Doctor of Philosophy (PHD)
Industrial Engineering (Engineering)
Date of Defense
First Committee Member
Second Committee Member
Shihab S. Asfour
Third Committee Member
Fourth Committee Member
Contingency inventory planning plays a significant role in many businesses. Firms must take low-probability big-impact events such as natural disasters and supply disruptions into consideration in their supply planning since they might cause crippling and irreversible effects on businesses. Companies that have operations in disaster-prone regions often face significant opportunity loss from shortages due to demand surge caused by rare events. With a robust contingency inventory planning policy, a firm can turn the negative effects of a low-probability event into an opportunity. Inventory pooling is one approach to keep contingency inventory for possible future use in case a demand surge caused by a disruptive low-probability event occurs. By pooling the inventory, the participating companies mitigate the risk of running out of stock when the demand surge occurs. In the first part of this study, we present a game theoretic analysis of a decision problem of two buyers and a supplier who keeps contingency stock for the buyers for the future use in case of a low-probability high-impact event such as a hurricane or an epidemic. The buyers operate their businesses in independent markets. However, they reserve contingency inventory from a single supplier. The proposed sequential game starts with the supplier’s unit reservation fee offer to the buyers. In the second stage, the buyers decide on their reservation quantities. In the last stage, the supplier acquires contingency inventory based on the reservation amounts. The reservation fees are kept and the reserved inventories are salvaged by the supplier if there is no low-probability event occurs. In the second part of this study, we present a game theoretic analysis of a decision problem of two buyers and a supplier where the buyers are the first movers (leaders) of the game. This time, the sequential game starts with each buyer’s individual reservation fee decision. At the beginning, buyers simultaneously move and offer deductible and non-refundable reservation fees to a single supplier for a unit product to be held as backup inventory over a single period. In the second stage, the supplier decides on her reservation quantities for each buyer based on their reservation fee offers. If a buyer is inflicted by the low-probability event, her reserved inventory is supplied by the supplier. If her reservation amount is not enough, the supplier can supply additional quantities from the reservation of the other buyer unless she exercises her reservation. Another approach to tackle shortages involves expedited replenishment. Expedited replenishment opportunities after obtaining updated demand information may exist to cover the potential shortages. However, usually such options impose high procurement costs. In the third part of this study, we study a finite horizon multi-period procurement and inventory control problem of a buyer where she has the option of ordering seasonal products with an advance contract before the beginning of the selling season. Purchasing large quantities with an advance contract creates cost saving opportunity to the buyer since the unit product cost is lower than the subsequent stages. However, at the time of the advance contract, the buyer is less informed about the market demand. The buyer can make additional expedited replenishments later during the season at predefined modes. During this time interval, the market signal for the buyer is updated. Our goal in this chapter is to determine the most efficient way to make procurement decisions for the buyer. The framework developed in this part of the study provides efficient policies to the buyer who has to deal with multiple sources of uncertainty such as market signal variability and demand across multiple periods during the selling season.
Inventory; contingency; advance supply contracts
Demir, Sercan, "Advance Supply Contracts for Contingency Inventory" (2017). Open Access Dissertations. 1982.