & Muda 2003; Stevenson, L. Hendry,
et al. 2005). In this configuration the routing is often somewhat flexible and can be changed by operators
if that fits the current shop floor conditions, or if it fits the product requirements. If for example, a job is
planned on machine A, but there is a long queue in front of this machine an operator can, if possible,
decide to use machine B for certain jobs. While this flexibility is one of the selling points of this
configuration, it has consequences for the production planning as variability and uncertainty often
causes control problems (Soepenberg et al. 2012). The consequences are especially prevalent with long
routings and even when both processing times and routings are known beforehand, predicting the future
state of an order is nearly impossible. Only a small disruption of an order at a station or a deviation of
the routing can have consequences for the progress of the order itself and for many other orders
(Soepenberg et al. 2012). Another variable adding to the routing uncertainty is the possibility to
outsource the production or part of the production while the product was already released to the shop
floor. Outsourcing can have various reasons, regulation of capacity through outsourcing can be one of
the reasons (Riezebos, 2001), The actual lead-‐times can be both negatively and positively affected by
outsourcing.
In an MRP system without buffers, whenever a routing is changed which negatively affects the lead-‐time,
due dates are not met and the lead-‐time will be expanded. On the contrary, in the same MRP system
when a routing is changed that positively affects the lead-‐time, this will only result in finished products
waiting at the Finished Goods Inventory (FGI) till its due date. Especially in capital-‐intensive MTO/ETO
companies this is considered a problem. Safety lead-‐time buffering can be used to avoid missing the due
date; however it can lead to high FGI. These two should thus be balanced, depending on the context.
However, in order to attain the desired service level, it is often necessary to buffer against uncertainty
(Koh & Saad 2003).
Moreover, in practice it appears that changes in the routing that occur just before or while the product is
on the shop floor, are often not translated to the MRP system. This can for example been done to avoid
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nervousness of the system or because no feedback mechanisms are present. Companies thus settle with
out-‐dated routing information, and base new order releases on old information.
2.3 MRP adjustments to cope with routing uncertainty
The robustness of a production plan relies heavily on the possibility of modifying the routing of a product
with no penalty in terms of lead-‐time performance in the companies’ objectives (Alfieri et al. 2012). Due
to the high product routing uncertainty in MTO/ETO companies, it is clear that a production planning
should be able to incorporate a certain degree of anticipation to these uncertain events, while providing
a robust schedule for the execution of activities and utilization of resources.
In the battle to make MRP systems more veracious, most papers focus on the relationship between
capacity loading and actual lead-‐times, and propose a variable PLT that is based on the shop floor
condition. Examples are dynamic lead time estimation (Jodlbauer & Reitner 2012; Ioannou & Dimitriou
2012), Advanced Resource Planning (Vandaele & De Boeck 2003) and Workload dependent lead times
(Pahl et al. 2007). These examples all base the PLTs on the system’s actual workload. The main
advantage of these approaches is that they effectively take into account the congestion that is caused by
the interference of different products in the shop floor. These authors however, do not include other
factors that influence actual lead-‐times in their model. It can even be argued that introducing these
extensions of MRP systems can make the influence of uncertainty and flexibility in product routings more
severe as the planned lead-‐time of an order will be based on the position of orders in production
according to the production planning. Due to the uncertainty and variability in product routing this
information can be incorrect. Besides, the robustness of the production plan will probably be low when
routings are modified on a regular basis, as the PLT will fluctuate even more heavily than in a standard
MRP system.
Most attention has been paid to incorporate the relation between capacity loading and lead-‐times in
planning systems, only a few papers focused on dealing with routing variability and uncertainty. These
algorithms are often concerned with determining optimal routings in other industries than MTO/ETO.
Several scholars have developed algorithms or decision frameworks to determine the optimal routings.
When the number of re-‐routing is high, these algorithms fail. Using the assumption that all other things
remain equal is not valid anymore and will result in sub-‐optimizing (Riezebos et al. 2011). Riezebos et al.
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(2011) propose the use of a heuristic to deal with re-‐routing, to support planning decisions However, in
MTO/ETO environments implementing a heuristic like this requires a lot of information to be available in
time. While this could be done by implementing and integrating manufacturing execution systems, this is
often expensive and difficult to accomplish in job shop environments (Saenz de Ugarte et al. 2009).
Another approach suggested in literature to make MRP more suitable for MTO/ETO companies is high-‐
level MRP (HL/MRP). The idea behind this approach is reflected in for example Period Batch Control and
QRM (Suri 2010; Riezebos 2001). It suggests that the planning system does not need to prescribe who
will work at the various tasks and when they have to start within a period, it suffices to know that there
will be enough capacity at the planning level to accomplish all tasks that are scheduled within this period
(Burbidge 1996; Riezebos 2013). In HL/MRP the amount of BOM levels is reduced by combining tasks
into one level. In a detailed MRP system the planning department has to specify PLTs per task, in a more
aggregated MRP this has to be done per subset of tasks. The rest of the logic of MRP remains unchanged
in HL/MRP.
In detailed MRP systems, every small change in the routing should be adjusted in the MRP system if one
wants to prevent a gap between the real process and the modelled process. These changes often lead to
nervous behaviour of the system, which influences the performance of the plant negatively. By reducing
the level of detail, and specifying PLTs for sets of operations that are performed within a department or
team, small changes will not effect the planning. This is illustrated with the following example: Station 1
to 4 forms a group within the HL/MRP and the group has a PLT of 5 hours. Product A is planned to flow
through station 1 and station 2, with both a processing time of 2 hours. In a HL/MRP system the planning
does not have to be updated when the routing has been changed to station 3 and 4, with differing
process times, because the new routing belongs to the same group. A change within the routing of the
department or team does not influence the planning within HL/MRP. Reducing the level of detail, using
subsets of resources is comparable to the time-‐bucket approach as for example discussed by Taal &
Wortmann (1997). They state that if aggregated information is used, nervousness of a plant can be
greatly reduced.
In MRP it is common to use buffers at each level of the BOM (Vandaele & De Boeck 2003). In a detailed
MRP system this implies using a safety lead-‐time buffer for every individual task. In a more aggregated
MRP system, several tasks are planned as one step, which means only one buffer (Vandaele & De Boeck
2003; Suri 2010). One of the advantages of a more aggregated MRP is variability pooling. A longer lead-‐
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time on one workstation can be compensated by a shorter lead-‐time in another workstation (Vandaele &
De Boeck 2003). Pooling variability tends to dampen the overall variability by making it less likely that a
single occurrence will dominate performance (Hopp & Spearman 2011). Due to the variability pooling,
HL/MRP needs lower buffers.
We propose that by reducing the level of detail, the uncertainty of the product routing will be reduced at
the planning level. Small deviations from the planned routing will no longer affect the product routing at
the planning level. This will positively influence the robustness of the planning. Moreover, buffering
against routing uncertainties is done at the group level, which will reduce the buffer that is needed due
to variability pooling. We propose that the reduction in uncertainty and the centralized buffers will
positively influence lead times.
2.4 Human influence
The influences of changes to the MRP system are hardly or not discussed in relation to their effects on
the human scheduler who will work with it. In complex manufacturing organizations, planning and
scheduling still requires significant human support to ensure effective performance. Planning should thus
not be considered as a mere technical problem. The scheduler is and will stay a critical factor in the
planning process (MacCarthy et al. 2001; Taal & Wortmann 1997). The absence of discussing how the
proposed extension influences the scheduler can be seen as a flaw in previous research extending or
changing MRP systems.
Most extensions of MRP systems are designed from a mathematical perspective and focus on finding a
mathematically optimal planning. Mathematical optimality does not always correspond to ‘real world’
optimality. It is the task of the scheduler to create a feasible and reasonable planning; the main function
of the planning system is supporting the planner in the planning process (Taal & Wortmann 1997). The
interaction between human-‐system should not be underestimated while researching revised MRP
systems.
Complex algorithms are often not well understood by the scheduler, and are considered as ‘black boxes’
(Taal & Wortmann 1997). Increasing complexity will affect the way people work with algorithms. If the
planner does not understand the system, meaningful system-‐human interaction will be complicated, as it
will be hard for a planner to detect problems, adjust parameters and keep overview. In HL/MRP, the
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simple logic of MRP is preserved, which probably leads to a better understanding than complex
algorithmic adjustments.
2.5 Research questions
The main question of this study, ‘How does the aggregation level of MRP influence the relationship
between routing uncertainty and lead-‐times?’ will be answered in the proceedings of this thesis. The sub
questions addressed are:
1. How does implementing HL/MRP influence the planned lead-‐times?
2. How is lead-‐time performance influenced when routing uncertainty is present?
3. How does the level of MRP aggregation affect lead-‐time performance when uncertainty is
present?
4. What are the implications for the scheduler by implementing a HL/MRP?
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3 METHODOLOGY
The purpose of this study is to broaden the insights into how the aggregation level of planning can
influence the relationship between routing uncertainty and lead-‐times. The core goals are to identify
how the aggregation level influences the lead-‐time performance, especially related to planned lead-‐
times, uncertainty and system-‐scheduler interaction.
3.1 Research design
After performing a literature study, empirical research is conducted through an exploratory case study
(Voss et al. 2002; Karlsson 2009). The objective of this study is to answer “how” questions, and the focus
is on a phenomenon within a real life context, which makes case study most appropriate (Yin, 2009). A
single case study is chosen to increase the depth of the analysis. Within the settings of the case
company, a broad range of methods is used to gather both quantitative and qualitative results. In order
to answer the (sub-‐) questions guiding this research, a combination of interviews, behavioural
observations and two interrelated simulation models have been used.
Within the empirical settings of the case organization, we have carefully developed two models; one
simulates the production system and the other simulates the planning system. Robinson (2004) defines
simulation as experimentation with a simplified imitation of an operations system as it progresses
through time, for the purpose of better understanding and/or improving the system. The use of
simulation enables us to test multiple scenarios in a relatively short amount of time, which is necessary
for answering our research questions. The simulation models are not only used to gather quantitative
data about lead-‐time performance, but the models were also used in a simulation game in which the
interaction with the human scheduler was assessed with both behavioural observations as interviews.
3.2 Measurements
The unit of analysis is the MRP of an MTO/ETO company, with the independent variable being the
planning aggregation level. The dependent variable is lead-‐time performance. In this thesis lead-‐time
performance refers to the actual lead-‐time, the service level and the average days spent in FGI. In this
thesis, lead-‐times are measured as the time between order release to the completion of processing
(Yücesan & de Groote 2000). Lead-‐time can have an effect on the moment of delivery; this due date
performance will be measured by the percentage of products that is ready to fulfil a customer order
17
before or on the due date. FGI exists because of deviations between the completion time of an order and
the due date agreed upon with a customer, and will be measured by the average time a product spends
in FGI. In short the variables under consideration are:
• Lead-‐time (hours)
• Customer service (% on-‐time)
• Average time in FGI (due to deviation between due-‐date and lead-‐time) (hours)
Furthermore, planned lead-‐times are measured, to evaluate the accurateness of the PLT and to evaluate
the difference between the two aggregation levels. The observations of the participant and the
interviews conducted are used to assess the scheduler-‐system interaction.
3.3 Case organization selection and description
We have selected a MTO/ETO company in which the production process could be categorized as a job
shop with varying, flexible routing. This company desires to reach shorter lead-‐times and the planners
were used to working with an MRP system. The case company, Trelleborg Ridderkerk, is a global supplier
of engineered rubber solutions in e.g. civil engineering, dredging and energy, and is located in the south
of the Netherlands. Trelleborg distributes products to more than 40 countries in the world. Although,
part of a larger group, the organization can be considered as a medium-‐sized enterprise (MSE) with
around 160 employees. It produces a mix of MTO and ETO products with a high level of customization
and variability, which make it appropriate for our research. Figure 3.1 gives a clear picture of their annual
results in terms of products and product mix.
The organization is currently involved in a Quick Response Manufacturing (QRM) transformation. QRM
pursues the reduction of lead-‐time in all aspects of an organizations operations, both internally and
externally (Suri 2010). The transformation has until now mainly focused on the office and engineering
practices of the case organization (Q-‐ROCs, redesigning processes). While the office is designed in QRM
cells, the shop floor can be classified as a job shop and is therefore suitable for this research. The level of
uncertainty in product routing is high due to the existence of late design changes, changes due to shop
floor conditions and the possibility of outsourcing.
18
FIGURE 3.1 TRELLEBORG PRODUCTION ORDERS
The case company implemented AX Dynamics, and started with designing and implementing the MRP
system in 2013. Currently, the MRP system is designed in a highly detailed manner. This level is used as
the basis for modelling the detailed MRP system. However, it should be noted that production is still not
controlled with the system as little faith is put in whether the performance would be positively
influenced.
19
4 SIMULATION DESIGN
This section will discuss the design, validation and settings of the simulation models that are used. It also
provides insights into how the experiments were conducted. This section is split up into four parts. First,
we explain the general design of the study, and show the interrelationship between the simulation
models. Second, a detailed description of the two simulation models and the in-‐ and outputs of the
models are discussed. Third, the experimental design is discussed. Fourth, the verification, validation and
experimental settings of these two models are discussed.
4.1 General design
To examine the influence of the aggregation level of MRP, we have simulated the MRP system. Two
configurations have to be possible within this simulation, a detailed planning and a high-‐level planning.
Furthermore, the simulation model is designed in such a manner that it allows for analysing the
interaction with the human-‐scheduler. In order analyse the influence of the aggregation level of the MRP
system on the lead-‐time performance, a simulation model of the production process is made. Input data
for both models is obtained from the ERP system, observations of the real system and interviews with
the production planner, the plant manager and several operators.
4.2 MRP Tool
The frame of the MRP model and its boundaries result from the scope of this dissertation: planning the
physical flow of the product. Therefore, the model needs to span the entire production operation. It
excludes process steps like engineering, designing and tendering that are likely to occur within MTO/ETO
companies (Hicks & McGovern 2009).
The model is built using Microsoft Excel. This software is mainly chosen because of the familiar interface.
As the scheduler has to interact with this model, it is suitable for the purpose of this study. The study
requires two configurations of the model: an MRP at task level and an MRP at team/department level.
The input data obtained for the purpose of this model is:
• Demand
• Order specifics
20
• Planned routings of orders
• Expected processing times per task
• Expected setup time per task
• Capacity of the departments/machines/people
• Buffer time per task/department to accommodate uncertainty
The output of the system is, in both configurations, an order release list for the coming period specifying
the day an order has to start. Next to that, a capacity requirement overview is presented in time buckets
of one week.
4.3 Production simulation
Again, the frame of this simulation model and its boundaries are a result of the scope of this dissertation:
planning the physical flow of the product. The aim of this simulation model is to assess the ability of
HL/MRP to cope with routing uncertainty in the physical flow, and the model should thus be a
representation of the whole job shop through which the products flow. Within the boundaries of the
simulation, there were almost limitless options for the routing. With the restriction of two stations, the
exit strategy was not limited and all other stations could be their successor, of course depending on the
characteristics of the product under consideration. The study requires two configurations of the model: a
production system with low routing uncertainty and one with high routing uncertainty. This is modelled
by increasing the deviation between the planned product routing and the actual product routing due to
late design changes, decisions on the shop floor and outsourcing of tasks.
The model is built using the simulation software Tecnomatrix Plant Simulation, which is developed by
Siemens with the purpose to model, simulate, analyse, visualize and optimize production systems,
material flows and logistic operations in an efficient way (Bangsow 2010). The ability of Plant Simulation
models to represent the variability, interconnectedness and complexity of a system makes it appropriate
software for modelling a job shop production environment. To give an indication of the content of this
simulation model, a small overview of some content is given in appendix A.
The input data for this model can be divided into two groups: production datasets and process
parameters. In table 4.1, the subsets the input types are presented. The results of a simulation model
depend heavily on the quality of the input data and the accuracy of the model compared to the
21
behaviour of the real production system (Robinson 2004). To assure data-‐validity, the source all data has
been checked for inconsistencies and unusual patterns and outliers. Knowing the distribution of a data
set matters, as using differences in distribution deliver great differences when implemented into a
simulation. The distribution of the process and the distribution of the setup time are determined for all
31 workstations within the boundaries of the production simulation. Evaluating the distributions of the
workstations has been performed with the use of DataFit, an Add-‐in of Plant Simulation 12. A
significance level of 95% is used for determining the distribution type. The results are summarized in
appendix B.
Input Type Dataset/parameter
Production datasets
Order release list
Planned Product Routing
Actual Product Routing
Process parameters
Work-‐hours per machine/employee
Process time parameters per process step
Setup time parameters per process step
Batch size parameter per process step
TABLE 4.1: INPUT DATA
In order to assess the lead-‐time performance of the system, all performance measures defined in the
methodology are tracked per order and are averaged over the run-‐length to make it possible to make
comparisons between the difference experimental settings. Descriptive statistics such as minima,
maxima and standard deviation are also calculated for the lead-‐time. Moreover, the simulation model
provides the scheduler with information for determining the PLTs. For this purpose, the simulation
model also tracks the average waiting times of each buffer, the average processing time together with
the capacity loading of the past period.
Several simplifications have been made to simplify the situation and to focus on the most important
characteristics of the job shop. The main purpose of these simplifications is to increase the utility of a
model while not significantly affecting the validity or credibility. However, some simplifications are also
made simply because required data was not available. These simplifications enabled a more rapid model
development and use, while not endangering sufficient accuracy for the purpose of this study (Robinson
22
2004). The simplifications made are based upon analysis of case company’s data and information
gathered longitudinally. The simplifications that have been made are:
• Machines never fail
• Input of external supplies are always present at the release moment specified by the MRP model
• Human capacity is not affected by illness or other factors other than lunch breaks
• Transport from one station to another does not require time
• Product Quality Check Type Three (QC3) does not require any time, and is done during ‘normal’
processing time
• Aggregation of one type of machines (caldron) to one machine
4.4 Experimental Design
This study will compare different configurations of both the MRP tool and the simulation model. This
section will provide an overview of the scenarios that are tested. However, we will start with outlining
the planning procedure that is used. In collaboration with the scheduler of the case company, in all
configurations of the MRP tool, order release lists are made on a weekly basis, for a period of five weeks,
indicating which order should be released to the shop floor on which day. Both determining and
adjusting the planned lead times, and readjusting the planning due to capacity limits was the
responsibility of the scheduler. This made it possible to study the behaviour of the planner, and study the
impact the level of aggregation has on the planner.
With these order release lists, several scenarios have been tested. The focus of the scenarios is on the
lead-‐time performance of these order release lists under routing uncertainty. The scenarios tested are
presented in table 4.2.
Scenario Settings Detailed MRP HL/MRP HL/MRP-‐PM
No routing uncertainty Scenario 1D Scenario 1H Scenario 1H-‐PM
Routing uncertainty Scenario 2D Scenario 2H Scenario 2H-‐PM
TABLE 4.2 SCENARIO SETTINGS
23
To give a clear insight into the several scenarios, the scenarios are discussed below.
• Scenario 1D: In this scenario all planned products are produced in exactly the routing as planned,
the planning is made with the use of the detailed MRP tool. Planned lead-‐times are intuitively set
based on the experience of the scheduler
• Scenario 2D: This scenario is similar to scenario 1D, but with routing uncertainty due to design
changes introduced.
• Scenario 1H: In this scenario all planned products are produced in exactly the routing as planned,
the planning is made with the use of the HL/MRP tool. Planned lead-‐times are intuitively set
based on the experience of the scheduler
• Scenario 2H: This scenario is similar to scenario 1H but with routing uncertainty due to design
changes introduced.
• Scenario 1H-‐PM: In this scenario all planned products are produced in exactly the routing as
planned, the planning is made with the use of the HL/MRP tool. Planned lead-‐times are set
according to the method proposed in the next chapter.
• Scenario 2H-‐PM: This scenario is similar to scenario 1H-‐PM but with routing uncertainty due to
design changes introduced.
4.5 Verification and validation
This section addresses the verification and validation of the two simulation models, in order to ensure
that the two models are working correctly and accurately. Verification according to Sargent (2013), is
‘ensuring that the computer program of the computerized model and its implementation is correct’.
Validation is defined as ‘the substantiation that a model within its domain of applicability possesses a
satisfactory range of accuracy consistent with the intended application of the model’ (Sargent 2013).
Validation consists of identifying issues, and adapting accordingly. Model validation is done in various
forms, based on the approaches discusses in Robinson (2004). Conceptual model validation of both
models was conducted with interview sessions and group discussions about the conceptual model and
the assumptions made. The results of a simulation model depend heavily on the quality of the data
(Robinson 2004). To assure data-‐validity, the source all data has been checked for inconsistencies and
unusual patterns and outliers. This is done both visually and with the use of DataFit, an Add-‐in of Plant
24
Simulation 12. We have tried to find the cause of outliers and whenever found appropriate the outliers
are removed from the data.
Doing several tests, and watching the models at a low running speed verified the models. This was done
to check whether the products are processed correctly and whether the methods do what they are
intended to do. Moreover, a semi-‐experienced user of Plant Simulation was asked to test the production
simulation model. Beforehand, he was informed about the goal of the model and a quick screening
through the model was done in collaboration.
By comparing the capacity loading of individual workstations in the commercial ERP system with the
MRP simulation, white-‐box validation of the MRP simulation model was done. At first high deviations in
capacity requirement were found for one particular workstation, however it appeared that the capacity
profile of this workstation (Bouwwikkel) was out-‐dated in the commercial ERP system as the merger of
two-‐production sites added extra capacity. When all settings within both models were the same, no
deviations were found in both the order release list as the capacity requirements. As the HL/MRP is just
an adjustment in configurations of the detailed MRP simulation, and the logic remains the some, not
specific white-‐box testing has been performed. We have verified the model by setting the PLTs to zero in
both configurations; this should result in exactly the same capacity requirements and order release lists.
White-‐box validation of the production simulation was done by inspecting the output reports for
individual stations, and discussing them with the production planner, the shop floor manager an
operator. Within these sessions in-‐ and output of the model were discussed, and important variables
were discussed like utilization and actual lead-‐time. Some minor adjustments were made, especially in
the hours a day a workstation worked. Moreover, breaks were reduced from one-‐hour to a half-‐hour per
shift. This as a rotating system is used, which implies that while every worker goes on a shift of an hour,
machinery is only standing still for a half hour.
After this, black-‐box validation is used to check the overall behaviour of the model (Robinson 2004). For
both models, extensive validation-‐sessions with the production planner and the shop floor manager
were conducted. Black-‐box validation of the MRP simulation model is done by comparing the order
release dates of the detailed MRP simulation with the commercial ERP system the company is currently
using. The issue of experimentation validity is discussed in the next section.
4.6 Experimental settings:
25
In the simulation game, the production simulation model used is a terminating process, as a one-‐week
production schedule in the end-‐point of the simulation. Therefore, the run length will be a week of
operations (Monday to Sunday). While often terminating process, returning to the empty setting after
each period is not a realistic starting point for this model. From the second week onwards, the situation
at the end of the previous week is the starting point for the week after. However, because the system
starts in an empty state, it has to be filled with products before a representative state is achieved for the
first week. The weekly order release lists from the previously described experiments were combined in
the simulation game so that the simulation model can be described as non-‐terminating.
In this case it is appropriate to use a warm-‐up period before obtaining results. The warm-‐up time is
defined as the period the model needs to reach a representative state (Robinson 2004). An appropriate
warm-‐up period is determined with the use of the Welch’s method. The Welch method is applied on the
average lead-‐time obtained from the first scenario (1D). After testing several window sizes, it is
concluded that a window size of 5 is best for this data as it smoothens the data best. After processing 43
products, a representative state is reached. This is equal to a warm-‐up period of 1 day. See Appendix C
for a complete overview of the Welch method. Furthermore, a run length of 5 weeks (35 days) will be
used, thus the end-‐time of the simulation will be set to 36 days (run length + warm-‐up).
As the model is stochastic, one run of the simulation model represents a single observation of the
system. In order to produce a better estimate of mean performance, multiple runs have to be done. The
confidence interval method (CIM) has been used to determine the simulation runs that are needed to
produce reliable results. The CIM is performed on the average lead-‐time of products. With 15 replicates,
a confidence level of 95% was achieved (See appendix D). Given that several experiments will be
conducted, we will stay at the secure side and use 20 runs. This amount of replications is used for all
experiments, and the appropriateness is checked after the experiments have been performed. There was
no need to adjust this; as for all experiments, 20 runs was enough to secure a 95% confidence level.
With the experimental settings used, and the verification and validation of the models taken into
consideration, we can conclude that sufficient accuracy is ensured for the exploratory nature of this
study.
26
5 FINDINGS
In this section first a method for specifying PLTs in HL/MRP is proposed and tested. This is necessary to
answer the first sub question, but also to be able to effectively use HL/MRP and thus to answer the
following sub-‐questions. After this, the outcomes of various experiments will be discussed. First, the
influence of uncertainty on lead-‐times is examined for both the detailed and the HL MRP. Then, the
influence of aggregation on lead-‐time performance is discussed. Since the scenarios relate to each other,
insights will originate from analysing the differences between the models. Finally, the observations made
of the planner and the information gathered with the use of interviews and a short questionnaire are
presented.
Several statistical tests have been performed with the use of SPSS. A significance level of 95% is being
applied for all statistical tests. An important assumption of the tests used is the normality of data.
Therefore, normality of the data-‐series used in these tests is assessed by a Shapiro-‐Wilk test. This is an
appropriate test as the tests are being performed on a rather small sample (n=amount of runs = 20).
With a p-‐value above 0,05 the samples can be classified as normally distributed. A complete overview of
the results is presented in Appendix E. The assumption of normality holds for all data-‐series, except for
one. In this case, an ANOVA-‐test was being applied. This test is fairly robust to small deviations from
normality particularly if the sample sizes are the same, and therefore this test is still appropriate (Lix et
al. 1996).
5.1 Framework for specifying planned lead-‐times
Based on the findings from both practice and theory, in this part a method for specifying PLTs in HL/MRP
will be proposed. This method is based on the literature presented before about planned and actual
lead-‐times, and interviews within the case company. In this method, the usability and intelligibility for a
human scheduler is taken into consideration. Finally, the proposed method will be compared with the
current way of specifying PLTs that is used in the case company, but then applied to the HL/MRP.
5.1.1 Design of the framework
Based on literature, we have established that the PLT should incorporate the estimated processing time,
the waiting time and an appropriate buffer. Furthermore, It should take into account the stochastic
nature of the real world. However, how this should be done in a HL/MRP in a MTO/ETO organization is
27
not yet discussed in literature. In fact, there is not even an agreement among scholars what method for
specifying PLTs works best in MTO/ETO environments. Before proposing a method for specifying PLTs in
HL/MRP, the objective of PLTs should be clear; estimating the time the job will take in such a manner
that the goals in terms of service level and FGI are reached.
Interviews within the case company made clear that no real method for specifying PLTs was in use, and
that it was based solely on the feeling and experience of the scheduler. Moreover, discussions about the
PLT methods proposed in literature made clear that using historical averages was considered the most
understandable and simple method. However, as discussed, using this method can have severe
consequences for the service level and FGI. Based on literature, and interviews the following
requirements for specifying the planned lead-‐times for high-‐level MRP in MTO/ETO companies are
determined:
1. In MTO/ETO environments, demand and product mix can fluctuate quite extensively, which has a
direct influence on the production flow. Therefore, the utilization level will also fluctuate. This
has an effect on the waiting times, and thus when utilization shifts happen this should be taken
into consideration when specifying PLTs.
2. Processing times are somewhat determined by the product, changes in demand and products
can thus effect the processing times. Therefore, the processing requirements of the products
should be taken into consideration when specifying PLTs.
3. As the safety lead-‐time buffer has an effect on both service level and FGI, an appropriate buffer
should be determined per product that does not lead to a large amount of FGI or to a very low
service level.
We propose a method that uses the expected processing time of a product (2) and expand it with a
method to determine an appropriate buffer (3) that takes both waiting times and uncertain events into
account. This buffer should depend on the group’s utilization level that is expected in the period under
consideration (1).
With expected processing time is meant the expected time the products will spend in processing,
summed for all tasks performed within the group. The buffer should be determined by looking at the
flow times of the group (waiting times + processing times) of a comparable period (same utilization level,
and preferable comparable average processing time). The standard deviation between the flow times
28
and the average processing time of that period should then be determined; this represents the standard
deviation of the waiting time. The buffer to cover waiting times and uncertain events should then be
determined per product with:
𝐵 = 𝑧 ∗ 𝑠.𝑑 𝑜𝑓 𝑝𝑒𝑟𝑖𝑜𝑑 𝑛
With:
B=buffer time s.d of n= standard deviation of the waiting time of a comparable period n z= standard score
The standard score is depending on the desired service level and the distribution of the standard
deviation of the waiting times. For reasons of simplicity, in this research the deviation between the
waiting times and the average processing times are all assumed to be normally distributed.
With this method the expected processing times are set per product (type) and the buffers are set
depending on the group it has to flow through. Thus the buffer time does not depend on what kind of
product is going through the group. The total PLT can then be determined per product with:
PLT= EPT + B
With:
PLT = planned lead-‐time EPT = expected processing time
5.1.2 Testing of the framework
A comparison is done between the PLTs specified purely on prior experience of the scheduler and PLTs
specified with the proposed method. Analysis of the PLTs shows a reduction of the average PLT. When
the PLTs are based on the planners experience, an average PLT per product of 6,5 days is estimated.
When the method proposed above is implemented this reduces to 3,8 days.
The influences of these two methods of PLT specifying are further examined with the use of the
production simulation. As uncertainty in routing is present in the case company, this is also present in
the simulation. The results, summarized in table 5.1, indicate a reduction in average lead-‐time of almost
35 hours when the PLT is specified with the use of the proposed method. On the contrary, the average
29
accuracy of the PLT and the actual LT has lowered slightly. When the PLTs were based on experience, the
PLTs were on average 0,5 hours longer than the actual lead times. Using the proposed method resulted
in a higher deviation of 1,75 hours. Moreover, the service level increased significantly to 86%. However,
this is still lower than the case company’s desired service level of 95%, which is used in the PLT
calculations.
Experimental
settings
Average
Lead-‐time
(hours)
Standard
Deviation
(hours)
Minima
(hours)
Maxima
(hours)
Left
Interval
bound
(hours)
Right
Interval
bound
(hours)
Service
level
(%)
Experiment 1:
PLT based on
experience 148,13 10,04 132,94 166,88 143,43 152,83
49%
Experiment 2:
PLT method
proposed 113,53 6,58 100,21 127,14 110,45 116,61
86%
TABLE 5.1 LEAD-TIME PERFORMANCE PLT METHOD
To test whether these results differ significantly from each other a one-‐way ANOVA was conducted. This
is appropriate since we are comparing the means of two independent samples. Results indicate that
there is a significant difference in lead-‐time performance between the two experiments (alpha<0,05).
See appendix F for the results of the ANOVA and Welch tests.
5.2 Influence of uncertainty on lead-‐time
In this section, the influence routing uncertainty has on lead-‐times in the different scenarios is discussed.
First, an analysis is done to assess the influence uncertainty has on lead-‐time performance for each way
of using the MRP system. Then, insights will be gathered by comparing the systems with each other. The
results are summarized in the table 5.2.
30
Scenario
Average
Lead-‐time
(hours)
Standard
Deviation
(hours)
Minima
(hours)
Maxima
(hours)
Left Interval
bound
(hours)
Right
Interval
bound
(hours)
Scenario 1D 123,94 9,74 106,03 139,46 119,37 128,50
Scenario 2D 124,63 9,53 107,07 139,82 120,17 129,09
Scenario 1H 148,99 10,28 133,93 168,04 144,17 153,80
Scenario 2H 148,13 10,04 132,94 166,88 143,43 152,83
Scenario 1H-‐PM 114,40 6,52 100,81 128,79 111,35 117,45
Scenario 2H-‐PM 113,53 6,58 100,21 127,14 110,45 116,61
TABLE 5.2 INFLUENCE OF UNCERTAINTY ON LEAD-TIME PERFORMANCE
When the detailed MRP system is being used for determining the order releases, the average lead-‐time
increases with the introduction of routing uncertainty. With routing uncertainty, the average lead-‐time is
47 minutes longer. When the HL/MRP is being used, and the PLTs are being set based on the experience
of the scheduler, the average lead-‐time deceases with the introduction of routing uncertainty. With
routing uncertainty, the average lead-‐time is 51 minutes shorter. When the HL/MRP is being used, and
the PLTs are based on the earlier proposed method, the average lead-‐time also decreases with the
introduction of routing uncertainty. With routing uncertainty, the average lead-‐time is 52 minutes
shorter.
For the three distinct planning methods, a paired sample T-‐test is performed to assess whether these
reactions to uncertainty were significant or not. The paired samples T-‐test is appropriate as we are
comparing the behaviour at two distinct situations (low uncertainty & high uncertainty). Results indicate
that the differences in lead-‐times are significant (alpha<0,05) for all methods. A complete overview of
the statistical results can be found in Appendix G.
Previous results indicate a difference in the ability to cope with uncertainty. When the detailed MRP
system was used the lead-‐times increased, while in the experiments with HL/MRP the lead-‐times
decreased. The ability of the planning method to cope with uncertainty is measured as the deviation
between lead-‐times with uncertainty and without uncertainty. Whether there is a significant difference
31
in the ability to cope with uncertainty is analysed by comparing these deviations. The average
percentage differences in lead-‐time are presented in figure 5.1
FIGURE 5.1 DIFFERENCE IN LEAD-TIME REACTIONS TO UNCERTAINTY
A one-‐way ANOVA test was performed to test whether there is a significant difference in reaction to
uncertainty between the three options. This is appropriate since we are comparing the means of three
independent samples. Results indicate that there is a significant difference between the reaction to
uncertainty between the detailed MRP and both experiments using HL/MRP (alpha<0,05). However,
there is no significant difference in the reaction to uncertainty between the two experiments using
HL/MRP (alpha>0,05). A complete overview of the results of the statistical test can be found in Appendix
H.
5.3 Influence of aggregation level on lead-‐time performance
In this section, the influence of the aggregation level on lead-‐time performance when routing
uncertainty is present is discussed. Insights will be gathered by comparing the scenarios with each other.
The results are summarized in the table 5.3.
-‐1,00%
-‐0,80%
-‐0,60%
-‐0,40%
-‐0,20%
0,00%
0,20%
0,40%
0,60%
0,80%
Detailed MRP High Level MRP High Level MRP & Proposed PLT
method
Hours
Average difference in Lead-‐time
32
Scenario
Average Lead-‐
time (hours)
Standard
Deviation
(hours)
Minima
(hours)
Maxima
(hours)
Average
time in FGI
(hours)
Service
level
(%)
Scenario 2D 124,63 9,53 107,07 139,82 51,94 49%
Scenario 2H 148,13 10,04 132,94 166,88 66,27 49%
Scenario 2H-‐PM 113,53 6,58 100,21 127,14 25,60 86%
TABLE 5.3 INFLUENCE OF AGGREGATION ON LEAD-TIME PERFORMANCE
In the table above we can observe that when the HL/MRP is used, and the PLTs are specified based on
the experience of the planner the average lead-‐time is almost a full day more than when the detailed
MRP is used. Furthermore, these two scenarios do not notably differ from each other in the sense of
service level and average time spent in FGI.
It also visible that the average lead-‐time in the scenario with the use of HL/MRP and the proposed PLT
method is performing best on both average lead-‐times and service level. Moreover, in the other two
scenarios products spend on average more time in FGI. Furthermore, in scenario 2H-‐PM product were on
average a little more than 1 day too early, while in the other two scenarios products were on average 2
days too late.
To assess whether the scenarios, significantly differ from each other a one-‐way anova was performed.
This is appropriate since we are comparing the means of three independent samples. Results indicate
that the three scenarios all significantly differ from each other in average lead-‐times. A complete
overview of the results of the statistical test can be found in Appendix I.
5.4 Human-‐system interaction
In this section the perceived human-‐system interaction is discussed for the detailed and the high-‐level
MRP. The results gathered with the use of observations, interviews and a short questionnaire are
presented below.
First of all, it was observed that less time was needed for composing an order release list when the
HL/MRP tool was used. Furthermore, observing the planner in determining the order release list made
visible that while the planner tried different options in the HL/MRP, he did not do this in the detailed
33
MRP. Questioning the planner about this, led to the insight that in the detailed MRP system he was
afraid that when he changed one setting, a lot of things would change in the system and he would lose
the overview. Observations also presented a clear difference in the amount of manual changes the
planner made to the system and the order release list. In the detailed MRP system, the planner felt the
necessity to bring forward the start date by manually adjusting this date more often than in an HL/MRP.
These adjustments were made to spread the capacity requirement. In the HL/MRP tool, the planner felt
this necessity less, he said about this: “I assume that a lot of the capacity problems observed on the
detailed level, are not a problem in reality due to the flexibility in routing of the job shop. In the HL/MRP I
am better able to estimate whether this is true in specific cases”.
After all experiments were conducted, the general opinion of the scheduler was asked about the two
planning tools. About the detailed MRP system, the planner said: “It doesn’t matter how much time I
spend on perfecting this tool, it will never be perfect nor will it be exactly correct”. And “Designing and
implementing a detailed MRP system is very time consuming, keeping the MRP system up to date is even
more time consuming”. His opinion about the HL/MRP is illustrated in the following statements. “While I
could still react to for example capacity problems in the future, I was less distracted by small details and
could focus more on giving a feasible production schedule for the whole factory”. And “Estimating
appropriate planned lead-‐times is hard at first, though it gets easier after some rounds. In the end, it is
easier as it is more averaged out than in a detailed MRP. However, you do need some time to adjust.”
To support the interview and observations with more quantitative data, the results of the short
questionnaire are presented in table 5.4. The planner was asked to rank each factor on a scale of 1 to 5.
It is visible, that the HL/MRP scores slightly better on all factors considered
Human-‐System interaction
Factor Detailed MRP HL/MRP
Ease to use 3 4
Ease to understand 3 4
Level of overview 3 4
Satisfaction 2 4
TABLE 5.4 RESULTS QUESTIONNAIRE
34
6 DISCUSSION
As this is an explorative case study we will discuss the most remarkable findings. Starting with the
influence of using an HL/MRP on planned lead-‐times. Then, we will continue the discussion with the
influence of uncertainty and aggregation level on lead-‐time performance. After that, we will discuss the
implications high-‐level MRP has on the human scheduler.
6.1 Planned lead-‐times
This research provides evidence suggesting that PLTs influences actual lead-‐times and supports previous
research done in this area. A literature study made clear that there is no agreement among scholars
about how PLTs should be set. Based on the relation between PLTs and lead-‐time performance, it has
become clear that it should cover processing times and waiting times. Furthermore, the amount of
buffering should be considered as a management decision as it is concerned with the desired service
level. In HL/ MRP, a lower buffer is needed to attain the same service level due to variability pooling.
Based on these insights a method to specify PLTs in HL/MRP systems is proposed. The outcomes of the
experiments indicate that the proposed PLT specifying method has a better lead-‐time performance than
an experienced based PLT method. The experience based PLT method leads to higher PLTs, resulting in
longer lead-‐times. These results support the theory of a self-‐fulfilling prophecy, as described by various
scholars (Karmarkar 1989; Pahl et al. 2007; Selçuk et al. 2006; Wedel & Lumsden 1995). The method
proposed for specifying PLTs should be seen as a starting point for further research in this area.
6.2 Lead-‐time performance
Results indicate that routing uncertainty has a negative influence on lead-‐time performance when
detailed MRP systems are used. These results support literature suggesting that uncertainty can be seen
as an amplifier that negatively affects lead-‐time (Van Nieuwenhuyse et al. 2011). However, our findings
suggest that routing uncertainty has a significant positive effect when high-‐level MRP is used.
Furthermore, experimental results show that MRP aggregation affects lead-‐time performance, but it
depends heavily on the way PLTs are specified. The detailed MRP outperforms the high-‐level MRP when
the same method for specifying PLTs is applied. Using the high-‐level MRP together with the PLT method
that is proposed in this paper, results in a significant better lead-‐time performance.
35
6.3 Implications for human scheduler
Even though only one planner has been observed, this research shows that it is important to consider
the role of the planner in research on lead-‐time performance. The role of the planner is especially
important because MTO/ETO environments involve a complexity that cannot be fully captured by
planning systems. Moreover, aggregating the MRP system, has a positive effect on the system-‐planner
interaction, and increases the ease of planning.
36
7 CONCLUSION
This study shows that the aggregation level of MRP positively influences the relation between routing
uncertainty and lead-‐times. While routing uncertainty disrupts the performance of systems controlled
with a detailed MRP, it enhances the lead-‐time performance when high-‐level MRP is used. Furthermore,
the results show that the method of specifying PLTs is of high influence on the performance of MRP
systems, and should not be ignored in research. Based on these findings we cannot conclude whether
HL/MRP is better than detailed MRP when uncertainty is present because that would require testing
more differing PLT settings. The final conclusion is that the scheduler who participated in this simulation
experiments favoured HL/MRP. It was easier to keep overview and the HL/MRP system was better
adjustable, a useful characteristic in an ever changing environment.
7.1 Theoretical implication
This paper adds to current research on MRP systems in MTO/ETO environments because it is not focused
on trying to capture the complexity with ever more complex algorithms. On the contrary this study tries
to reduce that complexity by planning on an aggregated level. This paper has shown that the concept of
HL/MRP is very promising both from a lead-‐time performance as human-‐system interaction perspective.
This study could be a starting point for more research that examines the possibilities of high-‐level MRP.
Moreover, this study contributes to the theory by providing insights in the relation between uncertainty
and lead-‐time performance. While existing literature suggests that uncertainty always has a negative
effect on lead-‐time performance, this study shows that under the right conditions uncertainty can also
have positive effects on lead-‐time performance.
7.2 Practical implications
The insights of this study have implications for MTO/ETO schedulers. Practitioners are investing more
and more resources in the development of MRP and MES systems to perfectly plan and re-‐plan
production. This study indicates that there are other simpler and most importantly cheaper solutions to
these problems. The proposed solution, even though underdeveloped, allows schedulers to attain more
control over the production process since it omits ‘black box’ planning.
37
8 LIMITATIONS AND FURTHER RESEARCH
It has to be noted that this research contains a number of limitations, which have to be taken into
account when assessing the findings and the conclusions of this research. A limitation that negatively
influences the generalizability of the conclusions is the fact that this paper concerns a single case study.
The problem with a single case study is that these findings may only be true for this case. However,
simulating the production and planning system of one company was extremely time consuming,
therefore a multiple case study was impossible. So we chose to perform an in-‐depth analysis over a
broad analysis at multiple cases. However, it is suggested that this study should be replicated with
multiple MTO/ETO companies.
We deliberately left lot-‐sizes out of the scope for this research. This is valid as lot-‐for-‐lot production is a
frequent configuration within MTO/ETO companies. However, lot sizes do influence lead-‐times(Enns
2001). Therefore, we suggest the inclusion of lot sizes in future research endeavors.
Moreover, we left the tasks performed before production out of the scope. For simplicity reasons it is
assumed that front-‐end development is never the bottleneck, and can always be performed on time. In
reality front-‐end development should be taken into consideration when a planning is created. We
suggest including factors like this when more is known on the potential of HL/ MRP.
In this paper we have explored the influences of the MRP aggregation level on the actual planning
performance. However, we have merely compared the current level against an aggregated level. We
believe that there must be an optimal aggregation level at which to perform the planning. Research
about that optimal aggregation, and how this depends on contextual factors could really contribute to
the overall understanding of the phenomena.
Furthermore, disadvantages of a simulation study are the lack of exact/optimal results, the bounded
generalizability and the big amount of data that is needed (Robinson 2004). Furthermore, model
development is time consuming. However, given the research objectives and the exploratory nature of
this study, the advantages outweigh the disadvantages associated with simulation methodology. For
future studies we suggest testing HL/MRP with pilots. In retrospect we spent a lot of time building the
simulation models, so there was relatively little time available to perform the experiments. Simulating an
MTO/ETO environment poses considerable problems to a researcher because of the high variety that
characterizes these environments.
38
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APPENDIX A: PRODUCTION SIMULATION MODEL
43
APPENDIX B: WORKSTATION SETTINGS Process parameters (variable)
Source of unpredictable variability Distribution Parameters
Processing time P1: Extrusion large
(Unit: hours) Lognormal
• Mu=1,1, Sigma=2,9 • Based on data extracted from
enterprise management system for 96 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P31: Press machine (unit:
hours)
Negative
exponential
• Beta = 3,2 • Based on data extracted from
enterprise management system for 100 jobs
• Data is negative exponential distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P32: Press machine (unit:
hours) Lognormal
• Mu=3,9, Sigma=4,8 • Based on data extracted from
enterprise management system for 34 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P33: Press machine (unit:
hours) Lognormal
• Mu=1,3, Sigma=0,2 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P34: Press machine (unit:
hours) Erlang
• Alpha=3,4, Beta=0,46 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Erlang distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P35: Press machine (unit:
hours) Lognormal
• Mu=2,2, Sigma=2,3 • Based on data extracted from
enterprise management system
44
for 25 jobs • Data is Lognormal distributed as
assessed by the Kolmogorov-‐Smirnov test
Processing time P36: Press machine (unit:
hours) Lognormal
• Mu=6,2, Sigma=17 • Based on data extracted from
enterprise management system for 50 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P37: Press machine (unit:
hours) Normal
• Mu=49, Sigma=21 • Based on data extracted from
enterprise management system for 25 jobs
• Data is Normal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P39: Press machine (unit:
hours) Lognormal
• Mu=1,9, Sigma=1,2 • Based on data extracted from
enterprise management system for 89 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P40: Press machine (unit:
hours) Uniform
• Min=0,5, Max=2,2 • Based on data extracted from
enterprise management system for 30 jobs
• Data is Uniform distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P42: Press machine (unit:
hours) Normal
• Mu=1,1, Sigma=0,6 • Based on data extracted from
enterprise management system for 92 jobs
• Data is Normal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P45: Press machine (unit:
hours)
Negative
exponential
• Beta = 3,8 • Based on data extracted from
enterprise management system for 25 jobs
45
• Data is negative exponential distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P47: Press machine (unit:
hours) Lognormal • Same distribution as P39
Processing time P48: Press machine (unit:
hours) Erlang
• Mu=1,9, Sigma=0,46 • Based on data extracted from
enterprise management system for 79 jobs
• Data is Erlang distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P51: Press machine (unit:
hours) Erlang
• Mu=0,91, Sigma=0,2 • Based on data extracted from
enterprise management system for 25 jobs
• Data is Erlang distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P55: Press machine (unit:
hours) Erlang
• Mu=139,2, Sigma=139,1 • Based on data extracted from
enterprise management system for 25 jobs
• Data is Erlang distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P511: Extrusie small
(Unit: hours) Lognormal
• Mu=0,73, Sigma=4,6 • Based on 100 measurements of
individual products extracted from the ERP system
• Data is Lognormal distributed as assessed by Kolmogorov-‐Smirnov test
Processing time P528: Hakken en Boren
(Unit: hours) Lognormal
• Mu=2,3, Sigma=4,8 • Based on 100 measurements of
individual products extracted from the ERP system
• Data is Lognormal distributed as assessed by Kolmogorov-‐Smirnov test
46
Processing time P530: Stralen
(Unit: hours) Uniform
• Min=0,5, Max=2,0 per batch of 20 • Based on observations and
interviews with the production manager and operators
Processing time P531: Smeren
(Unit: hours) Uniform
• Min=2,0 Max=3,0 per batch of 20 • Based on observations and
interviews with the production manager and operators
Processing time P535: Kalander
(Unit: hours)
Negative
Exponential
• Beta = 0,21 • Based on data extracted from
enterprise management system for 50 jobs
• Data is negative exponential distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P557: Bekleding
(Unit: hours) Lognormal
• Mu=56, Sigma=554 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P558: After processing
press machines
(Unit: hours) Lognormal
• Mu=3,6, Sigma=5,1 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P559: Pre-‐processing
(Unit: hours) Lognormal
• Mu=1,1, Sigma=2 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P561: Bouwwikkel
(Unit: hours) Lognormal
• Mu=7,3, Sigma=21 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
47
Processing time P563: Pre/after processing
extrusion
(Unit: hours)
Negative
Exponential
• Beta = 0,21 • Based on data extracted from
enterprise management system for 25 jobs
• Data is negative exponential distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P564: Bouwwikkel2
(Unit: hours) Lognormal
• Mu=3, Sigma=4,7 • Based on data extracted from
enterprise management system for 96 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Processing time P565: After processing
(Unit: hours) Lognormal
• Mu=3,7, Sigma=1,1 • Based on data extracted from
enterprise management system for 96 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
P567: Afwerken en testen
(Unit: hours) Weibull
• Beta = 0,21 • Based on data extracted from
enterprise management system for 25 jobs
• Data is negative exponential distributed as assessed by the Kolmogorov-‐Smirnov test
P579: Quality Check
(Unit: hours) Lognormal
• Mu=8,5, Sigma=24 • Based on data extracted from
enterprise management system for 100 jobs
• Data is Lognormal distributed as assessed by the Kolmogorov-‐Smirnov test
Process parameters (constant)
Parameter Distribution Parameters
Setup time P1: Extrusie large
(Unit: hours) Constant
• 0,5 hour per product ‘batch’ • Based on observations and
interviews with the production manager and operators
48
Recovery time P1: Extrusion large Constant
• 0,5 hour per product ‘batch’ • Based on observations and
interviews with the production manager and operators
Setup time P31 – P55: all pressing machines
(unit: hours) Constant
• 0,25 hours per product • Based on observations and
interviews with the production manager and operators
Processing time P65: Robot (unit: hours) Constant
• 1,13 hours per product • Based on observations and
interviews with the production manager and operators
Setup time P65: Robot
(Unit: hours) Constant
• 0,10 hour per product • Based on observations and
interviews with the production manager and operators
Setup time P530: Stralen
(Unit: hours) Constant
• 0,5 hours per batch of 20 products
• Based on observations and interviews with the production manager and operators
Setup time P531: Smeren
(Unit: hours) Constant
• 0,5 hours per batch of 20 products
• Based on observations and interviews with the production manager and operators
Setup time P535: Kalander
(Unit: hours) Constant
• 0,25 hours per product ‘batch’ • Based on observations and
interviews with the production manager and operators
Setup time P557: Bekleding
(Unit: hours) Constant
• 0,25 hours per product • Based on observations and
interviews with the production manager and operators
Setup time P561:
(Unit: hours) Constant
• 0,1 hours per product • Based on observations and
interviews with the production manager and operators
Setup time P564:
(Unit: hours) Constant
• 0,25 hours per product • Based on observations and
interviews with the production manager and operators
49
Processing time Ketels
(Unit: hours) Constant
• The caldron is heated once a day, during the night. Products remain inside the whole night (21.00 till 09.00).
APPENDIX C: WELCH’S METHOD
APPENDIX D: CONFIDENCE INTERVAL METHOD
!"100.000,00!!!"!!!!
!100.000,00!!!200.000,00!!!300.000,00!!!400.000,00!!!500.000,00!!!600.000,00!!!700.000,00!!!800.000,00!!!900.000,00!!
1! 5! 9! 13!17!21!25!29!33!37!41!45!49!53!57!61!65!69!73!77!81!85!89!93!97!
Cumula&
ve)m
ean)
Number)of)runs)
CIM,)S1D)
cumula4ve!mean!
Lower!Interval!
Upper!Interval!
50
APPENDIX E: NORMALLITY TESTS
51
APPENDIX F: INFLUENCE OF PLT METHOD
APPENDIX G: INFLUENCE OF UNCERTAINTY ON LEAD-TIMES
52
APPENDIX H: AVERAGE DIFFERENCE IN LEAD-TIME
53
APPENDIX I: INFLUENCE OF AGGREGATION ON LEAD-TIMES
