Sharing forecast information in a supply chain

This doctoral dissertation is centered around sharing forecast information within a supply chain. Based on a research study of the semiconductor equipment industry, this thesis examines the benefit and cost of sharing forecast information in the supply chain. It has three parts. First, I investigate the supplier\u27s cost trade-offs in order fulfillment using an ‘imputed cost’ approach. Next, I empirically test the effectiveness of forecast sharing measured by supplier delivery performance. I then go on to study the underlying incentives to share forecasts in the supply chain using a game-theoretic framework. It is found that sharing risky and volatile forecast information may not improve supply chain performance. Moreover, the customer has an incentive to inflate order forecasts. However, I demonstrate that truthful information sharing is achievable in a long-term supply chain relationship without recourse to explicit contracting mechanisms. This is because a long-run relationship gives supply chain parties opportunities to evaluate each other\u27s credibility and punish untruthful behavior, and therefore provides the right incentive for truthful forecast sharing. It is also found that such a long-run communicative equilibrium is more likely to form when the industry landscape is stable, firms value long-term relationships, and overforecasting is relatively easy to detect. These results are consistent with the empirical findings for the semiconductor equipment industry