Optimal Ordering and Pricing Policies for Seasonal Products: Impacts of Demand Uncertainty and Capital Constraint

With a stochastic price-dependent market demand, this paper investigates how demand uncertainty and capital constraint affect retailer’s integrated ordering and pricing policies towards seasonal products. The retailer with capital constraint is normalized to be with zero capital endowment while it can be financed by an external bank. The problems are studied under a low and high demand uncertainty scenario, respectively. Results show that when demand uncertainty level is relatively low, the retailer faced with demand uncertainty always sets a lower price than the riskless one, while its order quantity may be smaller or larger than the riskless retailer’s which depends on the level of market size. When adding a capital constraint, the retailer will strictly prefer a higher price but smaller quantity policy. However, in a high demand uncertainty scenario, the impacts are more intricate. The retailer faced with demand uncertainty will always order a larger quantity than the riskless one if demand uncertainty level is high enough (above a critical value), while the capital-constrained retailer is likely to set a lower price than the well-funded one when demand uncertainty level falls within a specific interval. Therefore, it can be further concluded that the impact of capital constraint on the retailer’s pricing decision can be influenced by different demand uncertainty levels

The floating contract between risk-averse supply chain partners in a volatile commodity price environment

In this dissertation, two separate but closely related decision making problems in environments of volatile commodity prices are addressed. In the first problem, a risk-averse commodity user\u27s purchasing policy and his risk-neutral supplier\u27s pricing decision, where the user can purchase his needs through contract with his supplier as well as directly from the spot market, are analyzed. The commodity user is assumed to be the supplier\u27s sole client, and the supplier can always expand capacity, at a cost to the user, to accommodate the user\u27s demand in excess of initially reserved capacity. In the more generalized second problem, both parties (commodity user and supplier) are assumed to be risk averse, and both can directly access the spot market. In addition to making pricing decisions, the supplier is also faced with the challenge of establishing the right combination of in-house production and spot market engagements to manage her risk of exposure to spot price volatility under the contract. While the supplier has a frictionless buy and sell access to the spot market, the user can only access this market for buying purposes and incurs an access fee that is linearly increasing in the purchased volume. In both problems, by adopting the mean-variance criterion to reflect aversion to risk, the decisions of both parties are explicitly characterized. Based on analytical results and numerical studies, managerial insights as to how changes in the model\u27s parameters would affect each party\u27s decisions are offered at length, and the implications of these results to the manager are discussed. A focal point for the dissertation is the consideration of a floating contract, the landing price of which is contingent on the realization of the commodity\u27s spot market price at the time of delivery. It was found that if properly designed, not only can this dynamic pricing arrangement strategically position a long-term supplier against spot market competition, but it also has the added benefit of leading to improved supply chain expected profits compared to a locked-in contract price setting. Another key finding is that when making her pricing decisions, the supplier runs the risk of overestimating the commodity user\u27s vulnerability at higher levels of the user\u27s aversion to risk as well as at higher volatility of spot prices

REVENUE AND ORDER MANAGEMENT UNDER DEMAND UNCERTAINTY

We consider a firm that delivers its products across several customers or markets, each with unique revenue and uncertain demand size for a single selling season. Given that the firm experiences a long procurement lead time, the firm must decide, far in advance of the selling season not only the markets to be pursued but also the procurement quantity. In this dissertation, we present several operational scenarios in which the firm must decide which customer demands to satisfy, at what level to satisfy each customer demand, and how much to produce (or order) in total. Traditionally, a newsvendor approach to the single period problem assumes the use of an expected profit objective. However, maximizing expected profit would not be appropriate for firms that cannot afford successive losses or negligible profits over several consecutive selling seasons. Such a setting would most likely require minimizing the downside risk of accepting uncertain demands into the production plan. We consider the implications of such competing objectives. We also investigate the impact that various forms of demand can have on the flexibility of a firm in their customer/market selection process. a firm may face a small set of unconfirmed orders, and each order will often either come in at a predefined level, or it will not come in at all. We explore optimization solution methods for this all-or-nothing demand case with risk-averse objective utilizing conditional value at risk (CVaR) concept from portfolio management. Finally, in this research, we explore extensions of the market selection problem. First, we consider the impact of incorporating market-specific expediting costs into the demand selection and procurement decisions. Using a lost sales assumption instead of an expediting assumption, we perform a similar analysis using market-specific lost sales costs. For each extension we investigate two different approaches: i) Greedy approach: here we allocate order quantity to market with lowest expediting cost (lowest expected revenue) first. ii) Rationing approach: here we find the shortage (lost sale) then ration it across all the markets. We present ideas and approaches for each of these extensions to the selective newsvendor problem

Online marketing:When to offer a refund for advanced sales

Advance selling is a marketing strategy commonly used by online retailers to increase sales by exploiting consumer valuation uncertainty. Recently, some online retailers have started to allow refunds on products sold in advance. On the one hand this reduces the net advance sales, but on the other hand it allows a higher advance sales price. This research is the first to explore the overall effect of allowing a refund on profits from advance sales, identifying conditions where advance selling with or without refunds (or no advance selling at all) is best. We analytically compare the profits of three advance selling strategies: none, without refund, and with refund. We show that selling in advance and allowing a refund is optimal for products with a relatively small profit margin and small strategic market size, and that the added profit can be considerable. Our results guide managers in selecting the right advance selling strategy. To facilitate this, we graphically display, based on the two dimensions of regular profit margin and strategic market size, under what conditions the different strategies are optimal

An optimal stock building strategy in a manufacturing company

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 54).This thesis aims to tackle a demand seasonality problem by building either work-in-process inventory or finished goods inventory before the peak season. A linear programming algorithm is developed to determine the optimal way for stock building in order to satisfy the demand with minimum inventory holding cost. An optimization software ILOG OPL Studio 5.1 is used to solve the LP model to optimality within several seconds. The purpose of the analysis is three fold: (1) to identify the bottleneck processes where the capacities fall short of demand; (2) to generate the optimal stock building policy that minimizes the inventory holding cost; (3) to derive a cost-efficient executable production plan with consideration of the operating labor cost. The strategy outlined can be applied easily in actual manufacturing. This efficient and robust model can be used to obtain optimal stock building policy with various demand and capacity scenarios. It can be a useful tool for the company to develop stock building strategy for future demand with improved installed capacity. Key words: Optimization, linear programming, inventory building policy Disclaimer: Theeontent of the thesis is modified to protect the real identity of the attachment company. Company name and confidential information are omitted.by Meng Li.M.Eng

Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach.

a b s t r a c t A stochastic fuzzy multi-objective programming model is developed for supply chain outsourcing risk management in presence of both random uncertainty and fuzzy uncertainty. Utility theory is proposed to treat stochastic data and fuzzy set theory is used to handle fuzzy data. An algorithm is designed to solve the proposed integrated model. The new model is solved using the proposed algorithm for a three stage supply chain example. Computation suggests an analysis of risk averse and procurement behavior, which indicates that a more risk-averse customer prefers to order less under uncertainty and risk. Tradeoff game analysis yields supported points on the trade-off curve, which can help decision makers to identify proper weighting scheme where Pareto optimum is achieved to select preferred suppliers