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The impact of a constraint buffer in a flow shop
ELSEVIER
prod&ion economics
Int. J. Production Economics 42 (1995) 175-185
The impact of a constraint buffer in a flow shop Leslie K. Duclos*, Department
of Management,
Michael
College of Business Administration,
University
S. Spencer of Northern
Iowa, Cedar Falls, IA 50614-0125,
USA
Received 1 February 1995; accepted 9 October 1995
Abstract This article presents the findings from a simulation using actual production data comparing the material requirements planning (MRP) scheduling method with that of drum-buffer-rope (DBR) from the theory of constraints (TOC), previously called OPT. The simulation is based on production information from a diesel engine factory currently using MRP. The same data were then used under DBR procedures. Care was taken to utilize the DBR method in the manner suggested in the TOC literature. A buffer-modified MRP was also modeled. This environment used MRP scheduling with a buffer inserted prior to the constraining operation. An analysis of variance of the results of all scenarios indicates that the constraint buffer used in DBR was successful in increasing output from the system. Keywords:
Production
planning;
Theory
of constraints
1. Introduction
One of the more controversial issues in the operations management field over the past ten years has been the investigation of the theory of constraints (TOC). Some of the controversy stems from the earliest applications of the principles, known as OPT, that predated TOC. The controversy was further enhanced by the confusion over just what the differences were between OPT and TOC and the operations management concepts presented in the book The Goal [l]. Confusion has also resulted from the introduction of a TOC performance measurement system distinctly different than traditional operations management performance measurement systems. This has clouded the discussion of the operations management principles associated with the TOC * Corresponding author. Tel.: 319 273 2964; fax: 319 273 2922.
(TOC); Drum-Buffer-Rope
(DBR);
Flow shop; Simulation
technique. What appears to be lacking is a clear statement of the operations management principles expressed in TOC, and a more objective evaluation of those principles. The purpose of this research is twofold. The first objective is to distill and clarify the operations management concepts in The Goal, and relevant OPT and TOC literature. The second objective is to test the operations management scheduling concepts by presenting the results of a simulation based on empirical data comparing TOC with an MRP system in a flow shop. A flow shop was selected because actual data were accessible and TOC scheduling components could be more easily examined than in a job shop. 2. Terminology There are two sources which provide details concerning OPT, TOC, and The Goal. Both sources were written by the principal author of
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TOC, Eliyahu M. Goldratt. In the first source, Goldratt [2] presented a genealogy of OPT describing in some detail the avenue of its development. OPT software was aggressively marketed as a solution to operations contentions at key resources. The scheduling algorithm used in the OPT software was copyrighted and not made available for research. The mystery surrounding the algorithm and the decision to maintain its confidentiality led to much of the controversy surrounding OPT. Only recently has the OPT software been examined in detail [3]. The underlying operations principles found in the OPT software were presented as the nine OPT rules [4]. Examples of these rules include: constraints determine non-bottleneck utilization; an hour lost at the bottleneck is an hour lost for the entire system; and an hour saved at a non-bottleneck is a mirage. The nine OPT rules were examined by some researchers [5,6], but OPT results were presented largely by practitioner articles [7-91. The proprietary nature of the scheduling algorithm discouraged research, and may have limited practitioner acceptance. In 1984, The Goal [l] was written to present the underlying conflicts involving the performance measurement systems and actual manufacturing through the use of a series of fictional vignettes. The term “OPT” did not appear in the book nor was there a clear and concise discussion of a scheduling methodology. The book did present a fivestep process used to improve a production facility. The five step process is: (1) identify the constraint; (2) exploit the constraint; (3) subordinate all other operations to the constraint; (4) elevate the constraint; and (5) avoid managerial inertia should the constraint be broken in step 4. Even this five-step process was not specifically presented in a concise manner until the 1992 revised version of the book. There were, however, instances where principles discussed in The Goal were implemented at actual companies without the OPT software [lo-121. A version of OPT, released late in 1985, focused on the management of the production process through a scheduling technique
called drum-buffer-rope (DBR). The DBR method was presented more fully in the book The Race [ 131 and by Schragenheim and Ronen [14]. It is the DBR method that is the core of the scheduling procedure under TOC and the principal subject of this research. Goldratt introduced the term “Theory of Constraints” in 1987 [15]. By this time TOC was a collection of various concepts including: (1) the performance measurement system, (2) the five-step improvement process, (3) a method of combining a bill of material with the associated routings called V-A-T logical structure trees, (4) identification of control points, and (5) DBR. The nine OPT rules had largely been abandoned. See Table 1 for a framework of TOC components and a glossary of terms. In 1990 another TOC component, buffer management [14], was developed to more effectively manage the buffer and avoid production problems that could jeopardize throughput.
3. TOC literature There has been some research concerning the various components of TOC. Finch and Cox [16] and Lockamy and Cox [ 171 examined the use of the logical structures. Reimer [18] discussed an application at Velmont Industries. Fawcett and Pearson [ 191 provided insights in a conceptual article. Lockamy [20] described the application at a Trane, Inc. factory. All examined the overall TOC system including the underlying performance measurement system. There have also been two articles comparing OPT [TOC] and MRP [7,6] although both were conceptual rather than empirically based. All drew the conclusion that operations management concepts underpinning TOC would improve the overall performance of MRP. In other conceptual articles, researchers have compared the underlying concepts of OPT [TOC], JIT and MRP. Everdell [21], Aggarwal and Aggarwal[22], and Grunwald et al. [23] concluded that all three systems have advantages and disadvantages, and their individual success would depend upon the specific environment and
L.K. Duclos, M.S. Table 1 Framework I.
II.
III.
for theory
SpencerlInt.
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Economics
42 (199.5) 175-185
171
of constraints
Logistics system focusing components: A. Five-step focusing process - A process of continuous improvement for a system focusing on the constraint [I]. B. V-A-T framework - A classification scheme that identifies the positioning of buffers and control points produced by the combination of the bill-of-material and routings. The name “V-A-T” originates from the shapes of the resulting diarams [I 5-l 71. Performance measurement system components: A. Throughput - The rate at which a system generates money through sales [ 131. B. Inventory ~ All of the money the system invests in purchasing things which it intends to sell valued at raw material cost [131. C. Operating expense - All the money the system spends in turning inventory into throughput [13]. Operations planning and control components: A. Drum - The pace of production in the system determined by the constraint [13, 141. B. Constraint buffer - A time offset in the schedule allocated to protect throughput of the system by maintaining a supply of material available for production at the constraint. The constraint buffer absorbs variability in the production process of operations feeding the constraint [13,14]. C. Rope - A communication device to limit (or choke) the flow of material into the system to match the actual production of the constraint [13, 141. D. Shipping buffer ~ A time offset in the schedule of constraint parts allocated to protect throughput of the system by main taining a supply of material available for shipping from the constraint. The shipping buffer absorbs the variability in the production process of operations downstream from the constraint [13,14]. E. Buffer management ~ A method of monitoring the presence or absence of material in a buffer and taking action to prevent disruption of a system’s throughput [14].
Plenert and Best [24] and Sohal and Howard [25] concluded that the underlying principles in TOC would make it superior to either JIT or MRP. Aggarwal [26] and Ptak [27] concluded that a combination of all three systems should be more successful that any individual system. Two efforts have evaluated TOC’s scheduling methodology using simulation. Ramsay et al. [28] developed a simulation based on a fictional product line consisting of two flashlights in a fictional production environment. The researchers named the various subsystems in their simulations based on the function being performed. For example, “squeeze” is used to describe the TOC scheduling function DBR, while “push” is used to describe MRP and “pull” describes JIT. They conclude, “At least as seen here, the squeeze approach appears to be the most useful of the three” [28, p. 451. However, this simulation was not based on field data and did not explicitly examine DBR as described in Schragenheim and Ronen [29]. The researchers did create a constraint buffer and did release material into the system based on the pace of production at the constraint. However, the simulation created a master production schedule (MPS) for end items, rather than creating the MPS managment.
for components at the constraint. Under TOC, the constraint determines the throughput for the system rather than the final assembly schedule. The MPS, in effect, is constructed based on the capabilities of performing specific operations on parts crossing the constraint, wherever it is located, rather than at the top level (level 0) in the bill of material as is common in MRP. Additionally, the Ramsay et al. [28] simulation excluded the shipping buffer of DBR, a mechanism to pull parts through the production process to ensure due-date performance. The simulation also suffered from “[t]he lack of intelligence in the STATUS variable [that controlled operation activation] which lessened the value of the program” [29, p. 451. In TOC terms, this simulation modeled a “T” logical structure, more closely associated with a flow shop, but without a complete implementation of the DBR technique. For an explanation of “T” structure characteristics see Finch and Cox [16]. The second simulation was conducted by Lambrecht and Segaert [30]. This simulation was also based on a fictional production environment. In one case, six work stations were arranged in a series while the second case consisted of a two-line 12-station environment where two components
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converge at work center 11. In both cases there is an assumption of infinite demand for a single end product. The researchers identified their “long pull strategy” as being similar to DBR (p. 54). The simulation was initialized with one work-piece at each station plus a 15-unit buffer positioned prior to the constraint. The researchers concluded, “It is shown that this long pull strategy outperforms other allocation strategies” (p. 61). The simulation did provide for an assembly buffer; however, the lack of a realistic MPS and the lack of a shipping buffer detract from the model. In TOC terms, this simulation models a simple “A” logical structure, more closely associated with a job shop, without determining the effects of the MPS. While both simulations make important contributions, they fail to include important elements of DBR.
4. Methodology Thus, to date, analytical research of the full DBR method does not support the theory that a strategically placed buffer in a “T” logical structure, or flow shop, will improve the performance of the manufacturing system. To better judge the impact of a buffer located at the constraining operation for a completely implemented DBR scheduling method, a simulation model was developed based on data and MRP scheduling methods used at one operating factory. The specific environment modeled was chosen because of accessibility to the facility by one of the authors. No effort was made to randomly select the site. The factory was known to have the highest due-date performance of any factory in the company, as well as being one of the most profitable. The factory’s successful application of MRP has been documented in both trade publications and professional journals [31-331. The production environment selected for the study is a 17-station automated transfer line used to produce a major component for the factory’s product line, the diesel engine cylinder head. The product line consists of five models of heads required for the daily production of finished engines or service requirements. Each of the engine heads is made from a single raw casting. Diesel engines are produced on a line-paced assembly line located at the same facility as the head line. Other
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major components, such as blocks, connecting rods, and crankshafts, are also manufactured at the same facility. The 17-station head transfer line is grouped into two main sections with separate actual cycle times established for each section. Raw castings are placed into the lower section consisting of seven stations, identified in this study as operation 10, where 9.74 standard minutes are required to perform the operations converting the raw casting into a semi-finished block. Most of the work in this section consists of milling and drilling operations. The work-in-process material is then transferred to the upper section, operation 20, where the additional ten operations are performed. In this section, several components are added to the semi-finished block, such as shims and pins. Every head passes through each station in the line. The line is automated such that blocks index from one station to another in a timed sequence dictated by the completion of the programmed cycle time. Should a problem occur at one station the entire line stops. The factory treats the line as two operations, thus the constraint, in TOC terms, is the section rather than a single station. The work done in the second section is more detailed with grinding, polishing and a wash operation as the final step. The standard time for the upper section is 10.38 min per piece. Once finished, the head is sent into the subassembly area to await its turn for final assembly onto a finished engine. The overall production of heads within this line is a flow shop with sequential operations rather than a functionally arranged job shop. Considerable work had been undertaken by the company to implement just-in-time production methods over the previous ten years. As a result, a 1 min setup time is all that is required for each change from one head model to another. Additionally, machine breakdowns are rare in this production line. No attempt was made to incorporate machine down-time due to breakdowns into the model.
4.1. Material
requirements
planning
environment
The factory supplies finished engines to several vehicle factories on a daily basis. In most cases,
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because of the relatively high value of a finished engine, production is paced according to the vehicle factories’ demand. Great efforts have been made throughout the company to provide diesel engines on a JIT basis to the vehicle factories. Requirements for service heads are received from a central distribution depot on a weekly basis. The master production schedule (MPS) is developed from requirements received on-line through an interfactory system. The MPS is stated in weekly time periods and requirements transmitted to an MRP system. Requirements for engine components are determined by traditional MRP calculations. The head line uses a lot-for-lot order policy with a 1 week lead time. The lead time calculation generates requirements for raw castings rather than for production scheduling. Because the factory is a repetitive manufacturer, the MPS from one week to another varies little. The aggregate daily production rate is held constant while the mix of engines changes to meet vehicle plant needs. Thus, the component requirements are repeated from week to week with only the mix quantities varying. The aggregate demand for heads remains unchanged until the entire factory changes the overall daily production level. Operating under MRP the factory produces a week’s worth of each head through the production line on a recurring basis. The MRP system is used to authorize the release of a week’s worth of heads as well as the purchasing of replenishment castings. However, since the line cannot hold a week’s supply, heads are loaded into the first operation using the MRP scheduled quantity as space becomes available in the line’s first station. Raw castings are held temporarily on roller conveyors awaiting entry into the first operation. Finished heads are placed into finished engine head stores in the subassembly area to await final assembly. Under MRP, a 5 day (1 week) supply of finished heads is maintained. As a mix of engines is assembled, heads are drawn from this finished stores area. As production occurs, the finished engine head store is replenished. (Some might criticize the factory for not adopting JIT in this area. Management does have plans to continue its implementation of JIT and eliminate the stores area. However, at this time, a stockout would essentially shut down entire
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vehicle factories since most JIT efforts have been directed at eliminating the more costly finished engine inventory.) The capacity requirements of the head line are determined through a closed-loop MRP process. The workforce is determined by the amount of standard hours required to produce a week’s requirement using MRP data. The workforce calculation is reviewed monthly and adjusted as required. Realistically, the workforce remains unchanged until the daily production rate of finished engines is changed. The workforce level is set to minimize labor delay. Industrial engineers monitor delay rather closely since labor-based costing is used. Fig. 1 depicts the MPS used for this simulation, the shop production schedule that results from MRP’s requirements generation procedure, and the logical structure of the head line under the MRP production environment.
4.2. Theory of constraints environment As further depicted in Fig. 1, under TOC the rules of engagement change. Product demand from the vehicle factories is scheduled on the constraint resource using a Gantt chart. This finite schedule becomes, in effect, the MPS and consists of the components that are routed across the constraint. Each time customer requirements change, the MPS is reviewed and updated to reflect the change in the mix and quantities of the components. Under TOC, the constraint buffer is created by moving inventory typically maintained in the finished engine head stores area into the constraint buffer, as work-in-progress. This is done to maximize throughput from the constraint by absorbing upstream production instabilities. The constraint buffer is monitored on a continuous basis to ensure full utilization of the constraint. A relatively small shipping buffer is maintained to cover processing times, resource contention, and time to absorb random fluctuations in the production process that occur after the constraining operation up to the shipping point. In this environment, all five heads are used for service parts. The finished engine head stores then also serves as a shipping buffer for each head.
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A) Master Production Schedule Diesel Engine 6466D 6466T 6466~ 6466~~ 6466Ax
Weekly Quantity 25 200 150 50 30
Daily Demand Rate 5 40 30 10 6
455
91
Total B) Shop Production Schedule Part A E
C D
E
l5tP Operating Evironment Due Date Ouantitv Start Date day 1 week 1 day 5 week 1 25 day 5 week 1 200 day 1 week 1 day 5 week 1 150 day 1 week 1 day 5 week 1 50 day 1 week 1 day 5 week 1 30 day 1 week 1
TOC Operating
Start day 1 day 2 day 3 day 4 day 5
Date week week week week week
1 1 1 1 1
Due day day day day day
Environment
Date 1 week 3 week 4 week 5 week 5 week
Quantitv 25 1 1 200 1 150 1 50 1 30
C) Production Environments MRP
64661
Production
!TOC Production
Environment
Finished Engine Head 6466~ 6466D 6466AX
6466TX
64661
Environment
Finished Engine Head 6466T 6466~ 6466AX
Operation 20 (10.38 std. min.)
Oper&i& 20 (10.38 std. min.)
Operation 10 (9.74 std. min.)
Constraint
, Operation 10 (9.74 std. min.)
Raw Material h
f
Raw Material
Fig.1
6466~~
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Material is released into the lower section, operation 10, only as actual production occurs at the constraint. In this manner the pace of production is set by the constraining operation rather than a calculated start date per MRP’s block scheduling calculations. The choking of material release to the pace of the constraint’s production is the rope in the drum-buffer-rope term. Consequently, the constraint becomes the drum setting the pace of facility production.
4.3. Bugler-modiJied MRP environment The third environment reflects a combination of the MRP scheduling mechanism with the TOC buffer mechanism. This research was done to isolate the impact of both the release mechanism and the buffer mechanism associated with DBR. In several of the earlier research efforts [7-91 the belief was expressed that by adding a constraint buffer to an MRP system the benefits of TOC could be obtained. We examined this by adding a constraint buffer only to the MRP model without adding the DBR scheduling mechanism. This environment is referred to as buffer-modified MRP. In this model, both the finished engine head stores and the constraint buffer locations are treated the same way as in the TOC environment and initialized as such. Unlike in traditional MRP, material now flows into a constraint buffer. The MRP production release mechanism, releasing orders as weekly lotsized batches at the beginning of each week, is maintained. Material flows from operation 10 into the buffer as long as sufficient buffer space is available. The space is set equal to 2.5 days of inventory.
5. Model construction Three simulation models were constructed to replicate the three manufacturing environments described in the previous section. Fifteen different scenarios of these environments were simulated representing the use of MRP, DBR, and the buffermodified MRP for five different levels of operation variability. Variability was implemented as increased process time variance for both machin-
I75-185
181
ing operations. The levels tested represent 0, 5, 10, 15, and 20% variability. The production capacity of the system, given no variability, was 461.95 units per week using a two-shift operation. The schedule requirements were 455 units as summarized in Fig. 1. In the three environments, the weekly requirement for engine heads was produced as a batch (all A’s, then B’s, then C’s, etc.). This resulted in five setup operations per week minimizing the effect on system capacity. The system constraint, operation 20, occurred as a result of processing time alone. Production time at operation 20 was 10.38 min per piece versus 9.74 at operation 10. In the simulation models, material release was implemented by creating an entity for each individual item to be produced as dictated by the scheduling mechanism. The MRP weekly time bucket resulted in release of all materials (as entities) to meet the week’s production on day one and these entities were stored in a queue node prior to operation 10. For the DBR model, release of materials to operation 10 occurred as entities departed operation 20. For the MRP model, the finished engine head stores inventory level for each product was initialized to 1 week of demand. Under the DBR model, both the finished stores buffer and the constraint buffer were initialized to 2: days of inventory. While as yet untested, TOC suggests that one-half of the inventory typically maintained at the finished stores level be moved to a buffer located prior to the bottleneck operation [34]. In this case, operation 20 was the bottleneck (i.e. constraining) operation. The buffer-modified MRP environment combined the buffer mechanism from DBR with MRP. (From Fig. 1, the MRP shop production schedule was combined with the TOC production environment.) In this model, both the finished engine head stores and the constraint buffer locations were initialized to 23 days of inventory as was the DBR model. The MRP production release mechanism operated the same as in the original MRP environment. Orders were released as weekly lot-sized batches for all products at the beginning of each week. The performance measures used for this simulation are machine utilization at operations 10 and
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20, and throughput. Throughput is reported in terms of units per hour along with cycle time. Throughput is defined as output per hour entering into finished engine head stores rather than the definition used in the TOC literature. Machine utilization and throughput were selected based on their importance to the actual operating factory and ability to be used to measure against TOC predictions. Under TOC, the focus is on the bottleneck operation. Production is ultimately determined by the utilization of that machine since any down-time at this machine decreases production. Machine utilization at the preceding machine is important only if it impacts the utilization of the bottleneck machine when, due to variability, processing time starts to exceed the production time of the bottleneck operation, starving the bottleneck. Inventory levels and stockouts would also be of concern to the supervisor of the manufacturing line represented by the simulation. As will be discussed in Section 6 however, neither measure reaches steady state under the assumptions of this MRP system. These measures are still reported and serve to show how the MRP system deteriorates without manual intervention. Tests were run to determine the point at which the simulation reached steady state using the per-
Table 2 Simulation Model I-MRP 2-DBR 3-MODMRP
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formance measures of machine utilization and throughput. (See Law and Kelton [35] for a discussion of tests for verifying steady state.) Both the MRP and the TOC models reached steady state fairly quickly, within a 4 week period, and for these models the output variables were cleared at the end of 4 weeks. For the buffer-modified MRP environment, steady state was not reached until 33 weeks; therefore, all output variables were cleared at the end of 33 weeks. Six independent simulation runs for each scenario were performed to provide sufficient, statistically independent data for an ANOVA. Each simulation run represents 24 weeks of production. Preliminary tests found no significant differences between production runs of 24, 48, and 72 weeks for machine utilization and throughput; therefore the shorter simulation run was chosen.
6. Results Table 2 provides a summary of the simulation results. Fifteen different scenarios, created by the combination of system variability for each of the three models, are reported. Each number reported is the average of six simulation runs.
results OP-10 util.
OP-20 util.
Throughput per hour
Avg. sys. inv.
Max. sys. inv.
Average number of stockouts
5 10 15 20
0.9337 0.9227 0.9100 0.8998 0.8918
0.9949 0.9837 0.9706 0.9605 0.9517
5.7500 5.6828 5.6052 5.5430 5.4942
531.84 445.47 348.77 271.86 218.42
748.00 647.33 593.33 558.00 541.83
0.0000 0.0000 0.0027 2.5350 10.9478
0 5 10 15 20
0.9375 0.9371 0.9368 0.9366 0.9365
0.9990 0.9990 0.9990 0.9990 0.9990
5.7734 5.7717 5.7709 5.7701 5.7693
561.68 557.99 556.25 554.85 553.74
795.00 790.17 787.50 785.67 783.83
0.0000 0.0000 0.0000 0.0000 0.0000
0 5 10 15 20
0.9232 0.9230 0.9231 0.9232 0.9233
0.9849 0.9853 0.9853 0.9852 0.9850
5.6959 5.6934 5.6924 5.6919 5.6909
482.52 482.59 482.59 482.53 482.50
67 1.OO 670.17 668.83 667.67 666.83
0.0000 0.0000 0.0000 0.0000 0.0000
V’bility
0
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The results of the MRP simulation model demonstrate the effect of variability on production operations and the need for increasing production capacity. In reality, the factory uses overtime to avoid stockouts. In the plant studied for this simulation, a stockout has devastating, perhaps career threatening, results throughout the company. As a result, management takes extraordinary actions to prevent a stockout, primarily through high finished engine head inventory levels, and stockouts are not allowed to occur. However, the potential for stockouts is evident in the plant. Operation variability in the lower section (operation 10) of the line causes starvation at the upper section (operation 20) of the line. Starvation of operation 20 decreases output and results in decreasing levels of finished engine head inventory. This finished engine head stores is used to maintain production of engines and meet the vehicle factories’ demand for engine deliveries. Variability continues to cause a depletion in finished engine head stores ever deeper until management schedules overtime for the line. Essentially the stores inventory level spirals down until a crisis emerges. The overtime is used to build back the inventory of finished engine heads in stores. The results of the MRP simulation demonstrate this effect. As displayed in Table 2, average and maximum system inventory levels deteriorate downward for increasing levels of processing variability. Without a method for replenishing finished engine head inventory (such as increasing capacity through overtime), stockouts begin to occur. Remember, too, that each simulation run represents 24 weeks of production. Increasing the length of the simulation run revealed that the finished engine head inventory levels eventually do reach zero. As product demand still exists, each demand occurrence produces a stockout resulting in a negative inventory balance. Using negative inventory levels to represent a backlog of unfilled demand, this backlog continues to grow over time. Hence, inventory never reaches steady state. In this simulation, as well as in reality, system performance is compromised unless overtime is scheduled. The impact of variability, and the importance of utilizing the bottleneck operation, is also demonstrated in Table 2. As variability increases, utiliza-
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tion of operation 20, the bottleneck operation, and system throughput both decrease. (Again, this occurs because the bottleneck operation is starved for engine heads whenever the processing time for the preceding operation, operation 10, exceeds operation 20’s processing time.) These measures were found to be significantly different at the 0.01 level for each MRP scenario tested. Given the same variabilities as in the MRP simulation, DBR did not produce a stockout condition. Moreover, for each of the DBR scenarios, system throughput was not found to be significantly different at the 0.01 level. Machine utilization at operation 20 did not change for each DBR scenario. Machine utilization for operation 10 is only significantly different at the 20% variability level with respect to the 0% variability. These results indicate that the buffer used in the simulation was sufficient to correct for the 20% process variability and prevent starvation of operation 20. When comparing MRP operations to DBR operations, machine utilization and throughput were found to be significantly different for all variability levels. Use of TOC (DBR) appeared to improve overall performance through improved utilization of the bottleneck operation. The buffermodified MRP results begin to reveal how the mechanisms in DBR can impact the successful performance of the production environment. As with the DBR environment, machine utilization for operation 20 and system throughput were found to be significantly different than those measures for the pure MRP environment. This demonstrates the effect of the buffer mechanism. Whenever operation 20 has completed an engine, another is released to the buffer. Operation 20, therefore, is not starved for parts and can operate nearly 100% of the time. The utilization of operation 10 was found to be significantly different at the 0.01 level as compared to both the pure MRP and DBR environments. This is a result of the MRP scheduling mechanism. In the DBR environment, operation 10 is idle only when the constraint buffer is full and the engine cannot be removed from the machine. In the buffer-modified MRP environment, however, operation 10 is idle when the constraint buffer is
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full, as well as for a significant time at the end of each week. This occurs because in a pure MRP environment, engines are released into production at the beginning of each week. Given the capacity of operation 10, the weekly production requirements can be met in 4431 min. Therefore, operation 10 sits idle almost 6 h per week. The result is decreasing average constraint buffer size as constraint buffer inventory becomes finished engine head inventory. Fig. 2 shows that over time, the constraint buffer essentially disappears, if only for short periods of time. Typically at the beginning of each week, operation 20 is starved for engine heads as raw material is just released to operation 10. The result is lower operation 20 utilization and system throughput as displayed in Table 2. If actually implemented in the factory, the results of a buffer-modified system would eventually mirror the results of the MRP scenario, since the constraint is idle for some amount of time. In this case, the scheduler could schedule overtime for the line or initiate an early release of engine head castings in order for production to maintain constraint utilization, provide sufficient constraint buffer size, and keep his job. The results of the buffer-modified scenario also suggest the need for further study of constraint buffer size within a pure DBR model. In the buffermodified scenario, inventory levels are deteriorating over time, as can partially be seen in Fig. 2, but not as quickly as with the original MRP model. Even with sometimes very low constraint buffer levels, machine utilization was close to that of the pure DBR environment. This leads to the question of
I, ,
,
“t=, week
,
f Time
Fig. 2 Buffer modified
t=33
weeks
MRP
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the appropriate constraint buffer size that can be used without jeopardizing constraint resource utilization and throughput for the pure DBR model.
7. Conclusions Based on this simulation of an actual operating production environment, the scheduling procedure under theory of constraints called drum-bufferrope produced significantly better results than the MRP methods used at the factory. This finding supports the results achieved by the two other model simulations comparing TOC and MRP. However, this research differs as actual production data, rather than fictionalized data, were used. Most importantly, the DBR method was fully implemented as described in TOC literature. This research also demonstrates that the DBR scheduling method can be separated from the performance measurement cost system, and produce schedules that meet customer demand. The implementation and use of DBR does not appear to be precluded by the other TOC components. This does not degrade from those components, but only suggests that the underlying philosophy embedded into TOC need not stand in the way of making operational improvements by adopting the DBR scheduling procedure. Additional research should evaluate whether or not DBR performance is enhanced by the adoption of the other TOC components. This research also demonstrates the impact of both the buffer mechanism and the release mechanism (the rope) of the DBR environment. Previous examination of TOC or OPT focused on the use of a constraint buffer while this study examined the material release function as well. This research also demonstrates that trying to combine the perceived strengths of two different production techniques may not yield satisfactory results. The size of the constraint buffer needed to absorb the variability in the production system remains a question unanswered by this simulation. Further research is required to establish the trade-off between buffer sizes and protective capacity at both locations.
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The location of the two buffers was determined by the TOC component called logical structure analysis. There are three logical structures, “V”, “A” and “T”. This simulation tested the “T” structure. Additional research is needed to test the assumptions concerning the location of the buffers and the use of DBR in an “A” structure, more commonly a job shop, and a “V” structure, more common in process industries. This simulation was based on modeling a single operating environment. While the results did support previous, less realistic, simulations, care should be taken in generalizing the findings. Managers and machine tool design engineers should, however, be aware that significant productivity improvements are likely with the implementation of the DBR method. References 111Goldratt,
E.M. and Cox, J., 1992. The Goal, 2nd revised ed. North River Press, Croton-on-Hudson, NY. shop floor schedul121Goldratt, E.M., 1988. Computerized ing. Int. J. Prod. Res., 26: 443-455. [31 Fry, T.D., Blackstone, J.H. and Cox, J.F., 1992. An analysis and discussion of the optimized production technology software and its use. Prod. Oper. Mgmt., 1: 2299242. [41 Lundrigan, R., 1986. What is this thing called OPT? Prod. Inv. Mgmt. J., 27(2): 2211. PI Jacobs, F.R., 1983. The OPT scheduling system: A review of a new production scheduling system. Prod. Inv. Mgmt. J., 24(3): 47751. to MRP Fl Vollmann, T.E., 1986. OPT as an enhancement II. Prod. Inv. Mgmt. J., 27(2): 3846. 171Swann, D., 1986. Using MRP for optimized schedules (emulating OPT). Prod. Inv. Mgmt. J., 27(2): 30-37. 181 Meleton, M.P. Jr., 1986. OPT ~ Fantasy or breakthrough? Prod. Inv. Mgmt. J., 27(2): 13-20. [91 Vollum, R.B. and O’Malley, T.J., 1989. Improving on JIT success with constraint management and synchronous flow. 32nd Ann. Conf. Proc. American Production and Inventory Control Society, pp. 583-584. Changing to low inventory man1101 Booth, J., 1988. Beavers ufacturing. Int. J. Prod. Res., 26: 3477413. M.R. and Decaluwe, L., 1988. JIT and con1111 Lambrecht, straint theory: The issue of bottleneck management. Prod. Inv. Mgmt. J., 29(3): 61-65. production’s potential. P21 Wheatly, M., 1989. OPTimizing Int. J. Oper. Prod. Mgmt., 9(2): 38-44. E.M. and Fox, R.E., 1986. The Race. North iI31 Goldratt, River Press, Croton-on-Hudson, NY. E. and Ronen, B., 1991. Buffer manageu41 Schragenheim, ment : A diagnostic tool for production control. Prod. Inv. Mgmt. J., 32(2): 7479.
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[15] Umble, M. and Srikanth, M.L., 1990. Synchronous Manufacturing. South-Western Publishing Co., Cincinnati, OH. [16] Finch, B.J. and Cox, J.F., 1989. Strategic use of WIP inventory: The impact of bill-of-material shape and plant type. Prod. Inv. Mgmt. J., 30(l): 63-66. [17] Lockamy, A. III and Cox III, J.F., 1991. Using V-A-T analysis for determining the priority and location of JIT manufacturing techniques. Int. J. Prod. Res., 29: 1661-1672. [ 181 Reimer, G., 199 1. Material requirements planning and theory of constraints: Can they coexist? A case study. Prod. Inv. Mgmt. J., 32(4): 48-52. [19] Fawcett, SE. and Pearson, J.N., 1991. Understanding and applying constraint management in today’s manufacturing environment. Prod. Inv. Mgmt. J., 32(3): 46-55. [20] Lockamy, A. III, 1993. How to compete in your industry. Prod. Inv. Mgmt. J., 34(l): l-5. [21] Everdell, R., 1984. MRP II, JIT, and OPT: Not a choice but a synergy. 27th Ann. Conf. Proc. ~ Readings in Zero Inventory. American Production and Inventory Control Society, pp. 144-148. [22] Aggarwal, S.C. and Aggarwal, S., 1985. The management of manufacturing operations: An appraisal of recent developments. Int. J. Oper. Prod. Mgmt., 5(3): 21-38. [23] Grunwald, H., Striekwold, P.E.T. and Weeda, P.J., 1989. A framework for quantitative comparison of production control concepts. Int. J. Prod. Res., 27: 281-292. [24] Plenert, G. and Best, T.D., 1986. MRP, JIT, and OPT: What’s ‘best’? Prod. Inv. Mgmt. J., 27(2): 22-28. [25] Sohal, A. and Howard, K., 1987. Trends in materials management. Int. J. Phys. Distribution Mater. Mgmt., 17(5): 3-41. [26] Aggarwal, S.C., 1985. MRP, JIT, OPT, FMS? Making sense of production operations systems. Harvard Bus. Rev., 63(5): 8-16. [27] Ptak, C.A., 1991. MRP, MRP II, OPT, JIT, and CIM Succession, evolution, of necessary combination. Prod. Inv. Mgmt. J., 32(2): 7-11. [28] Ramsay, M.L., Brown, S. and Tabibzadeh, K.. 1990. Push, pull and squeeze shop floor control with computer simulation. Ind. Eng., 22(2): 39945. [29] Schragenheim, E. and Ronen, B., 1990. Drum-buffer-rope shop floor control. Prod. Inv. Mgmt. J., 31(3): 18-22. [30] Lambrecht, M.R. and Segaert, A., 1990. Buffer stock allocation in serial and assembly type of production lines, Int. J. Oper. Prod. Mgmt., lO(2): 47761. [31] Spencer, M.S., 1986. Lessons from the JIT journey. 1986 Int. Conf. Proc. American Production and Inventory Control Society, Falls Church, VA, pp. 326330. [32] Spencer, M.S., 1988. Developing a finite schedule for cellular manufacturing. Prod. Inv. Mgmt. J., 29(l): 74-79. [33] Spencer, M.S., 1995. Production planning in a MRP/JIT repetitive manufacturing environment. Prod. Planning Control, 6: 176184. [34] TOC Workshop Materials, 1990. Avraham Y. Goldratt Institute, New Haven, CT. [35] Law, A.M. and Kelton, W.D., 1991. Simulation Modeling and Analysis. McGraw-Hill, New York, NY.
prod&ion economics
Int. J. Production Economics 42 (1995) 175-185
The impact of a constraint buffer in a flow shop Leslie K. Duclos*, Department
of Management,
Michael
College of Business Administration,
University
S. Spencer of Northern
Iowa, Cedar Falls, IA 50614-0125,
USA
Received 1 February 1995; accepted 9 October 1995
Abstract This article presents the findings from a simulation using actual production data comparing the material requirements planning (MRP) scheduling method with that of drum-buffer-rope (DBR) from the theory of constraints (TOC), previously called OPT. The simulation is based on production information from a diesel engine factory currently using MRP. The same data were then used under DBR procedures. Care was taken to utilize the DBR method in the manner suggested in the TOC literature. A buffer-modified MRP was also modeled. This environment used MRP scheduling with a buffer inserted prior to the constraining operation. An analysis of variance of the results of all scenarios indicates that the constraint buffer used in DBR was successful in increasing output from the system. Keywords:
Production
planning;
Theory
of constraints
1. Introduction
One of the more controversial issues in the operations management field over the past ten years has been the investigation of the theory of constraints (TOC). Some of the controversy stems from the earliest applications of the principles, known as OPT, that predated TOC. The controversy was further enhanced by the confusion over just what the differences were between OPT and TOC and the operations management concepts presented in the book The Goal [l]. Confusion has also resulted from the introduction of a TOC performance measurement system distinctly different than traditional operations management performance measurement systems. This has clouded the discussion of the operations management principles associated with the TOC * Corresponding author. Tel.: 319 273 2964; fax: 319 273 2922.
(TOC); Drum-Buffer-Rope
(DBR);
Flow shop; Simulation
technique. What appears to be lacking is a clear statement of the operations management principles expressed in TOC, and a more objective evaluation of those principles. The purpose of this research is twofold. The first objective is to distill and clarify the operations management concepts in The Goal, and relevant OPT and TOC literature. The second objective is to test the operations management scheduling concepts by presenting the results of a simulation based on empirical data comparing TOC with an MRP system in a flow shop. A flow shop was selected because actual data were accessible and TOC scheduling components could be more easily examined than in a job shop. 2. Terminology There are two sources which provide details concerning OPT, TOC, and The Goal. Both sources were written by the principal author of
0925-5273/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved SSDI 0925-5273(95)00197-2
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TOC, Eliyahu M. Goldratt. In the first source, Goldratt [2] presented a genealogy of OPT describing in some detail the avenue of its development. OPT software was aggressively marketed as a solution to operations contentions at key resources. The scheduling algorithm used in the OPT software was copyrighted and not made available for research. The mystery surrounding the algorithm and the decision to maintain its confidentiality led to much of the controversy surrounding OPT. Only recently has the OPT software been examined in detail [3]. The underlying operations principles found in the OPT software were presented as the nine OPT rules [4]. Examples of these rules include: constraints determine non-bottleneck utilization; an hour lost at the bottleneck is an hour lost for the entire system; and an hour saved at a non-bottleneck is a mirage. The nine OPT rules were examined by some researchers [5,6], but OPT results were presented largely by practitioner articles [7-91. The proprietary nature of the scheduling algorithm discouraged research, and may have limited practitioner acceptance. In 1984, The Goal [l] was written to present the underlying conflicts involving the performance measurement systems and actual manufacturing through the use of a series of fictional vignettes. The term “OPT” did not appear in the book nor was there a clear and concise discussion of a scheduling methodology. The book did present a fivestep process used to improve a production facility. The five step process is: (1) identify the constraint; (2) exploit the constraint; (3) subordinate all other operations to the constraint; (4) elevate the constraint; and (5) avoid managerial inertia should the constraint be broken in step 4. Even this five-step process was not specifically presented in a concise manner until the 1992 revised version of the book. There were, however, instances where principles discussed in The Goal were implemented at actual companies without the OPT software [lo-121. A version of OPT, released late in 1985, focused on the management of the production process through a scheduling technique
called drum-buffer-rope (DBR). The DBR method was presented more fully in the book The Race [ 131 and by Schragenheim and Ronen [14]. It is the DBR method that is the core of the scheduling procedure under TOC and the principal subject of this research. Goldratt introduced the term “Theory of Constraints” in 1987 [15]. By this time TOC was a collection of various concepts including: (1) the performance measurement system, (2) the five-step improvement process, (3) a method of combining a bill of material with the associated routings called V-A-T logical structure trees, (4) identification of control points, and (5) DBR. The nine OPT rules had largely been abandoned. See Table 1 for a framework of TOC components and a glossary of terms. In 1990 another TOC component, buffer management [14], was developed to more effectively manage the buffer and avoid production problems that could jeopardize throughput.
3. TOC literature There has been some research concerning the various components of TOC. Finch and Cox [16] and Lockamy and Cox [ 171 examined the use of the logical structures. Reimer [18] discussed an application at Velmont Industries. Fawcett and Pearson [ 191 provided insights in a conceptual article. Lockamy [20] described the application at a Trane, Inc. factory. All examined the overall TOC system including the underlying performance measurement system. There have also been two articles comparing OPT [TOC] and MRP [7,6] although both were conceptual rather than empirically based. All drew the conclusion that operations management concepts underpinning TOC would improve the overall performance of MRP. In other conceptual articles, researchers have compared the underlying concepts of OPT [TOC], JIT and MRP. Everdell [21], Aggarwal and Aggarwal[22], and Grunwald et al. [23] concluded that all three systems have advantages and disadvantages, and their individual success would depend upon the specific environment and
L.K. Duclos, M.S. Table 1 Framework I.
II.
III.
for theory
SpencerlInt.
J. Production
Economics
42 (199.5) 175-185
171
of constraints
Logistics system focusing components: A. Five-step focusing process - A process of continuous improvement for a system focusing on the constraint [I]. B. V-A-T framework - A classification scheme that identifies the positioning of buffers and control points produced by the combination of the bill-of-material and routings. The name “V-A-T” originates from the shapes of the resulting diarams [I 5-l 71. Performance measurement system components: A. Throughput - The rate at which a system generates money through sales [ 131. B. Inventory ~ All of the money the system invests in purchasing things which it intends to sell valued at raw material cost [131. C. Operating expense - All the money the system spends in turning inventory into throughput [13]. Operations planning and control components: A. Drum - The pace of production in the system determined by the constraint [13, 141. B. Constraint buffer - A time offset in the schedule allocated to protect throughput of the system by maintaining a supply of material available for production at the constraint. The constraint buffer absorbs variability in the production process of operations feeding the constraint [13,14]. C. Rope - A communication device to limit (or choke) the flow of material into the system to match the actual production of the constraint [13, 141. D. Shipping buffer ~ A time offset in the schedule of constraint parts allocated to protect throughput of the system by main taining a supply of material available for shipping from the constraint. The shipping buffer absorbs the variability in the production process of operations downstream from the constraint [13,14]. E. Buffer management ~ A method of monitoring the presence or absence of material in a buffer and taking action to prevent disruption of a system’s throughput [14].
Plenert and Best [24] and Sohal and Howard [25] concluded that the underlying principles in TOC would make it superior to either JIT or MRP. Aggarwal [26] and Ptak [27] concluded that a combination of all three systems should be more successful that any individual system. Two efforts have evaluated TOC’s scheduling methodology using simulation. Ramsay et al. [28] developed a simulation based on a fictional product line consisting of two flashlights in a fictional production environment. The researchers named the various subsystems in their simulations based on the function being performed. For example, “squeeze” is used to describe the TOC scheduling function DBR, while “push” is used to describe MRP and “pull” describes JIT. They conclude, “At least as seen here, the squeeze approach appears to be the most useful of the three” [28, p. 451. However, this simulation was not based on field data and did not explicitly examine DBR as described in Schragenheim and Ronen [29]. The researchers did create a constraint buffer and did release material into the system based on the pace of production at the constraint. However, the simulation created a master production schedule (MPS) for end items, rather than creating the MPS managment.
for components at the constraint. Under TOC, the constraint determines the throughput for the system rather than the final assembly schedule. The MPS, in effect, is constructed based on the capabilities of performing specific operations on parts crossing the constraint, wherever it is located, rather than at the top level (level 0) in the bill of material as is common in MRP. Additionally, the Ramsay et al. [28] simulation excluded the shipping buffer of DBR, a mechanism to pull parts through the production process to ensure due-date performance. The simulation also suffered from “[t]he lack of intelligence in the STATUS variable [that controlled operation activation] which lessened the value of the program” [29, p. 451. In TOC terms, this simulation modeled a “T” logical structure, more closely associated with a flow shop, but without a complete implementation of the DBR technique. For an explanation of “T” structure characteristics see Finch and Cox [16]. The second simulation was conducted by Lambrecht and Segaert [30]. This simulation was also based on a fictional production environment. In one case, six work stations were arranged in a series while the second case consisted of a two-line 12-station environment where two components
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converge at work center 11. In both cases there is an assumption of infinite demand for a single end product. The researchers identified their “long pull strategy” as being similar to DBR (p. 54). The simulation was initialized with one work-piece at each station plus a 15-unit buffer positioned prior to the constraint. The researchers concluded, “It is shown that this long pull strategy outperforms other allocation strategies” (p. 61). The simulation did provide for an assembly buffer; however, the lack of a realistic MPS and the lack of a shipping buffer detract from the model. In TOC terms, this simulation models a simple “A” logical structure, more closely associated with a job shop, without determining the effects of the MPS. While both simulations make important contributions, they fail to include important elements of DBR.
4. Methodology Thus, to date, analytical research of the full DBR method does not support the theory that a strategically placed buffer in a “T” logical structure, or flow shop, will improve the performance of the manufacturing system. To better judge the impact of a buffer located at the constraining operation for a completely implemented DBR scheduling method, a simulation model was developed based on data and MRP scheduling methods used at one operating factory. The specific environment modeled was chosen because of accessibility to the facility by one of the authors. No effort was made to randomly select the site. The factory was known to have the highest due-date performance of any factory in the company, as well as being one of the most profitable. The factory’s successful application of MRP has been documented in both trade publications and professional journals [31-331. The production environment selected for the study is a 17-station automated transfer line used to produce a major component for the factory’s product line, the diesel engine cylinder head. The product line consists of five models of heads required for the daily production of finished engines or service requirements. Each of the engine heads is made from a single raw casting. Diesel engines are produced on a line-paced assembly line located at the same facility as the head line. Other
175-185
major components, such as blocks, connecting rods, and crankshafts, are also manufactured at the same facility. The 17-station head transfer line is grouped into two main sections with separate actual cycle times established for each section. Raw castings are placed into the lower section consisting of seven stations, identified in this study as operation 10, where 9.74 standard minutes are required to perform the operations converting the raw casting into a semi-finished block. Most of the work in this section consists of milling and drilling operations. The work-in-process material is then transferred to the upper section, operation 20, where the additional ten operations are performed. In this section, several components are added to the semi-finished block, such as shims and pins. Every head passes through each station in the line. The line is automated such that blocks index from one station to another in a timed sequence dictated by the completion of the programmed cycle time. Should a problem occur at one station the entire line stops. The factory treats the line as two operations, thus the constraint, in TOC terms, is the section rather than a single station. The work done in the second section is more detailed with grinding, polishing and a wash operation as the final step. The standard time for the upper section is 10.38 min per piece. Once finished, the head is sent into the subassembly area to await its turn for final assembly onto a finished engine. The overall production of heads within this line is a flow shop with sequential operations rather than a functionally arranged job shop. Considerable work had been undertaken by the company to implement just-in-time production methods over the previous ten years. As a result, a 1 min setup time is all that is required for each change from one head model to another. Additionally, machine breakdowns are rare in this production line. No attempt was made to incorporate machine down-time due to breakdowns into the model.
4.1. Material
requirements
planning
environment
The factory supplies finished engines to several vehicle factories on a daily basis. In most cases,
L.K. Duclos, M.S.
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because of the relatively high value of a finished engine, production is paced according to the vehicle factories’ demand. Great efforts have been made throughout the company to provide diesel engines on a JIT basis to the vehicle factories. Requirements for service heads are received from a central distribution depot on a weekly basis. The master production schedule (MPS) is developed from requirements received on-line through an interfactory system. The MPS is stated in weekly time periods and requirements transmitted to an MRP system. Requirements for engine components are determined by traditional MRP calculations. The head line uses a lot-for-lot order policy with a 1 week lead time. The lead time calculation generates requirements for raw castings rather than for production scheduling. Because the factory is a repetitive manufacturer, the MPS from one week to another varies little. The aggregate daily production rate is held constant while the mix of engines changes to meet vehicle plant needs. Thus, the component requirements are repeated from week to week with only the mix quantities varying. The aggregate demand for heads remains unchanged until the entire factory changes the overall daily production level. Operating under MRP the factory produces a week’s worth of each head through the production line on a recurring basis. The MRP system is used to authorize the release of a week’s worth of heads as well as the purchasing of replenishment castings. However, since the line cannot hold a week’s supply, heads are loaded into the first operation using the MRP scheduled quantity as space becomes available in the line’s first station. Raw castings are held temporarily on roller conveyors awaiting entry into the first operation. Finished heads are placed into finished engine head stores in the subassembly area to await final assembly. Under MRP, a 5 day (1 week) supply of finished heads is maintained. As a mix of engines is assembled, heads are drawn from this finished stores area. As production occurs, the finished engine head store is replenished. (Some might criticize the factory for not adopting JIT in this area. Management does have plans to continue its implementation of JIT and eliminate the stores area. However, at this time, a stockout would essentially shut down entire
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vehicle factories since most JIT efforts have been directed at eliminating the more costly finished engine inventory.) The capacity requirements of the head line are determined through a closed-loop MRP process. The workforce is determined by the amount of standard hours required to produce a week’s requirement using MRP data. The workforce calculation is reviewed monthly and adjusted as required. Realistically, the workforce remains unchanged until the daily production rate of finished engines is changed. The workforce level is set to minimize labor delay. Industrial engineers monitor delay rather closely since labor-based costing is used. Fig. 1 depicts the MPS used for this simulation, the shop production schedule that results from MRP’s requirements generation procedure, and the logical structure of the head line under the MRP production environment.
4.2. Theory of constraints environment As further depicted in Fig. 1, under TOC the rules of engagement change. Product demand from the vehicle factories is scheduled on the constraint resource using a Gantt chart. This finite schedule becomes, in effect, the MPS and consists of the components that are routed across the constraint. Each time customer requirements change, the MPS is reviewed and updated to reflect the change in the mix and quantities of the components. Under TOC, the constraint buffer is created by moving inventory typically maintained in the finished engine head stores area into the constraint buffer, as work-in-progress. This is done to maximize throughput from the constraint by absorbing upstream production instabilities. The constraint buffer is monitored on a continuous basis to ensure full utilization of the constraint. A relatively small shipping buffer is maintained to cover processing times, resource contention, and time to absorb random fluctuations in the production process that occur after the constraining operation up to the shipping point. In this environment, all five heads are used for service parts. The finished engine head stores then also serves as a shipping buffer for each head.
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A) Master Production Schedule Diesel Engine 6466D 6466T 6466~ 6466~~ 6466Ax
Weekly Quantity 25 200 150 50 30
Daily Demand Rate 5 40 30 10 6
455
91
Total B) Shop Production Schedule Part A E
C D
E
l5tP Operating Evironment Due Date Ouantitv Start Date day 1 week 1 day 5 week 1 25 day 5 week 1 200 day 1 week 1 day 5 week 1 150 day 1 week 1 day 5 week 1 50 day 1 week 1 day 5 week 1 30 day 1 week 1
TOC Operating
Start day 1 day 2 day 3 day 4 day 5
Date week week week week week
1 1 1 1 1
Due day day day day day
Environment
Date 1 week 3 week 4 week 5 week 5 week
Quantitv 25 1 1 200 1 150 1 50 1 30
C) Production Environments MRP
64661
Production
!TOC Production
Environment
Finished Engine Head 6466~ 6466D 6466AX
6466TX
64661
Environment
Finished Engine Head 6466T 6466~ 6466AX
Operation 20 (10.38 std. min.)
Oper&i& 20 (10.38 std. min.)
Operation 10 (9.74 std. min.)
Constraint
, Operation 10 (9.74 std. min.)
Raw Material h
f
Raw Material
Fig.1
6466~~
L.K. Duclos. M.S. SpencerlInt.
J. Production Economics 42 (1995)
Material is released into the lower section, operation 10, only as actual production occurs at the constraint. In this manner the pace of production is set by the constraining operation rather than a calculated start date per MRP’s block scheduling calculations. The choking of material release to the pace of the constraint’s production is the rope in the drum-buffer-rope term. Consequently, the constraint becomes the drum setting the pace of facility production.
4.3. Bugler-modiJied MRP environment The third environment reflects a combination of the MRP scheduling mechanism with the TOC buffer mechanism. This research was done to isolate the impact of both the release mechanism and the buffer mechanism associated with DBR. In several of the earlier research efforts [7-91 the belief was expressed that by adding a constraint buffer to an MRP system the benefits of TOC could be obtained. We examined this by adding a constraint buffer only to the MRP model without adding the DBR scheduling mechanism. This environment is referred to as buffer-modified MRP. In this model, both the finished engine head stores and the constraint buffer locations are treated the same way as in the TOC environment and initialized as such. Unlike in traditional MRP, material now flows into a constraint buffer. The MRP production release mechanism, releasing orders as weekly lotsized batches at the beginning of each week, is maintained. Material flows from operation 10 into the buffer as long as sufficient buffer space is available. The space is set equal to 2.5 days of inventory.
5. Model construction Three simulation models were constructed to replicate the three manufacturing environments described in the previous section. Fifteen different scenarios of these environments were simulated representing the use of MRP, DBR, and the buffermodified MRP for five different levels of operation variability. Variability was implemented as increased process time variance for both machin-
I75-185
181
ing operations. The levels tested represent 0, 5, 10, 15, and 20% variability. The production capacity of the system, given no variability, was 461.95 units per week using a two-shift operation. The schedule requirements were 455 units as summarized in Fig. 1. In the three environments, the weekly requirement for engine heads was produced as a batch (all A’s, then B’s, then C’s, etc.). This resulted in five setup operations per week minimizing the effect on system capacity. The system constraint, operation 20, occurred as a result of processing time alone. Production time at operation 20 was 10.38 min per piece versus 9.74 at operation 10. In the simulation models, material release was implemented by creating an entity for each individual item to be produced as dictated by the scheduling mechanism. The MRP weekly time bucket resulted in release of all materials (as entities) to meet the week’s production on day one and these entities were stored in a queue node prior to operation 10. For the DBR model, release of materials to operation 10 occurred as entities departed operation 20. For the MRP model, the finished engine head stores inventory level for each product was initialized to 1 week of demand. Under the DBR model, both the finished stores buffer and the constraint buffer were initialized to 2: days of inventory. While as yet untested, TOC suggests that one-half of the inventory typically maintained at the finished stores level be moved to a buffer located prior to the bottleneck operation [34]. In this case, operation 20 was the bottleneck (i.e. constraining) operation. The buffer-modified MRP environment combined the buffer mechanism from DBR with MRP. (From Fig. 1, the MRP shop production schedule was combined with the TOC production environment.) In this model, both the finished engine head stores and the constraint buffer locations were initialized to 23 days of inventory as was the DBR model. The MRP production release mechanism operated the same as in the original MRP environment. Orders were released as weekly lot-sized batches for all products at the beginning of each week. The performance measures used for this simulation are machine utilization at operations 10 and
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20, and throughput. Throughput is reported in terms of units per hour along with cycle time. Throughput is defined as output per hour entering into finished engine head stores rather than the definition used in the TOC literature. Machine utilization and throughput were selected based on their importance to the actual operating factory and ability to be used to measure against TOC predictions. Under TOC, the focus is on the bottleneck operation. Production is ultimately determined by the utilization of that machine since any down-time at this machine decreases production. Machine utilization at the preceding machine is important only if it impacts the utilization of the bottleneck machine when, due to variability, processing time starts to exceed the production time of the bottleneck operation, starving the bottleneck. Inventory levels and stockouts would also be of concern to the supervisor of the manufacturing line represented by the simulation. As will be discussed in Section 6 however, neither measure reaches steady state under the assumptions of this MRP system. These measures are still reported and serve to show how the MRP system deteriorates without manual intervention. Tests were run to determine the point at which the simulation reached steady state using the per-
Table 2 Simulation Model I-MRP 2-DBR 3-MODMRP
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formance measures of machine utilization and throughput. (See Law and Kelton [35] for a discussion of tests for verifying steady state.) Both the MRP and the TOC models reached steady state fairly quickly, within a 4 week period, and for these models the output variables were cleared at the end of 4 weeks. For the buffer-modified MRP environment, steady state was not reached until 33 weeks; therefore, all output variables were cleared at the end of 33 weeks. Six independent simulation runs for each scenario were performed to provide sufficient, statistically independent data for an ANOVA. Each simulation run represents 24 weeks of production. Preliminary tests found no significant differences between production runs of 24, 48, and 72 weeks for machine utilization and throughput; therefore the shorter simulation run was chosen.
6. Results Table 2 provides a summary of the simulation results. Fifteen different scenarios, created by the combination of system variability for each of the three models, are reported. Each number reported is the average of six simulation runs.
results OP-10 util.
OP-20 util.
Throughput per hour
Avg. sys. inv.
Max. sys. inv.
Average number of stockouts
5 10 15 20
0.9337 0.9227 0.9100 0.8998 0.8918
0.9949 0.9837 0.9706 0.9605 0.9517
5.7500 5.6828 5.6052 5.5430 5.4942
531.84 445.47 348.77 271.86 218.42
748.00 647.33 593.33 558.00 541.83
0.0000 0.0000 0.0027 2.5350 10.9478
0 5 10 15 20
0.9375 0.9371 0.9368 0.9366 0.9365
0.9990 0.9990 0.9990 0.9990 0.9990
5.7734 5.7717 5.7709 5.7701 5.7693
561.68 557.99 556.25 554.85 553.74
795.00 790.17 787.50 785.67 783.83
0.0000 0.0000 0.0000 0.0000 0.0000
0 5 10 15 20
0.9232 0.9230 0.9231 0.9232 0.9233
0.9849 0.9853 0.9853 0.9852 0.9850
5.6959 5.6934 5.6924 5.6919 5.6909
482.52 482.59 482.59 482.53 482.50
67 1.OO 670.17 668.83 667.67 666.83
0.0000 0.0000 0.0000 0.0000 0.0000
V’bility
0
L.K. Duclos. M.S.
SpencerlInt.
J. Production
The results of the MRP simulation model demonstrate the effect of variability on production operations and the need for increasing production capacity. In reality, the factory uses overtime to avoid stockouts. In the plant studied for this simulation, a stockout has devastating, perhaps career threatening, results throughout the company. As a result, management takes extraordinary actions to prevent a stockout, primarily through high finished engine head inventory levels, and stockouts are not allowed to occur. However, the potential for stockouts is evident in the plant. Operation variability in the lower section (operation 10) of the line causes starvation at the upper section (operation 20) of the line. Starvation of operation 20 decreases output and results in decreasing levels of finished engine head inventory. This finished engine head stores is used to maintain production of engines and meet the vehicle factories’ demand for engine deliveries. Variability continues to cause a depletion in finished engine head stores ever deeper until management schedules overtime for the line. Essentially the stores inventory level spirals down until a crisis emerges. The overtime is used to build back the inventory of finished engine heads in stores. The results of the MRP simulation demonstrate this effect. As displayed in Table 2, average and maximum system inventory levels deteriorate downward for increasing levels of processing variability. Without a method for replenishing finished engine head inventory (such as increasing capacity through overtime), stockouts begin to occur. Remember, too, that each simulation run represents 24 weeks of production. Increasing the length of the simulation run revealed that the finished engine head inventory levels eventually do reach zero. As product demand still exists, each demand occurrence produces a stockout resulting in a negative inventory balance. Using negative inventory levels to represent a backlog of unfilled demand, this backlog continues to grow over time. Hence, inventory never reaches steady state. In this simulation, as well as in reality, system performance is compromised unless overtime is scheduled. The impact of variability, and the importance of utilizing the bottleneck operation, is also demonstrated in Table 2. As variability increases, utiliza-
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183
tion of operation 20, the bottleneck operation, and system throughput both decrease. (Again, this occurs because the bottleneck operation is starved for engine heads whenever the processing time for the preceding operation, operation 10, exceeds operation 20’s processing time.) These measures were found to be significantly different at the 0.01 level for each MRP scenario tested. Given the same variabilities as in the MRP simulation, DBR did not produce a stockout condition. Moreover, for each of the DBR scenarios, system throughput was not found to be significantly different at the 0.01 level. Machine utilization at operation 20 did not change for each DBR scenario. Machine utilization for operation 10 is only significantly different at the 20% variability level with respect to the 0% variability. These results indicate that the buffer used in the simulation was sufficient to correct for the 20% process variability and prevent starvation of operation 20. When comparing MRP operations to DBR operations, machine utilization and throughput were found to be significantly different for all variability levels. Use of TOC (DBR) appeared to improve overall performance through improved utilization of the bottleneck operation. The buffermodified MRP results begin to reveal how the mechanisms in DBR can impact the successful performance of the production environment. As with the DBR environment, machine utilization for operation 20 and system throughput were found to be significantly different than those measures for the pure MRP environment. This demonstrates the effect of the buffer mechanism. Whenever operation 20 has completed an engine, another is released to the buffer. Operation 20, therefore, is not starved for parts and can operate nearly 100% of the time. The utilization of operation 10 was found to be significantly different at the 0.01 level as compared to both the pure MRP and DBR environments. This is a result of the MRP scheduling mechanism. In the DBR environment, operation 10 is idle only when the constraint buffer is full and the engine cannot be removed from the machine. In the buffer-modified MRP environment, however, operation 10 is idle when the constraint buffer is
L.K. Duclos, M.S. SpencerlInt.
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J. Production
full, as well as for a significant time at the end of each week. This occurs because in a pure MRP environment, engines are released into production at the beginning of each week. Given the capacity of operation 10, the weekly production requirements can be met in 4431 min. Therefore, operation 10 sits idle almost 6 h per week. The result is decreasing average constraint buffer size as constraint buffer inventory becomes finished engine head inventory. Fig. 2 shows that over time, the constraint buffer essentially disappears, if only for short periods of time. Typically at the beginning of each week, operation 20 is starved for engine heads as raw material is just released to operation 10. The result is lower operation 20 utilization and system throughput as displayed in Table 2. If actually implemented in the factory, the results of a buffer-modified system would eventually mirror the results of the MRP scenario, since the constraint is idle for some amount of time. In this case, the scheduler could schedule overtime for the line or initiate an early release of engine head castings in order for production to maintain constraint utilization, provide sufficient constraint buffer size, and keep his job. The results of the buffer-modified scenario also suggest the need for further study of constraint buffer size within a pure DBR model. In the buffermodified scenario, inventory levels are deteriorating over time, as can partially be seen in Fig. 2, but not as quickly as with the original MRP model. Even with sometimes very low constraint buffer levels, machine utilization was close to that of the pure DBR environment. This leads to the question of
I, ,
,
“t=, week
,
f Time
Fig. 2 Buffer modified
t=33
weeks
MRP
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42 (1995)
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the appropriate constraint buffer size that can be used without jeopardizing constraint resource utilization and throughput for the pure DBR model.
7. Conclusions Based on this simulation of an actual operating production environment, the scheduling procedure under theory of constraints called drum-bufferrope produced significantly better results than the MRP methods used at the factory. This finding supports the results achieved by the two other model simulations comparing TOC and MRP. However, this research differs as actual production data, rather than fictionalized data, were used. Most importantly, the DBR method was fully implemented as described in TOC literature. This research also demonstrates that the DBR scheduling method can be separated from the performance measurement cost system, and produce schedules that meet customer demand. The implementation and use of DBR does not appear to be precluded by the other TOC components. This does not degrade from those components, but only suggests that the underlying philosophy embedded into TOC need not stand in the way of making operational improvements by adopting the DBR scheduling procedure. Additional research should evaluate whether or not DBR performance is enhanced by the adoption of the other TOC components. This research also demonstrates the impact of both the buffer mechanism and the release mechanism (the rope) of the DBR environment. Previous examination of TOC or OPT focused on the use of a constraint buffer while this study examined the material release function as well. This research also demonstrates that trying to combine the perceived strengths of two different production techniques may not yield satisfactory results. The size of the constraint buffer needed to absorb the variability in the production system remains a question unanswered by this simulation. Further research is required to establish the trade-off between buffer sizes and protective capacity at both locations.
L.K. Duclos, M.S. Spencer/Int. J. Production Economics 42 (1995) 175-185
The location of the two buffers was determined by the TOC component called logical structure analysis. There are three logical structures, “V”, “A” and “T”. This simulation tested the “T” structure. Additional research is needed to test the assumptions concerning the location of the buffers and the use of DBR in an “A” structure, more commonly a job shop, and a “V” structure, more common in process industries. This simulation was based on modeling a single operating environment. While the results did support previous, less realistic, simulations, care should be taken in generalizing the findings. Managers and machine tool design engineers should, however, be aware that significant productivity improvements are likely with the implementation of the DBR method. References 111Goldratt,
E.M. and Cox, J., 1992. The Goal, 2nd revised ed. North River Press, Croton-on-Hudson, NY. shop floor schedul121Goldratt, E.M., 1988. Computerized ing. Int. J. Prod. Res., 26: 443-455. [31 Fry, T.D., Blackstone, J.H. and Cox, J.F., 1992. An analysis and discussion of the optimized production technology software and its use. Prod. Oper. Mgmt., 1: 2299242. [41 Lundrigan, R., 1986. What is this thing called OPT? Prod. Inv. Mgmt. J., 27(2): 2211. PI Jacobs, F.R., 1983. The OPT scheduling system: A review of a new production scheduling system. Prod. Inv. Mgmt. J., 24(3): 47751. to MRP Fl Vollmann, T.E., 1986. OPT as an enhancement II. Prod. Inv. Mgmt. J., 27(2): 3846. 171Swann, D., 1986. Using MRP for optimized schedules (emulating OPT). Prod. Inv. Mgmt. J., 27(2): 30-37. 181 Meleton, M.P. Jr., 1986. OPT ~ Fantasy or breakthrough? Prod. Inv. Mgmt. J., 27(2): 13-20. [91 Vollum, R.B. and O’Malley, T.J., 1989. Improving on JIT success with constraint management and synchronous flow. 32nd Ann. Conf. Proc. American Production and Inventory Control Society, pp. 583-584. Changing to low inventory man1101 Booth, J., 1988. Beavers ufacturing. Int. J. Prod. Res., 26: 3477413. M.R. and Decaluwe, L., 1988. JIT and con1111 Lambrecht, straint theory: The issue of bottleneck management. Prod. Inv. Mgmt. J., 29(3): 61-65. production’s potential. P21 Wheatly, M., 1989. OPTimizing Int. J. Oper. Prod. Mgmt., 9(2): 38-44. E.M. and Fox, R.E., 1986. The Race. North iI31 Goldratt, River Press, Croton-on-Hudson, NY. E. and Ronen, B., 1991. Buffer manageu41 Schragenheim, ment : A diagnostic tool for production control. Prod. Inv. Mgmt. J., 32(2): 7479.
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[15] Umble, M. and Srikanth, M.L., 1990. Synchronous Manufacturing. South-Western Publishing Co., Cincinnati, OH. [16] Finch, B.J. and Cox, J.F., 1989. Strategic use of WIP inventory: The impact of bill-of-material shape and plant type. Prod. Inv. Mgmt. J., 30(l): 63-66. [17] Lockamy, A. III and Cox III, J.F., 1991. Using V-A-T analysis for determining the priority and location of JIT manufacturing techniques. Int. J. Prod. Res., 29: 1661-1672. [ 181 Reimer, G., 199 1. Material requirements planning and theory of constraints: Can they coexist? A case study. Prod. Inv. Mgmt. J., 32(4): 48-52. [19] Fawcett, SE. and Pearson, J.N., 1991. Understanding and applying constraint management in today’s manufacturing environment. Prod. Inv. Mgmt. J., 32(3): 46-55. [20] Lockamy, A. III, 1993. How to compete in your industry. Prod. Inv. Mgmt. J., 34(l): l-5. [21] Everdell, R., 1984. MRP II, JIT, and OPT: Not a choice but a synergy. 27th Ann. Conf. Proc. ~ Readings in Zero Inventory. American Production and Inventory Control Society, pp. 144-148. [22] Aggarwal, S.C. and Aggarwal, S., 1985. The management of manufacturing operations: An appraisal of recent developments. Int. J. Oper. Prod. Mgmt., 5(3): 21-38. [23] Grunwald, H., Striekwold, P.E.T. and Weeda, P.J., 1989. A framework for quantitative comparison of production control concepts. Int. J. Prod. Res., 27: 281-292. [24] Plenert, G. and Best, T.D., 1986. MRP, JIT, and OPT: What’s ‘best’? Prod. Inv. Mgmt. J., 27(2): 22-28. [25] Sohal, A. and Howard, K., 1987. Trends in materials management. Int. J. Phys. Distribution Mater. Mgmt., 17(5): 3-41. [26] Aggarwal, S.C., 1985. MRP, JIT, OPT, FMS? Making sense of production operations systems. Harvard Bus. Rev., 63(5): 8-16. [27] Ptak, C.A., 1991. MRP, MRP II, OPT, JIT, and CIM Succession, evolution, of necessary combination. Prod. Inv. Mgmt. J., 32(2): 7-11. [28] Ramsay, M.L., Brown, S. and Tabibzadeh, K.. 1990. Push, pull and squeeze shop floor control with computer simulation. Ind. Eng., 22(2): 39945. [29] Schragenheim, E. and Ronen, B., 1990. Drum-buffer-rope shop floor control. Prod. Inv. Mgmt. J., 31(3): 18-22. [30] Lambrecht, M.R. and Segaert, A., 1990. Buffer stock allocation in serial and assembly type of production lines, Int. J. Oper. Prod. Mgmt., lO(2): 47761. [31] Spencer, M.S., 1986. Lessons from the JIT journey. 1986 Int. Conf. Proc. American Production and Inventory Control Society, Falls Church, VA, pp. 326330. [32] Spencer, M.S., 1988. Developing a finite schedule for cellular manufacturing. Prod. Inv. Mgmt. J., 29(l): 74-79. [33] Spencer, M.S., 1995. Production planning in a MRP/JIT repetitive manufacturing environment. Prod. Planning Control, 6: 176184. [34] TOC Workshop Materials, 1990. Avraham Y. Goldratt Institute, New Haven, CT. [35] Law, A.M. and Kelton, W.D., 1991. Simulation Modeling and Analysis. McGraw-Hill, New York, NY.