ارايه‌ي يك چارچوب تصميم گيري براي بهبود عملكرد بنگاه‌هاي زنجيره‌ي عرضه با به كارگيري رگرسيون ريج

A DECISION MAKING FRAMEWORK FOR PERFORMANCE IMPROVEMENT OF SUPPLY CHAIN FIRMS USING RIDGE REGRESSION

پيچيدگي تصميم گيري در زنجيره‌هاي عرضه باعث نياز روزافزون به روش هايي براي مدل‌سازي اين سيستم ها شده است. بسياري از بنگاه‌هاي توليدي و بازرگاني به دنبال مدل‌هايي هستند كه به شناسايي رفتار بنگاه‌هاي زنجيره در شرايط مختلف و تصميم گيري براي بهبود عملكرد آن‌ها كمك كند. اين نوشتار روشي آماري براي مدل‌سازي و ارزيابي عملكرد يك بنگاه زنجيره‌ي عرضه پيشنهاد مي‌كند. هدف از به كارگيري اين روش، شناخت عوامل كليدي تاثيرگذار بر عملكرد سيستم و پيش‌بيني رفتار آن در شرايط مختلف است. در اين نوشتار دو معيار عملكرد پيشنهاد و رابطه‌ي آماري هريك، با عوامل موثر بر عملكرد سيستم تعيين مي‌شود. روابط كمّي به دست آمده يك چارچوب تصميم گيري براي بهبود عملكرد سيستم در راستاي راهبردهاي رقابتي كل زنجيره فراهم مي‌كند. كاربرد روش پيشنهادي در دنياي واقعي با يك مطالعه‌ي موردي نشان داده مي‌شود.

The complexity of decision making in supply chain management has resulted in a growing need for modeling methodologies. A large number of production and distribution firms are looking for models that can help identify firm performance and make decisions for improvement. In this paper, a systematic procedure to model and evaluate the operating performances of a supply chain, in terms of both efficiency and effectiveness, using a ridge-regression approach is first proposed. The goal is to identify significant factors affecting firm performance and to predict system behavior under different operating conditions. The statistical relations of the performance measures with significant factors are then determined. These relations provide a decision-making framework to improve system performances within the competitive strategy of the whole supply chain. The simplicity and less time requirement of the proposed approach compared to other approaches, such as discrete and continuous simulation, as well as its clear conclusions and its applicability to different supply chain systems, make the proposed methodology more appealing. Finally, a real-world application of the proposed methodology is demonstrated through a case study. Although the results are applicable to the system under consideration, the proposed methodology can be easily adapted to other real-world supply chain systems.