Adaptive logistic controller for integrated design of distributed supply chains (original) (raw)
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Supply chains are complicated dynamical systems triggered by customer demands. Proper selection of equipment, machinery, buildings and transportation fleets is a key component for the success of such systems. However, efficiency of supply chains mostly depends on management decisions, which are often based on intuition and experience. Due to the increasing complexity of supply chain systems (which is the result of changes in customer preferences, the globalization of the economy and the stringy competition among companies), these decisions are often far from optimum. Another factor that causes difficulties in decision making is that different stages in supply chains are often supervised by different groups of people with different managing philosophies. From the early 1950s it became evident that a rigorous framework for analyzing the dynamics of supply chains and taking proper decisions could improve substantially the performance of the systems. Due to the resemblance of supply chains to engineering dynamical systems, control theory has provided a solid background for building such a framework. During the last half century many mathematical tools emerging from the control literature have been applied to the supply chain management problem. These tools vary from classical transfer function analysis to highly sophisticated control methodologies, such as model predictive control (MPC) and neuro-dynamic programming. The aim of this paper is to provide a review of this effort. The reader will find representative references of many alternative control philosophies and identify the advantages, weaknesses and complexities of each one. The bottom line of this review is that a joint co-operation between control experts and supply chain managers has the potential to introduce more realism to the dynamical models and develop improved supply chain management policies. ᭧
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Several alternative approaches have been proposed for supply chain modeling majority of which steady-state models. These models cannot adequately deal with dynamic characteristics of supply chain system affected by lead time, demand fluctuation, sale prediction and so forth. Static models in particular cannot describe, analyze and provide solutions for a key issue in supply chains called bullwhip effect. The bullwhip effect is information deviation from one end of the supply chain to the other which intensifies fluctuation and change in demand from downstream to upstream. This issue leads to major deficiencies. One of the approaches used to cope with dynamic issues is control systems approach. In present study, a predicting model controller was developed to minimize the bullwhip effect in supply chain. In addition, a prediction methodology is integrated into predicting model control framework to predict uncertainty in distorting demand behavior. Integration of a prediction methodolo...
Dynamic Analysis and Control of Supply Chain Systems
Supply Chain the Way to Flat Organisation, 2009
Globalization has changed in the last decade the shape of world trade, from independent and local markets to highly interrelated value chains demanding a large variety of goods with high quality standards. A supply chain is thus understood as a highly interconnected demand network composed of different stages or nodes that include raw materials, distributors of raw materials, manufacturers or producers, distributors of manufactured products, retailers and customers. Between these stages or nodes there is an interchange of information (orders) and flows of materials. The above mentioned elements of a supply chain and their relations give rise to complex structures, whose interactions affect the performance of the entire system. Nowadays the markets require flexibility, speed and productivity in order to satisfy an environmentally-conscious consumer demanding a larger variety of manufactured goods. These trade conditions impose low costs and effectiveness in production. Therefore, most supply chains strive for minimizing raw material and finished products inventories in fast distribution networks (Fujimoto, 2002), (Steidtmann, 2004) while fulfilling astringent manufacturing rules. This tendency has resulted on what is called synchronization of production and distribution at the supply chain. Supply chain synchronization occurs when the consumer business world is linked togethe r b y t e c h n o l o g y , m a k i n g e a c h o f t h e constitutive parts, consumers, suppliers, producers, associates and distributors synchronize with the whole. Thus, when the consumer places a request for an end-product, there is a synchronized retailer or distributor there to deliver it. For example (Koudal, 2003) studied the demand and supply dynamics in the automotive value chain, yielding flexibility and fast consumer respond. In textile industry, TAL Appareal Group applied electronic and communications platforms to evolve into a flexible manufacturer, growing from a single local textile mill to a global multinational company (Koudal & Wei-teh, 2005). From an economic point of view the potential impact of performance improvements on production systems is tremendous. The arising dynamics complexity of highly synchronized supply chains poses demanding control challenges for their operation and management, for which representative models are required. Two decision levels can be considered in the supply chain operation: the first is the tactical and is referred to the decision making process that optimizes the supply chain performance, and the second involves the operational activities. A well-constructed supply