Managing commodity price uncertainty is an integral part of many firms' business process. Firms adopt a variety of operational strategies to manage this uncertainty, subject to operational constraints such as finite procurement and processing capacities. The availability of financial derivative instruments provide firms with additional options to manage the risk from commodity operations. This dissertation explores different aspects of managing the price uncertainty for a commodity processing firm in a series of four related essays.;The first three essays consider the integrated procurement, processing and trade decisions for a firm operating a single location with procurement and processing capacity constraints under risk-neutral and risk-averse objective functions. These essays focus on deriving the optimal policy structure and developing computationally tractable heuristics where required. The first essay considers a risk-neutral firm maximizing expected profits from operations over a multi-period horizon and derives the optimal operational policy for the firm. The second essay deals with the issue of time-consistent decision making in risk-averse settings while the third essay looks at the value of operational hedging, such as excess procurement or processing capacity.;The fourth essay extends the single location problem to a network setting and considers a 'star' network configuration. While solving the network problem optimally is hard, this essay proposes heuristics based on insights from the optimal policy structure for the single node problem to address the computational complexities. In addition, this essay also proposes a myopic heuristic to manage the commodity procurement and processing decisions in a network. Numerical studies indicate that these heuristics provide a significant improvement in expected profits, compared to heuristics used in practice.
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