Es 08 forecasting topic final

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Forecasting

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Es 08 forecasting topic final

  1. 1. Forecasting
  2. 2. ForecastingProcess of predicting a future eventUnderlying basis ofall business decisions Sales will be $200 Production Million! Inventory Personnel Facilities
  3. 3. Types of Forecasts by Time Horizon Short-range forecast  Up to 1 year; usually less than 3 months  Job scheduling, worker assignments Medium-range forecast  3 months to 3 years  Sales & production planning, budgeting Long-range forecast  3+ years  New product planning, facility location
  4. 4. Short-term vs. Longer-term Forecasting• Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes.• Short-term forecasting usually employs different methodologies than longer-term forecasting• Short-term forecasts tend to be more accurate than longer-term forecasts.
  5. 5. Types of Forecasts• Economic forecasts – Address business cycle, e.g., inflation rate, money supply etc.• Technological forecasts – Predict rate of technological progress – Predict acceptance of new product• Demand forecasts – Predict sales of existing product
  6. 6. Seven Steps in Forecasting• Determine the use of the forecast• Select the items to be forecasted• Determine the time horizon of the forecast• Select the forecasting model(s)• Gather the data• Make the forecast• Validate and implement results
  7. 7. Realities of Forecasting• Forecasts are seldom perfect• Most forecasting methods assume that there is some underlying stability in the system• Both product family and aggregated product forecasts are more accurate than individual product forecasts
  8. 8. Forecasting ApproachesQualitative Methods Quantitative Methods Used when situation Used when situation is vague & little data is ‘stable’ & historical exist data exist New products Existing products New technology Current technology Involves intuition, Involves experience mathematical e.g., forecasting sales on techniques Internet e.g., forecasting sales of color televisions
  9. 9. Overview of Qualitative Methods Jury of executive opinion  Pool opinions of high-level executives, sometimes augment by statistical models Delphi method  Panel of experts, queried iteratively Sales force composite  Estimates from individual salespersons are reviewed for reasonableness, then aggregated Consumer Market Survey  Ask the customer
  10. 10. Overview of Quantitative Approaches• Naïve approach• Moving averages Time-series Models• Exponential smoothing• Trend projection• Linear regression Associative models
  11. 11. Naive ApproachAssumes demand in nextperiod is the same asdemand in most recentperiod e.g., If May sales were 48, then June sales will be 48Sometimes cost effective& efficient © 1995 Corel Corp.
  12. 12. Moving Average Method MA is a series of arithmetic means Used if little or no trend Used often for smoothing  Provides overall impression of data over time Equation Demand in Previous n Periods MA n
  13. 13. Weighted Moving Average Method• Used when trend is present – Older data usually less important• Weights based on intuition – Often lay between 0 & 1, & sum to 1.0• Equation Σ(Weight for period n) (Demand in period n) WMA = ΣWeights
  14. 14. • Forecasting• 1. The following gives the number of pints of Type A blood used at Woodlawn Hospital in the past 6 weeks:• Week of Pints Used• Aug 3 360• Sept 7 389• Sept 14 410• Sept 21 381• Sept 28 368• Oct 5 374• a. Forecast the demand for the week of October 12 using a 3 week moving average.• b. Use a 3 week weighted moving average, with weights of 0.1, 0.3 and 0.6 using 0.6 for the most recent week.
  15. 15. Disadvantages of Moving Average Methods• Increasing n makes forecast less sensitive to changes• Do not forecast trend well• Require much historical data
  16. 16. Linear Trend Projection• Used for forecasting linear trend line• Assumes relationship between response variable, Y, and time, X, is a linear function Yi a bX i• Estimated by least squares method – Minimizes sum of squared errors
  17. 17. Linear Regression Equations Equation ˆ Yi a bxi : n x i yi nx y i 1 Slope: b n 2 2 x i nx i 1 Y-Intercept: a y bx
  18. 18. X YJAN 1 9FEB 2 7MARCH 3 10APRIL 4 8MAY 5 7JUNE 6 12JULY 7 10AUG 8 11SERPT 9 12OCT 10 10NOV 11 14DEC 12 16

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