İleri imalat teknolojilerinin ekonomik analizi ve esneklik faktörünün sayısallaştırılmasına bulanık kümeler yaklaşımı
Başlık çevirisi mevcut değil.
- Tez No: 56024
- Danışmanlar: PROF.DR. ETHEM TOLGA
- Tez Türü: Doktora
- Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1996
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 252
Özet
ÖZET Bu çalışmada, ileri imalat sistemleri sınıflandırılarak, her birinin ekonomik analizi için literatürde kullanılmış yaklaşımlar verilmiştir. İleri imalat sistemleri modüler sistemlerden tam otomasyona kadar, değişirken, kullanılan ekonomik analiz yaklaşımları ise iskonto edilmiş nakit akışı yöntemlerinden uzman sistemlere, bulanık dilsel yöntemlere kadar değişmektedir. İleri imalat sistemlerinin ekonomik analize dahil edilmesi gereken daha iyi kalite, esneklik, rekabet avantajı gibi stratejik kazançları vardır. Bu çalışmada, gözönüne alınan esneklik faktörü, sınıflandırma şekilleri ve tanımları ile verilmiş ve sayısallaştırılması zor olan bu faktörü hangi araştırmacının ne şekilde sayısallaştırdığı anlatılmıştır. Esneklik elemanlarının tanımında kullanılan parametrelerin tahmini, üçgensel bulanık sayılar kullanılarak yapılmıştır. Böylece tahmin hatalarının ortadan kaldırılması amaçlanmıştır. İncelenen teçhizat esnekliği, mamul esnekliği, proses esnekliği, talep esnekliği ve rotalama esnekliği fırsat maliyetleri olarak değerlendirilmiştir. Bulanık şimdiki değer analizi esaslı bir model kullanılarak, belirli bir planlama ufku için fırsat maliyetlerinin bulanık şimdiki değeri hesaplanmıştır. Parametre değerlerinin dönemden döneme değiştiği varsayılmıştır. O
Özet (Çeviri)
SÜMMARY THE ECONOMIC JÜSTIPICATION OF ADVANCED MANUFACTURİNG TECHNOLOGIES AND THE FÜZZY SETS APPROACH TO THE QÜANTIFICATION OF THE MANÜFACTURING FLEXIBILITY The introduction of microprocessors and computer controlled production tools into industry has given a new perspective to manufacturing processes in many countries. Computer Aided Design (CAD), Computer Aided Manufacturing (CAM), Group Technology (GT), and Computer Integrated Manufacturing (CİM) are considered by many as viable tools which can reduce direet and indirect manufacturing costs, improve product guality and increase the variety of products offered. The advanced manufacturing concept includes an effort to implement the follovings: (1) new approaches to guality, (2) new production control philosophies, (3) a change in management thinking regarding the work force, and (4) more flexible approaches to customer reguirements. The objectives of advanced manufacturing are becoming the best competitör, growing more rapidly and being more profitable than competitors, hiring and retaining the best people, being able to respond guickly and decisively to changing market conditions. The economic justification process is an important hurdle to the adoption of advanced automated manufacturing eguipments have been justified by doing the analyses of investment ver sus the resulting cost savings; however, benefits of advanced manufacturing technology lie in the strategic areas. These strategic benefits, such as, shorter lead times, consistent guality, timely delivery schedule and improved capability to react to changing demand are difficult to guantify. The intangible benefists, traditionally considered as secondary effects, become the key issues in justifying investment in advanced manufacturing technologies (AMTs). If these benefits are not explicitly considered, the adaption of new technologies is incorrectly discouraged. There is a number of rather differerit approaches available för the financial justification of AMTs. A few classification approaches have been proposed by researchers in the' past. Öne is single objestive deterministle methods, multi-objective deterministle methods, probabilistic methods, and fuzzy set methods. Another öne is single criterion methods that are divided xiiiinto deterministle and non-deterministic methods and multiple criteria methods that are again divided into deterministic and non-deterministic methods. Single criterion deterministic methods include net present value methods, internal rate of return method, benefit/cost ratio method, payback period, mathematical programming, and minimal annual revenue reguirement. Single criterion non-deterministic methods include sensitivity ana ly sis, decision trees optimistic/pessimistiç, Monte-Carlo simulation. Multiple criteria deterministic methods include scoring models, analytic hierarchy process, goal programming, decison support systems, productivity model, dynamic programming. Multiple criteria non-deterministic methods include fuzzy linguistics, expert system, utility models, game theoritic model. The net present value can be defined as the sum of the net cash flows discounted at some minimum acceptable rate of return to time zero. The proper choice of a discount rate and recovery period is very important becâuse high technology projects are many times rejected when a high hurdle rate and short recovery period are used in the analysis to compute the net present value. Some authors present a new technigue called the state- price net present value. internal rate of return is the interest rate which makes the sum of the discounted cash flows equal to zero. Since the acceptability of a project depends on minimum attractive rate of return (MARR), it is important to choose an appropriate value of MARR based on certain considerations such as risk of the project. Benefit/eost ratio method is highly dependent upon the proper definition of benefits and costs and provides no advantage över the net present value of the internal rate of return methods. Payback period is the time reguired to recover the initial investment. This method fails to incorporate the benefits resulting from strategic issues involved in the investment. O Modified minimum annual revenue reguirement is simply the minimum income reguired to cover ali incremental costs, including capital recovery on the incremental investment, return on the incremental investment, incremental costs of goods sold, and the incremental taxes inherent in an investment alternative. xivMathematical programming choose a subset of interrelated projects för investment f rom a given set of proj ects. Sensitivity analysis is done when there may be. inherent errors in the estimation of the values of parameters in the problem, ör when preferences associated with the various projects change. Decision tree approach in justifying automated manüfacturing technology is appropriate f ör situations wherein the analyst is making several similar decisions över a period of time. Optimlstic/pessimistic. analysis is used to find optimistic and pessimistic estimates of ali decision variables and compare the available alternâtives based on some measure of effectiveness. Monte Carlo simulation is useful when other analytical approaches are difficult ör not feasible to use. An outcome f ör each variable of interest is randomly selected from a probability distribution assumed to represent each criterion of interest and these outcomes are then combined. Scoring models feature the ability to accommodate the consideration of intangible, ör economically non- guantifiable, elements involved in an investment decision in an analytical fashion. A linear additive scoring model aids decision makers in evaluating the desirability of a firm's long-term and short-term advanced automated manufacturi.ng technologies. Analytic hierarchy process (AHP) uses a hierarchical representation of factors influencing a system, and makes pairwise comparisons among the factors in order to rank alternâtives to solve a decison problem. The strength of the AHP method lies in its ability t o structure a complex, multiattribute, and multiperiod problem hierarchically. Goal programming features the ability to analyze multiple, conflicting goals. it has been used to model investment decisions in a flexible mahufacturing system from a mülti-objective context. O Decision support system refers to a computerized approach to establishing an information system för managerial analysis. The application of traditional economic evaluation methods, simulation, mathematical programming ör accounting technigues for the evaluation of advanced manufacturing systems suffer from limitations XVwhen used alone, but good analysis is possible when ali the techniques are combined. With a combined decision support system several alternatives can be studied in less time. Productivity model is used to assess the impact of the propösed egüipment investment on profitability from the standpoint of productivity. If a project has a total productivity level greater than, ör egual to, a predetermined productivity level, it is accepted. Dynamic programming is an appropriate tool for use in the justification problem since the decision on the level of investment for automated manufacturing depends not only on the current realizâtions of costs and revenues but also on previous decisions regarding invsetment in automated manufacturing systems. Fuzzy linguistic models.permit the translation of verbal expressions into numerical ones, thereby dealing guantitatively with imprecision in the expressions of the importance of each strategic goal and the enabling technology involved in implementing CİM systems. Expert systems are computer programs that have the capability of solving complex problems by certain rules and logical reasoning mechanism. These rules represent the problem solving approaches of experts. The main feature of expert systems is the ability to handle problems with inexact data. Several applicatinos of expert system have been developed by many researchers. Utility models evaluate the decision-marker's preferences expressed in the form of utility functions of multiple attributes in order to determine the choice which satisfies him the most. Game-theoretic models feature the ability to handle the element of strategic interdependence betveen firms, which is an important factor because the industries which invest in AMT exhibit significant strategic interdependence between competitors. Maj^Çıf acturing flexibility is a complex, multidimensional and difficult-to-synthesize concept. it is def ined as the ability to çöpe with changing circumstances ör instability caused by the enviroranent. The term“manufacturing flexibility”isn't very well understood. At least 50 different terms for various flexibilities can be found in the literatüre. This is due to (1) the scope of the flexibility-related terms xvioverlap considerably, (2) some terms are aggregates of others, (3) identical terms used by various writers often do not necessarily mean the same thing. Some types of flexibility measures proposed some researchers are equipment flexibility, product flexibility, process flexibility, demand flexibility, routing flexibility, volume flexibility, expansion flexibility, market flexibility. We define four kinds of flexibility and evaluate them as opportunity costs. The def ined flexibilities are equipment flexibility, product flexibility, process flexibility, demand flexibility, and routing flexibility. We propose a procedure for quantifying these flexibilities in monetary terms and an evaluation model based on fuzzy present value method. In an economic analysis, an opportunity cost can be defined as the potential after-tax profit that is lost or sacrificed when the choice of one course of action requires the giving up of an alternative course of action. Opportunity costs are not usually entered on an organization's books. They represent the economic benefits that are foregone as a result of pursuing some alternative course of action, and thus they are relevant to investment decisions. The parameters used for the definition of the flexibilities have been accepted as triangular fuzzy numbers. Triangullar fuzzy numbers have three parametres: the smallest possible value, the most promising value, and the largest possible value. This provides a true estimation of each parameter. After used the fuzzy present value method, the fuzzy present value of the considered flexibility is also a triangular fuzzy number. XV 11
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