Chapter 4-Forecasting Operations management
- 1. 4 - 1 © 2014 Pearson Education, Inc. Forecasting PowerPoint presentation to accompany Heizer and Render Operations Management, Eleventh Edition Principles of Operations Management, Ninth Edition PowerPoint slides by Jeff Heyl 4 © 2014 Pearson Education, Inc.
- 2. 4 - 2 © 2014 Pearson Education, Inc. What is Forecasting? ► Process of predicting a future event ► Underlying basis of all business decisions ► Production ► Inventory ► Personnel ► Facilities ??
- 3. 4 - 3 © 2014 Pearson Education, Inc. 1. Short-range forecast ► Up to 1 year, generally less than 3 months ► Purchasing, job scheduling, workforce levels, job assignments, production levels 2. Medium-range forecast ► 3 months to 3 years ► Sales and production planning, budgeting 3. Long-range forecast ► 3+ years ► New product planning, facility location, research and development Forecasting Time Horizons
- 4. 4 - 4 © 2014 Pearson Education, Inc. Types of Forecasts 1. Economic forecasts ► Address business cycle – inflation rate, money supply, housing starts, etc. 2. Technological forecasts ► Predict rate of technological progress ► Impacts development of new products 3. Demand forecasts ► Predict sales of existing products and services
- 5. 4 - 5 © 2014 Pearson Education, Inc. Seven Steps in Forecasting 1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model(s) 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement results
- 6. 4 - 6 © 2014 Pearson Education, Inc. Forecasting Approaches ► Used when situation is vague and little data exist ► New products ► New technology ► Involves intuition, experience ► e.g., forecasting sales on Internet Qualitative Methods
- 7. 4 - 7 © 2014 Pearson Education, Inc. Forecasting Approaches ► Used when situation is ‘stable’ and historical data exist ► Existing products ► Current technology ► Involves mathematical techniques ► e.g., forecasting sales of color televisions Quantitative Methods
- 8. 4 - 8 © 2014 Pearson Education, Inc. Overview of Qualitative Methods 1. Jury of executive opinion ► Pool opinions of high-level experts, sometimes augment by statistical models 2. Delphi method ► Panel of experts, queried iteratively
- 9. 4 - 9 © 2014 Pearson Education, Inc. Overview of Qualitative Methods 3. Sales force composite ► Estimates from individual salespersons are reviewed for reasonableness, then aggregated 4. Market Survey ► Ask the customer
- 10. 4 - 10 © 2014 Pearson Education, Inc. ► Involves small group of high-level experts and managers ► Group estimates demand by working together ► Combines managerial experience with statistical models ► Relatively quick ► ‘Group-think’ disadvantage Jury of Executive Opinion
- 11. 4 - 11 © 2014 Pearson Education, Inc. Delphi Method ► Iterative group process, continues until consensus is reached ► 3 types of participants ► Decision makers ► Staff ► Respondents Staff (Administering survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments)
- 12. 4 - 12 © 2014 Pearson Education, Inc. Sales Force Composite ► Each salesperson projects his or her sales ► Combined at district and national levels ► Sales reps know customers’ wants ► May be overly optimistic
- 13. 4 - 13 © 2014 Pearson Education, Inc. Market Survey ► Ask customers about purchasing plans ► Useful for demand and product design and planning ► What consumers say, and what they actually do may be different ► May be overly optimistic
- 14. 4 - 14 © 2014 Pearson Education, Inc. Overview of Quantitative Approaches 1. Moving averages 2. Exponential smoothing 3. Linear regression
- 15. 4 - 15 © 2014 Pearson Education, Inc. ► Set of evenly spaced numerical data ► Obtained by observing response variable at regular time periods ► Forecast based only on past values, no other variables important ► Assumes that factors influencing past and present will continue influence in future Time-Series Forecasting
- 16. 4 - 16 © 2014 Pearson Education, Inc. ► MA is a series of arithmetic means ► Used if little or no trend ► Used often for smoothing ► Provides overall impression of data over time Moving Average Method Moving average = demand in previous n periods å n
- 17. 4 - 17 © 2014 Pearson Education, Inc. ► Used when some trend might be present ► Older data usually less important ► Weights based on experience and intuition Weighted Moving Average = Weight for period n ( ) Demand in period n ( ) ( ) å Weights å Weighted moving average
- 18. 4 - 18 © 2014 Pearson Education, Inc. ► Form of weighted moving average ► Weights decline exponentially ► Most recent data weighted most ► Requires smoothing constant () ► Ranges from 0 to 1 ► Subjectively chosen ► Involves little record keeping of past data Exponential Smoothing
- 19. 4 - 19 © 2014 Pearson Education, Inc. Exponential Smoothing New forecast = Last period’s forecast + (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + (At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous period’s forecast = smoothing (or weighting) constant (0 ≤ ≤ 1) At – 1 = previous period’s actual demand
- 20. 4 - 20 © 2014 Pearson Education, Inc. Choosing The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand – Forecast value = At – Ft
- 21. 4 - 21 © 2014 Pearson Education, Inc. Least Squares Method Figure 4.4 Deviation1 (error) Deviation5 Deviation7 Deviation2 Deviation6 Deviation4 Deviation3 Actual observation (y-value) Trend line, y = a + bx ^ Time period Values of Dependent Variable (y-values) | | | | | | | 1 2 3 4 5 6 7 Least squares method minimizes the sum of the squared errors (deviations)
- 22. 4 - 22 © 2014 Pearson Education, Inc. Least Squares Method Equations to calculate the regression variables ŷ = a+bx b = xy - nxy å x2 - nx2 å a = y -bx
- 23. 4 - 23 © 2014 Pearson Education, Inc. Least Squares Requirements 1. We always plot the data to insure a linear relationship 2. We do not predict time periods far beyond the database 3. Deviations around the least squares line are assumed to be random
- 24. 4 - 24 © 2014 Pearson Education, Inc. Associative Forecasting Used when changes in one or more independent variables can be used to predict the changes in the dependent variable Most common technique is linear regression analysis We apply this technique just as we did in the time-series example
- 25. 4 - 25 © 2014 Pearson Education, Inc. Associative Forecasting Forecasting an outcome based on predictor variables using the least squares technique y = a + bx ^ where y = value of the dependent variable (in our example, sales) a = y-axis intercept b = slope of the regression line x = the independent variable ^
- 26. 4 - 26 © 2014 Pearson Education, Inc. ► How strong is the linear relationship between the variables? ► Correlation does not necessarily imply causality! ► Coefficient of correlation, r, measures degree of association ► Values range from -1 to +1 Correlation
- 27. 4 - 27 © 2014 Pearson Education, Inc. Correlation Coefficient r = n xy - x y å å å n x2 - x å ( ) 2 å é ë ê ù û ú n y2 - y å ( ) 2 å é ë ê ù û ú
- 28. 4 - 28 © 2014 Pearson Education, Inc. Correlation Coefficient y x (a) Perfect negative correlation y x (c) No correlation y x (d) Positive correlation y x (e) Perfect positive correlation y x (b) Negative correlation High Moderate Low Correlation coefficient values High Moderate Low | | | | | | | | | –1.0 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1.0 Figure 4.10
- 29. 4 - 29 © 2014 Pearson Education, Inc. Correlation Coefficient r = (6)(51.5) – (18)(15.0) (6)(80) – (18)2 é ë ù û (16)(39.5) – (15.0)2 é ë ù û y x x2 xy y2 2.0 1 1 2.0 4.0 3.0 3 9 9.0 9.0 2.5 4 16 10.0 6.25 2.0 2 4 4.0 4.0 2.0 1 1 2.0 4.0 3.5 7 49 24.5 12.25 Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5 Σy2 = 39.5 = 309-270 (156)(12) = 39 1,872 = 39 43.3 = .901
- 30. 4 - 30 © 2014 Pearson Education, Inc. ► Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x ► Values range from 0 to 1 ► Easy to interpret Correlation For the Nodel Construction example: r = .901 r2 = .81