The Multi-strategies Response Models Of The Manufacturer Under Uncertain Supply And Demand

With the increase in supply chain uncertainty, supply chain management can not only concentrate on supply chain performance improvement, but also need to pay attention to supply chain risk or uncertainty management. Risks may come in a form of economic instability, environmental concerns, volatile fuel costs, strike, work stoppages, supply shortages, and quality concern. The current coping strategies and methodologies on supply chain uncertainty are generally limited to a specific objective. For example, the majority of the supply chain research focuses on coordination methods and managing supply chain under demand uncertainty. Most of them lack a comprehensive deliberation and are based on idealized assumptions. This dissertation will study multi-dimensional response models of supply chain from the perspective of manufacturer. The three response models address the supply uncertainty, demand uncertainty, and production uncertainty caused by supply and demand uncertainty.The purpose of this study is to propose several multi-dimensional response models. By characterizing the uncertainties in the supply chain, this research establishes a number of response strategies to tackle various scenarios under uncertain supply and demand. The details are as follows:(1) The research established the multi-echelon production planning model for manufacturer under supply price and demand uncertainty. We proposed a scatter evolutionary algorithm that integrates fuzzy stochastic chance-constrained programming to solve the model. First for a generalized supply chain network with fuzzy raw material prices and normally distributed market demand. a set of planning models were established from raw material procurement to final product sales. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issuel. Finally, a computational study is conducted to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach, and the impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis.. We found that taking into account supply and demand uncertainty jointly can optimize the supply and demand structure to increase supply chain profits and improve the overall customer service. In addition, the proposed method can provide good solution for complex, realistic, large-scale problems.(2) Under the partial or full supply uncertainty information, this research applies mechanism design theory (reverse game theory) to address the manufacturer's contract coping strategy for long-term and short-term contracts selection. This research considers supply disruption as the most common and detrimental risk in supply uncertainty. We first study the contracts under the risk of supply disruptions with perfect and imperfect information based on the two-part price mechanism and mechanism design theory. We identify the design parameters that maximize profit for the manufacturer under the default supply uncertainty, and determine the amount the manufacturer with incomplete information is willing to pay to obtain the full information. Next we examine the long-term and short-term contract design strategy if secondary ordering is allowed with incomplete and complete default information. We also optimize contract design and contract selection under different conditions. Finally, we extend above results to the conditions with uncertain demand, and consider the two-stage information updating in the optimal contracts design. This study found that given the disruption information as suppliers'private information, the Stackelberg dominant manufacture can alleviate the supply disruption risk using the proposed two-part contracts based on the mechanism design theory. Under the complete information condition, the manufacturer could extract the entire channel profit. The manufacturer can extract partial channel profit under incomplete information condition, and the profit of the supplier with high disruption probability is zero. We also found the secondary ordering can be used as another strategy to respond to the supply uncertainty for the manufacturer. Under certain conditions, long-term contracts are not more favorable than short-term contracts to manufacturers. The decision makers thus need to determine the contract type according to external parameters.(3) In order to cope with the supply disruption risk, the author developed an Information Granulation Entropy-based model for supplier selection. In the proposed model, experts input fuzzy language to form an evaluation matrix. After defuziffying the matrix, the K-means clustering method is applied to discretize the matrix. An innovative information granulation entropy approach, based on information science theory and data mining technique, is developed to determine the weights of criteria. Finally, the TOPSIS closeness rating method is applied to derive the priorities of the alternatives. To demonstrate the validity of the proposed method, the author illustrates the model with a real-world application faced by a large company for selecting a supplier, and compared and analyzed the method with AHP in 7 aspects. The proposed evaluation framework is especially beneficial when dealing with large-scale problems with diverse criteria and/or alternatives. The study found that this method extract the information of evaluation data to weight, which validates the result of evaluation.(4) For the endogenous product demand that depends on the price and dynamic group behavior, we proposed an optimal Group Buying strategy and dual-channel marketing response model. For price-sensitive demand, we take into account the impact of group opinions on customers'purchasing decision. The herd behavior often leads to distorted demand, which in turn affects pricing under various market conditions. We proposed retailers various optimal pricing and packet-size determination models. Along with instant buying (individual buying), we contrast and determine the optimal selling strategy for different market parameters. Under the MIX strategy GB is complementary and substitutable for IB. We extended these models to multi-product business cases, and obtained the optimal price and quantity under various combinations of Group Buying and Instant Buying. On this basis, competition between merchants of substitutable products is analyzed. It is found that the competition has a Nash Equilibrium Solution. We also analyzed the payoff of both competitors under the conditions of the various parameters, and thus obtained competitive equilibrium solution. We found that the impact of group pressure to a customer is generally much greater than the waiting costs in the Group Buying strategy. Finally social welfare and social effectiveness were analyzed for the various strategies.This dissertation focuses on the multi-dimensional responses of the manufacturer as the core of the supply chain, using the mathematical modeling and optimization techniques under supply and demand uncertainty to improve the responsiveness, and provide a viable way to effectively manage a variety of uncertainty and risks, and enrich the supply chain uncertainty management theory.