The Study of Comparing Predict Methods for Bullwhip Effect in Supply Chain Management

• Main Authors: Pin-Hsiao, Hsieh, 謝秉孝
• Other Authors: Ming-Kuen Chen
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/82279645014867670935

碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 92 === The essence of Bullwhip Effect is the reciprocation among upper, middle, and lower sections in an industrial system. Any slight variation of need in lower section could make a substantial alteration in middle section; proceed to higher section a gigantic needing shift. In other words, the needing shift of lower section magnifies the transference to upper section, higher the transference reached, bigger the influence impacted. Making a comprehensive survey of industrial yield, the types of products are transformed into various, low quantity, shortened product life-cycle and delivery time, and globalization in both purchasing and manufacturing. In supply chain, the-upper section-manufactory in behalf of dealing with the short terminal product life-cycle and fast-responding customer service, so that to cause every sections magnifying the manufactory’s stocks extrusion and demand, thereupon making excess end items and materials to run out of stock. Furthermore, this essay focuses on issue of predicting supply chain need. By the way of applying the beer game software to set the three parameters in supply chain: stock policy, external customer needs and lead time. In order to stimulate twenty-four types of supply chain circumstances, and collecting the value of all sections in the stimulated supply chain. This research analyzed the data on using two models. The first model is taking all the explanatory variables, including stock policy, external customer needs, and lead time, into consider. This uses the range of order standard deviation between factory and retailer as explanatory variable. Utilize two unitary analytical tools, Back Propagation Network and Multivariate Adaptive Regression Splines, separately, in order to prove . The outcome is BPN has better predict performancethan MARS. The second model extracts the essential explanatory variables through MARS in advance. Then, data analyze them with MARS and BPN, respectively, so as to prove . Then this research found out that two steps predict model are better than just one step predict model. This research report is trying to estimate the predicting method of Bullwhip Effect, to use as diminishing the impact it brings out. This essay provides enterprise for managing supply chain.