(3) Price fluctuations . Because of promotions and trade deals, the price of a product fluctuates, which increases variability of demand. When the price of a product is low, a customer buys in bigger quantities than needed. When the price returns to normal, the customer buys less than needed to deplete its inventory. Stabilizing prices and decreasing the number of promotions is a way of reducing this effect. (4) Rationing and shortage gaming . When product demand exceeds supply, a supplier needs to ration its product to customers. Knowing that, customers may order more than they really need. Later, when there are no shortages, orders disappear. Introducing rationing methods based on past sales rather than on orders placed takes away the incentive for customers to inflate order sizes. There are many conceptual issues related to measuring the bullwhip effect that are hardly discussed in the literature. First, different choices can be made regarding the way data are aggregated. Neither current literature nor business practice is clear about this matter. For example, in the paper by Holmstro ¨m (1997) it is not indicated at which level of detail the measurements have been carried out. Sometimes the issue is that available data are too aggregated. For example, at retail outlets, point-of-sale information is often aggregated directly (before being transferred to a database) into daily or weekly demand. This may hamper an analysis for which hourly demand is needed. Second, measuring the total bullwhip effect does not tell which of the different causes contributes most and which solutions are most relevant. For example, to assess the possible benefits of exchanging demand information, it is important to be able to measure which part of the bullwhip effect is due to incomplete demand information in a particular supply chain. Benefits for transferring EPOS data must be clear before a system is set up to make this possible. In many cases a significant investment in information systems is needed to collect and process the data. Furthermore, EPOS data is very valuable marketing information, that retailers may not want to make available to the manufacturer. By measuring the bullwhip effect at various echelons in the supply chain, we may be able to find out at which echelon sharing information is particularly useful. By specifying at which level of aggregation the information is useful, the various companies Figure 1 Example of increased demand variability 80 Measuring the bullwhip effect in the supply chain Jan C. Fransoo and Marc J.F. Wouters Supply Chain Management: An International Journal Volume 5 . Number 2 . 2000 . 78–89 Downloaded by IQRA UNIVERSITY At 04:07 20 September 2015 (PT)
may find that a particular level of aggregated data may be exchanged to dampen the bullwhip effect and to improve operations, while detailed EPOS data are not exchanged if retailers want to protect that for marketing purposes.
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