3. Why is demand forecasting important for effective supply chain management? * Demand forecasting is important for effective supply chain management because it provides critical‚ accurate and timely demand information. It estimates the future demand and the basis for planning and sound business decisions. Demand forecasting helps to minimize the deviation between actual demand and the forecast and having accurate demand forecasts allows a supply chain to run smoothly. 4. Explain the impact
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Cross-Functional Alignment in Supply Chain Planning: A Case Study of Sales and Operations Planning Rogelio Oliva Noel H. Watson Working Paper 07-001 Copyright © 2007‚ 2008‚ 2009 by Rogelio Oliva and Noel H. Watson Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Cross-Functional Alignment in Supply
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BOSTON COLLEGE CARROLL SCHOOL OF MANAGEMENT OPERATIONS AND STRATEGIC MANAGEMENT Capacity Management at Littlefield Technologies: DSS Manufacturing Issues During Spring 2006 Professor Field’s Version Background In early January 2006‚ Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS’s in more complex
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of the trend value of forecast for period t TAFt = Trend Adjusted Forecast for Period t made at the end of t-1) TAFt+k=Trend Adjusted Forecast for period t+k made at the end of period t Exponential Smoothing Ft+1 = aAt + (1 - a) Ft Forecasting next period= Forecast for the current period+ a fraction of the error for the current period Trend Adjusted Exponential Smoothing St = Smoothed Forecast at the end of period t Tt = Trend Estimate at the end of period t St = a1 At + (1 - a1)
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presented here: • Naive forecasting models are simple models in which it is assumed that the more recent time periods of data represent the best predictions or forecasts for future outcomes. Naive models do not take into account data trend‚ cyclical effects‚ or seasonality. For this reason‚ naive models seem to work better with data that are reported on a daily or weekly basis or in situations that show no trend or seasonality. The simplest of the naive forecasting methods is the model in which
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sales reps based on amount of product sold. Therefore‚ they would push product during the promotional period but were not able to sell as much during a non-promotional period. Lack of Forecasting: Although nearly all of the distributors had computer supported ordering systems‚ few had sophisticated forecasting systems for determining order quantities. Lack of Order Control: Barilla does not require its distributors to place minimum orders‚ or have a maximum order amount in place during its promotional
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•Soumendu Mukhopadhyay FT151034 •Vivek Anand FT153113 •Anand M FT152020 •Manzoor FT152099 •Lokesh Chandana FT152087 Case – Problems & Issues Problems : •To identify a forecasting technique so as to forecast the demand for existing products as well as new products. Issues : •Best fit model ?? •Problems associated with each forecasting model ?? •Performance of the used model ?? •Fundamental things to be accounted while looking at forecast performance ?? •Managerial issues to be addressed ?? •Quantification
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Mindy Sidwell MG-495 Midterm Exam Student’s Answer Sheet Each Multiple Choice Question is worth 3 points. Please place the letter that corresponds with your answer(a‚ b‚ c‚ d‚ or e) in the appropriate box below. 1. b 14. b 2. a 15. a 3. a 16. a 4. a 17. d 5. c 18. a 6. e 19. e 7. b 20. d 8. a 21. b 9. b 22. c 10. a 23. c 11. a 24. c 12. a 25. b 13. c . . Each
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Inventory Crisis. The uncertainty of demand made it hard for them to forecast which model to produce. And since they have variety of product because of localization‚ it is harder to manage inventory. A lead time of four to five weeks also made forecasting a difficult job and it also caused higher safety stocks. Another cause of the crisis is that the people in HP got their noses up. No one wants to talk to the other. There’s a poor communication inside the organization. If they don’t want to ride
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65055_18_ch18_p765-811.qxd 10/11/06 12:29 PM Page 808 808 Chapter 18 TABLE 18.14 Month January February March April May June July August September October November December Forecasting DEPARTMENT STORE SALES FOR THE COUNTY‚ SEPTEMBER 2002 THROUGH DECEMBER 2006 ($ MILLIONS) 2002 2003 2004 2005 2006 55.8 56.4 71.4 117.6 46.8 48.0 60.0 57.6 61.8 58.2 56.4 63.0 57.6 53.4 71.4 114.0 46.8 48.6 59.4 58.2 60.6 55.2 51.0 58.8 49.8
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