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transfer of the EPOS data would improve on the trade-off between downpricing and out- of-stock. This is due to the fact that a chain- wide trade-off is likely to be very different from a local trade-off made by the franchisee. This can be easily understood by the newsboy model, in which a trade-off is made between overage cost (cost of having ordered too much) and underage cost (cost of having ordered too little). It is clear that, taking the cost structure of the chain as a whole, the ratio of overage and underage cost is smaller than in the case of the cost structure for the individual franchisee. The overage cost for the whole chain includes all purchased raw material and labor put into the product, while the overage cost for the franchisee additionally includes the profit margins and the fixed costs of the manufacturer and the retail organization (DC). Thus, the retail franchisee is unlikely to change its ordering policy without the proper financial incentives from the retail organization. A viable option of reducing the bullwhip is by eliminating the amplification in variability that is caused by the DC. For the salads chain, the effect of the DC is considerable. This would mean that transferring EPOS data would be beneficial. Given the discussion above, and thus assuming the retail franchisee may not be willing to change his ordering policy if there is no financial incentive to do so, transferring the (aggregate) retail order may be a better option to reduce the bullwhip effect than transferring detailed consumer demand. Assuming the same financial conditions in the meals chain, it may not be beneficial at all to transfer any information about demand for meals to the manufacturer, since the DC hardly causes a bullwhip effect in the supply chain. Benefits of transferring information are likely to be marginal and an investment in information systems to make the data exchange possible would probably not pay off. It pays off to find out the detailed mechanics of the bullwhip effect in a specific supply chain. Filtering out the various causes of the bullwhip effect at each of the echelons in the chain, and assuming other policies (such as pricing) would not be changed, can make the specific benefits of transferring EPOS data clear and help decide whether an investment in information systems would pay off. Conclusions In this paper, we have discussed the measurement of the bullwhip effect. A correct measurement is an essential start to investigating problems caused by demand amplification and to assess which measures can be taken to reduce this amplification. We conclude that: . A correct bullwhip measurement needs to be well defined. The correct sequence of data aggregation needs to be agreed upon in order to be able to compare data. As we have demonstrated in section 3, considerable differences in measurement can occur if a different sequence of aggregation data is applied. Aggregating data is essential, since the inbound demand at an echelon and the outbound
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