Study Of Supply Chain Inventory And Pricing Problems Under Environment Of Uncertainty

Uncertain environment in the real world often causes mismatch between supply and demand, and it brings about inventory problems as well. In addition, its role in corporations’operation and performances can never be overestimated, because profit loss due to uncertainties may sometimes even make companies go bankrupt. Hence, to aim at weakening negative effects of uncertainties in industry, inventory and pricing problems are discussed with risk consideration in this paper. This paper studies three types of uncertain environment and related issues:1) profit fluctuation and bull whip effect caused by demand uncertainty;2) mismatch of seasonal uncertain demand and supply, the capacity of which can hardly be frequently changed in short term;3) joint effect of uncertainty of capacity due to unreliable machine and demand uncertainty.In this paper, methods and theories such as mean-variance analysis, probability and stochastic processes, optimization, dynamic pricing and markov decision process are. applied. Combined with traditional inventory and pricing issues, new problems and contributions are provided in uncertain environment as follows.First, this paper discuss the impact of demand uncertainty that causes bullwhip effect and profit variance along a supply chain. Essentially, these two management challenges can be viewed as inventory problem. To address this issue and provide effective solution, this paper applies mean-variance analysis to study the impact of information sharing on the bullwhip effect under risk aversion in a sequential supply chain with two stages. This paper first finds that the risk-averse ordering decision at each stage is unique and show that the corresponding risk-neutral solution is an upper bound on the risk-averse one. Assuming that the demand follows a normal distribution and applying a common demand forecasting approach, further show that risk aversion does not affect the bullwhip effect, while still reduces the it with information sharing. And information sharing benefits the decision makers by improving their expected profits and fill rates, by maintaining the shirked inventory safety factors due to risk aversion. The beneficial impact of information sharing is more significant as the risk-averse ratio or demand standard deviation increases. And the extension shows that the benefit amplifies as firms’lead time or the number of stages increases in the supply chain.Second, seasonal and uncertain demand often fetter the development of aviation industry, due to the lack of flexibility of fleet capacity in a short-term. As a result, aviation travelling demand often mismatches capacities offered by airlines. To avoid loss caused by the mismatch due to seasonal and uncertain demand, this paper addresses the issue of by introducing the aircraft leasing business. Focusing on the decisions and interactions between one leasing company and a group of airlines, we find that the pure strategy exists in a game that leasing company knows the airlines’ reactions towards different leasing rates. We present a tractable algorithm to find the integrated optimal decisions of the players, including the ticket pricing policy, the leasing number, and the leasing rate. We also observe that deterministic approximation works well under stochastic demand if the leasing company yields a small amount of the revenue to the airlines. The value of the leasing business is derived from the risk pooling effect, which stems from flexible capacity allocation and the flexible leasing rate. To better understand the pooling effect, we numerically compare the performance between a centralized system, which represents the scenario with leasing, and a decentralized system, which represents the scenario without leasing, by varying various key factors.Third, from observations of industry, mismatch of supply and uncertain demand becomes increasingly crucial, when the production facilities are unreliable. Hence, this article also attempt to solve this kind of problem by searching a joint pricing and inventory policy with consideration of demand uncertainty and machine unreliability. We consider a make-to-stock system served by an unreliable machine that produces one type of product, which is sold to customers at one of two possible prices depending on the inventory level at the time when a customer arrives (i.e., the decision point). The system manager must determine the production level and selling price at each decision point. We first show that the optimal production and pricing policy is a threshold control, which is characterized by three threshold parameters under both the long-run discounted profit and long-run average profit criteria. We then establish the structural relationships among the three threshold parameters that production is off when inventory is above the threshold, and that the optimal selling price should be low when inventory is above the threshold under the scenario where the machine is down or up. Finally we provide some numerical examples to illustrate the analytical results and gain additional insights.Finally, all contributions and results are concluded in the last chapter, including the direction of future research.To sum up, the importance of uncertainties from reality in companies’ operations can never be overestimated. This paper attempt to provide solutions for inventory and pricing problems in industry with all kinds of uncertainties. Then, corporations may become more competitive facing complex uncertain environment. Meanwhile, this paper based on manufacturing and aviation industrial background, and extend academic results by uncertainty consideration and combine inventory policy with pricing decisions. Hence, this paper has instructive to industrial practice and theoretical innovation.