Modified drum–buffer–rope scheduling mechanism for a non-identical parallel machine flow shop with processing-time variation

The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.     

The study of an Easy-to-Use DBR and BM system

The concept and advantages of the Drum–Buffer–Rope (DBR) scheduling and buffer management (BM) system are now widely accepted and recognized by the industrial communities. Therefore, there are several types of commercial DBR and BM business solution software such as OPT21?, DISASTER?, Visual DBR?, Synchrono?, Drummer?, etc., on the market. However, prior to implementation of any of these, factories must first gather a complete data set for their perspective throughput nets. This means that the users will be required to enter and maintain a complicated database and the degree of difficulty of the entire software system implementation process will also increase. Furthermore, it is also unrealistic to maintain the accuracy of these dynamic data in the fast-paced and competitive business environment. Therefore, in this article, we have proposed the Easy-to-Use DBR and BM system concept. The term ‘Easy-to-Use’ refers to the fact that users will be required to enter and maintain a minimal set of fundamental data to satisfy the factories’ operation needs. The system framework mentioned in our article contains the full and complete function of the DBR and BM system but with very limited data maintenance by the users. Therefore, with less procedural complexity, this system can produce a higher range of operational application and can attract higher interest from the users. The concept of simplified throughput net design proposed in our article cannot only be used as a reference when factories develop their own information systems, but can also provide a new design model and algorithm for the system software developers as well. Since the database maintenance is significantly simplified, the factories can quickly adapt to any changes in the dynamic, rapid-changing, and highly competitive production environment. In the article, we first describe the concept of the Easy-to-Use DBR and BM system and the steps to simplify the information system by using a simple throughput net. We then explain the system framework and design methodology. At the end, we have used a prototype system to demonstrate and to verify the applicability and efficiency of the concept and framework mentioned in our article.     

A comparison of JIT and TOC buffering philosophies on system performance with unplanned machine downtime

The impact of buffering under just in time (JIT) and theory of constraints (TOC) is studied to determine whether a difference in performance exists in systems faced with unplanned machine downtime. Comparisons are based on results obtained from simulation of a five-station cell utilised in computer substrate manufacturing. Analysis of the simulation output suggests that the TOC technique, drum–buffer–rope (DBR), achieves higher levels of performance as measured by total output and lead time while reducing inventory requirements relative to the tested JIT technique, Kanban. Improved system performance stems from the strategic placement of buffers in DBR, which maximises protection of the constraint from variation rather than attempting to protect each individual station. Further, analysis suggests that DBR systems are more robust than JIT systems in that they are able to maintain higher levels of system performance across a range of inventory levels.     

Comparisons between drum–buffer–rope and material requirements planning: a case study

Drum–Buffer–Rope (DBR) is an alternative approach to manufacturing planning and control that is not as formally tested as Material Requirements Planning (MRP) systems which have traditionally been around for years. Yet, some reports indicate very good performance for DBR and the associated use of synchronous manufacturing principles. But how do these systems compare and relate to one another? Based on our experiences of studying a Bearing Manufacturing Company that actually made the transition from an MRP system to a DBR system, we conduct simulation-based experiments in this paper with the objective of providing a more formal comparison between these two systems than what has been offered in prior literature. To our knowledge, this is the only study of its kind that uses a real-world setting to evaluate key differences and convergence points between comprehensive MRP and DBR systems. Our results show that even though the MRP and DBR systems position inventory differently and provide different dynamic responses to customer demand, there are several operating policies that can be implemented in either system. While the DBR performance in our simulation model was clearly superior to a nominal MRP implementation, we show that even within the constraints of the structural design of MRP system, policy modification based on DBR principles can significantly reduce these performance differences. This finding has an important implication for practising managers who need not necessarily switch from a MRP system to a DBR type of a system (as was done by our case-study firm) in order to take advantage of attractive features of the DBR system. Future researchers can use our study to understand more fully how these Structural Design and Operating Policy differences can be further exploited to implement unique systems that combine the best features of both DBR and MRP systems.     

Implementing theory of constraints in a traditional Japanese manufacturing environment: The case of Hitachi Tool Engineering

This research presents a case study of a virtual ‘textbook’ application of the theory of constraints (TOC) in a Japanese tool manufacturing company. Hitachi Tool Engineering uses state-of-the-art technology to design and manufacture cutting tools known as End-mills. The plant described in this study is a classic V-plant and exhibited all of the standard problems of a traditionally managed V-plant, existing within the unique framework of Japanese work culture. Plant management applied the five focusing steps and used the operations strategy tools, including drum-buffer-rope and buffer management, to improve the system. Following the approach recommended by Eli Goldratt, the thinking process tools of current reality tree and evaporating clouds were used to help identify and resolve problems when the implementation encountered major obstacles. While the implementation was a huge success, the devastating effect of a core problem being left unresolved is well documented. The implementation generated significant improvements in work-in-process inventory, production lead time, on-time delivery, productive capacity, inventory turnover, product quality, sales volume, and profitability. Moreover, management has extended the introduction of TOC to the non-manufacturing functions and TOC is becoming the common company culture that bridges four culturally diverse manufacturing plants.     

TOC-based performance measures and five focusing steps in a job-shop manufacturing environment

Theory of constraints (TOC) views a company as a set of interdependent processes working in harmony to achieve the profit goal of the company as a whole, and thus it emphasizes total system performance over localized measures to guide operational decisions. This paper demonstrates the usefulness of employing TOCbased global performance measures to make operational-decisions (e.g. product mix, continuous improvement, inventory management, production planning and scheduling) to strengthen the internal supply chain in a relatively complex manufacturing environment, i.e. a job shop. An ARENA-based simulation model is presented and a number of scenarios are discussed that provide insights regarding the characteristic features of TOC, such as goals and necessary conditions, performance measures, five-focusing steps for continuous improvement, and drumbuffer-rope scheduling. These insights will assist managers in making important decisions regarding approaches to successful TOC implementations, and will provide academics with a broad range of future research opportunities.     

Revolutionizing supply chain management the theory of constraints way: a case study

This research describes in detail an application of theory of constraints (TOC) and its resulting benefits on the supply chain performance of India's largest lock manufacturing company over a period of seven years. Using TOC's thinking process, the core constraints that had limited the company's performance in the areas of production, distribution, supply group and projects were identified and eliminated. TOC's unique approach helped the company achieve a significant reduction in its finished goods, raw material and work-in-process inventories at various levels across the supply chain. The stock-outs and excess in the distribution system nearly disappeared. The existing lead times saw a drastic reduction while the availability of items increased to nearly 100% despite significant decrease in inventory levels in the supply chain. The inventory turns of the distributors and retailers more than tripled and their profitability increased significantly. The overall sales of the company grew nearly three times during the six years post TOC implementation. TOC's holistic approach helped the company to double its profits and improve its cash position during the Great Recession.     

Takt Time Grouping: implementing kanban-flow manufacturing in an unbalanced,high variation cycle-time process with moving constraints

One-piece flow and kanban/pull methods have been used to reduce work-in-process (WIP) and flowtime in manufacturing flow processes; however, these methods have limitations. For example, one-piece flow does not work well when there are relatively large set-up times required between different components. One-piece flow also requires operations to be well balanced with a minimum of variability in processing times at each operation. Unfortunately, these conditions often do not exist. The theory of constraints drum-buffer-rope (DBR) method is designed for unbalanced processes, and it has been shown to be effective for products with large operation time variation. However, DBR does not generally optimise flowtime and cannot handle a process with moving constraints (bottlenecks). We have developed a method called Takt Time Grouping (TTG) for implementing kanban-flow manufacturing, when one-piece flow or DBR do not provide good results. TTG combines one-piece flow manufacturing, transfer-batch sizing and DBR concepts through the use of a constraints-based transfer-batch sizing formula. Using a discrete event simulation model, it is shown that TTG increases throughput rate as compared to one-piece flow, CONWIP and DBR approaches, with much lower WIP inventory and faster flowtime than CONWIP and DBR.     

Throughput/inventory dollar-days: TOC-based measures for supply chain collaboration

As the semiconductor industry moves away from vertical integration, performance measures play an increasingly important role to ensure effective collaboration. This paper demonstrates that the theory of constraints (TOC)-based measures, Throughput and Inventory Dollar-Days (T/IDD), induce autonomous supply chain (SC) links to function as a synergistic whole and thereby, improve the performance of the whole SC network significantly. We model an SC network of a well-known TOC case study using discrete event simulation and discuss managerial implications of these measures via a set of scenarios. The scenarios explain how these measures – without sharing sensitive financial data – allow members of an SC network to monitor both the effectiveness (TDD) and efficiency (IDD) of SC members and lead them to create win-win solutions following well-known TOC-based planning and control concepts. We conclude this paper by discussing some limitations of the proposed research and provide directions for future theoretical research.     

An exploratory study of protective inventory in a re-entrant line with protective capacity

While the effect of protective inventory on the performance of simple lines has received considerable attention, the same cannot be said for re-entrant lines. This paper attempts to meet that deficiency. This paper examines two different but related issues. First, the theory of constraints (TOC) evaporating cloud method is employed to show the traditional dilemma of increasing work-in-process (WIP) to fully utilise resources versus decreasing WIP inventory to reduce cycle time. The assumptions and implications of three different management philosophies (traditional, JIT/lean, and TOC) are explored in addressing this dilemma with respect to the use of both protective inventory and protective capacity. Second, given an unbalanced re-entrant line with fixed capacity, simulation is used to explore the effectiveness of using protective inventory by changing the level of WIP on two dependent variables: cycle time and throughput. Two sources of variation are simulated: processing time and breakdowns (machine failures). At a given WIP level, it was found that the amount of protective capacity at non-bottlenecks changed with increases in variability. Therefore the level of WIP inventory (with its protective inventory) and the level of protective capacity needed to protect against variability play a critical role in determining cycle time and throughput of the re-entrant line. While this is an exploratory study, comments on protective inventory and protective capacity are provided based on the three different management philosophies.     

A Decision Support System for Determining Optimal Retention Stocks for Service Parts Inventories

This paper reports on the development and successful implementation of a decision support system (DSS) for analyzing service parts inventory retention stocks. The DSS was implemented in a Fortune 100 company with an initial $50,000,000 investment in service parts inventory. During the last two years of use, approximately $13,000,000 of service parts have been scrapped (disposed) with the help of the DSS. Very few of these parts have had to be repurchased from scrap dealers. This has resulted in approximately $6,000,000 in tax savings alone. The system continues 'to be used regularly by the company.

The paper contributes to the service parts management literature in three ways. First, the paper proposes a new forecasting model for the retention stock problem. Second, the paper develops a new inventory model which captures a richer operating environment. Third, the paper suggests how these models may be integrated in an interactive, menu-driven, databased DSS. Although the forecasting model, inventory model, and DSS are described in the context of a specific company, the DSS and the embedded models are applicable to managing service parts in a wide variety of environments.     

MRPⅡ系统中TOC的研究及应用

通过分析传统ＭＲＰⅡ系统存在的局限，并简要介绍ＴＯＣ管理思想与ＤＢＲ方法，就ＭＲＰⅡ与ＴＯＣ的结合做初步探讨，进而在ＴＯＣ哲理的基础之上提出一种ＭＲＰⅡ与ＴＯＣ相结合的生产计划与控制系统的实现模型。     

On the beat of the drum: improving the flow shop performance of the Drum–Buffer–Rope scheduling mechanism

One of the main elements of the theory of constraints is its Drum–Buffer–Rope (DBR) scheduling (or release) mechanism that controls the release of jobs to the system. Jobs are not released directly to the shop floor – they are withheld in a backlog and released in accordance with the output rate of the bottleneck (i.e. the drum). The sequence in which jobs are considered for release from the backlog is determined by the schedule of the drum, which also determines in which order jobs are processed or dispatched on the shop floor. In the DBR literature, the focus is on the urgency of jobs and the same procedure is used both for backlog sequencing and dispatching. In this study, we explore the potential of using different combinations of rules for sequencing and dispatching to improve DBR performance. Based on controlled simulation experiments in a pure and general flow shop we demonstrate that, although the original procedure works well in a pure flow shop, it becomes dysfunctional in a general flow shop where job routings vary. Performance can be significantly enhanced by switching from a focus on urgency to a focus on the shortest bottleneck processing time during periods of high load.     

Comparing JIT,MRP and TOC,and embedding TOC into MRP

The traditional material requirements planning (MRP) system for planning and controlling production systems is being replaced more and more by just-in-time (JIT) and the theory of constraints (TOC). Because JIT and TOC share many elements with MRP and because MRP is very flexible, it is not difficult to make MRP behave like JIT or TOC. Consequently manufacturers with MRP systems need not dismantle them to implement JIT or TOC. A five-step technical procedure for embedding TOC into MRP is presented in this paper along with an illustrative example from a microelectronics plant. Next a simple production line is analysed using a Markov chain model to examine the types of improvements each approach makes and the effect of these improvements on the performance of the line. Performance measures are the mean and variance of the output, shortage, inventory level, and cycle time of the production line. Insights are developed into the reasons for the superior performance of JIT and TOC.     

Heuristic dispatching rule to maximize TDD and IDD performance

Although mean flow time and tardiness have been used for a long time as indicators in both manufacturing plants and academic research on dispatching rules, according to Theory of Constraints (TOC), neither indicator properly measures deviation from production plans. TOC claims that using throughput dollar-day (TDD) and inventory dollar-day (IDD) can induce the factory to take appropriate actions for the organization as a whole, and that these can be applied to replace various key performance indices used by most factories. However, no one has studied dispatching rules based on TDD and IDD performance indicators. The study addresses two interesting issues. (1) If TDD and IDD are used as performance indicators, do those dispatching rules that yield a better performance in tardiness and mean flow time still yield satisfactory results in terms of TDD and IDD performance? (2) Does a dispatching rule exist to outperform the current dispatching rules in terms of TDD and IDD performance? First, a TDD/IDD-based heuristic dispatching rule is developed to answer these questions. Second, a computational experiment is performed, involving six simulation examples, to compare the proposed TDD/IDD-based heuristic-dispatching rule with the currently used dispatching rules. Five dispatching rules, shortest processing time, earliest due date, total profit, minimum slack and apparent tardiness cost, are adopted herein. The results demonstrate that the developed TDD/IDD-based heuristic dispatching rule is feasible and outperforms the selected dispatching rules in terms of TDD and IDD.     

基于DBR系统的敏捷供应链协同机制

基于单一生产系统内部DBR生产计划与控制系统的实现机制,通过对其应用范围的进一步扩展,提出了供应链管理环境下DBR系统运作的六条基本原理,构建了供应链环境下DBR系统的基本框架,从计划、缓冲与控制机制三个方面对其实现机制进行了探讨.提出了支持DBR系统运作的供应链协同计划与控制模型,该模型基于单一节点企业内部DBR系统和供应链DBR系统的共同运作,通过面向订单对象的分布式生产控制模式和电子看板,实现供应链系统的协同计划与控制.     

A capacity available-to-promise model for drum-buffer-rope systems

The capacity available-to-promise (CATP) concept, which is designed to enhance the available-to-promise (ATP) feature in MRPII for changing production strategy from make-to-stock (MTS) to make-to-order (MTO), was developed recently. In contrast to an ATP, which is the future uncommitted inventory as projected by master production schedule, a CATP provides a detailed and time-phased diagram of unused production capacity. Hence, a CATP can allow marketing personnel to establish realistic order promise dates and concentrate on selling idle capacity in the future, and enable customers to select their preferred future capacity. This study proposes a CATP model for drum-buffer-rope (DBR) systems. The DBR scheduling system is one of finite capacity schedule systems and is currently being implemented by a growing number of manufacturing companies. This CATP model can help DBR users improve the due-date promising and exploitation of bottleneck. This CATP model can also be embedded in current commercial or private DBR scheduling systems so as to enhance their effectiveness.     

Comparison of DBR with CONWIP in an unbalanced production line with three stations

The recently developed alternatives to traditional production planning and control systems such as material requirement planning (MRP) and Kanban are the drum–buffer–rope (DBR) and CONWIP (CONstant Work In Process) systems. Each system is best described as a combination push (like an MRP)/pull (like a Kanban) logistical procedure. Materials are pulled into the shop via the appropriate logic, and once released, materials are then pushed to subsequent workcentres. The performance of the DBR and CONWIP control policies are analysed and compared in a three-stage unbalanced tandem production line. Using a continuous Markov process model, steady-state probability distributions for the systems are derived, and then the performance measures of the systems can be evaluated. To compare the two systems, an optimization model for each system is proposed. From sensitivity analyses for the optimization models, the proposed models are validated, the differences of the two systems are investigated, and it is found that DBR is better than CONWIP under the proposed performance measures.     

Implementation of a demand-pull system in a job shop environment

This paper describes an implementation of JIT concepts in a medium-sized make-to-order manufacturing company. We develop a hybrid production control system for the company based on separating jobs with high production volumes following a standard routeing from relatively low-volume jobs with more complex routeings. The machines processing the high-volume standard-routeing jobs are treated as a virtual flow shop and controlled by a hybrid push/pull system, while the remainder of the floor is managed as a job shop. Initial tests have shown that this system can significantly improve throughput and simplify the production scheduling task. We believe that the system can be applied to a wide range of industries with similar characteristics. We also describe the testing of the system and the circumstances under which its implementation was discontinued, which indicate some of the difficulties faced by small manufacturers in implementing such systems.     

Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains

A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production–distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production–distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production–distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.