Research On Capacity And Pricing Decisions Of SMPEs Operating System Orienting Complex Demands

Since the reform and opening up from 1978, Chinese small and medium-sized production enterprises(SMPEs) have develop rapidly, and become an important force in promoting economic development. China is experiencing the critical period of economic reforming which involves economic mechanism transformation and restructuring, SMPEs are especially confronting a serious challenge owning to the limitation. At the same time, with the economic development, improvement of social life and people’s living standards, the market demand are more diversified/individualized and polytropic. And new challenge and chance can bring to our native manufacturing development by the unpredictablly complex demand. Within the enterprise, it exists problems of low management level, weak planning functions. Dynamically changeable market demands and unstable capacity will result in a mismatch, so that enterprises are facing issues of imbalances production schedule, serious deferred delivery, instability product quality and poor operational performance. How to adapt to the complex and changeable market environment? How to balance the supply and demand? These are key issueses to be solved urgently for SMPEs at present. This paper makes SMPEs as the object, analysis and research on capacity decision and pricing strategies in complex and changeful demand. The main contributions can be concluded as follows:(1) Capacity Decisions of SMPEs in complex demand.① Considering market demand uncertainty and effective output random, we construct the model single period production capacity decision targeting with profit maximization, the optimum algorithm is given through deduction.② The model of multi-period profit maximization is set up, we obtain the optimal solution of capacity decision. And analyzing the sensitivity of main parameters of the model, we discuss the relationship between optimal capacity/maximum profit and initial inventory/inventory cost/shortage cost.(2) Capacity optimization of production system in complex demand. ① We introduce the concept and characteristics of virtual procedure databases (VPDs), deal with the organization schema and production processes.② In order to meet the demand of complex multi-orders and minimize production cost as the goal, we construct the mode of VPDs and get the optimal capacity scheduling, through using the improved genetic algorithm of elitism preservation. Through repeated simulation experiments and comparison, it shows that the improved genetic algorithm has the faster speed of convergence, which takes the shorter time and results show that it is effective.③ The GSCPN system model of VPDs is established, and transformed the system by using markov chain, according to the isomorphism of GSCPN and the markov chain. We evaluate the performance of VPDs, quickly find bottlenecks in the production process, and adjust/optimize VPDs operating system.(3) Production and pricing strategy SMPEs in complex demand.① Assumed that market demand in dual- channel is stochastic, we obtained the optimal quantity and optimal pricing in a dual-channel by using two-stage resolving algorithm.② Considering there are many types of customers with switching in a dual-channel, we respectively establish the decentralized decision game model--"offline channel leading" and "online channel leading". Backward induction is used to solve the question, and get the equilibrium solution of optimal pricing and optimum production capacity.③ The model of pricing centralized decision is established, by judging Hessen matrix is negative definite, manufacturer has a set of equilibrium optimal pricing policies and production. On the basis of cases analysis and various parameters sensitivity analysis, the relevant conclusions are drawn.(4) Production and pricing strategies with capacity constraints in complex demand.① We establish pricing decision model of dual-channel with demand random under capacity constrains, and the optimal pricing and production strategy by solving optimization problem of Kuhn-tucker (KKT) conditions.② The models of "offline channel leading" and "online channel leading" is established with capacity constraint, we get the optimal quantity and pricing strategy by using backward induction method and to solve the optimization problem of KKT conditions.③ Considering existence of outsourcing when manufacturer is under-capacity, we respectively establish the models of non-cooperation and cooperation with OEM/manufacturer/distributor, it is concluded that the optimal pricing and production strategies of different decision models through analysis and deduction.