Qing Ding

On the Integration of Production and Financial Hedging Decisions in Global Markets

We study the integrated operational and financial hedging decisions faced by a global firm who sells to both home and foreign markets. Production occurs either at a single facility located in one of the markets or at two facilities, one in each market. The company has to invest in capacity before the selling season starts when the demand in both markets and the currency exchange rate are uncertain. The currency exchange rate risk can be hedged by delaying allocation of the capacity to specific markets until both the currency and demand uncertainties are resolved and/or by buying financial option contracts on the currency exchange rate when capacity commitment is made. A mean-variance utility function is used to model the firms risk aversion in decision making. We derive the joint optimal capacity and financial option decision, and analyze the impact of the delayed allocation option and the financial options on capacity commitment and the firms performance. We show that the firms financial hedging strategy ties closely to, and can have both quantitative and qualitative impact on, the firms operational strategy. The use, or lack of use of financial hedges, can go beyond affecting the magnitude of capacity levels by altering global supply chain structural choices, such as the desired location and number of production facilities to be employed to meet global demand.

Managing Storable Commodity Risks: The Role of Inventory and Financial Hedge

We study how to manage commodity risks price and consumption volume via physical inventory and financial hedge in a multiperiod problem with an interperiod utility function for a risk-averse firm procuring a storable commodity from a spot market at a random price and a long-term supplier at a fixed price. The firm also has access to financial contracts written on the commodity price, such as futures contracts and call and put options. We examine different cases of financial hedging, for example, single-contract and multicontract hedges. For each case, we dynamically maximize the mean-variance utility of the firms cash flow and characterize an optimal integrated policy of inventory and hedging, which is easy to compute and implement. We find that as long as futures are used in each period, alone or not, the optimal inventory policy is myopic. The optimal hedging policy, however, is never myopic, but depends on all the future optimal decisions. This is contrary to findings of the literature using intraperiod utility functions, which finds myopic hedging to be optimal. Moreover, we find that hedging may lead to inventory reduction in multiperiod problems. Thus the insights from the single-period studies in the literature---hedging leads to inventory increase---do not apply. Finally, insights are offered on the role and impact of inventory and financial hedge on profitability, variance control, and service level, using both analytical and numerical results.

Dynamic Pricing Through Discounts for Optimizing Multiple-Class Demand Fulfillment

In a multiple-customer-class model of demand fulfillment for a single item, we consider the use of dynamic price discounts to encourage backlogging of demand for customer classes denied immediate service. Customers are assumed to arrive over several stages in a period, and customer classes are distinguished by their contractual price and sensitivity to discounts. Through dynamic programming we determine the optimal discounts to offer, assuming a linear model for the sensitivity of customers to such inducements. We show that customers are served in class order, and allocation of inventory to demand is determined by considering the current number of customers backlogged, as well as the current inventory position. Through comparison to a naive supplier allocating inventory first come/first served with no discounting, we show that profits are primarily influenced by the allocation of capacity, and the use of price discounts primarily benefits the second-class customers overall fill rate. Heuristics for implementation of the solution in real-time settings are given.

Dynamic pricing for multiple class deterministic demand fulfillment

We consider how a firm should allocate inventory to multiple customer classes that differ based on the price they pay and their willingness to incur delay in fulfillment of their demand. The problem is set in a deterministic demand, economic-order-quantity-like environment with holding, backorder, lost demand and setup costs. The firm either fulfills demand or offers a price discount to induce the demand to wait for fulfillment from the next reorder. We determine the optimal policy and discuss how changes in various parameters affect profitability, customer service, and operational measures such as order frequency and base stock levels. We compare the results to a policy that only rations inventory without dynamic discounting and to a policy that only provides discounts. Through the comparison, we observe that dynamic pricing can be seen as a combination of a pricing mechanism which determines demand and an allocation mechanism that differentiates between customer classes, serving each ones needs. We show that if lower-value customers are distinguished by accepting reduced service, it is possible that both high and low-value customer classes see better levels of service under the optimal policy than under a discounting only policy. In addition we demonstrate the applicability of the results to a stochastic version of the problem.