A Virtual Cellular Manufacturing Approach to Batch Production*

In this paper, Virtual Cellular Manufacturing (VCM), an alternative approach to implementing cellular manufacturing, is investigated. VCM combines the setup efficiency typically obtained by Group Technology (GT) cellular manufacturing (CM) systems with the routing flexibility of a job shop. Unlike traditional CM systems in which the shop is physically designed as a series of cells, family-based scheduling criteria are used to form logical cells within a shop using a process layout. The result is the formation of temporary, virtual cells as opposed to the more traditional, permanent, physical cells present in GT systems. Virtual cells allow the shop to be more responsive to changes in demand and workload patterns. Production using VCM is compared to production using traditional cellular and job shop approaches. Results indicate that VCM yields significantly better flow time and due date performance over a wide range of common operating conditions, as well as being more robust to demand variability.     

Planning and Control in Multi-Cell Manufacturing

This research compares Material Requirements Planning (MRP), Kanban, and Period Batch Control (PBC) as alternative approaches to the planning and control of multi-cell manufacturing involving flow cells and assembly. Since previous research on performance of these systems in cellular manufacturing has been primarily conceptual, the experiments reported here provide new insights into their comparative performance. The results show that the production environment is a major factor in system choice. Three operating factors—Master Production Schedule (MPS) volume variation, MPS mix variation, and setup time/lot size—clearly affect system choice. All systems performed well under Justin-Time (JIT) conditions; there was no advantage to Kanban. Under the mixed conditions of high MPS variation, but small setup time/lot sizes, PBC produced superior performance compared to Kanban and MRP. Under non-JIT conditions, MRP was seen as clearly more effective. Finally, the results indicate that when conditions permit very small lot sizes relative to requirements, Kanban may perform best, even when MPS variation is high.     

基于同质设备多台配置的独立生产单元构建决策

单元化生产作为一种较好实现生产柔性与生产效率融合的先进生产方式,在变种变量需求环境下它已被大量生产企业特别是装配式生产企业所采用。生产单元构建问题是单元化生产系统设计的关键问题和首要问题,也是单元化生产研究领域的一个热点。本文研究设备易复制情形下,通过配置多台同质设备来实现生产单元间无物料移动,并保证生产单元间工作量均衡的独立生产单元构建问题。本文在综合考虑换装时间、加工顺序、设备生产能力、产品需求量等多个实际生产要素的基础上,建立了以平均总流程时间和各生产单元总流程时间与平均总流程时间偏差最小为目标的数学模型,并开发了一个启发式算法来求解数学模型,最后通过数值算例验证了模型和算法的有效性。     

Partitioning Work Centers for Group Technology: Analytical Extension and Shop-Level Simulation Investigation*

This paper investigates the effects of partitioning job shop work centers to implement cellular manufacturing. Analytical models are utilized to show that partitioning leads to adverse effects on flow characteristics. The setup reduction introduced in partitioned systems has to overcome these adverse effects before leading to the benefits associated with cellular manufacturing. It is shown that partitioned systems with an insufficient degree of setup reduction are inferior to unpartitioned systems. Two new parameters relevant to this context, the breakeven setup reduction factor and flow ratio, are introduced for the design of viable cellular manufacturing systems. These insights are verified using a shop-level simulation experiment, assuming non-Markovian conditions. The experimental factors include lot size, setup reduction factor, cell size and allowance of inter-cell movements. It is shown that the results are consistent with analytical insights in indicating the range of parameters in which cellular manufacturing may compare favorably with the best of the functional-layout systems.     

RFID as an Enabler of Improved Manufacturing Performance*

The use of radio frequency identification (RFID) versus bar coding has been debated with little quantitative research about how to best use RFID's capabilities and when RFID is more advantageous. This article responds to that need by qualitatively and quantitatively analyzing how RFID facilitates increased traceability and control in manufacturing, which in turn enables the use of more lot splitting and smaller lot sizes. We develop insights about operating policies (RFID vs. bar‐code tracking mechanisms, extent of lot splitting, and dispatching rules) and an operating condition (setup to processing time ratio) that affect the mean flow time and proportion of jobs tardy in a job shop. A simulation model is used to control factors in the experimental design and the output is evaluated using analysis of variance. The results show the following: (i) performance worsens when bar coding is used with extensive lot splitting, (ii) process changes such as extensive lot splitting may be required to justify RFID use instead of bar coding, (iii) the earliest operation due date dispatching rule offers an attractive alternative to other rules studied in previous lot splitting research, and (iv) the performance improvements with RFID and increased lot splitting are larger when the setup to processing time ratio is smaller. In a broader context, we fill a research void by quantitatively showing how RFID can be used as an advanced manufacturing technology that enables more factory automation and better performance along several dimensions. The article concludes by summarizing the results and identifying ideas for future research.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

OPTIMAL AND NEAR OPTIMAL CONTROL OF A TWO-PART-TYPE STOCHASTIC MANUFACTURING SYSTEM WITH DYNAMIC SETUPS

This paper deals with a manufacturing system consisting of a single machine subject to random failures and repairs. The machine can produce two types of parts. When the production is switched from one part type to the other, a random setup time is incurred at a constant cost rate. The objective is to track the demand, while keeping the work-in-process as close as possible to zero for both products. The problem is formulated as an optimal stochastic control problem. The optimal policy is obtained numerically by discretizing the continuous time continuous state opti-mality conditions using a Markov chain approximation technique. The discretized optimality conditions are shown to correspond to an infinite horizon, discrete time, discrete state dynamic programming problem. The optimal setup policy is shown to have two different structures depending on the parameters of the system. A heuristic policy is proposed to approximate the optimal setup policy. Simulation results show that the heuristic policy is a very good approximation for sufficiently reliable systems.     

ON THE RELATIVE PERFORMANCE OF FUNCTIONAL AND CELLULAR LAYOUTS- AN ANALYSIS OF THE MODEL-BASED COMPARATIVE STUDIES LITERATURE

Paralleling the increased interest in cellular manufacturing in recent years, a large number of studies have emerged that focus on the relative performance of cell systems and the functionally organized systems they replace. This paper is an analysis of studies that use model-based, controlled experimentation to seek answers to the questions of if, when, and why cellular layouts outperform their functional counterparts. Twenty-four model-based studies are analyzed with respect to their definitions of cellular versus functional layouts, the experimental factors that have been investigated, the results concerning relative performance, and the findings' relevance to layout choice. We conclude that although the comparative studies have been very valuable in their efforts at identifying the factors that embody the essence of cells and shape performance improvements, most of the basic findings are known from past research. Furthermore, the studies' findings cannot assist practitioners in making specific choices between existing layouts and alternative cell systems. However, the totality of the findings reinforce our knowledge of how work systems in general can be designed and organized to reduce throughput time, and can also be used to identify environments and conditions for which conversions to cells may have more or less potential.     

A Theoretical Model of Manufacturing Lead Times and Their Relationship to a Manufacturing Goal Hierarchy*

In recent years, manufacturing firms have realized that a new, higher level of global competition causes them to compete simultaneously on multiple manufacturing goals, such as quality, delivery, cost, and flexibility. In response to this realization, considerable research now focuses on the relationship of manufacturing improvement programs to manufacturing goals. However, to date, this research has not investigated the specific underlying statistical relationships between manufacturing goals and the shop floor. This study investigates manufacturing lead time linkages with manufacturing programs and manufacturing goals. The basic purpose of this study is to understand and explain how programs affect the elements of manufacturing lead time and how manufacturing lead time affects manufacturing goal capabilities. By understanding these linkages, managers can logically trace the effects of specific programs to their eventual effects on manufacturing goal capabilities. This study's most important finding is that statistical variations in the elements of lead time cause a tendency for certain manufacturing goals to be more difficult to control and achieve than others because of canonical relationships of lead time variances. To control these lead time variances, successful firms concentrate their early program targets first on achieving “fitness for use” quality, followed by delivery reliability, short delivery lead time and cost, current product flexibility, and lastly, new product flexibility. This study mathematically illustrates which improvement programs most affect manufacturing goals through their relationship to manufacturing lead time variance reduction. It suggests that firms improve goal performance by initially targeting improvement through setup time reduction programs, defect reduction programs, and preventive maintenance programs, to facilitate quality improvements. By targeting specific programs and their related lead time variances, firms improve their manufacturing facility competitiveness with minimum obstacles.     

A joint economic lot size model for raw material ordering,manufacturing setup,and finished goods delivering

In this research we consider a single-manufacturer single-buyer supply chain problem where the manufacturer orders raw materials from its supplier, then using its manufacturing processes converts the raw materials to finished goods, and finally delivers the finished goods to its customer. The manufacturer produces the product in batches at a finite rate and periodically delivers the finished goods at a fixed lot size to its customer, who has a constant demand rate. An integrated inventory control model, making joint economic lot sizes of manufacturer's raw material ordering, production batch, and buyer's ordering, is developed to minimize the mean total cost per unit time of the raw materials ordering and holding, manufacturer's setup and finished goods holding, the buyer's ordering, and inventory holding. Numerical examples are also setup to illustrate that jointly considering the inventory costs above results in less mean total cost than that of considering them separately.     

A Study of Labor Allocation Strategies in Cellular Manufacturing

Current research studies in cellular manufacturing have considered environments constrained only by the machine resource, when in fact the flexibility of manufacturing cells is derived mainly from its allocation of the labor resource. This research specifically examines the labor resource in cellular manufacturing, also known as group technology (GT), through a series of simulation experiments on a hypothetical GT shop and recommends conditions under which different labor allocation strategies may be appropriate. The effects of various product routings and scheduling policies on labor allocation are also examined.     

A multicriteria model for supporting setup reduction investment decisions

One critical manufacturing challenge of the 1990s is for firms to effectively apply new operations management techniques while embracing wider philosophies such as total quality management (TQM) and computer integrated manufacturing (CIM), etc. Setup cost and/or time reduction is one such technique capable of producing many benefits for manufacturing firms, including reduced inventory, better equipment utilization, and improved quality. It is thereby viewed as an important component of just-in-time (JIT) manufacturing practice. Existing problems with the setup reduction decision include the many factors that must be considered, as well of an absence of validated and usable models for estimating potential benefits from setup reduction investment made in different contexts. This paper discusses the attainment of gains from setup reduction mainly by improving existing equipment and work practices rather than purchasing new equipment or technology. The model proposed in this paper is based on the application of the analytic hierarchical process (AHP) on seven weighted factors to obtain a preliminary indication as to whether investment in setup reduction is desirable in a given manufacturing context, and the expected benefits of such investment. A flexible scaling system, thus obtained, allows the model to handle a wide range of managerial predispositions to setup reduction.     

Applications and Implementation: AN EXPERIMENTAL COMPARISON OF CELLULAR (GROUP TECHNOLOGY) LAYOUT WITH PROCESS LAYOUT

This study compares different strategies for arranging machines in a facility. Computer simulation of two different machine shops was used to compare process layout (the arrangement of groups of machines where the machines within a group are interchangeable) to cellular layout designed using group technology concepts (the use of manufacturing cells where each cell contains different types of machines dedicated to the production of similar parts). Four layout strategies, including process layout, cellular layout, and two hybrid layouts, were compared in two machine-shop models. The shops that used cellular layouts had shorter setup times, lower machine utilization, and shorter distances traveled, on average. The shops with process layout, however, had better performance on queue-related statistics such as work-in-process inventory level and average flow time. This suggests that a well-organized traditional job shop may be able to achieve overall performance that at least is comparable to that of the same shop using cellular (group technology) layout.     

Economic Production Lot Sizing with Periodic Costs and Overtime*

Traditional approaches for modeling economic production lot‐sizing problems assume that a single, fixed equipment setup cost is incurred each time a product is run, regardless of the quantity manufactured. This permits multiple days of production from one production setup. In this paper, we extend the model to consider additional fixed charges, such as cleanup or inspection costs, that are associated with each time period's production. This manufacturing cost structure is common in the food, chemical, and pharmaceutical industries, where process equipment must be sanitized between item changeovers and at the end of each day's production. We propose two mathematical problem formulations and optimization algorithms. The models' unique features include regular time production constraints, a fixed charge for each time period's production, and the availability of overtime production capacity. Experimental results indicate the conditions under which our algorithms' performance is superior to traditional approaches. We also test the procedures on a set of lot‐sizing problems facing a national food processor and document their potential economic benefit.     

An evaluation of product commonality and group technology production methods in a pre-defined multiple-machine scenario

Group technology has been used successfully for a number of years as a setup reduction tool. It is especially valuable in low-volume high-mix manufacturing environments where products and machines may be partitioned into product families and machine cells. The partitioning of machines or processes into cells may be limited by practical constraints, and the partitioning of products is complicated as the number of products and processes increases. In this paper, the authors examine the behaviour of various grouping strategies lauded as being appropriate in situations where machine partitioning and product routeing are determined by technological constraints. Of specific experimental concern in this paper is the effect of the mean and variance of component part commonality between products on system performance under various grouping strategies in a multiple-machine environment.     

Order Dispatching and Labor Assignment in Cellular Manufacturing Systems

Although order and labor dispatching in the job shop manufacturing setting have been investigated extensively over the last three decades, its representation of actual processes found in practice today is limited due to the move to cellular manufacturing (CM). Manufacturing cells have become an important approach to batch manufacturing in the last two decades, and their layout structure provides a dominant flow structure for the part routings. The flow shop nature of manufacturing cells adds a simplifying structure to the problem of planning worker assignments and order releases, which makes it more amenable to the use of optimization techniques. In this paper we exploit this characteristic and present two mathematical modeling approaches for making order dispatching and labor assignment/reassignment decisions in two different CM settings. The two formulations are evaluated in a dynamic simulation setting and compared to a heuristic procedure using tardiness as the primary performance measure. The formulations are superior to the heuristic approach and can be incorporated into detail scheduling systems that are being implemented by corporations employing enterprise resource planning (ERP) systems today.     

An Optimal Model for Cell Formation Decisions

This study develops an approach to cell formation decisions for cellular manufacturing layouts in group technology settings. An optimal 0–1 integer programming model is used to provide an analysis for determining which machines and parts should be assigned to cells in cellular manufacturing layouts. This approach minimizes the cost of manufacturing exceptional parts outside the cellular system, subject to machine capacity constraints. Part–machine matrices are partitioned into disconnected cells and use far fewer 0–1 variables than earlier approaches. Formulation of the model is described with a numerical example and computer solutions to realistic problems are obtained. The characteristics of computer run times, model performance, and applications of the model are discussed.     

Production and changeover control policies of a class of failure prone buffered flow-shops

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.     

Relative Performance Evaluation and Risk Taking in Delegated Investment Decisions*

Providing proper incentives to firm managers is increasingly important in the current competitive environment. Analytical research has suggested that evaluating a manager's performance relative to that of a peer group, in conjunction with standard-based pay, can induce efficient risk-sharing between firm owners and managers while maintaining the latter's incentives to exert effort. To date, direct empirical tests of this proposition have not been reported. This study uses a laboratory experiment to test the effect of relative performance evaluation on the risk-aversion of delegated investment decisions. Project-specific risk is operationalized using operating leverage, in part because the variability of a project's operating profits generally increases with this variable, and in part because many of the new manufacturing approaches held to be important to competitive advantage require significant capital investments and attendant increases in operating leverage. Across two levels of environmental uncertainty, subjects under a relative (as opposed to absolute) performance standard selected investments with significantly higher project-specific risk. Also, as environmental uncertainty increased, subjects under an absolute standard significantly reduced the riskiness of their investments. In contrast, subjects shielded from environmental uncertainty by a relative standard chose investments of about equal riskiness under both low and high uncertainty conditions. If supported by future research, these findings would suggest that relative performance evaluation may reduce managers' reluctance to adopt risky capital investments, especially in firms operating in high-risk economic or technological environments.     

A Knowledge Based Decision Support System for Setup Reduction

The issue of setup reduction is important for firms seeking to incorporate advanced procedures and concepts such as flexible manufacturing systems (FMS) and just-in-time (JIT) manufacturing to improve manufacturing productivity. Investing adequate amounts in setup reduction is a complex decision affected by many factors including the existing level of automation, worker characteristics, product features, and the manufacturing environment. The consequences of improper investment (i.e., over- and underinvestment) includes possible disruption of manufacturing operations as well as wasted resources. Much of the knowledge involved in the investment decision is heuristic and experience-based, and takes on greater significance when the form of the setup reduction function is not known a priori. This paper describes the development of a knowledge-based decision support system (KBDSS) that combines both heuristic and procedural knowledge to provide support for the setup reduction investment decision. The system uses codified manufacturing expertise to improve both the accuracy and effectiveness of setup-reduction decision making. Use of the KBDSS is illustrated by two examples based on disparate manufacturing contexts.