توسعه یک سیستم پشتیبانی تصمیم گیری برای ارزیابی تامین کننده و تخصیص سفارش
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21291||2012||11 صفحه PDF||سفارش دهید||6870 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 5, April 2012, Pages 4927–4937
This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.
Successful supply chain management requires an effective and efficient sourcing strategy to eliminate the uncertainties in both supply and demand. Sourcing decisions are critical more than ever, since with the increase of the purchasing costs as compared to the overall costs, the purchasing function and the purchasing decisions have gained a considerable importance at each firm. On average, a typical manufacturing company spends 60% of its total turnover in purchasing materials, goods and services acquired from external suppliers (Bayrak, Çelebi, & Taşkın, 2007). Thus purchasing decisions have significant effects on lowering costs and increasing profits. Sourcing decisions have some characteristics which are affected by globalization and the recent advances in information technologies. These decisions require the analysis of large amount of data obtained globally and this raises the issue of using advanced models in decision making. Secondly, sourcing decisions require the involvement of several decision makers in the global environments that further increases the complexity of decision making. Moreover sourcing decisions are made periodically and require tracking of the supplier performances on a regular basis. Computerized decision support systems (DSS) are often proposed as a remedy to overcome the difficulties and complexities involved in such decision processes. Purchasing processes are analyzed in two stages: first stage is the selection of suppliers formally by filtering them through an evaluation process that includes both qualitative and quantitative measures. Second stage is the order allocation where the order amounts for each supplier are determined. Although there are numerous studies in the literature for supplier evaluation and order allocation, very few companies consider these approaches in their decision making processes. The reasoning is mostly due to the fact that manual application of these models is quite time consuming, complex and most often requires a model expert. Besides, these decisions are repetitive processes; companies not only seek for a single evaluation but also need to keep track of past performances of the suppliers. Moreover, the targets and the related constraints in the decision process are subject to change in time. Thus the models should be supported by integrated databases. In application, all these features should be embedded into a DSS that provides a dynamic, fast and flexible environment for decision making. This fact is heavily emphasized in the recent studies by Ordoobadi, 2009a, Ordoobadi, 2009b, Pal and Kumar, 2008 and Ting and Cho, 2008 as well as the earlier studies by Yang and Chen (2005) and Lee, Ha, and Kim (2001). In this study, such a DSS is developed and experimented in one of the leading white goods manufacturers in Turkey. The model base includes an analytic hierarchy process (AHP) model which is developed for supplier evaluation by using qualitative and quantitative criteria. Furthermore, a goal programming (GP) model is developed that uses the evaluations of the AHP model and allocates orders among suppliers. The organization of the study is as follows: in Section 2, a literature survey of the recent studies on supplier selection problem and DSS applications are provided. In the third and fourth sections, the methodology of the study is introduced and the multi criteria decision models are developed. In Section 5, the DSS is presented with illustrations. Finally, conclusion and future work are proposed.
نتیجه گیری انگلیسی
In this study, integrated AHP-GP models and a DSS are developed for one of the leaders of the white-goods manufacturing sector in Turkey. The DSS environment provides the decision maker the ability to evaluate the possible suppliers according to the pre-defined criteria and sub criteria. The user has the opportunity to optimally diversify the annual quota to these suppliers according to the selected goals and the purchasing policies. The efficiency of the DSS is assessed by the company and the software is enhanced in accordance to these feedbacks. Supplier evaluation and order allocation problem includes several qualitative and quantitative comparisons. Major drawback of optimization models is their inability to cope with qualitative measures. This problem is overcome by the inclusion of the AHP methodology in the evaluation process. Nevertheless, based on these evaluations, optimal allocation is made by the GP model. Second issue that is raised by this study is the incorporation of the DSS environment that supports the model applications. In accordance with the rapidly changing marketing and manufacturing conditions, decision maker needs a fast, dynamic and flexible decision making environment where he/she can select from a set of objectives, define new ones, change the existing ones, etc. The decision maker needs to learn the behavior of the system by the use of scenario and sensitivity analysis. Finally the DSS can be used to monitor the supplier performances with respect to time. As future work, the DSS environment may be enhanced by adding simulation features to observe the percentage of late deliveries and order fulfillment when the demand rate and order lead times are random. Another enhancement option is to develop the supplier evaluation model with the group decision making perspective that allows group collaboration and discussion.