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Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
Forecasting
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Forecasting

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  • 23
  • Transcript

    • 1. Chapter Two Forecasting
    • 2. Planning Forecast Customer Production Process Finished Goods Inputs
    • 3. Forecasting
      • Marketin g : forecasts sales for new and
      • existing products.
      • Productio n : uses sales forecasts to plan
      • production and operations; sometimes
      • involved in generating sales forecasts.
    • 4. Characteristics of Forecasts
      • They are usually wrong
      • A good forecast is usually more than a single number
      • Aggregate forecast are more accurate
      • The longer the forecasting horizon, the less accurate the forecasts will be
      • Forecasts should not be used to the exclusion of known information
    • 5. Forecasting Horizon
      • Short term
        • (inventory management, production plans..)
      • Intermediate term
        • (sales patterns for product families..)
      • Long term
        • (long term planning of capacity needs)
    • 6. Forecasting Techniques Judgmental Models Time Series Methods Causal Methods Forecasting Technique Delphi Method Moving Average Exponential Smoothing Regression Analysis Seasonality Models
    • 7. Types of forecasting Methods
      • Subjective methods
        • Sales force composites
        • Customer survey
        • Jury of executive opinion
        • The Delphi method
      • Objective methods
        • Causal methods
        • Time series methods
    • 8. Qualitative Methods
      • Don’t have data
      • Don’t have time to develop a forecast
      • Often used in practice
      • “Close enough”
      • Depend on expert opinions
      • Market surveys
      • More appropriate for long term forecasts
    • 9. Delphi Technique
      • A method to obtain a consensus forecast by using opinions from a group of “experts”
        • expert opinion
        • consulting salespersons
        • consulting consumers
    • 10. Causal Methods
      • Causal methods use data from sources other than the series being predicted.
      • If Y is the phenomenon to be forecast and X 1 , X 2 , . .., X n are the n variables we believe to be related to Y, then a causal model is one in which the forecast for Y is some function of these variables:
      • Y = f (X 1 , X 2 , . .., X n )
      • Econometric models are causal models in which the relationship between Y and (X 1 , X 2 , . .., X n ) is linear.
      • That is
      • Y = a o + a 1 X 1 + a 2 X 2 + … a n X n
      • for some constants a 1 , a 2 , . . . , a n
    • 11. Forecasting Steps for Quantitative Methods
      • Collect data
      • Reduce/clean data
      • Build and evaluate model(s)
      • Forecast (model extrapolation)
      • Track the forecast
    • 12. Identify the correct pattern
        • Collect data. Look for possible cause/effect relationships
        • Determine which form can be used to generate the pattern
        • Determine specific values of the parameters
    • 13.  
    • 14. Building Models
      • Plot data over time. (remove outliers & get right scale).
      • Using part of the data, estimate model parameters.
      • Forecast the rest of the data with the model.
      • Evaluate accuracy of the model.
      • Use judgment to modify.
      • Keep track of model accuracy over time (redo, if needed).
    • 15. Forecasting Stationary Series
    • 16. Time series Analysis
      • Patterns that arise most often
      • Trend
      • Seasonality
      • Cycles
      • Randomness
    • 17. Time Series Patterns Fig. 2-2
    • 18. Notation
      • : Observed value of the demand during period t
      • time series we would like to predict
      • forecast made for period t in period t-1
      • forecast made at the end of t-1 after having
      • observed , , …
    • 19. Time Series Forecast
      • For some set of weights
    • 20. Evaluating forecasts
      • Forecast error in period t
      • For multiple-step-ahead
    • 21. Evaluating Forecasts
      • Mean Absolute Deviation
      • Mean Square Error
    • 22. Forecast Errors Over Time Fig. 2-3
    • 23. TIME SERIES METHODS Stationary Series
      • A stationary time series is represented by a
      • constant plus a random fluctuation:
      • D t = µ+ ε t
      • where µ is an unknown constant corresponding to the mean of the series and ε t is a random error with mean 0 and variance σ 2 .
      • The methods described for stationary series are:
        • Moving Averages
        • Exponential Smoothing
    • 24. Methods of Forecasting Stationary Series
      • Moving Averages
      • Exponential Smoothing
    • 25. Moving Average
    • 26. 90 Oct 110 Sep 130 Aug 75 Jul 50 Jun 110 May 75 Apr 100 Mar 90 Feb 120 Jan Deliveries Month
    • 27. 94 110 92 90 83 91 MA(6) 105 85 78 78 95 88 103 MA(3) 90 Oct 110 Sep 130 Aug 75 Jul 50 Jun 110 May 75 Apr 100 Mar 90 Feb 120 Jan Deliveries Month
    • 28.  
    • 29. 17 20 24 12 19 22 15 18 22 11 13 16 20 10 11 14 18 9 9 12 16 8 7 10 14 7 8 12 6 6 10 5 4 8 4 6 3 4 2 2 1 MA(6) MA(3) Deliveries Month
    • 30. Moving-Average Forecasts Lag Behind a Trend Fig. 2-4
    • 31. EXPONENTIAL SMOOTHING
      • Current forecast is a weighted average of the last forecast and the current value of demand
      • New forecast = α (current observation of demand)
      • + (1- α ) (last forecast)
    • 32. Exponential Smoothing F t = F t-1 – (fraction of the observed forecast error in t-1) If we forecast high in period t-1  error is positive  adjustment to decrease current forecast If we forecast low in period t-1  error is negative  adjustment to increase current forecast
    • 33.  
    • 34. Example 220 190 8 211 305 7 203 285 6 201 225 5 202 186 4 205 175 3 200 250 2 200 200 1 Forecast Failures Quarter
    • 35.  
    • 36. Weights in Exponential Smoothing Fig. 2-5
    • 37. Exponential Smoothing for Different Values of Alpha Fig. 2-6
    • 38. Smaller values of α produce more stable forecasts, whereas larger values of α will produce forecasts which react more quickly to changes in the demand pattern.
    • 39. Comparison
    • 40. Similarities & Differences
      • Stationary series
      • Single parameter
      • Lag behind a trend
      • When α =2/(N+1)
      • Same distribution of forecast error
      • ES weighted average of all past data
      • MA only last N periods
      • MA : save past N data
      • ES : only last forecast
    • 41. Multiple-Step-Ahead Forecasts
      • Same as one-step-ahead-forecast
    • 42. Trend Based Methods
      • Regression Analysis
      • Double Exponential Smoothing
    • 43. Double Exponential Smoothing
      • Intercept at time t
      • and slope at time t

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