This dissertation focuses on addressing emerging challenges in modern supply chains, with a primary emphasis on risk management and data-driven decision-making, and aims to understand their implications for operational efficiency, financial performance, and environmental sustainability. The first chapter investigates financial risk within modern supply chains, specifically on cash flow management through payables finance, also known as supply chain finance. Payables finance enables a supplier to receive a buyer’s payables early while allowing the buyer to extend its payment due date. This area of research has gained renewed interest, particularly due to the recent adoption of blockchain technologies. Using a dynamic programming approach, we quantify the value of payables finance to small suppliers and determine the maximum payment term extensions for buyers. Through collaboration with a major US chemical company, we apply our model to its cash flow datasets, showing that payables finance can provide considerable cost savings (around 3%) for its suppliers and significant payment term extensions (up to 435 days) for the company. The second chapter explores operational risks in perishable product supply chains and their profound implications for food waste. This work uncovers a previously overlooked driver of the well-studied bullwhip effect: product perishability. Surprisingly, perishability can further attenuate upstream order variability. The extent of this variability amplification/attenuation is modulated by the buyer’s order quantity. Our real-world data-driven model calibration demonstrated the benefits of identifying novel contracts that coordinate on the buyer’s order quantity to limit variability amplification, resulting in 3-6% less food waste and 2-10% higher profits in the supply chain compared to those under wholesale-price contracts. These findings offer valuable guidance for optimizing supply chain profits while simultaneously achieving sustainability goals, reaching win-win scenarios. The third chapter focuses on how data-driven decision-making is reshaping the landscape of supply chain contracting. More specifically, this work studies the performance of revenue-sharing and wholesale-price contracts in supply chains where firms make data-driven inventory/pricing decisions. In these supply chains, each tier uses historical and contemporaneous data on demand and demand-relevant covariates to directly arrive at their optimal decisions, as opposed to the traditional paradigm where demand estimates are first exogenously specified, followed by a separate optimization stage. Our findings reveal that wholesale-price contracts tend to unexpectedly yield higher supply chain profits than revenue-sharing contracts—a stark contrast with well-known findings in the supply chain literature. Furthermore, we uncover significant interdependence between the choice of data-driven algorithm and contract, influenced by factors such as skewness and uncertainty of demand-related covariates and the number of past data used for decision-making. This highlights the need for fresh insights into developing new coordinating strategies tailored to data-driven supply chains.