Eight Steps to Forecasting • Determine the use of the forecast □ What objective are we trying to obtain? • Select the items to be forecast • Determine the time horizon of the forecast □ Short time horizon – 1 to 30 days □ Medium time horizon – 1 to 12 months □ Long time horizon – more than 1 year • Select the forecasting model(s) |Description |Qualitative Approach |Quantitative Approach
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considering demand from all sources Customer orders Service part demands Forecasts Forecasts are consumed by orders ?? All supply chain partners MUST understand demand!! Start with understanding the customer and the 7 rights Product Quantity Time Place Condition Price Information **We are going to replace forecasts with knowledge wherever possible** Forecast= Guess of the timing and quantity of customer demand Goal of forecast = is to make forecast accurate and less bias Plan= How
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61% 4.63% 5.77 24.64% ARIMA(1‚0‚0)(2‚0‚0) 7.23% 5.11 8.51% 8.02% 6.56 28.01% *Mean of NHS for the historical period is 60.08 and for the holdout period is 23.42 The best model should be the one with the smallest error. Among these three time-series models‚ the decomposition with exponential smoothing trend has the smallest MAPE and RMSE for both historical period and holdout period. Therefore‚ we use this model and the data from January 2001 to October 2011 to perform the ex-ante point and
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Nicole-line breaks mean new slide important questions Forecasts are needed to predict demand all different teams within the company need the forecast different users have different time requirements and detail reqts you might have to collect more data if you don’t have enough cost depends on the scope of the project need to engage the users‚ so have to provide a feedback system The top chart appears to be a ore difficult to forecast but they just narrowed the y axiz 2nd chart down
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PowerPoint Slides Prepared by Robert F. Brooker‚ Ph.D. Copyright 2007 by Oxford University Press‚ Inc. Slide 2 PowerPoint Slides Prepared by Robert F. Brooker‚ Ph.D. Copyright 2007 by Oxford University Press‚ Inc. Slide 3 Time-Series Analysis • Secular Trend – Long-Run Increase or Decrease in Data • Cyclical Fluctuations – Long-Run Cycles of Expansion and Contraction • Seasonal Variation – Regularly Occurring Fluctuations • Irregular or Random Influences PowerPoint
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Chapter 5: Problems 1‚ 5‚ 6‚ and 9 1) A)$66‚000 B) S will then become $754.29 C) We can find K by using the regression line method and time series data or cross sectional data. D) The potential weakness for this model is variable t because it is unknown. 5) 2000 | 800 | x x x x 2001 925 x x 800 800 2002 900 x x 913 838 2003 1025 x 875 901 857 2004 1150 x 950 1013 907 2005 1160 960 1025 1136 980 2006 1200 1032 1112 1158 1034 2007 1150 1087 1170 1196 1084 2008 1270
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Excel Problems 1. The following time series represent the total population of the United States‚ in thousands‚ over the last 12 years. |Year |Population (in 000‚s) | |1991 |253‚493 | |1992 |256‚894 | |1993 |260‚255 | |1994 |263‚436 | |1995 |266‚557 | |1996 |269‚667 | |1997 |272‚912
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applies to your work or personal life. Prepare a project proposal in which you: • Describe the organization‚ the inventory problem it faces‚ and the expected benefits that are motivating the organization to implement a solution. • Convert time series data collected in Week Two to seasonal indices. You may choose to use the University of Phoenix Material: Summer Historical Inventory Data or University of Phoenix Material: Winter Historical Inventory Data if the data you collected is insufficient
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Time Series behaviour of BOT in India: Evidence from Co integration Analysis and Error Correction Model xxxxxxxxxxxxxxxx Assistant Professor‚ Department of Business Administration‚ Xxxxxxxxxx West Bengal University of technology Kolkata‚ India Tel: +91-9231058348 E-mail: partha.s.sarkar@gmail.com Abstract India‚ a developing economy contains trade deficit from its very inception. The main objective of the study is to portray some characteristics of India’s trade in pre liberalization
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individuals is taken. This could be from panels of experts‚ surveys‚ management‚ or any other regarded and reputable source of knowledge. The time series forecasting method is part of the quantitative forecasting method in which the analysis of historical data; usually measured within successive intervals or over successive periods is used. The time series forecasting method makes use of assumptions of past patterns observable within data‚ data points from which data is derived from for the forecasting
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