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.     

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.     

A simulation analysis of the impact of family configuration on virtual cellular manufacturing

Past research has shown that it is possible to simultaneously achieve the setup efficiencies of traditional cellular manufacturing systems and the routeing flexibility of a job shop by viewing cells not as permanent, physical structures, but as temporary, 'virtual' entities. This research demonstrates that the advantages of virtual manufacturing cells can be obtained over a range of part family configurations. In particular, virtual cellular manufacturing is robust to changes in the number and size of families being processed. Further, the research shows that the benefits can be obtained under setup conditions impartial to a family-oriented part environment.     

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.     

Fundamental Insights into Part Family Scheduling: The Single Machine Case

A simulation study was conducted to investigate the behavior of family scheduling procedures in a dynamic dispatching environment. Two scheduling rules that incorporate setup avoidance mechanisms (FCFS-F and SPT-F) and two that do not (FCFS and SPT) were applied to a single machine. The scheduling environment was varied by controlling several important factors: the machine utilization, the number of setup configurations (families), the size of the family setup times relative to the job run times, the frequency by which members of the part families were released for processing, and the distribution of job interarrival and job run times. The major results from the study are the following: (1) The degree of stability in the system is the most influential factor with respect to mean flow time and flow time variance. Under low variance service and interarrival time distributions, the impact of scheduling rule selection is minor. (2) Conversely, under unstable scheduling situations, family scheduling procedures can have a substantial impact. (3) Clear interaction effects are noticed between all factors. The environment most conducive to family scheduling is characterized by high resource utilization, low setup-to-run time ratio, few part families, and erratic job arrivals. (4) Under conditions favorable to family scheduling, setup avoiding procedures can be used to increase output while at the same time reduce the mean and variance of flow time. (5) The shortest processing time rule (SPT) performs well with respect to mean flow time when relative setup times are small. Overall, however, SPT-F generates the lowest mean flow time while FCFS-F produces the lowest flow time variance. This study shows that scheduling procedures that consider setups in their structure can outperform rules that do not under many different operating conditions. However, the magnitude of this advantage very much depends on the scheduling environment. The results also highlight the fact that it may be better to try to reshape the manufacturing environment than worry about selecting the correct scheduling rule. If the environment cannot be stabilized, then the choice of a setup avoiding procedure, allocation of families to machines, and setup reduction become important issues.     

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.     

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。     

Optimizing lot size and setup time in a JIT environment: a multiple objective application

The just-in-time (JIT) system has been studied extensively and implemented by a number of US firms as an ell'ective production system. The core of JIT involves determination of lot size and setup time reduction so as to increase manufacturing flexibility while minimizing the inventory level. This decision problem usually involves multiple conflicting objectives and mixed-model production. In this paper, goal programming (GP) is applied to a real-world JIT problem involving fabrication of different automotive and industrial rubber composite belts. The model results provide new insights concerning the conflicting nature of several goals, especially between meeting demand and reducing setup or idle time. Also, the GP solution is superior to the current JIT practice of the company.     

Partitioning Work Centers for Group Technology: Insights from an Analytical Model*

This paper develops analytical approximations based on an M/M/c queuing model, for the operating characteristics of job shop work centers. The model is used to analyze the effects of partitioning a work center and introducing setup reduction into the cell components. The results using this model are shown to be consistent with the results of prior simulation-based studies of group technology under certain parameter ranges. A simulation experiment was conducted to verify the effects with multi-item, non-Markovian assumptions, and the general effects predicted by the model were found to apply.     

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.     

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.     

Optimal investment in setup reduction for finite horizon periodic review inventory system under deterministic time varying demand

This paper examines the impact of setup reduction on a finite horizon, periodic review inventory system, under deterministic time varying demand. A total relevant cost function is developed for such systems. Using this, the impact of setup reduction is examined under various forms of setup reduction cost functions that have been suggested in the literature. The operating characteristics and optimization of the various scenarios are discussed. Our analysis shows that the effects of setup reduction in a periodic review system are similar to those in a reorder point system. Our results are likely to help practitioners who use similar periodic review systems towards decreasing total inventory related costs by investing in setup reduction.     

Cost models for lot streaming in a multistage flow shop

Lot streaming is a technique used to split a processing batch into several transfer batches. In this way, overlapping operations can be performed in different manufacturing stages, and production can be accelerated. This paper proposes two cost models for solving lot streaming problems in a multistage flow shop. The purpose is to determine the optimal processing batch size and the optimal number of transfer batches that minimize the total annual cost in each model. In the first model, a more complete and accurate method is developed to compute the costs of raw materials, work-in-process, and finished-product inventories. The total cost includes the setup cost, the transfer batch movement cost, the three-type inventory holding cost, and the finished-product shipment cost. The second model contains not only the four costs in the first model, but also the imputed cost associated with the makespan time. The total annual cost functions in both models are shown to be convex, and two solution approaches are suggested. An experiment consisting of three phases was conducted to explore the effect on the optimal solution when changing the value of one parameter at a time. The results indicate that three parameters have significant effects on the optimal solution.     

Planning a cost-effective cellular manufacturing process—a case study

The drive key is one of the most important high-precision components of aircraft braking systems. In this paper, a cost-effective drive key manufacturing process is developed. This proposed manufacturing process makes use of near net shape manufacturing, CNC machining, and cellular manufacturing methods. To make the proposed process a reality, proper engineering changes of the parts along with group technology are applied to the product design, in conjunction with the pro posed manufacturing methods. In addition, a general spread sheet program is developed for each style of fixture module to assist in part program coding. Moreover, statistical process control (SPC) is implemented to verify product quality and accept the product at the point of manufacture. It is observed that the implementation of the proposed drive key cellular manufacturing process results in a tremendous improvement in quality, throughput and productivity as well as a drastic reduction in manufacturing operations, workholding fixtures, cutting tools, and inspection media.     

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.     

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.     

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.     

A Comparison between Basic Cyclic Scheduling and Variable Cyclic Scheduling in a Two‐stage Hybrid Flow Shop

In this paper the impacts of two types of repetitive scheduling systems on the makespan in a two‐stage hybrid flow shop, which consists of one machine in the first stage and multiple process lines in the second stage, are compared. First, we analyzed, through a simulation, how the makespan is affected by the setup frequency and sequencing rules for products under two types of scheduling systems: One is repetitive scheduling with only one batch per product family per scheduling cycle (basic cyclic scheduling system). The other is repetitive cyclic scheduling with various batches per product family per scheduling cycle (variable cyclic scheduling system). Second, we compared which scheduling system is superior under various manufacturing situations. The following points were noted. (1) The superior scheduling system can be shown by a two‐dimensional diagram of the setup frequency and the imbalance in workload for processing among process lines in the second stage. (2) Variable cyclic scheduling is superior in comparison with basic cyclic scheduling when there is a large imbalance in the workload to be processed among process lines in the second stage, or the workload in the second stage is larger than that in the first stage. The result of this research provides guidelines for selecting which scheduling system should be adopted.     

Productivity gains by cellular manufacturing

This paper discusses some of the major factual results and discussions of the effects of implementation of a cellular manufacturing environment on the productivity of people, management and facilities, as was experienced in this study. It is suggested that significant improvements at very low costs are possible at managerial, and other work force levels, by introduction of appropriate production management systems. This is based on analysis of the changes in production costs and more importantly the behaviour of employees brought about by a first time implementation of cellular manufacturing in a company.     

Scheduling two-stage production lines with multiple machines

Most manufacturing process maintain separate fabrication and assembly centres. Based on this observation, the author coincides a manufacturing process that contains two stages of production with multiple machines. The manufacturer produces a variety of products to satisfy customer demands, operates under a 'push' mode and in a ‘ make-to-order’ environment. Each customer order consists of known quantities of different products which must be delivered as a whole shipment. Periodically, the manufacturer schedules all the accumulated unscheduled customer orders. The scheduling objective is to minimize the sum of weighted customer order lead times. Such manufacturing systems are formulated as a mathematical programming problem. It is then shown that this problem is unary NP-hard and remains unary NP-hard even when all the weights are equal. Some insights about the structure of the optimal schedule(s) are provided and some special cases solved in polynomial time. Several polynomial time heuristics are proposed, and worst-case analysis of some of the heuristics are provided. Tight lower bounds are developed in order to measure the performance of the proposed heuristics. Numerical examples are presented and possible extensions are discussed.