Production and Operations Management

1. Production and Operations Management Forecasting

2. 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.

3. 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.

4. Introduction • A statement about the future value of a variable of interest. • We make forecasts about such things as weather, demand, and resource availability • Forecasts are important to making informed decisions • Forecasting is used to make informed decisions. • Long-range • Short-range

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

6. Introduction -- Uses of Forecasts

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

8. Two Important Aspects of Forecasts • Expected level of demand • The level of demand may be a function of some structural variation such as trend or seasonal variation • Accuracy • Related to the potential size of forecast error

9. Forecast Uses • Plan the system • Generally involves long-range plans related to: • Types of products and services to offer • Facility and equipment levels • Facility location • Plan the use of the system • Generally involves short- and medium-range plans related to: • Inventory management • Workforce levels • Purchasing • Production • Budgeting • Scheduling

10. Features Common to All Forecasts • Techniques assume some underlying causal system that existed in the past will persist into the future • Forecasts are not perfect • Forecasts for groups of items are more accurate than those for individual items • Forecast accuracy decreases as the forecasting horizon increases

11. Forecasts Are Not Perfect • Forecasts are not perfect: • Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for.

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

13. “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 Steps in the Forecasting Process

14. 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

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

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

17. Time Series -- Forecast Variations Irregularvariation Trend Cycles 90 89 88 Seasonal variations

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

19. Time Series -- Naive 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

20. Time Series -- Uses of Naive 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))

21. Time Series -- Techniques for Averaging • Moving average • Weighted moving average • Exponential smoothing

22. At-n+ … At-2 + At-1 Ft = MAn= n wnAt-n+ … wn-1At-2 + w1At-1 Ft = WMAn= n Time Series -- Moving Averages • 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.

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

24. Time Series -- Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1) • 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.

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

26. Time Series -- Example 3: Exponential Smoothing

27. Actual .4  .1 Time Series -- Picking a Smoothing Constant

28. Parabolic Exponential Growth Time Series -- Common Nonlinear Trends

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

30. n (ty) - t y    b = 2 2 n t - ( t)   y - b t   a = n Time Series – Calculating a and b

31. Time Series -- Linear Trend Equation Example

32. 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 Time Series -- Linear Trend Calculation

33. Time Series -- 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

34. Time Series -- 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

35. Computedrelationship Time Series -- Linear Model Seems Reasonable A straight line is fitted to a set of sample points.

36. Time Series -- 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

37. 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

38. 2 ( Actual  forecast)  MSE = n - 1  ( Actual forecast / Actual*100) MAPE = n Forecast Accuracy -- MAD, MSE, and MAPE   Actual forecast MAD = n

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

40. Forecast Accuracy -- Example

41. 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

42. Controlling the Forecast -- Sources of Forecast Errors • Model may be inadequate • Irregular variations • Incorrect use of forecasting technique

43.  (Actual - forecast) Tracking signal = MAD Controlling the Forecast -- Tracking Signal • Tracking signal • Ratio of cumulative error to MAD Bias: Persistent tendency for forecasts to be greater or less than actual values.

44. 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

45. Forecasting -- 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

46. Supply Chain Forecasts • Sharing forecasts with supply can • Improve forecast quality in the supply chain • Lower costs • Shorter lead times