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Forecasting

3. Forecasting. FORECAST : A statement about the future value of a variable of interest such as demand. Forecasting is used to make informed decisions. Long-range Short-range. Forecasts. Forecasts affect decisions and activities throughout an organization Accounting, finance

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Forecasting

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  1. 3 Forecasting

  2. FORECAST: • A statement about the future value of a variable of interest such as demand. • Forecasting is used to make informed decisions. • Long-range • Short-range

  3. Forecasts • Forecasts affect decisions and activities throughout an organization • Accounting, finance • Human resources • Marketing • MIS • Operations • Product / service design

  4. Uses of Forecasts

  5. Features of Forecasts I see that you willget an A this semester. • Assumes causal systempast ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate forgroups vs. individuals • Forecast accuracy decreases as time horizon increases

  6. Elements of a Good Forecast Timely Accurate Reliable Easy to use Written Meaningful

  7. Steps in the Forecasting Process “The forecast” Step 6 Monitor the forecast Step 5 Make the forecast Step 4 Obtain, clean and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast

  8. Types of Forecasts • Judgmental - uses subjective inputs • Time series - uses historical data assuming the future will be like the past • Associative models - uses explanatory variables to predict the future

  9. Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method • Opinions of managers and staff • Achieves a consensus forecast

  10. Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance

  11. Forecast Variations Figure 3.1 Irregularvariation Trend Cycles 90 89 88 Seasonal variations

  12. Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.

  13. Naïve Forecasts • Simple to use • Virtually no cost • Quick and easy to prepare • Easily understandable • Cannot provide high accuracy

  14. Uses for Naïve Forecasts • Stable time series data • F(t) = A(t-1) • Seasonal variations • F(t) = A(t-n)

  15. Techniques for Averaging • Moving average • Weighted moving average • Exponential smoothing

  16. Moving Averages At-n+ … At-2 + At-1 Ft = MAn= n wnAt-n+ … wn-1At-2 + w1At-1 Ft = WMAn= • Moving average – A technique that averages a number of recent actual values, updated as new values become available. • Weighted moving average – More recent values in a series are given more weight in computing the forecast.

  17. Simple Moving Average At-n+ … At-2 + At-1 Ft = MAn= n Actual MA5 MA3

  18. Exponential Smoothing • Premise--The most recent observations might have the highest predictive value. • Therefore, we should give more weight to the more recent time periods when forecasting. Ft = Ft-1 + (At-1 - Ft-1)

  19. Exponential Smoothing • Weighted averaging method based on previous forecast plus a percentage of the forecast error • A-F is the error term,  is the % feedback Ft = Ft-1 + (At-1 - Ft-1)

  20. Example 3 - Exponential Smoothing

  21. Picking a Smoothing Constant Actual .4  .1

  22. Common Nonlinear Trends Parabolic Exponential Growth Figure 3.5

  23. Linear Trend Equation Ft Ft = a + bt 0 1 2 3 4 5 t • Ft = Forecast for period t • t = Specified number of time periods • a = Value of Ft at t = 0 • b = Slope of the line

  24. Calculating a and b n (ty) - t y    b = 2 2 n t - ( t)   y - b t   a = n

  25. Linear Trend Equation Example

  26. Linear Trend Calculation 5 (2499) - 15(812) 12495 - 12180 = 6.3 b = = 5(55) - 225 275 - 225 812 - 6.3(15) a = = 143.5 5 y = 143.5 + 6.3t

  27. Techniques for Seasonality • Seasonal variations • Regularly repeating movements in series values that can be tied to recurring events. • Seasonal relative • Percentage of average or trend • Centered moving average • A moving average positioned at the center of the data that were used to compute it.

  28. Associative Forecasting • Predictor variables - used to predict values of variable interest • Regression - technique for fitting a line to a set of points • Least squares line - minimizes sum of squared deviations around the line

  29. Linear Model Seems Reasonable Computedrelationship A straight line is fitted to a set of sample points.

  30. Linear Regression Assumptions • Variations around the line are random • Deviations around the line normally distributed • Predictions are being made only within the range of observed values • For best results: • Always plot the data to verify linearity • Check for data being time-dependent • Small correlation may imply that other variables are important

  31. Forecast Accuracy • Error - difference between actual value and predicted value • Mean Absolute Deviation (MAD) • Average absolute error • Mean Squared Error (MSE) • Average of squared error • Mean Absolute Percent Error (MAPE) • Average absolute percent error

  32. MAD, MSE, and MAPE 2 ( Actual  forecast)  MSE = n - 1  ( Actual forecast / Actual*100) MAPE = n   Actual forecast MAD = n

  33. MAD, MSE and MAPE • MAD • Easy to compute • Weights errors linearly • MSE • Squares error • More weight to large errors • MAPE • Puts errors in perspective

  34. Example 10

  35. Controlling the Forecast • Control chart • A visual tool for monitoring forecast errors • Used to detect non-randomness in errors • Forecasting errors are in control if • All errors are within the control limits • No patterns, such as trends or cycles, are present

  36. Sources of Forecast errors • Model may be inadequate • Irregular variations • Incorrect use of forecasting technique

  37. Tracking Signal  (Actual - forecast) Tracking signal = MAD • Tracking signal • Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values.

  38. Choosing a Forecasting Technique • No single technique works in every situation • Two most important factors • Cost • Accuracy • Other factors include the availability of: • Historical data • Computers • Time needed to gather and analyze the data • Forecast horizon

  39. Operations Strategy • Forecasts are the basis for many decisions • Work to improve short-term forecasts • Accurate short-term forecasts improve • Profits • Lower inventory levels • Reduce inventory shortages • Improve customer service levels • Enhance forecasting credibility

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