دانلود مقاله ISI انگلیسی شماره 3550
عنوان فارسی مقاله

روش تصمیم گیری فازی چند معیاره برای ارزیابی سرمایه گذاری های پیشرفته سیستم تولید

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
3550 2001 16 صفحه PDF سفارش دهید 7390 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Volume 69, Issue 1, 7 January 2001, Pages 49–64

کلمات کلیدی
تجزیه و تحلیل تصمیم گیری فازی - تصمیم گیری چند معیاره - تجزیه و تحلیل سرمایه گذاری - سیستم های تولید انعطاف پذیر
پیش نمایش مقاله
پیش نمایش مقاله روش تصمیم گیری فازی چند معیاره برای ارزیابی سرمایه گذاری های پیشرفته سیستم تولید

چکیده انگلیسی

In this paper, a fuzzy decision algorithm is proposed to select the most suitable advanced manufacturing system (AMS) alternative from a set of mutually exclusive alternatives. Both economic evaluation criterion and strategic criteria such as flexibility, quality improvement, which are not quantitative in nature, are considered for selection. The economic aspects of the AMS selection process are addressed using the fuzzy discounted cash flow analysis. The decision algorithm aggregates the experts’ preference ratings for the economic and strategic criteria weights, and the suitability of AMS investment alternatives versus the selection criteria to calculate fuzzy suitability indices. The fuzzy indices are then used to rank the AMS investment alternatives. Triangular fuzzy numbers are used throughout the analysis to quantify the vagueness inherent in the financial estimates such as periodic cash flows, interest rate and inflation rates, experts’ linguistic assessments for strategic justification criteria, and importance weight of each criterion. A comprehensive numerical example is provided to illustrate the results of the analysis.

مقدمه انگلیسی

Investment evaluation methods play an important role in today's competitive manufacturing environment. Shrinking profit margins and diversification require careful analysis of investments, and the decisions regarding these investments are crucial to the survival of the manufacturing firm. Lately, the manufacturing firms have been investing in advanced manufacturing technologies such as group technology, flexible manufacturing systems, computer-integrated manufacturing systems, etc. to improve manufacturing performance in terms of cost, productivity, flexibility and quality, in an effort to compete with other industrialized firms in the global marketplace. Flexibility in a manufacturing environment can be defined as the capability and ease of accommodating changes in the system. Flexibility ensures that manufacturing can be both cost effective and customized at the same time [1]. A single widely-accepted measure for flexibility does not exist, and thus, there is a continuing research on this subject [2]. Flexibility of advanced manufacturing systems provides faster throughput, reduces cost of retooling for design changes, allows for smoother scheduling, and provides an ability for production volume adjustments to handle unanticipated demand changes with low levels of inventory [3]. In summary, flexibility results in considerable enhancement in responding to changes in market demand, product design and product mix. According to Meredith and Suresh [4], investment justification methods in advanced manufacturing technologies are classified into economic analysis techniques, analytical methods, and strategic approaches. These methods deviate from each other mainly due to the treatment of non-monetary factors. Economic justification methods of manufacturing investments have been discussed thoroughly in the past couple of decades [5]. Economic analysis methods are the basic discounted cash flow techniques such as present worth, annual worth, internal rate of return, etc., and other techniques such as payback period and return on investment which ignore time value of money. The application of these techniques to the evaluation of flexible manufacturing system (FMS) investments is analyzed in [6]. It is well known by engineering economy practitioners that accounting methods, which ignore time value of money, would produce inaccurate or at best approximate results. Discounted cash flow (DCF) methods appear as the most popular economic justification methodology; however, determining cash flows (revenues, expenses) and discount rates as crisp values can lead to erroneous results in most of the real-life applications. The probabilistic cash flow analysis can be used if the probabilities of the possible outcomes are known. However, when the frequency distribution of the possible outcomes is not known as for the revenues and expenses of a new product line, most decision-makers employ experts’ knowledge in modeling cash flows in the evaluation phase [7] and [8]. The conventional DCF methods do not appear to be suitable on their own for the evaluation of an advanced manufacturing system (AMS) investment due to the non-monetary impacts posed by the system. Sullivan [9] points out the inadequacy of traditional financial justification measures of project worth such as return on investment, payback, net present worth in considering the strategic merits of advanced manufacturing technologies. The results of the surveys conducted by Lefley [10] for justification of advanced manufacturing technology (AMT) in the UK, and by Lefley and Sarkis [11] for appraisal of AMT investments in the UK and US both indicate the support for the difficulty in assessing AMT investments due to their non-quantifiable benefits. Due to this difficulty, over 80% of the respondents in the US and UK point out that not all potential benefits of AMT investments are considered in the financial justification process. Furthermore, the results of the surveys state that subjective assessment of AMT investment with/without financial justification is observed in approximately 60% of the manufacturing firms responding to the questionnaire. Improvements in product quality, reliability, production efficiencies, competitiveness as a result of the versatility and flexibility of the system are the focal points in the justification stage of an AMS investment. Productivity, quality, flexibility and other intangibles should be examined in terms of potential returns through enhancement of long-term business competitiveness as well as in terms of a comprehensive evaluation of internal costs [5]. When flexibility, risk and non-monetary benefits are expected, and particularly if the probability distributions can be subjectively estimated, analytical procedures may be used. Strategic justification methods are qualitative in nature, and are concerned with issues such as technical importance, business objectives, competitive advantage, etc. [4]. When strategic approaches are employed, the justification is made by considering long-term intangible benefits. Hence, using these techniques with economic or analytical methods would be more appropriate. Fig. 1, which is an updated version of the classification initially proposed by Meredith and Suresh [4], resumes the justification methods for advanced manufacturing technologies. Since certain criteria cannot be expressed in quantitative terms, a number of articles focus on integrating the qualitative and quantitative aspects to evaluate the benefits of AMS. Wabalickis [12] develops a justification procedure based on the analytic hierarchy process (AHP) to evaluate the numerous tangible and intangible benefits of an FMS investment. Naik and Chakravarty [13] point out the need for integrating the non-financial and strategic benefits of AMS with the financial benefits, and propose a hierarchical evaluation procedure involving strategic evaluation, operational evaluation and financial evaluation. Shang and Sueyoshi [14] propose a selection procedure for an FMS employing the AHP, simulation, and data envelopment analysis (DEA). Small and Chen [15] discuss the results of a survey conducted in the US that investigates the use of justification approaches for AMS. According to their findings, manufacturing firms using hybrid strategies, which employ both economic and strategic justification techniques, attain significantly higher levels of success from advanced technology projects. Sambasivarao and Deshmukh [16] present a decision support system integrating multi-attribute analysis, economic analysis and risk evaluation analysis. They have suggested AHP, TOPSIS, and linear additive utility model as alternative multi-attribute analysis methods. An integrated multi-criteria procedure that takes into account both the economic criteria and the strategic justification criteria is required for proper evaluation of AMS investment alternatives. In general, scoring models, the analytic hierarchy process, outranking methods and goal programming can be listed among the deterministic methods for solving multiple criteria problems. Non-deterministic methods include game theoretical models, multi-attribute utility models, fuzzy linguistic methods and expert systems. In this paper, a fuzzy decision-making procedure is proposed as a computational-effective alternative to rectify some of the difficulties posed by the existing evaluation techniques.

نتیجه گیری انگلیسی

Diverse approaches have been proposed in the past decade to deal with evaluating AMS investment alternatives. The research articles pertaining to AMS evaluation focus primarily on the economic justification. Fuzzy discounted cash flow techniques are suggested for use in economic evaluation as an alternative to the traditional cash flow analysis. This paper presents a fuzzy present worth model for financial evaluation of AMS investments under conditions of inflation. The inaccuracy of the results obtained using the analysis ignoring inflation increases when after-tax cash flow analysis is performed. Consequently, differential rates of inflation are used in the fuzzy after-tax present worth model to properly account for the price-level changes. Global competition in manufacturing environment has forced the firms to increase the quality and responsiveness to customization, while lowering the costs. An automated manufacturing system, when properly implemented, provides major strategic benefits to the manufacturing firm such as flexibility, improved product quality, and reduced lead time. In order to incorporate these notable benefits that cannot be reduced to monetary terms into the justification and selection process of the AMS, an integrated approach considering both economic and strategic criteria is required. Since numerical estimates require more mental effort than linguistic descriptors, people are more likely to bias their evaluations if they are forced to provide numerical estimates of vague or imprecise items [37]. The strategic importance of the AMS investments can be effectively expressed using the fuzzy decision approach, as the fuzzy approach employs linguistic variables that are close to common language. In this paper, strategic criteria, which cannot be reduced to monetary terms, are integrated in the AMS selection procedure using a fuzzy multi-criteria decision-making algorithm. The fuzzy decision algorithm proposed in here helps to resolve the vagueness in AMS evaluation process by quantifying the non-monetary impacts. Hence, the decision-makers obtain a final ranking for the AMS alternatives by taking into account not only the economic criterion, but also the key strategic justification criteria by utilizing linguistic variables. The most suitable alternative among a set of mutually exclusive AMS investment alternatives is determined by applying a consistent and easily implemented method for ranking the fuzzy numbers. The decision analysis presented in this paper can be easily computerized, and allows for assessment of AMS investments taking into account an excessive criteria set and a large number of alternatives. Considering its effectiveness in quantifying the vagueness and imprecision in human judgment, the fuzzy decision-making approach appears as a consistent and computational-efficient alternative to existing methods. Nevertheless, it is worth noting that the benefit obtained due to the ease in computation in applying fuzzy decision models may be balanced by a possible loss in precision since fuzzy models provide only best and worst case analysis and do not assume that errors compensate.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.