Research On The Optimization Decision-making Of Supply Chain Implementation Postponement Strategy In Mass Customization

Facing to the increasingly intense market competition and customized demand, manufacturer could produce customized, low-cost, short-response-time and high-quality products by developing mass customization technology. Postpone strategy could satisfy customized demand on the basis of nether increasing cost level nor prolonging lead time by postponing the customized activities in the respectives of time and space. It could be seen as an effective method to realize mass customization. Thus, both industries and acedmics are intensively paying attention to building a high effective postponed system in mass customization environment. One of the key problems of building such high effective postponed system is its optimization decision-making. Aiming to the problem, the dissertation, from the respectives of manufacturer and supply chain, studies the optimal position of customer order decoupling point which could result the lowest production cost of manufacturer and supply chain. Also, the affecting factors of the optimal position of customer order decoupling point are studied. After the decision of customer order decoupling point postion, the dissertation studies the optimization planning and scheduling problem of this Push-Pull supply chain.First, the paper analyzes the production system of manufacturer implementation postponement strategy in mass customization. Then, it reveals that time and cost optimization could be realized by optimization of customer order decoupling point position. Considering these two optimization objectives, the basic time and cost optimization model and its extended queuing model of manufacturer implementation postponement strategy in mass customization are built. And the optimal customer order decoupling point position which results in optimal production system and its affecting factors are studied. It reveals that the optimal customer order decoupling point position is determinated by the produce cost of common semi-finished products. If the produce costs of common semi-finished products are relatively low, moving to the final process of the production of customer order decoupling point could be favorable to realize the system optimization of manufacturer implementation postponement strategy.Second, on the basis of the research before, the dissertation expands the implementer from manufacturer into supply chain. The problem of supply chain implementation postponement strategy in mass customization is studied. By building the basic model and its extended model of supply chain implementation postponement strategy, the optimal customer order decoupling point position, which results in optimal supply chain system, and its affecting factors are studied. The study reveals that the optimal customer order decoupling point position is not only affected by manufacturer production cost, but also by supplier cost. Furthermore, it shows that the optimal customer order decoupling point positions, which makes lowest manufacturer production cost, do not definitely result in the lowest supply chain cost. Thus, the decision making of customer order decoupling point should include the consideration of supplier cost in the condition of supply chain.At last, on the basis of indentified optimal customer order decoupling point position, the dissertation studies the planning and scheduling problems of Push-Pull postponed supply chain. It reveals that the planning and scheduling of such supply chain could be diviede into two parts by customer order decoupling point. As for the push supply chain which is located before customer order decoupling point, the key problem is to decide the optimal starting time to push the production. As for the pull supply chain which is located after customer order decoupling point, the key problem is to select supply chain partners reasonablely. By anlysis of an example, it reveals that supply chain cost could decrease dramatically by optimization of supply chain production arrangement. After that, a self-adaptive multiple ant colony algorithms is proposed in order to make a solution for planning and scheduling optimization model of Push-Pull supply chain implementation pstponement strategy in mass customization.