Secure Collaborative Planning, Forecasting, and Replenishment (SCPFR)

Although the benefits of information sharing between supply-chain partners are well known, many companies are averse to share their “private” information due to fear of adverse impact of information leakage. This paper uses techniques from Secure Multiparty Computation (SMC) to develop “secure protocols” for the CPFR r © † (Collaborative Planning, Forecasting, and Replenishment) business process. The result is a process that permits supply-chain partners to capture all of the benefits of information-sharing and collaborative decision-making, but without disclosing their “private” demand-signal (e.g., promotions) and cost information to one another. In our collaborative (CPFR) scenario, the retailer and supplier engage in SMC protocols that result in: (1) a forecast that uses both the retailers and the suppliers observed demand signals to better forecast demand; and (2) prescribed order/shipment quantities based on system-wide costs and inventory levels (and on the joint forecasts) that minimize supply-chain expected cost/period. Our contributions are as follows: (1) we demonstrate that CPFR can be securely implemented without disclosing the private information of either partner; (2) we show that the CPFR business process is not incentive compatible without transfer payments and develop an incentive-compatible linear transfer-payment scheme for collaborative forecasting; (3) we demonstrate that our protocols are not only secure (i.e., privacy preserving), but that neither partner is able to make accurate inferences about the others future demand signals from the outputs of the protocols; and (4) we illustrate the benefits of secure collaboration using simulation.