Midterm 1 SG

Midterm 1 SG - Trade-offs in SC Rpacket Integrated SC...

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Trade-offs in SC- Rpacket Integrated SC perspective/organization of SC- Rpacket Gap analysis p. 58- Rpacket Forecasting- Ch 3: p. 64/ p. 7 in packet Errors- p. 72 Issues- p. 14 in packet Methods- p. 68, 72 Exponential Smoothing- p. 70 CPFR- p. 74/ P. 14 in packet Customer Service- p. 49 CHAPTER 3 Customer Service: Service Outputs and Customer Utility Logistics is valued because it increases the utility of goods and services to customers. If goods have more utility to customers, they will either pay higher prices, purchase more often and with greater loyalty, or both. Always about customers - Logistics increases space utility by moving goods to where the customer needs them. - Logistics increases time utility by delivering goods when the customer needs them. - Logistics increases quantity and assortment utility by delivering exactly what the customer wants in the quantities the customer desires. Forecast Model Ft = (Bt × St × T × Ct × Pt) + I Where: Ft = Forecast quantity for period t; Bt = Base level demand for period t; St = Seasonality factor for period t; T = Trend component – index reflecting increase or decrease per period; Ct = Cyclical factor for period t; Pt = Promotional factor for period t; I = Irregular or random quantity. The Exponential Smoothing Model (Actual*smoothing constant) + (forecast*(1-smoothing constant)) So, exponential smoothing is like updating the latest forecast with the latest actual to get a new forecast. If you prefer, you can say exponential smoothing generates a new forecast based on a weighted average of the previous forecast and the latest actual.
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However, when there is a substantial trend or a significant seasonal pattern in the data, the inherent forecast lag in this type of model may give unacceptable
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This note was uploaded on 06/24/2008 for the course SCM 345 taught by Professor Maltz during the Spring '08 term at ASU.

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Midterm 1 SG - Trade-offs in SC Rpacket Integrated SC...

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