QI FENG, University of Texas at Austin - Red McCombs School of Business
Email: Annabelle.Feng@mccombs.utexas.edu
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

This chapter examines the interaction between supply price uncertainty and demand uncertainty. We consider a manufacturer who sources a key component using different procurement options: a long-term order on a price-only contract, short-term orders on an adjustment contract, and short-term purchases directly from the market. At the beginning of the planning cycle, the manufacturer places a long-term order and reserves a certain amount of supply capacity for the purpose of adjusting the long-term order, if needed. Before the selling season, the manufacturer has multiple options to place supplementary orders from the reserved capacity or from the market.

We compare two types of capacity arrangements: dedicated capacity and overall capacity. Under a dedicated capacity arrangement, the manufacturer reserves capacities separately for different adjustment opportunities. On the overall capacity arrangement, she keeps the flexibility of using the reserved capacity within the given period for possibly multiple adjustments. We discuss the optimal procurement strategies and the criteria for capacity allocations, as well as the policy behavior and service performance in different situations.

XIULI HE, University of North Carolina at Charlotte
Email: xhe8@uncc.edu
ASHUTOSH PRASAD, University of Texas at Dallas - School of Management
Email: aprasad@utdallas.edu
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu
GENARO GUTIERREZ, University of Texas at Austin - Red McCombs School of Business
Email: Genaro.Gutierrez@mccombs.utexas.edu

The presentation of Table 2 in the original version of this article ("A Survey of Stackelberg Differential Game Models in Supply and Marketing Channels", Journal of Systems Science and Systems Engineering, Vol. 16, No. 4, pp., 385-413, 2007) contained a few typos. The corrected Table 2 is given below.

VISHAL GAUR, Johnson Graduate School of Management, Cornell University
Email: vg77@cornell.edu
NIKOLAY OSADCHIY, New York University - Leonard N. Stern School of Business
Email: nosadchiy@yahoo.com
SRIDHAR SESHADRI, University of Texas at Austin - McCombs School of Business
Email: sseshadr@stern.nyu.edu

The volume of retail sales is commonly understood to be correlated with the state of the economy. This information can potentially be employed in demand forecasting, operations decisions, and risk management. We propose a model in which the total sales of a retailer is a function of sales forecasts generated by equity analysts, the term of the forecast, and the return on an aggregate financial market index over the term of the forecast. We test this model on a panel of 4,698 observations of annual firm-level sales forecasts for 97 retailers over 10 years, each year containing multiple forecasts of varying terms. We show that the correlation coefficient of sales forecast error with the financial market return is significant, and varies across firms depending on the retail segment, the gross margin, and the term of the forecast. Our model provides results on other parameters for forecasting as well, and a method for forecast updating. We show that forecast updates from our model provide new information not contained in the forecast updates by equity analysts, so that a combined forecast leads to improved forecast accuracy. These results have applications in forecast updating, decision postponement, production planning, and risk management.