Abstract
Information distortion is common in supply chains, especially demand information distortion. An easily-thought way of eliminating information distortion is information sharing. Although there exist many information sharing mechanisms that can coordinate the supply chains, they are not easy to implement due to their rigid implementation conditions. Consequently, many upstream manufacturers in reality commonly buy data from downstream retailers to eliminate demand information distortion, or may build a blockchain-based information transparency system (BITS), which is an alternative way proposed in this paper. The main purpose of this paper is to investigate how the two alternative ways perform in eliminating demand information distortion within supply chains. In specific, we study a two-echelon supply chain consisting of one manufacturer and one retailer under three scenarios: benchmark scenario with demand distortion, demand distortion elimination through data buying and through constructing BITS. Our findings are the following (Babich and Hilary, 2020): Eliminating demand distortion by BITS can always bring the manufacturer additional value, but a higher information transparency of BITS is not always better to the manufacturer, and the manufacturer chooses a fully transparent BITS to eliminate demand distortion only when the demand distortion is relatively large (Carley and Lin, 1997); The data purchase way of eliminating demand distortion can bring additional value to the manufacturer only when the data-buying cost is not very large (Chen and Huang, 2007); As compared to the data-buying scenario to eliminate demand distortion, the BITS construction scenario can generate more additional values for the manufacturer if the demand volatility is relatively small, or when the cost coefficient of building BITS is relatively small (CachonGérard and Fisher, 2000); If the retailer bears the BITS construction cost, the retailer prefers to proactively share the BITS construction cost rather than passively bear it completely.
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