Forecast Integration in Supply Chains under Freight Rejection

Freight transportation is a major source of supply chain cost, and one critical challenge is freight rejection, whereby a contract carrier declines a tendered shipment when spot market conditions are favorable. To mitigate this risk, digital freight platforms such as DAT and Uber Freight provide both backup freight and spot rate forecasts. Through API integration, these forecasts can be integrated into supply chains so firms can adjust contract terms and procurement decisions based on anticipated logistics costs. We study the supply chain implications of spot rate forecast integration under freight rejection. We develop a game-theoretic model of a manufacturer, a retailer, and a digital freight platform. We examine how forecast integration affects wholesale pricing, retail ordering, and platform rate decisions, and characterize its value to market participants and the adoption of forecast integration in equilibrium. We find that forecast integration enables the manufacturer to make the wholesale price responsive to the spot rate forecast, which in turn induces a countervailing response from the platform in its freight-rate decision. This platform rate counter-response reduces the retailer's expected shipping cost and leads forecast integration to always benefit the supply chain, although its value to the manufacturer, the retailer, and the platform is more nuanced and depends on the degree of production (dis)economy. In equilibrium, forecast integration is adopted when production exhibits sufficiently strong economy or diseconomy. We further show that mitigating freight rejection by, e.g., cultivating shipper-carrier relationships and improving contract explicitness, can unintentionally reduce retailer and supply chain profits by weakening supply chain responsiveness to spot market conditions, and that this effect can be amplified under forecast integration. We calibrate our model to real-world data and show that forecast integration increases system profit by 2.69% on average and by as much as 25.56%. Our results demonstrate the value of forecast integration in supply chains, provide prescriptive guidance for designing operational decision rules under forecast integration, and highlight how production (dis)economy shapes the value created by forecast integration.