سیستم ارزیابی تولید بر اساس رویکرد AHP / ANP برای صنعت ساخت ویفر

The critical role played by manufacturing performance measurement systems in achieving competitive success is increasingly recognized. Manufacturing success may depend on the compatibility between a performance measurement system in operation at subordinate organizational levels and an organization’s global goals. Therefore, developing an integrated performance measurement model is significant for strategy management. This study proposes an integrated process that allows manufacturing systems to construct performance measurement model. Performance criteria from the literature and an expert questionnaire were utilized prior to building the performance measurement model. The analytical hierarchy process (AHP) and the analytical network process (ANP) are utilized to determine the weight of each criterion when generating the performance model for manufacturing systems.

Many manufacturers have faced increased competition from both overseas and local manufacturers in recent years. Global competition and rapid technological development have changed customer requirements and business patterns. To improve the ability of manufacturers to compete in the global marketplace, manufacturers have changed operating philosophies to total quality management, just-in-time, and continuous improvement. Many manufacturers utilize performance indicators based on cost accounting systems; these indicators are inconsistent with new operational philosophies. Furthermore, many improvement efforts are difficult to be quantified in financial terms. Several studies have indicated that performance measures should support a company’s strategic goals, and should present both financial and non-financial information (Ghalayini et al., 1997, Wang et al., 2008 and Yuan and Chiu, 2007). Performance indicators have been introduced in manufacturing systems to evaluate the system performance and improve manufacturing competitiveness. To achieve these goals, selection of a range of performance indicators appropriate for manufacturers should be made based on a company’s strategic intentions that suit competitive environments and the nature of business. When choosing an appropriate range of performance measures, it is necessary to balance these measures, to ensure that one dimension or a set of dimensions of performance is not emphasized to the detriment of other measures. Moreover, the performance indicators selected must be measurable, and allow managers to monitor performance and goal realization. Numerous studies have focused on developing overall performance measurement models that combine more than one aspect. This study proposes a measurement model for evaluating overall manufacturing performance, and generates indications of performance to assist managers in realizing the advantages/disadvantages of operational conditions. The general model creates relationships between strategic criteria for evaluation in a multi-attribute decision analysis. However, performance evaluation models in manufacturing usually encompass several interdependent criteria, and each criterion has numerous detailed sub-criteria. How to best balance these indicators is an important issue. An incomplete measurement model can result in inappropriate actions that may harm company competitiveness. To accurately evaluate the influence of these criteria in terms of goals and detailed criteria with respect to upper level criteria, the analytic hierarchy process (AHP) approach and analytic network process (ANP) approach are utilized. The remainder of this paper is organized as follows: In Section 2, the literature pertaining to performance measurement criteria is reviewed. In Section 3, brief reviews of AHP and ANP approaches are provided. In Section 4, the proposed AHP/ANP-based performance evaluation model is presented, and the model components and relationships among components are described in detail. In Section 5, the proposed evaluation model is applied to evaluate the efficiency of manufactory for three companies that manufacture wafers. Conclusions are presented in the last section.

In this study, an AHP/ANP-based evaluation model was proposed that supports managers in identifying opportunities for improvement, comparing performance against internal standards, and comparing performance against external competitors. A three-stage modeling procedure was used that includes building an initial model, modifying criteria, and building evaluation model. The evaluation model has six dimensions (quality, utilization, delivery dependence, flexibility, cost, and employee) and 44 criteria. The top three dimensions are quality (31%), utilization (23.5%), and delivery dependence (21.6%). These dimensions play important roles in evaluating manufacturing performance for the wafer fabricating industry. Three companies were analyzed using the proposed model. The outcomes of the proposed model and the data from financial reports were compared. Comparison of results shows that the proposed model reflects real performance. Using the proposed measurement model, all dimensions and detailed criteria reflect customer requirements, clarify manufacturing processes and progress made, identify manufacturing efforts and engage in a never-ending improvement cycle. To assist managers in easily identifying which areas require improvement, the outcomes of the proposed model were represented as four colored lights: green, yellow, orange, and red. The performance of company A for all dimensions is indicated by a green light, which implies that the production condition is excellent, and customers are satisfied. Manufacturing performance is affected by numerous factors, and the interdependence between these factors is complex. The effects and relationships between factors of manufacturing performance will be the focus of future research. Additionally, we assume that the relationships between criteria are independent and that they reduce the number of questions in pairwise comparisons. How to assess the trade-offs between a great effect model and an easy answering model will be another direction in future research.