Optimizing Customer Forecasts for Forecast‐Commitment Contracts

Abstract

W e study a ''Forecast-Commitment'' contract motivated by a manufacturer's desire to provide good service in the form of delivery commitments in exchange for reasonable forecasts and a purchase commitment from the customer. The customer provides a forecast for a future order and a guarantee to purchase a portion of it. In return, the supplier commits to satisfy some or all of the forecast. The supplier pays penalties for shortfalls of the commitment quantity from the forecast, and for shortfalls of the delivered quantity from the customer's final order (not exceeding the commitment quantity). These penalties allow differential service among customers.In Durango-Cohen and Yano (2006), we analyzed the supplier's problem for a given customer forecast. In this paper, we analyze the customer's problem under symmetric information, both when the customer is honest and when he strategically orders more than his demand when doing so is advantageous. We show that the customer gains little from lying, so the supplier can use his control over the contract parameters to encourage honesty. When the customer is honest, the contract achieves (near-)coordination of the supply chain in a great majority of instances, and thus provides both excellent performance and flexibility in structuring contracts.

Uncertainty in competitive bidding – a framework for product–service systems

“…For this research, uncertainty is defined as a potential deficiency in any phase or activity of the process, which can be characterised as not definite, not known or not reliable (Huyse andWalters 2001, Soanes 2005). Within this context, forecasting can be used to reduce this uncertainty and support decision-making (Lawrence et al 2006, Durango-Cohen andYano 2011). An example is the estimation of the maintenance cost of a product (Fornasiero et al 2012).…”

Soft Order Commitment in Supply Chains: Role of Penalties and Rationing Rules

“…Due to the improved reliability of soft orders and the manufacturer's commitment, buyers' soft orders can induce the system‐optimal production quantity and achieve supply chain coordination. As with Dong and Zhu (), Cho and Tang (), and Durango‐Cohen and Yano (, ), we assume that the buyers' demand distributions and the manufacturer's output are common knowledge in the supply chain. This assumption allows us to focus on supply chain coordination rather than information asymmetry.…”

“…Hence, we assume that demand distribution is public information and are known to all parties, i.e., the manufacturer and the two buyers all know and . (Dong & Zhu, ; Cho & Tang, ; and Durango‐Cohen & Yano, , , assume that demand distribution is public information. )…”

“…The representative works of the second stream are Durango‐Cohen and Yano (, ), Mathur and Shah (), Gan, Sethi, and Zhou (), Yuan, Chua, Liu, and Chen (), and Barnes‐Schuster, Bassok, and Anupindi (). Durango‐Cohen and Yano () studied a forecast‐commitment contract, in which a customer provided a forecast of its order and purchased at least a portion of the forecast.…”

“…Through numerical examples, these authors illustrated that this contract may achieve a centralized supply chain's profit. Durango‐Cohen and Yano () considered the strategic interaction between the supplier and the buyer and concluded that the forecast‐commitment contract could induce a customer to provide a reasonable forecast. The commitment penalty contract can significantly improve the profits for the supplier and its customer compared to those with no contracts, but the system profit is unlikely the same as that of the centralized supply chain.…”