A COMPARATIVE SIMULATION STUDY OF MANUFACTURING RESOURCE PLANNING, JUST-IN-TIME AND THEORY OF CONSTRAINTS

A COMPARATIVE SIMULATION STUDY OF MANUFACTURING RESOURCE PLANNING, JUST-IN-TIME AND THEORY OF CONSTRAINTS IN VAT CLASSIFIED FLOW SHOPS FACING SMOOTH AND LUMPY DEMAND

by

EDMUNDO JOSE GAMAS BUENTELLO, B.A., M.B.A.

A DISSERTATION IN

BUSINESS ADMINISTRATION Submitted to the Graduate Faculty

of Texas Tech University in Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY Approved

Accepted

2002, Edmundo Jose Gamas Buentello

ACKNOWLEDGEMENTS

I am especially indebted to Ramon Lecuona Valenzuela for his encouragement and steadfast support in the pursuit of my doctoral studies. I am grateful to Dr. Carl Stem, Dr.

Carlton Whitehead, and Dr. Surya Yadav for their respective institutional contributions toward making my participation in the doctoral program a reality.

I wish to extend my appreciation to Dr. Paul Randolph for initiating me into the world of production and operations management, and for his academic guidance throughout the doctoral program, particularly during the writing of this dissertation. I wish to thank Dr.

Ronald Bremer for his academic advice with respect to this dissertation.

I am indebted to Nancy Dodge for her assistance on countless administrative issues, and for her personal support, during my period of enrollment in the doctoral program. I express my gratitude to Patsy Fisher for her help with many administrative chores that made my completion of the doctoral program possible, and to Israel Avila Amaro for his

assistance with the flow shop layout figures.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ii

ABSTRACT x LIST OF TABLES xii

LIST OF FIGURES xiii CHAPTER

1. INTRODUCTION 1 1.1 Importance of the Research and Dissertation Goal 1

1.2 Problem Statement and Research Question 2

1.3 Motivation for the Research 3 1.4 Background for the Research 4 1.5 Format of the Remaining Research Presentation 6

2. LITERATURE REVIEW 10 2.1 Comparative Simulation of MRP, JIT and TOC 10

2.1.1 Three Philosophical Frameworks for Production 10

2.1.2 Previous Comparative Simulation Studies 13

2.1.2.1 Simulating Two Workstations and

One Product 14 2.1.2.2 Simulating Five Workstations and

One Product 16 2.1.2.3 Simulating the Consequences of WIP

Inventory Control Systems 21 2.1.2.4 Simulating the Consequences of an

Adaptive Model ^{T l}

2.1.3 Various Key Considerations in Simulation 23

2.1.3.1 Demand Variability 23

2.1.3.2 Processing Time Variation 23

2.1.3.3 WIP Levels 24 2.1.3.4 Bottlenecks 24 2.2 VAT Flow Shop Classification 25

2.2.1 Archetypal Product and Process Combinations 25 2.2.2 Stereotypical Row Shop Characteristics and

Problems 27 2.3 Production in a Lumpy Demand Environment 30

2.3.1 Lumpy Demand as a Phenomenon 30 2.3.1.1 Nature of Lumpy Demand 30 2.3.1.2 Lumpy Demand in the Context of

Uncertainty 30 2.3.1.3 Origins of Lumpy Demand 31

2.3.2 Impact of Lumpiness on Production Systems 32 2.3.2.1 Dire Consequences of Scheduling

Changes 32 2.3.2.2 Analogy Between Lumpiness and

Some Conceptions of Nervousness 32 2.3.2.3 Immediate Causes of Lumpiness 33 2.3.2.4 Adaptive Capabilities of MRP and JIT 33

3. SIMULATION MODEL ARCHITECTURE 35

3.1 Simulation Framework 35 3.1.1 Research Procedure 35

3.1.2 Simulation Models as Appropriate Research

Tools 35 3.1.3 Selected Simulation Language 36

3.2 Demand Configuration 36 3.2.1 Production Environment 36

3.2.2 Demand Profiles and Row Shop Production

Capacity 37 3.2.3 Demand Stability and Cadence 39

3.3 Row Shop Stmcture 40 3.3.1 Flow Shop Symmetry, Staging and Layout 40

3.3.2 End Item Models, Material Routing and

Product Stmcture 41 3.3.3 Production Line Row 43 3.3.4 Balanced Production Capacity 43

3.3.5 Material Buffers 44 3.4 Workstation Processing Times 45

3.4.1 MRP Processing Time Distributions and

Resulting Plant Capacity 46 3.4.2 JIT Processing Time Distributions 47

3.4.3 TOC Processing Time Distributions 48

3.5 Row Shop Operating Conditions 50 3.5.1 Material Availability 50 3.5.2 Model Classification and Production Path

Sequencing 51 3.5.3 Setup Times 51 3.5.4 Dispatching Technique 52

3.5.5 Process and Transfer Batch Sizes 52 3.5.6 Lead Time Uncertainty 5 3

3.5.7 Defects 53 3.6 Collected Performance Measures 53

3.7 Simulation Run Parameters 34 3.7.1 Starting Conditions 54 3.7.2 Simulation Run Duration 54

3.7.3 Steady State Performance 54 3.7.4 Simulation Run Replication 55 3.7.5 Random Number Generation 55

4. SIMULATION MODEL VALIDATION 62 4.1 Validation Process Background 62 4.2 Equivalences in Processing Times and Material Buffers 63

4.3 Vahdation Outcome 65 4.3.1 Mean Total Output Figures 65

4.3.2 Mean Row Time Figures 66

4.3.3 Mean WIP Figures 68 5. SIMULATION MODEL ANALYSIS 75

5.1 Calculation of Performance Measures 75 5.2 Implications of the Calculated Values 76

5.3 Analysis of Variance 76 5.4 Comparison of Sample Mean Values 79

6. RESEARCH DESIGN 84 6.1 Overarching Research Framework 84

6.2 Research Tool Selection 85 6.3 Experimental Design 85 6.4 Enumeration of Hypotheses 86

6.5 Motivation for Hypotheses 87 6.6 Discussion of Hypotheses 89 6.7 Implications of the Hypotheses of Interest 90

7- RESEARCH FINDINGS 92 7.1 Flow Shop Mean Flow Time 92

7.1.1 "V" Classified Flow Shop Mean Flow Time ^)2

7.1.1.1 "V" Classified Flow Shop Mean

Row Time ANOVA 92 7.1.1.2 "V" Classified Flow Shop Mean

Row Time Comparison of Sample

Mean Values 93 7.1.2 "A" Classified Flow Shop Mean Flow Time 95

7.1.2.1 "A" Classified Flow Shop Mean

Row Time ANOVA 95 7.1.2.2 "A" Classified Flow Shop Mean

Row Time Comparison of Sample

Mean Values 96 7.1.3 "T" Classified Flow Shop Mean Flow Time 98

7.1.3.1 "T" Classified Flow Shop Mean

Row Time ANOVA 98 7.1.3.2 "T" Classified Flow Shop Mean

Flow Time Comparison of Sample

Mean Values 98

7.2 Flow Shop Mean WIP 101 7.2.1 "V" Classified Flow Shop Mean WIP 101

7.2.1.1 "V" Classified Flow Shop Mean

WIP ANOVA 101 7.2.1.2 "V" Classified Flow Shop Mean

WIP Comparison of Sample Mean

Values 102 7.2.2 "A" Classified Flow Shop Mean WIP 104

7.2.2.1 "A" Classified Flow Shop Mean

WIP ANOVA 104 7.2.2.2 "A" Classified Flow Shop Mean

WIP Comparison of Sample Mean

Values 105 7.2.3 "T" Classified Flow Shop Mean WIP 107

7.2.3.1 "T" Classified Flow Shop Mean

WIP ANOVA 107

7.2.3.2 "T" Classified Flow Shop Mean WIP Comparison of Sample Mean

Values 108

8. CONCLUSIONS 136 8.1 Research Contributions 136

8.1.1 Research Contributions Conceming Mean

Total Output 136 8.1.2 Research Contributions Conceming Mean

Row Time 136 8.1.2.1 Corroboration of Previous Findings

on Mean Row Time 136 8.1.2.2 Qualification of Previous Findings on

Mean Row Time 137 8.1.2.3 Contribution of New Findings on

Mean Row Time 138 8.1.3 Research Contributions Conceming Mean

Tardiness 139 8.1.4 Research Contributions Conceming Mean WIP 139

8.1.4.1 Corroboration of Previous Findings

on Mean WIP 139 8.1.4.2 Contribution of New Findings on

Mean WIP 139 8.1.5 Research Contributions Conceming Simulation

Models 140 8.1.6 Rationahzation of Research Contributions 141

8.2 Research Limitations 142 8.2.1 Research Limitations Regarding Simulation

Model Design 142 8.2.2 Research Limitations Regarding Simulation

Model Results 144 8.2.3 Research Limitations Regarding Simulation

Model Utilization 144 8.3 Avenues for Future Research 144

8.3.1 Analysis of Meaningful Mean Total Output and

Mean Tardiness Variables 144 8.3.2 Correction for Simulation Model Bias 145

8.3.3 Avenues for Advanced Research 147

REFERENCES 149

ABSTRACT

Three production planning and control methods currently vie for supremacy in real- worid industrial application. One of the production planning and control methods in

question is Manufacturing Resource Planning, which is founded upon the logic of its predecessor, Material Requirements Planning. The other two production planning and control methods concerned are Just-In-Time and Theory Of Constraints.

Each one of these production planning and control methods purports to endow a production manager with a comprehensive conceptual framework for the planning and control of his or her industrial operations. The question of whether one of the three

production planning and control methods mentioned is superior to the others is by logical extension a valid and important one for a research undertaking.

Two previous simulation studies compare the performance of the three

aforementioned production planning and control methods. One study was conducted by Fogarty, Blackstone, and Hoffmann (1991), and the more recent study was undertaken by Cook (1994). Both simulation studies contrast the three production planning and control methods in an "I" classified flow shop, which is characterized by a single production path.

This dissertation compares the performance of the production planning and control methods referred to in flow shops with a more complex layout, known as "V". "A" and

"T" classified flow shops, respectively, in accordance with the shape of their layout. It designs and constmcts three simulation models, one to represent each of these three flow shop classifications. Each simulation model is mn under each of the mentioned production planning and control methods, to satisfy either a smooth or a lumpy demand profile, and at four different total material buffer capacity levels.

The simulation models constmcted for the "V" and "T" classified flow shops arc first validated against Cook's (1994) simulation model for an "I" classified flow shop.

The simulation model constructed for the "A" classified flow shop cannot be validated in this manner, due to the inherently different nature of its industrial plant layout, but is similar in the logic of its design to the two validated simulation models.

Four performance measures are collected during the simulation mns; mean total output, mean flow time, mean tardiness and mean work-in-progress. Since the simulated flow shops always deliver their daily quantity demanded, two performance measures, mean total output and mean tardiness, lose their relevance for further statistical analyses.

The remaining two performance measures, mean flow time and mean WIP, broadly support the assertions made by Cook (1994) with respect to them. They generally favor the JIT production planning and control method, followed by the MRP production planning

and control method, in the "V" and "T" classified flow shops. However, in the "A"

classified flow shop, the TOC production planning and control method is generally

superior. Furthermore, a three-way interaction effect is found to exist in almost every case with respect to the production planning and control method employed, the demand profile faced and the total material buffer capacity level present.

These findings may be the consequence of the innate characteristics of the

production planning and control methods studied, or they may be the result of simulation model bias with respect to them. Future research activities are proposed to investigate these issues further.

LIST OF TABLES

1.1 Comparison Among Production Planning and Control Methods 8

3.1 Simulation Model Parameters 56 4.1 Validation Process Parameters 69 4.2 Validation of Mean Total Output 72 4.3 Validation of Mean Flow Time 73

4.4 Validation of Mean WIP 74 5.1 "V" Classified Flow Shop Total Output Mean Values 81

5.2 "A" Classified Flow Shop Total Output Mean Values 82 5.3 "T" Classified Flow Shop Total Output Mean Values 83 7.1 "V" Classified Flow Shop Mean Flow Time ANOVA Results 112

7.2 "V" Classified Flow Shop Mean Flow Time Sample Mean Values 113 7.3 "A" Classified Flow Shop Mean Row Time ANOVA Results 116 7.4 "A" Classified Row Shop Mean Row Time Sample Mean Values 117 7.5 "T" Classified Flow Shop Mean Flow Time ANOVA Results 120 7.6 "T" Classified Flow Shop Mean Flow Time Sample Mean Values 121

7.7 "V" Classified Flow Shop Mean WIP ANOVA Results 124 7.8 "V" Classified Flow Shop Mean WIP Sample Mean Values 125 7.9 "A" Classified Flow Shop Mean WIP ANOVA Results 128 7.10 "A" Classified Flow Shop Mean WIP Sample Mean Values 129 7.11 "T" Classified Flow Shop Mean WIP ANOVA Results 132 7.12 "T" Classified Flow Shop Mean WIP Sample Mean Values 13^^

LIST OF FIGURES

3.1 "V" Classified Flow Shop Layout 59 3.2 "A" Classified Flow Shop Layout 60 3.3 "T" Classified Flow Shop Layout 61 7.1 "V" Classified Flow Shop Mean Flow Time Facing Smooth Demand 114

7.2 "V" Classified Flow Shop Mean Row Time Facing Lumpy Demand 115 7.3 "A" Classified Flow Shop Mean Flow Time Facing Smooth Demand 118 7.4 "A" Classified Flow Shop Mean Flow Time Facing Lumpy Demand 119 7.5 "T" Classified Flow Shop Mean Flow Time Facing Smooth Demand 122 7.6 "T" Classified Row Shop Mean Row Time Facing Lumpy Demand 123 7.7 "V" Classified Flow Shop Mean WIP Facing Smooth Demand 126 7.8 "V" Classified Flow Shop Mean WIP Facing Lumpy Demand 127 7.9 "A" Classified Flow Shop Mean WIP Facing Smooth Demand 130 7.10 "A" Classified Flow Shop Mean WIP Facing Lumpy Demand 131 7.11 "T" Classified Flow Shop Mean WIP Facing Smooth Demand 134 7.12 "T" Classified Flow Shop Mean WIP Facing Lumpy Demand 135

CHAPTER 1 INTRODUCTION

1.1 Importance of the Research and Dissertation Goal

Three production planning and control methods currently vie for supremacy in real- world industrial application. One of the production planning and control methods in

question is Manufacturing Resource Planning (MRPII), which is founded upon the logic of its predecessor. Material Requirements Planning (MRP). The other two production

planning and control methods concemed are Just-In-Time (JIT) and Theory Of Constraints (TOC).

Each one of these production planning and control methods purports to endow a production manager with a comprehensive conceptual framework for the planning and control of his or her industrial operations, although hybrid applications have been devised which are outside the scope of this dissertation. The question of whether one of the three production planning and control methods mentioned is superior to the others is by logieal extension a valid and important one for a research undertaking.

Nonetheless, there exists a dearth of quantitative comparative analyses with respect to the three production planning and control methods in question. For this reason, the

research undertaken in this dissertation is preponderantly theoretical in its scope. It seeks to advance the state of knowledge with respect to the differential performance of the slated production planning and control methods, but is obligated to do so employing assumptions too simple to accurately replicate real-world industrial plants.

The goal of this dissertation is to analyze the performance of all three production planning and control methods in the context of complex flow shop layouts and alternative demand profiles. These flow shop layouts and demand profiles are not considered in the previous literature on the subject. In time this study and others to follow it, steadily

incorporating more complicated and realistic assumptions, should enable a production manager to select one of the three production planning and control methods studied in the knowledge of its relative performance.

1.2 Problem Statement and Research Question

There are three widespread production planning and control methods, MRPII/MRP (hereinafter jointly referred to as MRP), JIT and TOC. The problem confronted b\'

production managers, and so the problem statement that operations researchers must address, is the following. One of these production planning and control methods must be selected over the other two, given their relative total output, flow time, tardiness and Work- in-Progress (WIP), among other performance measures, in light of the specific flow shop layout and distinct demand profile present.

This dissertation seeks to bestow upon operations researchers a performance comparison of the MRP, JIT and TOC production planning and control methods, in the context of three specific complex flow shop layouts or classifications and two distinct demand profiles. The three specific complex flow shop classifications under scrutiny are

"V", "A" and "T", and are collectively known as VAT Classification. The two distinct demand profiles considered correspond to smooth and lumpy demand.

The research question associated with the problem statement, and addressed in this dissertation, in hght of the aforementioned flow shop classifications and demand profiles, is set forth as follows. What is the impact of employing the MRP, JIT and TOC production planning and control methods with respect to total output, flow time, tardiness and WIP, in the context of VAT flow shop classifications, and of smooth and lumpy demand profiles^

Each one of the possible production planning and control method-tlow shop

classification-demand profile combinations is studied and contrasted to the others by means of a specific implementation employing simulation. Each specific implementation attempts

to capture the essential elements that define the appropriate production planning and control method, flow shop classification and demand profile in question. The key assumptions incorporated in each specific implementation are numerous and complex, and the\ are expounded in later chapters, most notably Chapter 3, since each one of them requires background explanation and detailed technical description.

1.3 Motivation for the Research

As noted by Cox and Spencer (1998), Eliyahu M. Goldratt coined the terms "T".

"V", "A", and "T" to represent the major flow shop classifications or logical structures.

In essence, the terms describe flow shops' intemal layout or shape.

Previous performance comparisons among the three production planning and control methods of the computer age, already identified, have focused exclusively on flow shops with an "I" classification. This, of course, represents the simplest flow shop, with only one production path. In practice, prior studies have simplified circumstances even further by assuming only one end item model flowing through the flow shop, and a make- to-stock production environment.

The assumptions made in the previous research are thus of little consequence for the more complex VAT classified flow shops, often producing a full array of end item models, in many cases to satisfy either a smooth or a lumpy set of customer orders. The intention of this dissertation is to provide operations researchers with a performance comparison of production planning and control methods that is more closely tailored to those specific real- world situations.

The performance-oriented simulation models contained in this dissertation allow an operations researcher to assess the aforementioned production planning and control

methods with respect to total output, flow time, tardiness and WIP. The assessment is fashioned for each VAT classification and demand profile mentioned.

Furthermore, the performance-oriented simulation models constmcted in this dissertation contain information useful for the development of more detailed simulation models in future. These future simulation models can incorporate parameters and v ariables of special interest to operations researchers.

1.4 Background for the Research

Wight (1984) established five functions common to production management. These functions afford invaluable points of reference in the comparison among the production planning and control methods that have appeared over time. They are as follows.

1. Master Production Schedule (MPS), 2. Priority Planning,

3. Capacity Planning, 4. Priority Control, 5. Capacity Control.

According to Cox and Spencer (1998), whose book is used as the basis for the

remainder of this section, prior to the development of the computer, production planning and control was undertaken by a method of order launch and expedite. In a typical make-to- stock situation, when the predefined reorder point was reached, a replenishment order was triggered in the industrial plant. The replenishment order encompassed the expected demand during the time required for stock replenishment and safety stock to absorb an\

uncertainty in said demand. The central concern in this process was to balance the costs of ordering and holding stock.

In the aforementioned classical production planning and control method, .MPS was informal. Capacity planning revolved around the use of economic order quantities, w hile priority planning did so around the order point calculation. Capacity and prionts control were both carried out by production foremen, through an informal communication system.

Often, a mle of thumb or period order quantity was adopted in the place of an economic order quantity calculation.

MRP is the first production planning and control method to gain hold in the age of the computer. It is founded upon the time-phasing of production orders and their required materials, and is credited to J. Oriicky at the J.I. Case Company. The MPS uses both a sales forecast and actual customer orders as inputs. Capacity planning occurs in

conjuncfion with this forward-looking MPS. Priority planning occurs within the MRP calculation itself, as production orders and materials are time-phased. Priority control is established through the analysis of deviations from the production plan. At this stage capacity control is executed as the production plan is adjusted in accordance with the capacity available.

JIT, the next production planning and control method to appear, was developed at Toyota in the mid-1970s. Its bedrock is the elimination of all waste through a process of continuous improvement. The MPS is customer-driven through the use of actual orders.

As in the MRP production planning and control method, it is concomitant with capacity planning. Priority planning is carried out through the use of kanbans, JIT's well-known system of visual queues to communicate between workstations. Capacity and priority control are engineered into the pull system of production to meet the industrial plants specific demand, with protective capacity built into the schedule.

The TOC production planning and control method has its origins in a softw are package called Optimized Production Technology (OPT) that was developed and released by Goldratt in the late 1970s and early 1980s. This software incorporated a set of rules that developed an industrial plant's MPS in conjunction with capacity planning. Priority

planning is at the heart of the TOC production planning and control method, w hich guarantees that end items will flow through an industrial plant's bottleneck resources in strict descending order of their cash value-added per unit of time. Capacity and priorit\

control are undertaken jointly, as every possible action is carried out in order to ensure that bottlenecks are never starved for work on the requisite end item or the raw materials, parts and components that integrate it.

The TOC production planning and control method introduces the concept of Drum- Buffer-Rope (DBR) control. Dmm is the industrial plant's bottleneck, which necessaril\

sets the pace for the entire plant. Buffer is the material that is placed in front of the bottleneck, taking advantage of the superior production capacity of the preceding

workstations, in order to make sure that said bottleneck is never starved for work. Rope is the linkage that is established between the bottleneck and the rest of the workstations in the industrial plant, non-bottlenecks by definition, preventing a buildup of excessive WIP that can never flow through the bottleneck.

A brief comparison of the four production planning and control methods described here is to be found in Table 1.1. This table highlights the overarching philosophy, dri\ ing force and principal characteristics of each production planning and control method.

1.5 Format of the Remaining Research Presentation

Chapter 2 of this dissertation contains a review of the research literature that is most relevant to the investigation. It expounds prior simulation models comparing the

performance of the MRP, JIT and TOC production planning and control methods. It also addresses the nature of VAT Classification and lumpy demand.

Chapter 3 specifies the simulation models that constitute the quantitative research tool in this dissertaUon. These models represent "V'\ "A" and "T'' classified flow shops operating under the MRP, JIT and TOC production planning and control methods, w hile facing either a smooth or a lumpy demand profile.

Chapter 4 undertakes the validation of the simulation models. This research acti\ it\

is the cornerstones of confidence in the final results of the stud\.

Chapter 5 discusses the analytical framework for the simulation models. It details the variables of interest and the statistical analyses employed.

Chapter 6 surveys the experimental design of the research. It proffers the hypotheses and places them in the context of the overarching research framework.

Chapter 7 presents the results obtained from the simulation models. It then conducts the requisite hypotheses testing.

Chapter 8 concludes the dissertation with an overview of the contributions made b>

the research. It also lists its hmitations and the avenues for further research.

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CHAPTER 2

LITERATURE REVffiW

2.1 Comparative Simulation of MRP, JIT and TOC 2.1.1 Three Philosophical Frameworks for Production

According to Byrne and Jackson (1994), there are three basic contemporary

methods of planning and control in industrial plants; MRP, JIT and TOC. Each production planning and control method has reaped extensive adoption in industry and rests on a

coherent theoretical framework. It has also become more and more common for real-worid industrial plants to employ some ad-hoc combination of these production planning and control methods, but as has akeady been stated, the resulting hybrid methods are outside the scope of this dissertation.

MRP constitutes the oldest of these production planning and control methods. This production planning and control method sets a priority on matching stock to workstations, and avoiding idleness through the backward scheduling of materials from the end item due dates.

MRP is a push production planning and control method. Savsar and Al-Jawini (1995) described what this means:

In conventional production flow systems, which are caWed push systems, material or semi-finished parts are pushed downstream the line as long as they can move. (p.

67)

Thus, the MRP production planning and control method is predicated upon pushing materials downstream. This feature of the MRP production planning and control method distinguishes it from both the JIT and the TOC production planning and control methods.

JIT is a method of planning and control in an industrial plant that has its roots in the Toyota company in particular, and Japanese industry in general. Its overriding aim is to

eliminate waste, responding to demand pull by producing only what is needed in small lots, and receiving materials from suppliers just-in-time to do so.

The TOC production planning and control method has also variously been known as synchronous manufacturing or as OPT, the name of the early software application that employed the production planning and control method's guiding principles and logic. This production planning and control method is concemed with optimizing the flow of work through the bottlenecks present in any industrial plant, so as to achieve the maximum possible throughput, as measured by profits.

Fry, Cox, and Blackstone (1992) described the logic incorporated in the OPT software. OPT is rooted in the following TOC production planning and control method's principles, in as much as it:

.. .combines a finite scheduler for factory bottlenecks with a backward scheduler for non-botdenecks.. .separates the engineering network into a critical network and a noncritical network. The critical network contains all bottleneck resources and the nonbottleneck resources that are fed by the bottlenecks.. .After the MPS has been developed.. .The completion dates for orders that are processed by a bottleneck may be different from the customer due dates. The completion dates for orders totally processed by nonbottlenecks, however, coincide with the customer due dates.. .For orders that are processed by a bottleneck, a second master production scheduling point is the bottleneck, and these orders are back scheduled from the planned production date at the bottleneck. For orders routed only through nonbottlenecks, back scheduling is from the customer promised delivery date. (pp. 229-239)

The distinction between bottlenecks and non-botdenecks is thus a characteristic feature of the TOC production planning and control method. This production planning and control method focuses its efforts on the smooth operation of the former.

Veral (1995) studied the differences relative to shop floor control among the three production planning and control methods under scrutiny. He also identified those elements of the MRP production planning and control method that have become universal!) accepted:

Manufacturing control approaches such as JIT's Kanban system, and OPT's drum- buffer-rope system concentrate on the above-mentioned fine-tuning of lead times lor initiating and / or throttiing order releases. Both propose the elimination of fixed planning parameters the MRP uses. In OPT, a signal is sent to the material entry point to withhold any order which will be routed to an overioaded work station.

This signal prevents additional work to pile up at akeady congested stations and increase WIP. In Kanban systems, OPT's signal is substituted by a physical buffer hmitation which chokes the output of the immediate upstream (feeding) workstation, thus allowing the overloaded station to work off its current load. However, for

highly diverse production activities with numerous routings, the Kanban system is subject to frequent stop-and-go sequences, causing interruptions. Consequently, one of the limitations of Kanban systems is that they are not very suitable for

variable or flexible routing environments.. .Both OPT and Kanban systems advocate the deliberate use of idle time and resultant capacity under-utilization.. .In practice, aspects of MRP which address MPS development, bills of material, routing and inventory files have come to be universally accepted as basic necessities for all manufacturing systems even if they are not used in a strictly MRP context. So, the problem lies with incorporating the information provided by MRP (or the MRP function) and the dynamic information generated by shop floor activities for achieving shop floor effectiveness, (p. 90)

We can conclude that each of the MRP, JIT and TOC production planning and control methods has a set of distinguishing characteristics. These serve to set them apart under observation.

The MRP production planning and control method has also been researched by Adenso-Diaz and Laguna (1996), Amar (1987), Awate (1988), Bogataj and Horvat (1996), Bregman (1991), Buzacott (1989), Buzacott and Shanthikumar (1994), Daniels (1986), Gardiner and Blackstone (1993), Grubbstrom and MoHnder (1994, 1996), Ho (1993, 1994), Kanet (1986, 1988), Lagodimos (1993), Love and Barekat (1989), Migliorelli and Swan (1988), New and Mapes (1984), Ross (1989), Segerstedt (1995, 1996), St. John (1985), Sum (1993), Veral (1989), Walter (1990), Zapfel (1996) and Zhao and Lee (1993).

Additional studies conceming the JIT production planning and conU-ol method are those by Al-Asseri and Hariga (1995), Arogyaswamy and Simmons (1991), Banerjee and Kim (1995), Barker (1994), Bartezzaghi, Turco, and Spina (1992), Chapman (1990), Co and Zhu (1995), Cowton and Vail (1994), Egbelu and Wang (1989), Goyal and Deshmukh (1992), Gunasekaran, Goyal, Martikainen, and Yli-Olli (1993). Gutierrez and Sahinidis (1996), Hahm and Ohta (1993), Halim, Miyazaki, and Ohta (1994), Houghton and Portougal (1995), Kamoun and Yano (1996), Kim and Schniederjans (1993), Kubiak,

Steiner, and Yeomans (1997), Miltenburg and Goldstein (1991), Miltenburg and Sinnamon (1989, 1995), Morabito and Kraus (1995), Safayeni, Purdy, van Engelen. and Pal (1991), Shin and Min (1991), Steiner and Yeomans (1993), Swenseth and Buffa (1991), Swenseth.

Muralidhar, and Wilson (1993) and Toomey (1989).

The TOC production planning and control method has also been scmtinized by Ashcroft (1989), Gardiner, Blackstone, and Gardiner (1993), Guide (1996), Lee and Plenert (1993), Lundrigan (1986), Schragenheim, Cox, and Ronen (1994), Schragenheim and

Ronen (1990), Simons, Simpson, Carlson, James, Lettiere, and Mediate (1996), Spencer (1994), Wahlers and Cox (1994) and Wheatley (1989).

Other research into production planning and control, in some instances including comparison among different methods, is by Belt (1987), Flapper, Miltenburg, and

Wijngaard (1991), Grosfeld-Nir and Ronen (1993), Hopp and Simon (1993), Lambrecht and Decaluwe (1988), Lander (1988), Matsuura and Tsubone (1993), Miltenburg (1990,

1993), Plossl (1988), Ptak (1991), Swann (1986), Tamura and Fujita (1995) and Yilmaz and Boe (1988).

2.1.2 Previous Comparative Simulation Studies

Gupta and Brennan (1995) discussed the advantages of simulation as a research tool. Although they specifically addressed the MRP production planning and control method, their conclusions can be equally applied to the JIT and TOC production planning and control methods in industrial plants:

In the context of MRP, the advantage of simulation is that it allows researchers to consider variables originating from the MRP system (e.g. lot-sizing rules) as well as those originating from the manufacturing environment (e.g. product structure)

simultaneously, thus providing insights into MRP performance, (p. 206)

Surprisingly few simulation exercises have been undertaken in order to compare the performance of these three different production planning and control methods. This

adversely impacts both production managers and operations researchers, who respectively implement and study them.

Yavuz and Satir (1995) pinpointed the nature of comparative studies. The\

attributed the following goal to them:

Comparative studies deal with the performance comparison of two policies working under identical production system configurations and environment factor settings, (p. 1028)

This conclusion highlights the importance of operating the three production planning and control methods reviewed under identical intemal and extemal conditions.

This ensures an accurate comparison among their respective performance measures.

2.1.2.1 Simulating Two Workstations and One Product

Perhaps the most influential simulation exercise comparing the MRP, JIT and TOC production planning and control methods was that carried out by Fogarty, Blackstone, and Hoffmann (1991). The authors of this simulation exercise expressly intended to

demonstrate the differences among the three production planning and control methods.

They simulated an extremely simple flow shop with this goal in mind, hoping this layout would make it easier to explore the implications of each production planning and control method.

The flow shop they designed had a sequential assembly line with two workstations, and so an "I" classification, and produced only one end item model, thus requiring no setups. Each workstation had an identical production capacity, with three possible dail>

output quantities, and was allowed to process material so long as it was idle and the

following material buffer had free capacity. The WIP level between the two workstations was capped, and an unlimited supply of raw materials was always available for the first workstation.