Vendor's performance evaluation is an important subject which has strategic implications for managing an efficient company. However, there are many important criteria for prospering company. These criteria may contradict together. In other words, while a criterion is improved, the other may worsen. Indeed, similar to manufacturing manager in global market, purchasing manager who has significant practical implications deals with this issue. The vendor selection problem (VSP) is obviously affected by the complexity and uncertainty due to the lack of information associated with related business environment of countries in a global market. On the other hand, in the automotive industry which plays an important role in the worldwide market, these decisions will be exacerbated by increasing the outsourcing and opportunities. There are varieties of techniques, from simple weighted scoring methods to complex mathematical programming, for handling VSP.In this study, we propose a new cost efficiency data envelopment analysis (CE-DEA) approach with price uncertainty for finding the most cost efficient unit. Potential uses are then illustrated with an application to automotive industry involving 73 vendors in Turkey. (C) 2014 Elsevier Ltd. All rights reserved.

The importance of responding to customer demand to stay competitive in the global market and to increase market share has been increasing for companies lately. Due to demand fluctuations and difficulties to estimate it, gradually it becomes more difficult to sustain profitability and to fulfill demand. The company’s main problem is how to cut costs while producing small numbers of many types of products. For that reason, cost-conscious companies have focused on monitoring and controlling manufacturing and supplier-related activities by means of manual/electronic control devices in order to enhance the efficiency in the supply chain management and logistics process. This paper presents a case study about deployment of radio frequency identification (RFID) technology-based electronic Kanban system in an automotive industry supplier firm. In this project, by deploying RFID technology in a pilot area within the current manual Kanban system of this company, it has been possible to measure the true value added time in the production process. Value stream mapping is used to exhibit the mandatory requirements of RFID technology deployment in the shop floor. As a part of the study, we generate a current value stream map and a future value stream map, which contain the recommended revisions for the automotive supplier company. To evaluate the return of the investment, performance metrics were established and benefit–cost analysis is made. Obviously, future gains will include better inventory management to reduce the inventory levels within the production system.

Energy is a critical foundation for economic growth and social progress. It is estimated that 70% of the world energy consumption could be provided from renewable resources by the year 2050. Renewable energy is the inevitable choice for sustainable economic growth, for the harmonious coexistence of human and environment as well as for the sustainable development. The aim of this paper is to evaluate the renewable energy alternatives as a key way for resolving the Turkey's energy-related challenges because of the fact that Turkey's energy consumption has risen dramatically over the past three decades as a consequence of economic and social development. In order to realize this aim, we comparatively use MACBETH and AHP-based multicriteria methods for the evaluation of renewable energy alternatives under fuzziness. We use 4 main attributes and 15 sub-attributes in the evaluation. The potential renewable energy alternatives in Turkey are determined as Solar, Wind, Hydropower, and Geothermal.

New part introduction is an important topic because customer expectations expanded and a large variety of parts must be produced by the production systems. Therefore, effect of new parts to the system performance at the operational level should be analysed in order to verify that the new parts can be processed within the cells using the current capacity of machines and workers or to identify if there will be any inter-cell flow due to new parts. In the current study, a methodology that incorporates both simulation methodology and Information Axiom of Axiomatic Design (AD) to identify the best system parameter levels is developed. The structural properties of a manufacturing system will be investigated with simulation technique to observe system performance metrics. Then based on observations, information content of each scenario is calculated and the best scenario with the most suitable parameter levels is selected. To indicate applicability of the methodology, a real hybrid manufacturing system where new parts are introduced is modelled for different levels of system parameters including different setup levels, numbers of workers and lot size, using the simulation technique and different scenarios are populated. The performance of the system for each scenario is observed using the simulation models. Then, based on the Information Axiom of Axiomatic Design total information content is calculated for each scenario, the best scenario that represents the most suitable system parameters is determined. Integration of simulation and Information Axiom of AD makes a distinction from other new product introduction studies.

Supplier selection is a key task for firms, enabling them to achieve the objectives of a supply chain. Selecting a supplier is based on multiple conflicting factors, such as quality and cost, which are represented by a multi-criteria description of the problem. In this article, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to overcome the supplier selection problem. First, criteria that are determined for the problem are reduced by applying ANFIS input selection method. Then, the ANFIS structure is built using data related to selected criteria and the output of the problem. The proposed method is illustrated by a case study in a textile firm. Finally, results obtained from the ANFIS approach we developed are compared with the results of the multiple regression method, demonstrating that the ANFIS method performed well.

In the last decades, supply chain management (SCM) has become a significant issue in real life and in the literature due to increasing globalisation. Moreover, supplier selection and periodical evaluation has become an important tool for the companies in order to maintain an effective SCM. The main goal of this study is to construct an integrated method to build a decision support system for supplier evaluation and selection that incorporates quantitative and qualitative calculations together to deal with vague and uncertain data available to decision makers. A methodology, which is capable of evaluating and monitoring suppliers' performance, is constructed, using fuzzy analytic hierarchy process (AHP) to weight the established decision criteria and ELECTRE III to evaluate, rank and classify performance of suppliers regarding relative criteria. The proposed methodology is applied to a real-life supplier-selection and classification problem of a pharmaceutical company.

This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry.

This paper proposes a method based on MACBETH under multicriteria evaluation techniques for evaluation of renewable energy alternatives. The aim of this study is to evaluate Turkey's potential renewable energy alternatives under four main criteria and sixteen sub criteria. The potential renewable energy alternatives are determined as Solar, Wind, Hydropower and Geothermal. MACBETH Software is used for the solution of this problem.

One-piece flow is a design rule that entails production in manufacturing cells on a ‘make one, check one, and move-on one’ basis (Black, J.T., 2007. Design rules for implementing Toyota Production System. International Journal of Production Research, 45 (16), 3639–3664), which reduces manufacturing lead time significantly. This paper proposes a sequential methodology comprised of a mathematical model and a heuristic approach (HA) for the design of a hybrid cellular manufacturing system (HMS), to facilitate one-piece flow practice. The mathematical model is employed in the cases of small- and medium-sized problems, and it attempts to minimise the total number of exceptional operations, while considering machine capacities and alternative machines. The machine-part matrix achieved by the mathematical model is input into the flow line design stage of the HA, where backflow within the cells is eliminated. However, for industrial problems, the proposed HA is utilised. After the formation of the cells by clustering, the HA attempts to eliminate exceptional operations of a given cellular configuration together with a functional structure by employing alternative machines, based on the decision rules developed. Later, unidirectional flow within the cells is achieved and the capacity and budget constraints are satisfied. A medium-sized problem is solved by using both of the approaches, namely, the model integrated with the flow-line design stage of the HA and the complete HA. The results are discussed and the limitations are explained.

To increase customer satisfaction, quality function deployment is used to translate customer needs into technical design requirements (DRs). Determination of DRs for product development is very important because these requirements are the vital keys to successful products. The methods used to evaluate DRs in the literature can be categorized into multicriteria evaluation methods such as scoring methods, the analytic hierarchy method, analytic network process, and so forth. There are few papers using fuzzy multi-attribute outranking methods to evaluate DRs. This article aims to compare the results of three different fuzzy outranking methods to evaluate the DRs in the PVC windows industry. A sensitivity analysis is also made by using the software, FOuR. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1229–1250, 2007.

In this paper, we consider an interactive goal programming approach for fuzzy multi objective linear programming application to aggregate production planning problems. Our aim is to determine the overall degree of decision maker satisfaction with the multiple fuzzy goal values and to give the exactly satisfactory solution results for decision maker in illustrative example.

In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and product development system achieving higher customer satisfaction. Quality function deployment (QFD) is used as a powerful tool for improving product design and quality, and procuring a customer-driven quality system. In this paper, an integrated framework based on fuzzy-QFD and a fuzzy optimization model is proposed to determine the product technical requirements (PTRs) to be considered in designing a product. The coefficients of the objective function are obtained from a fuzzy analytic network process (ANP) approach. Fuzzy analytic hierarchy process (AHP) is also used in the proposed framework. An application in a Turkish Company producing PVC window and door systems is presented to illustrate the proposed framework.

Facility layout design (FLD) has a very important effect on the performance of a manufacturing system. The concept of FLD is usually considered as a multiobjective problem. For this reason, a layout generation and its evaluation are often challenging and time consuming due to their inherent multiple objectives in nature and their data collection process. In addition, an effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g., flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, and material handling vehicle utilization in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating FLD. The criteria that are to be minimized are viewed as inputs whereas the criteria to be maximized are considered as outputs. A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data by using exact and vague data by means of fuzzy set theory. Analytic hierarchy process (AHP) is then applied to collect qualitative data related to quality and flexibility. The DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set of a case study, which consists of 19 FLDs provided of the plastic profile production system.

In general, facility layout problems are occurred if there are changes in requirements of space, people and equipments. When the changes of requirements are shown frequently, the problem goes toward a dynamic structured problem. Designing the facility layout that can respond to requirements related to changes in economic situation of the scope, variability of demand in terms of volume or variety, and changes in production technologies is a comprehensive and complicated study.

The rapid progress in digital data acquisition and storage technology has lead to the fast growing tremendous and amount of data stored in databases, data warehouses, or other kinds of data repositories such as the World Wide Web [40].

This paper presents a decision making approach based on data envelopment analysis (DEA) for determining the most efficient number of operators and the efficient measurement of labor assignment in cellular manufacturing system (CMS). The DEA approach is performed by employing the average lead time, the average operator utilization as the output variables and using the number of operators, transfer batch size, demand level as the input variables. Both inputs and outputs are procured by means of simulation of CMS. The objective is to determine the labor assignment in CMS environment.

Quality Function Deployment (QFD) is a cross-functional planning tool, which is used to ensure that the voice of the customer is deployed throughout the product planning and design stages. The QFD process involves various inputs in the form of linguistic data, e.g., human perception, judgment, and evaluation on importance or relationship strength. Such data are usually ambiguous and uncertain. This paper aims to implement of QFD under a fuzzy environment. In addition, the analytic network process (ANP), the general form of the analytic hierarchy process (AHP) is used, to prioritize design requirements by taking into account the degree of the interdependence between the customer needs and design requirements and the inner dependence among them. The developed approach is used to study the basic product planning stage of a car design.

An effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g. flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, material handling vehicle utilization, etc. in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating Facility Layout Design (FLD). A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data. Fuzzy AHP is then applied to collect qualitative data related to quality and flexibility. DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set consisting of twelve FLDs provided of the plastic profile production system.

To increase customer satisfaction, Quality Function Deployment (QFD) is used to translate customer needs (CNs) into technical design requirements (DRs). Determination of DRs for product development is very important since these requirements are the vital keys of successful products. The methods used to evaluate DRs in the literature can be categorized into multi-criteria evaluation methods such as scoring methods, analytic hierarchy method (AHP), analytic network process (ANP), etc. There is a limited number of papers using multi-attribute outranking methods to evaluate DRs. This paper aims at comparing the results of three different fuzzy outranking methods to evaluate the DRs. A numerical example is presented to illustrate the use of these methods. A sensitivity analysis by changing thresholds is also made by using a software.

Quality function deployment (QFD) has been used to translate customer needs (CNs) and wants into technical design requirements (DRs) in order to increase customer satisfaction. QFD uses the house of quality (HOQ), which is a matrix providing a conceptual map for the design process, as a construct for understanding CNs and establishing priorities of DRs to satisfy them. This article uses the analytic network process (ANP), the general form of the analytic hierarchy process (AHP), to prioritize DRs by taking into account the degree of the interdependence between the CNs and DRs and the inner dependence among them. In addition, because human judgment on the importance of requirements is always imprecise and vague, this work concentrates on a fuzzy ANP approach in which triangular fuzzy numbers are used to improve the quality of the responsiveness to CNs and DRs. A numerical example is presented to show the proposed methodology. © 2004 Wiley Periodicals, Inc.