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Workload balancing capabilities of load based pull production control systems

An evaluation of various load based mechanisms using discrete event simulation

Master's Thesis 2017 70 Pages

Business economics - Supply, Production, Logistics

Excerpt

Table of Contents

Abstract

Table of figures

Table of tables

List of Abbreviations

Glossary

1. Introduction

2. Background
2.1 Workload Balancing
2.2 Production Planning and Control Systems
2.3 PullSystems
2.3.1 Structure of pull systems
2.3.2 Configuration of pull systems
2.4 Release mechanisms
2.5 CONWIP and m-CONWIP
2.6 Performance predictions and research questions

3 Methodology
3.1 Research method: simulation
3.2 Model description
3.3 System parameter and experiment designs

4. Results
4.1 Set 1: Negative exponential inter arrival rates and constant processing times
4.2 Set 2: Negative exponential inter arrival rates and random processing times
4.3 Set 3: Constant inter arrival rate and random processing time
4.4 Set 4: Experiments with different ratios oflarge to small orders and 80% & 90% utilization

5. Discussion

6. Conclusion

References

Appendix A

Appendix B

Table of figures

Figure 1: total throughput times components (adapted from Germs & Riezebos, 2010)

Figure 2: Influence of card count on TTT and STT (adapted from Germs & Riezebos, 2010)...

Figure 3: Production Planning and Control (adapted from Fredendall et. al. 2010)

Figure 4: Flow chart release mechanism 1

Figure 5: Start situation release mechanism 1

Figure 6: Situation after card allocation release mechanism 1

Figure 7: Flow chart release mechanism 1

Figure 8: Start situation release mechanism 2

Figure 9: Situation after card allocation release mechanism 2

Figure 10: CONWIP ( adapted from Germs & Riezebos, 2010)

Figure 11: m-CONWIP system (adapted from Germs & Riezebos, 2010)

Figure 12: Simulation modelling approaches (Robinson, 2004, 42)

Figure 13: Schematic representation m-CONWIP and CONWIP system

Figure 14: Graph of the different systems: random arrival rates and constant processing times.

Figure 15:Graph of the different systems: random arrival rates and processing times

Figure 16: TTT reductions of different ratios for load-based system “one”

Figure 17: TTT reductions of different ratios for load-based system “two”

Table of tables

Table 1: Overview experimental factors

Table 2: Sets of experiments

Table 3: Results random arrival rates and constant processing times

Table 4: Results random arrival rates and processing times

Table 5: Results random arrival rates and processing times. 20/80 ratio

Table 6: Results random arrival rates and processing times. 80/20 ratio

Table 7: Results constant arrival rates and random processing times

Table 8: Results constant arrival rates and random processing times 20/80 ratio

Table 9: Results constant arrival rates and random processing times 80/20 ratio

Table 10: Results constant arrival rates and random processing times for different ratios

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

Abstract

Pull systems can be simple but effective production control systems. However, most pull systems do not perform well with random processing times and have a limited applicability for the MTO sector. The systems do not achieve a decent reduction of total throughput times (TTT) or only under special conditions. The unsolved problem is how to design a load-based system that is able to perform well with random processing times. To overcome this problem a load-based pull system with an adapted release mechanism is proposed. Pull systems need to balance the workload effectively over the different routes and stations in order to reduce TTT. By prioritizing orders regarding their size, the adapted release mechanism balances the workload within the routes and between the different routes. A discrete event simulation with several sets of experiments was conducted to test this release mechanism in a load-based system and compare it to various other pull systems. The results show an improved performance of the proposed system compared to the other systems. The magnitude of this performance improvement strongly depends on the used ratio of large to small orders.

Keywords: [production control], [pull system], [load-based], [workload balancing], [process time variability] [discrete event simulation]

Glossary

Large and small orders:

Every order has a specific amount of needed processing times at the station along its route. Load-based cards display a certain amount of processing times. Orders that need less or equal processing times than displayed by one load-based card are called small orders and need only 1 card to be released. Orders with more needed processing times than displayed by one card are called large orders and need 2 cards to be released.

Production Planning and Control Systems (PPCS):

PPCS are tools to control and plan the production. This is done over functions like order scheduling or material and capacity planning. The goal is to reduce WIP, production and lead times (Stevenson et. al., 2005).

Pull Systems:

Pull systems control the production by limiting the amount ofWIP on the shop floor. Signals e.g. in form of tokens are used to signal free capacity on the shop floor. Releases of orders are regulated through this signals to keep the WIP under the limit (Hopp & Spearman, 2004).

Workload Balancing Capability

Workload balancing capability is the capability of a system to balance incoming orders in such a way, that the workload and the utilization of the different routes and stations is equally high. The workload balancing of a system is considered effectively when the total throughput times of the orders are lower than in an unrestricted/unbalanced system (Land & Gaalman, 1998).

1. Introduction

In the manufacturing industry controlling the workload as well as the input/output is important to realize short lead times and total throughput times (TTT) (Moreira & Alves, 2009). Production planning and control systems (PPCS) are used to gain an advantage over competitors by limiting the work in progress (WIP) and reducing shop floor throughput times (STT) and TTT. Especially in the increasingly important make-to-order (MTO) sector it is most importance to have these advantages in lead times as products cannot be produced to stock and the competition in this sector is high (Stevenson et. al., 2005).

The described advantages can be achieved by using card-based pull systems to control the production and throughput times by limiting the WIP (Germs & Riezebos, 2010). But limiting the WIP does not automatically reduce the TTT. For the reduction of TTT the pull systems need to be able to effectively balance the workload over the different workstations on the shop floor. For each workstation, the workload should be kept on a target level. In order to do this, the WIP has to be balanced between the different routes and between the different station within each route (Fand & Gaalman, 1998).

As the products in the MTO sector have a high variety, products often need to take a specific route and have specific processing times at the different production stations (Germs & Riezebos, 2010). The problem is most pull systems fail to achieve an improved production performance in these environments with random processing times, which means they are not applicable for MTO companies (Germs & Riezbos, 2010). In this research, random processing times stand for different processing times of products, but do not include events like preemptive and non-preemptive outages (Hopp & Spearman, 2000).

Previous researches from Germs & Riezbos (2010) and Ziengs et. al. (2012) have shown that widely used straightforward unit-based pull systems are able to balance the workload effectively by dividing the WIP between the different specific routes. But the systems failed to achieve reduced TTT when random processing times were simulated.

Bodnar (2016) studied a load-based pull system with a simple release mechanism that balances the workload between the routes. This system showed effective workload balancing capabilities and was even able to achieve reduced TTT with random processing times, but only under specific conditions.

It can be said, that the applicability of the most card-based pull systems is limited for MTO companies (Thurer et. al., 2016). Thus, further research ofload based pull systems is needed to overcome the problem with processing time variability. The purpose of this research should be to improve the applicability and robustness of card-based pull systems in the MTO environment. A system that shall overcome this problem has to be able to balance the workload effectively between and within the routes under a variety of processing time distributions.

In this research, a load-based pull system will be investigated that is supposed to be able to balance the workload between the routes and within the routes. This shall be possible by prioritizing the orders by the sum of the needed processing times at the different stations. It releases orders, which’s added processing times are smaller than a certain threshold, first onto the shop floor, whereas the system researched by Bodnar (2016) releases the orders according to the FIFO rule.

Thurer et. al. (2010 & 2014) used prioritization of orders regarding the amount of needed processing times in their COBACABANA simulation and found that prioritizing large orders improves the performance.

The gap is that simple types of pull systems using different prioritizations regarding processing times were not researched yet.

The aim of this research is to explore the proposed load-based release mechanism and to investigate if this release mechanism is able to effectively balance the workload under the influence of processing time variability. Further, the performances of the different described unit- and load- based pull system will be assessed. The purpose is to research how currently in use load-based pull systems need to be designed or adapted to be able to cope with processing time variety.

For this, a discrete event simulation with unit- and load-based CONWIP and m-CONWIP systems will be conducted to investigate the performance of the systems. The throughput time performances of those systems will be compared in various environments to analyze the workload balancing capabilities.

By this, the research will contribute new insights to the literature about how load-based pull systems react to and perform under processing time variability regarding their workload balancing capabilities, and if they are more useful than the unit-based systems. For the managerial contribution, finding suitable pull systems for the MTO environment would enable companies to use simple pull systems or adapt the systems currently in use to control the production of products with processing time variety.

This is important as companies in the MTO sector often do not have the resources to invest in more complex and expensive control systems and thus need less expensive card-based solutions (Thurer et. al. 2015). And as a lot of the MTO companies are small and medium sized enterprises with limited financial resources choosing the right production control system is essential and implementing a not applicable system can have serious financial consequences (Stevenson et. al., 2005).

The structure of this thesis is the following: in chapter 2 the background literature is reviewed, chapter 3 describes the used methodology and models, chapter 4 presents the results of the simulation, which are then discussed in chapter 5. The last chapter 6 concludes the findings.

2. Background

In chapter 2, first workload balancing will be discussed and then the relevant literature for production planning and control systems (PPCS) and pull systems will be reviewed. Further, the in this study used CONWIP and m-CONWIP pull systems will be described.

2.1 Workload Balancing

According to Land and Gaalman (1998), control systems capability to reduce the TTT depends on the workload balancing capability. Total throughput time consists out of different times and these times get influenced differently by the limitation of WIP and workload balancing.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: total throughput times components (adapted from Germs & Riezebos, 2010)

The TTT is all the time it takes an order from the moment it gets accepted until its production is finished and it gets released out of the system. It can be divided into the order pool time (OPT), which is between acceptance and the release onto the shop floor, and the shop floor throughput time (STT), which is all the time the order spends in the queues and getting processed at the different processing stations.

Germs & Riezebos (2010) stated, workload balancing improves the control of arrival times of orders at the different stations on the shop floor. Because of this control, the required queue length to achieve a certain utilization level becomes shorter. This will result in shorter STT, but the orders may have to spend more time in the order pool, as the amount of orders allowed on the shop floor is limited and thus the orders have to wait longer until they get released. The workload balancing is only effective if the TTT gets reduced, which implies that the reduction in STT needs to be bigger than the increase in OPT (Ziengs et. al., 2012).

This trade-off between STT and OPT depends on the allowed amount of WIP on the shop floor, which is in pull systems regulated by the card count. The following graph shows the influence of different card counts on TTT and STT.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: Influence of card count on TTT and STT (adapted from Germs & Riezebos, 2010)

The points on the graph display different card counts, from few cards on the left to an increasing number on the right. As it can be seen for a system that has no constrains on the WIP (push system), the STT is the highest. With limiting the amount of WIP the STT and TTT get reduced. But if the card count becomes to small the TTT increases, which is, as explained above, due to an increasing OPT that overcomes the STT reduction. This graph displays why it is important to find the right number of cards in order to reduce TTT.

Germs & Riezebos (2010) say that the workload gets balanced by the release mechanism of the system. This can be the release mechanism, that releases the orders from the order pool onto the shop floor, or the dispatching mechanism on the shop floor. In order to do this, control loops (explained in chapter 2.3) are needed that can detect workload imbalance in the system and signal that to the release mechanism. This control loops should be designed route-specific to detect the workload imbalance between the routes (Ziengs et. al. 2012). The workload of the system is imbalanced when one route is way more congested than others routes. This can be seen by the availability of cards for the different routes. Once the imbalance is signaled to the release mechanism, the orders will be released prioritizing less congested routes, which will balance the workload in the system. As stated above, this will reduce the queue lengths and by this the STT and TTT.

In conclusion, in addition to the setting the right card count the appropriate implementation of control loops is necessary to achieve effective workload balance.

2.2 Production Planning and Control Systems

PPCS systems can help to reduce cycle times, improve quality, reduce costs and holding due dates. Those aspects are especial important for MTO companies, as products cannot be produced in advance or get stored (Stevenson et. al., 2005). The purpose of such a PPCS is to plan capacity and materiel requirements, demand management and to schedule the orders and by this reduce cycle and lead times (Stevenson et. al., 2005).

PPCS can be divided into several functions or decisions. Different researches like Moreira & Alves (2009) or Fredendall et. al. (2010) name them differently but the functional steps are basically the same. These functions are order entry, order release, and order dispatching. The following figure shows the model from Fredendall et. al. (2010).

Abbildung in dieser Leseprobe nicht enthalten

Figure 3: Production Planning and Control (adaptedfrom Fredendall et. al. 2010)

The first step order entry starts when an order arrives. Based on properties of the order like the production route, processing times, revenue and due date, a decision about the order is made. The order can either be accepted, rejected or new negotiated. Different strategies like total acceptance or due date negotiation can be used. This stage also sets the due dates and takes the workload capacity into account. At the end, accepted order get released into the order pool (Fredendall et. al., 2010; Moreira & Alves, 2009). More detailed descriptions can be found in Fredendall et. al. (2010) and Moreira & Alves (2009).

In the order release stage, the orders get released from the order pool on to the shop floor. The release stage manages when, how many and which orders get released (Land & Gaalmann, 1996). Again, there are different rules for the decision which order should be released when, e.g. immediate release or planned input/output control. This rules are used to prioritize the orders based on their above described properties. The order release stage also takes the capacity of the stations on the shop floor into account. The decision which order to release when is very important as it decides how much WIP is on the shop floor. The order release also decides about the workload balancing as it controls which orders get released and by this the amount of workload of the different routes and stations of a production system (Moreira & Alves, 2009). For further descriptions of these stage and the different used rules look at Moreira & Alves (2009).

The last stage is the priority dispatching, which controls the way of the order on the shop floor and the prioritization of orders in the shop floor queues. Different rules need different information to be performed, like processing time or routing of the order (Fredendall et. al., 2010; Moreira & Alves, 2009). When the queues are small, order dispatching is less important as orders do not have to wait long and the possibilities to prioritize orders are limited due to the low count of orders in the queues. Long queues indicate that the workload is not balanced and should be prevented by a correct order release (Land & Gaalmann, 1996). This means, if the order release fails to do this, order dispatching is needed to balance the workload and gets more important (Fredendall et. al.,2010).

2.3 Pull Systems

Pull systems are production control systems that follow the principle of controlling and limiting the workload on the shop floor (Hopp & Spearman, 2000). The pull system limits WIP by only letting an order onto the shop floor when it gets a signal that is generated by a downstream workstation. Signals are transferred in so called loops, which connect workstations. The signal is usually conveyed by means of a card that signals that capacity is now or will soon be available. This means the releases are based on information about the system status. The aim of pull systems is to limit the WIP on the shop floor and achieve a reduction of TTT by effectively balancing the workload (Hopp & Spearman, 2000).

Pull systems are production control systems and can perform the above described functions of order release and order dispatching. As they do not plan the production, the order entry stage is not handled by pull systems. This stage is done before the pull systems starts to control the production. The pull system does not decide which orders get into the order pool.

The order release stage is handled by cards. Orders orjobs in the order pool need a certain number of cards to be released onto the shop floor, as long as the cards are not available the order has to wait. Once the cards are available, they get attached and the order gets released onto the shop floor. When the order got processed, the cards are detached and send back to get attached to new waiting orders. With this system, the number of cards displays and controls the maximal amount of workload in the system (Hopp & Spearman, 2000).

For pull systems, priority dispatching is also handled by cards and works the same as order release. Order dispatching is performed at different positions on the shop floor by so called loops. This loops divide the shop floor into smaller sub systems and control the production in these subsystems with the above described card mechanism. An example of a system with such loops is the Kanban pull system. If only one loop per route is installed, the production of the order cannot be changed once it entered the shop floor (Gonzalez-R et. al., 2011).

According to Gaury (2000) pull systems consist out of a structure and configuration. The structure stands for the number and pattern of the control loops, which define the way the cards flow. Configuration defines the number of cards and what the cards stand for. First, the for this thesis important structural aspects will be explained and then the configurational ones. At last the different used systems will get described.

2.3.1 Structure of pull systems

The structure of a pull systems is defined by the location and amount of the used control loops. Finding the best specific pattern of loops for the specific environment of the system is crucial for its performance (Gaury, 2000). For this research the structural aspect of route specific and non-route specific loops are important.

(1) Route specific vs non-route specific

In a route specific structure the cards belong to a specific production route or a part of it. Incoming orders need to take a specific route and therefore need specific cards which displays free or soon available capacity for this specific route. One or multiple loops per route are necessary for a route specific structure in order to differentiate between the routes. The in this research used m- CONWIP system is an example for a route specific structure (Germs & Riezebos, 2010).

Non-route specific structures do not have route specific loops. As an example, the CONWIP system hasjust one loop for all routes. Orders are released on to the shop floor regardless which route they need to take and cards just display available capacity for the whole system but not for a specific route (Germs & Riezebos, 2010, Ziengs et. al., 2012).

As orders in the MTO sector are often route specific, the systems need a route specific structure to be able to balance the workload effectively (Ziengs et. al., 2012).

2.3.2 Configuration of pull systems

The configuration of a pull systems describes the characteristics of the used cards and their amount. For this research the below described aspects of the configuration are important.

(1) Number of cards

The number of cards that are used in a loop represents the amount of work which is allowed in the loop (Liberopoulos & Dallery, 2000). The optimal number depends on the characteristics of the system. If to many cards are in the systems, the queues are getting long and a lot of WIP is on the shop floor. This reduces capability of the system to reduce STT, especially if order dispatching is not possible. If to less cards are used, the idle times of the stations increase as not enough WIP is in the system. A lot of research focus on ways to find the optimal card count (Gaury, 2000).

(2) Product anonymous vs product specific

The used cards in a pull system can contain different information. One distinction is if cards are product anonymous or product specific. Product specific cards can only be used to produce one specific product and signal available capacity for a specific product. Product anonymous cards do not contain such information and signal available capacity for the loop they are used in. For a route specific structure, product anonymous cards have to be used (Ziengs et. al. 2012). Ziengs et. al. (2012) states that previous research (Spearman et al. 1990, Krishamurthy et. al. 2004) have

shown that product specific cards are not suitable for an MTO environment because of the routing and product variety, although product specific cards can theoretically be route specific.

(3) Unit-based vs load-based

Load-based cards represent a certain amount of processing times, whereas unit-based cards just represent one order. This means the number of unit-based cards equals the maximal amount of orders that can be simultaneously on the shop floor. As unit-based cards display only an order without the exact processing time, the needed capacity for the order isjust a rough approximation (Ziengs et. Al. 2012). This makes unit-based pull systems less suitable for environments with a high variability in processing times, as the needed time to process the orders on the shop floor can only be estimated.

Load-based cards signal a certain maximal amount of processing time or capacity. By this load-based cards can differentiate orders based on the required processing time or processing capacity and assign a needed number of cards regarding the processing time of the order. For every order the processing time needs to be known. If an order needs more capacity than displayed by one card, more than one card needs to be attached to release the order. By this buckets of processing times are created and each bucket is represented by a certain number of cards.

As the information of process times for every order is taken into account, load-based cards lead to a more accurate approximation of the needed capacity (Ziengs et. al. 2012). The accuracy of this approximation depends on the range of processing times a card displays and how well the different orders fit into this ranges. As load-based cards display the needed processing time more accurate, they are more suitable for an MTO environment than unit-based cards.

In summary, a system that is used in an environment with high process and route variety, should use product anonymous load-based cards and have a route specific structure to be able to reduce TTT.

2.4 Release mechanisms

In this research two different release mechanisms will be simulated. This release mechanisms differ in the way they prioritize orders. For the understanding of the mechanisms and the prioritization the following is important to know.

As already described load-based cards display a certain amount of processing time. Orders that need this amount or less processing time, need one card to be released and will be called small orders. While orders that need more processing times than displayed by one card, need 2 or more cards to be released and will be called large orders. Hence, the ratio of large to small orders is the ratio of orders that need 2 cards to the amount of orders that need only one card.

(1) Release mechanism 1: Balancing workload between different routes

The first mechanism uses route specific cards and routes specific control loops to get information about the workload status in the different routes and to control the WIP and balance the workload. With this information, orders for less congested routes can be prioritized and released first and thus the workload can be balanced between the routes (Ziengs et. al., 2012).

The following figure shows the flow chart for this release mechanism.

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Details

Pages
70
Year
2017
ISBN (eBook)
9783668687110
ISBN (Book)
9783668687127
File size
1.1 MB
Language
English
Catalog Number
v421018
Institution / College
University of Groningen – Economics and Business
Grade
1,7
Tags
production control canban pull systems conwip produciton planning workload balancing simulation process time variability load-based

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Title: Workload balancing capabilities of load based pull production control systems