Holmstro \u00c8m 1997 has analysed the orders flowing upstream from retail outlets

Holmstro èm 1997 has analysed the orders flowing

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Holmstro Èm 1997) has analysed the orders flowing upstream from retail outlets back to the factory. He studied in depth a traffic building high volume), low margin product, and a low traffic low volume), high margin product. Demand amplification was Table III Typical bullwhip results from playing the beer game Multiplicative Supply chain bullwhip factor Inventories echelon ex. marketplace Peak stock Peak backlog Swing stock Marketplace 1.0 ± ± ± Retailer 3.5 20 10 30 Wholesaler 4.5 55 30 85 Distributor 11.5 20 50 70 Brewery 14.00 75 25 100 Source : Authors, based on data by Sterman 1989) 172 Diagnosis and reduction of bullwhip in supply chains Peter McCullen and Denis Towill Supply Chain Management: An International Journal Volume 7 . Number 3 . 2002 . 164±179 Downloaded by IQRA UNIVERSITY At 04:10 20 September 2015 (PT)
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estimated via the coefficient of variation measure standard deviation/average value), as discussed in detail by Fransoo and Wouters 2000). We have calculated the bullwhip factor as the ratio of the coefficients of variation estimated as the orders are passed up successive echelons in the supply chain. The results are shown in Table IV. They demonstrate real-world bullwhip, which is even larger than that predicted by the beer game, and promulgates wildly upstream exactly as Van Aken 1978) has related, based on his experiences in Philips' Eindhoven plants. Note that the bullwhip factors yield important insights into the behaviour of the various ``players'' in the chain. The downstream players shops and wholesalers) are the biggest culprits in the sense of bullwhip generation. They exhibit little difference in attitude to ordering policies for either low margin or high margin products, with bullwhip factors at around three to one. Not so the factory scheduler. He treats the two products differently, and significantly dampens down the demand volatility in the factory orders placed for the high volume product. This is most likely to have been achieved via some version of level scheduling Suzaki, 1987). In contrast, the same scheduler is quite prepared to induce further substantial bullwhip into the system, when considering the low volume product. Deliveries from the factory also exhibit some bullwhip, but it is of a smaller order of magnitude than that generated by the downstream ``players''. The total bullwhip factor over the entire chain is 9:1 for the high volume product, and nearly 29:1 for the low volume product; considerably worse than the 14:1 bullwhip factor reported for the beer game in Table III. In the confectionery supply chain the extent of the bullwhip problem is therefore considerably worse than the beer game would suggest for one of the value streams. Hence on this evidence the beer game provides an inadequate account of bullwhip. In other words, in the real world there are even more triggers of bullwhip ready to be activated!
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