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Share and share alike? : The value of pooling demand data across retail locations Colby, Sarah M.


We consider the value of pooling demand data across multiple retail outlets in a multiple-period newsvendor problem. In particular, our setting consists of a single firm with two newsvendor retail locations. The retailers each face normally distributed, stationary demand for a product. The variance of demand is known, but the mean is unknown and estimated. This estimate is updated as consumer demand at the retail locations is observed. We contrast the situation in which a central agent pools the retailers' demand data when updating the demand forecast with that when the retailers act with local data only. In a numerical study, we investigate the effects of the following factors on the value of pooling: (1) the relative size of the retailers' markets; (2) whether the product is a high-profit or a low-profit product; (3) the number of periods in the horizon; and (4) the forecast updating method. Further, we study the effects of these factors under a number of different scenarios characterized by the a priori beliefs about mean demand. Specifically, these scenarios represent initial beliefs about mean demand which are either overconfident, underconfident or reasonable. In our numerical study we find that the effect of the relative size of the retailers' markets on the value of pooling is not interesting. The value of pooling is greater for low-profit products than for high-profit products. As expected, pooling is more valuable when the a priori beliefs about mean demand are overconfident or underconfident than when they are reasonable. The effect of the forecast updating method on the value of pooling depends on the initial beliefs about mean demand. Likewise, the effect of the horizon length depends on the forecast updating method, as well as the initial beliefs about mean demand.

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