Monetizing demand forecasts in an e-commerce supply chain: da-ta-product pricing and information-sharing strategies

Purpose As an emerging factor in production, data serve as a core resource of the digital economy. This study aims to examine when an e-commerce platform can monetize a processed demand forecast as a data product while preserving the supplier’s incentive to purchase the information. Design/methodology/approach The authors develop three data strategies for an e-commerce supply chain consisting of an online platform and a sup-plier. A multistage Bayesian Stackelberg model with correlated forecast errors compares no processed-forecast sharing, free full sharing and paid full sharing. A centralized benchmark and numerical robustness checks distinguish monetization from operational coordination. Findings The data-valuation-driven strategy can improve both firms’ profits when the data-product fee remains within a feasible range. The platform’s lower participation threshold must not exceed the supplier’s willingness-to-pay threshold. The interval expands with incremental forecast value and the external buyer market and contracts with productization cost. Because the fixed fee does not change wholesale or retail prices, it monetizes information and reallocates profit but does not eliminate double marginalization. Originality/value This study connects supply-chain forecast sharing with data-product monetization. It separates operational information value, the focal license payment, external-buyer revenue and productization cost and derives distinct conditions for bilateral participation, joint profit and operational coordination.