Forecasting lecture MBA AAST OPERATION M

forecasting operation management

1. Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Forecast Forecast – 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 an important element in making informed decisions Instructor Slides 3- 2

3. Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services Forecasts affect decisions and activities throughout an organization

4. Elements of a Good Forecast The forecast should be timely should be accurate should be reliable should be expressed in meaningful units should be in writing technique should be simple to understand and use should be cost effective Instructor Slides 3- 4

5. Forecasting Approaches Qualitative Forecasting Qualitative techniques permit the inclusion of soft information such as: Human factors Personal opinions Hunches These factors are difficult, or impossible, to quantify Quantitative Forecasting Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast These techniques rely on hard data

6. Forecasting Techniques Judgmental Forecasts (Qualitative) Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts. Time-Series Forecasts (Quantitative) Forecasts that project patterns identified in recent time-series observations. Associative Model (Quantitative) Forecasting technique that uses explanatory variables to predict future demand.

7. Judgmental Forecasts Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts Salesforce opinions Executive opinions Consumer surveys Delphi method

8. Time-Series Forecasts Forecasts that project patterns identified in recent time-series observations Time-series - a time-ordered sequence of observations taken at regular time intervals Assume that future values of the time-series can be estimated from past values of the time-series Instructor Slides 3- 8

9. Time-Series Behaviors Trend Seasonality Cycles Irregular variations Random variation Instructor Slides 3- 9

10. Trends and Seasonality Trend A long-term upward or downward movement in data Population shifts Changing income Seasonality Short-term, fairly regular variations related to the calendar or time of day Restaurants, service call centers, and theaters all experience seasonal demand

11. Cycles and Variations Cycle Wavelike variations lasting more than one year These are often related to a variety of economic, political, or even agricultural conditions Random Variation Residual variation that remains after all other behaviors have been accounted for Irregular variation Due to unusual circumstances that do not reflect typical behavior Labor strike Weather event

12. Time-Series Behaviors Instructor Slides 3- 12

13. Time-Series Forecasting - Naïve Forecast Naïve Forecast Uses a single previous value of a time series as the basis for a forecast The forecast for a time period is equal to the previous time period’s value Can be used with a stable time series seasonal variations trend Instructor Slides 3- 13

14. 3- 14 Forecast for any period = previous period’s actual value F t = A t-1 F: forecast A: Actual t: time period Naïve Forecasts

15. 3- 15 Naïve Forecast Example

16. 3- 16 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 Naïve Forecasts

17. 3- 17 Uses for Naïve Forecasts

18. Time-Series Forecasting - Averaging These Techniques work best when a series tends to vary about an average Averaging techniques smooth variations in the data They can handle step changes or gradual changes in the level of a series Techniques Moving average Weighted moving average Exponential smoothing Instructor Slides 3- 18

19. Technique that averages a number of the most recent actual values in generating a forecast Moving Average Instructor Slides 3- 19

20. Moving Average As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then re-computing the average The number of data points included in the average determines the model’s sensitivity Fewer data points used-- more responsive More data points used-- less responsive Instructor Slides 3- 20

21. Week Sales (actual) Sales (forecast) Error t A F = MA3 A - F 1 20 -   2 25 -   3 15 -   4 30 20 10 5 27 23.3333 3.66667 6   24   Moving Average Example

22. 3- 22 Simple Moving Average Figure 3-4 Revised Actual Forecast (MA3) Forecast (MA5) Responsiveness vs. Stability Smaller m, responsiveness , stability  Larger m, responsiveness , stability  Must maintain stability when fluctuations are high.

23. The most recent values in a time series are given more weight in computing a forecast The choice of weights, w , is somewhat arbitrary and involves some trial and error Weighted Moving Average Instructor Slides 3- 23

24. 3- 24 Weighted Moving Average Example

25. A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error Exponential Smoothing Instructor Slides 3- 25

26. 3- 26 Exponential Smoothing Exponential Smoothing method based on previous forecast plus a percentage of the previous forecast error. A-F is the error term,  is the % feedback F t = F t-1 +  (A t-1 - F t-1 )

27. 3- 27 Example 3 - Exponential Smoothing

28. 3- 28 Picking a Smoothing Constant   .1  .4 Actual

29. Associative Forecasting Techniques Associative techniques are based on the development of an equation that summarizes the effects of predictor variables Predictor variables - variables that can be used to predict values of the variable of interest Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms Instructor Slides 3- 29

30. Simple Linear Regression Regression - a technique for fitting a line to a set of data points Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion ) Instructor Slides 3- 30

31. Simple Linear Regression Instructor Slides 3- 31

32. Backup Instructor Slides 4- 32

33. Simple Linear Regression Assumptions Variations around the line are random Deviations around the average value (the line) should be normally distributed Predictions are made only within the range of observed values Instructor Slides 3- 33

34. Least Squares Line Instructor Slides 3- 34

35. Forecast Accuracy and Control Forecasters want to minimize forecast errors It is nearly impossible to correctly forecast real-world variable values on a regular basis So, it is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs Forecast accuracy should be an important forecasting technique selection criterion Error = Actual – Forecast If errors fall beyond acceptable bounds, corrective action may be necessary Instructor Slides 3- 35

36. Sources of forecast errors The model may be inadequate. Irregular variation may be occur. The forecasting technique may be used incorrectly or the results misinterpreted. There are always random variation in the data.

37. Monitoring the Forecast Tracking forecast errors and analyzing them can provide useful insight into whether forecasts are performing satisfactorily Sources of forecast errors The model may be inadequate Irregular variations may have occurred The forecasting technique has been incorrectly applied Random variation Control charts are useful for identifying the presence of non-random error in forecasts Tracking signals can be used to detect forecast bias Instructor Slides 3- 37

38. Choosing a Forecasting Technique Factors to consider Cost Accuracy Availability of historical data Availability of forecasting software Time needed to gather and analyze data and prepare a forecast Forecast horizon Instructor Slides 3- 38

39. Thank You Instructor Slides 4- 39