Technological Forecasting – Learning Objectives

1. Technological Forecasting – Learning Objectives • List the elements of a good forecast. • Outline the steps in the forecasting process. • Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each. • Compare and contrast qualitative and quantitative approaches to forecasting.

2. Learning Objectives • Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems. • Describe two measures of forecast accuracy. • Describe two ways of evaluating and controlling forecasts. • Identify the major factors to consider when choosing a forecasting technique.

3. 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

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

5. Uses of Forecasts

6. 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

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

8. 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

9. 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

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

11. 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

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

13. 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.

14. Naïve Forecasts • Simple to use • Virtually no cost • Quick and easy to prepare • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy • Can be a standard for accuracy

15. Uses for Naïve Forecasts • Stable time series data • F(t) = A(t-1) • Seasonal variations • F(t) = A(t-n) • Data with trends • F(t) = A(t-1) + (A(t-1) – A(t-2))

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

17. Moving Averages At-n+ … At-2 + At-1 Ft = MAn= n 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. • Ft = WMAn= wnAt-n + … wn-1At-2 + w1At-1 • or: Ft+1 = wtAt & wt = 1.0 where t = (1,n)

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

19. 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)

20. 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)

21. Example 3 - Exponential Smoothing

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

23. Common Nonlinear Trends Parabolic Exponential Growth Figure 3.5

24. 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

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

26. Linear Trend Equation Example

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

28. 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.

29. 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

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

31. 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

32. 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

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

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

35. Example 10

36. 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

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

38. 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.

39. 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

40. 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

41. Supply Chain Forecasts • Sharing forecasts with supply can • Improve forecast quality in the supply chain • Lower costs • Shorter lead times • Gazing at the Crystal Ball (reading in text)

42. Exponential Smoothing

43. Linear Trend Equation

44. Simple Linear Regression