Globalization and increased product variety have impacted the uncertainty in demand and supply. The recent financial instability adds another layer of uncertainty regarding financing and investment. The changes, while gradual, have accumulated over time and posed enormous difficulties in planning procurement. This thesis focuses on inventory procurement strategies that help firms tackle challenges due to uncertainties in the demand/supply and financial concerns. The first part is on employing dynamic inventory procurement strategies to achieve cost efficiency and tackle the uncertainties in demand and supply. The second and third parts focus on the interaction between Finance and Operations in both its analytic aspects and empirical aspects. A synopsis of the three parts of the thesis follows. Part 1: “Inventory Management and Stochastic Lead Time” This chapter analyzes a continuous time back-ordered inventory system with stochastic demand and stochastic delivery lags for placed orders. This problem in general has an infinite dimensional state space and is hence intractable. We first obtain the set of minimal conditions for reducing such a system’s state space to one-dimension and show how this reduction is done. Next, by modeling demand as a diffusion process, we reformulate the inventory control problem as an impulse control problem. We simplify the impulse control problem to a Quasi-Variation Inequality (QVI). Based on the QVI formulation, we obtain the optimality of the (s, S) policy and the limiting distribution of the inventory level. We also obtain the long run average cost of such an inventory system. Finally, we provide a method to solve the QVI formulation. Using a set of computational experiments, we show that significant losses are incurred in approximating a stochastic lead time system with a fixed lead time system, thereby highlighting the need for such stochastic lead time models. We also provide insights into the dependence of this value loss on various problem parameters. Part 2: “Inventory Financing and Trade Credit” In this chapter, we study the inventory performance of publicly listed retailers between 1980 and 2010 based on a panel dataset from COMPUSTAT, CRSP, I/B/E/S and a hand-collected dataset on bankruptcy. We quantify the effect of a carefully-defined financial holding cost on inventory decisions, after controlling for operational factors and considering access to trade credit. This finding provides empirical evidence of the failure of the Modigliani-Miller Theorem in the inventory management context. We are also able to infer several unobservable costs based on historical inventory decisions. For example, the average cost of trade credit is estimated to be about 20% per year, which matches the typical trade credit terms in the United States. We find that the cost of trade credit computed has a strong connection to inventory per- formance. Our findings are robust to alternative econometric specifications, alternative measures of variables and model estimates for subsets of data. Part 3: “Joint Inventory and Cash Management Decisions” In this chapter, we address this question by considering a general con- tinuous time model of a dynamic inventory system that incurs costs in both managing the inventory and managing the cash flow. To support its inventory and operational cost, this system has access to both the financial market and trade credit from suppliers. We show how the inventory procurement decision and financing decision are made jointly. Specifically, we show that, with friction of financing, not only does the Modigliani-Miller Theorem not hold but also the two decisions interact in a dynamic and complex manner. We are also able to show how the value of the inventory system can be improved by using trade credit. / text