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Justifying new manufacturing technology is usually very difficult since the most important benefits are often strategic and difficult to quantify. Traditional capital budgeting procedures that rely on return measures based on direct cost savings and incremental future cash flows do not normally capture the strategic benefits of higher quality, faster responses to wider ranges of customer needs, and the options for future growth made available by flexible manufacturing technology. Adding to these limitations is the difficulty of using traditional cost accounting systems to generate the information necessary for justifying new manufacturing investments. This paper reviews these problems and recommends procedures useful for assessing investments in flexible manufacturing technology.

Against a background of a customization imperative embraced by manufacturing firms in industrialised nations and the concomitant call for more balanced performance measurement systems (PMS), this study seeks to examine the mediating role of both non‐financial and financial performance measures in the relationship between a firm's strategic orientation of flexible manufacturing and organisational performance.

A path‐analytical model is adopted using questionnaire data from 84 Australian manufacturing firms.

The results indicate that, first, firms emphasising a flexible manufacturing strategy utilise non‐financial as well as financial performance measures; second, these performance measures are associated with higher organisational performance; and third, there is a positive association between a firm's strategic emphasis on flexible manufacturing and organisation performance via non‐financial and financial performance measures.

While there is agreement on the beneficial role of non‐financial performance measures in supporting strategic priorities associated with customization strategies, equivocal research results have emerged on the role of financial performance measures in this context. The study underscores the importance of both non‐financial and financial performance measures in this context.

The paper reinstates the value of financial performance measures for firms pursuing customization type strategies and adds to one's knowledge of PMSs by exploring the intervening role of such systems in linking flexible manufacturing strategy to organisation performance.

In the real world many companies combine the operations of manufacturing, assembly and disassembly. Thus, the integration of just‐in‐time FMS, FAS, and flexible disassembly system (DAS) models poses an interesting problem. The purpose of this paper is to provide major emphasis on a new simulation model for design and performance evaluation of a flexible assembly and disassembly system with dual Kanban under a stochastic system. This paper also primarily investigates the effect of varying the number of kanban cards, mean inter‐arrival time of demand and locations of the bottlenecks on the performance integration of JIT flexible manufacturing, assembly and disassembly systems.

Simulation is carried out in ARENA and data is analyzed using multivariate analysis of variance (MANOVA). This paper investigates the effect of varying number of kanban cards, mean inter‐arrival time of demand, and locations of the bottlenecks on the performance integration of JIT flexible manufacturing, assembly and disassembly systems. The performance measures that are simultaneously considered are the fill rate, work in process, and mean cycle time. This paper emphasizes that understanding the interactions between the variables and their effects on system performance is of utmost importance for managers in improving performance processes.

In manufacturing practice, there are many industrial units that represent the mixture of the referred three models. This paper presents a new simulation model for design and performance evaluation of a flexible assembly and disassembly system with dual kanban. The simulation results are statistically compared with MANOVA. MANOVA is used to perform the test with multiple objective functions, e.g. with the average production cycle time, percentage average fill rate, and work‐in‐process. The conclusion to be drawn is that minimized WIP can be obtained by higher percentage average fill rate, lower WIP, small average part cycles times, and increasing in kanban cards while simultaneously retaining full customer satisfaction.

The researcher presents the newly developed kanban system into the production system of JIT flexible manufacturing, assembly and disassembly system with simulation technique. Furthermore, by assigning time factors to the models, several performance measures can be easily computed. Then, the researcher tests the effect of the number of kanban card on integration of JIT flexible manufacturing, assembly and disassembly systems using a simulation approach, the simulation model is developed using the ARENA simulation package. The results are applied to a small case study. For a single product under the integration of JIT flexible manufacturing, assembly and disassembly systems, as the number of kanban cards increase, the fill rate along with work in process and the mean cycle time increases as well.

Discusses flexible manufacturing systems (FMS) and measures the benefits of substituting production flexibility for inventory. Compares the trade‐offs that arise in balanced operations, graphical models and some of the associated risks of flexible manufacturing implementation, operation and management. Also addresses the question of selecting flexibility over other techniques as well as benefits which flexibility provides. Examines a brief comparison between traditional and flexible manufacturing. Examples include illustrations of the benefits of FMS and cost flexibility, plus a view of the future role of FMS.

A conceptual framework for structuring a flexible manufacturing system (FMS) environment called an open automation architecture (OAA) is proposed. The framework is based on the open‐systems approach to information system development. OAA proposes partitioning of the manufacturing environment into autonomous manufacturing entities called logical manufacturing shops, that communicate and cooperate during the manufacturing of products. The partitioning in OAA is not based on the traditional structuring of machines on the shop floor. Each logical manufacturing shop is structured to provide for data and process integration within its processing domain, supported by a stable grouping of machines on the shop floor. The framework is illustrated with a manufacturing case and a discussion of information system support in the form of expert systems.

This paper describes work undertaken to support an integrated approach to the autonomous maintenance management of machine tools and flexible manufacturing systems. The work described was initiated as part of a collaborative research project, funded under the European Community Eureka Maine project initiative as EU744: Integrated Condition and Machining Process Monitoring System for Flexible Manufacturing Systems and for Stand‐alone CNC Machine Tools. The paper considers the steps needed to develop and implement truly autonomous machine tool maintenance management systems, and outlines the benefits which can be obtained from such approaches. The systems described have been deployed within collaborating industrial partners, each of whom is an existing user of advanced manufacturing technology. Their impact on these manufacturing systems is considered, with reference to the improvements in overall equipment effectiveness (OEE). The paper cites the improvements made in relation to specific flexible manufacturing cells as justification for the outlined system improvements.

In today′s rapidly changing market environment it has become necessary for manufacturing systems to have quick response times and high flexibility. This has led to the development of flexible manufacturing systems (FMS). Owing to its highly automated nature, a typical FMS has a high investment cost. Hence it becomes necessary to select the best configuration and decision rules at the design stage itself. The decision rules can have a substantial effect on the system performance. Attempts to simulate a particular FMS system. Applies different combinations of scheduling rules at the workstation buffers and at the loading buffer to the system and evaluates their effect on system performance.

Modern manufacturing facilities include conventional work centres as well as flexible manufacturing systems (FMSs). Discusses computer simulation software which simulates and graphically animates in real time an integrated job shop/FMS. The program interfaces a FORTRAN‐based simulator with a mainframe computer aided design (CAD) system which provides for 3‐D wire‐frame computer graphics and real‐time animation. The program also employs a batch‐oriented material requirements planning (MRP) system to provide daily updates of outstanding production centre loadings on a monthly planning horizon. Presents an example of an evaluation of an actual manufacturing facility.

The performance of flexible manufacturing systems, (FMS) has been one of the major topics that has attracted the attention of production and operations management (POM) researchers for the last two decades. In this paper we study the relationship between the degree of machine flexibility and the level of system performance. A machine loading and routing model is developed and used to investigate the effect of changing machine flexibility on the performance measures of an FMS. The measures used in this paper include makespan, routing flexibility, capacity flexibility, and inventory effects. The results indicate that changes in the machine flexibility affect some measures more than others. The results also show that, for a decrease of 24 per cent in the degree of machine flexibility, makespan increases by 17 per cent, routing flexibility decreases by as much as 13 per cent, and capacity flexibility decreases by as much as 38 per cent, and the inventory decreases by as much as 26 per cent. Furthermore, as the workload on the FMS increases, the differences in the performance between two FMSs (with different degrees of machine flexibility) increase.

The purpose of this paper is to develop a computer aided decision‐making model for flexible manufacturing system (FMS) situations when multiple conflicting objectives are addressed by the management.

It is assumed that the problem is the managerial level schedule rather than the operational schedule. As a tool, goal programming has been employed for measuring the trade‐offs among the objectives. As a safeguard, the level of the reliability of the constraints associated with the random coefficients is taken into consideration. As an optimization technique, the approach of chance constrained programming which has been an operational way for introducing probabilistic constraints into the collection of the linear programming and goal programming problem constraints is stated and mathematically formulated.

The approach of chance constrained programming is suitable to introduce management concerns about the reliability of the constraints of the problem in the FMS.

The paper gives an overview of the FMS and proposes a goal programming model for the analysis of problem. The proposed model acknowledges the randomness of customer demands for better standardization of production planning and inventory management systems. By the fact that customer demands are not always deterministic the hypothesis that sale level for each period is normally distributed is imposed. A sample example problem is provided to show how the proposed model can work.