مدیریت زنجیره تامین، عامل کمک کننده : تجزیه و تحلیل و درس های آموخته شده
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23591||2014||11 صفحه PDF||سفارش دهید||7180 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 57, January 2014, Pages 274–284
This work explores “big data” analysis in the context of supply chain management. Specifically we propose the use of agent-based competitive simulation as a tool to develop complex decision making strategies and to stress test them under a variety of market conditions. We propose an extensive set of business key performance indicators (KPIs) and apply them to analyze market dynamics. We present these results through statistics and visualizations. Our testbed is a competitive simulation, the Trading Agent Competition for Supply-Chain Management (TAC SCM), which simulates a one-year product life-cycle where six autonomous agents compete to procure component parts and sell finished products to customers. The paper provides analysis techniques and insights applicable to other supply chain environments.
Supply-chain management is becoming increasingly sophisticated, driven by expectations of greater business agility and more closely coupled processes within as well as across organizations. For example, in smart markets participants have to use computational tools to understand the market characteristics and to anticipate market needs . Central to smart markets are software agents with adjustable autonomy, i.e. programs capable of autonomous or semi-autonomous decision making, which are used to assist humans.
نتیجه گیری انگلیسی
When automating business processes, designers should be concerned with business agility and particularly with how the automated process will respond to situations where the standard assumptions of the market may be violated. The KPIs we present facilitate this process by providing characteristics to measure across the automated supply chain, and realistic simulation techniques (of which TAC SCM is an example) provide rich data sets over which to accurately measure behavior in different situations. Although TAC SCM may not match a particular real-world market scenario, these tools can provide valuable insights. For simulated scenarios, realism requires complexity.