1. 13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13

2. 13 – 2 Demand Patterns HorizontalTrend SeasonalCyclical

3. 13 – 3 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Linear Regression Dependent variable Independent variable X Y Estimate of Y from regression equation Regression equation: Y = a + bX Actual value of Y Value of X used to estimate Y Deviation, or error Figure 13.2 – Linear Regression Line Relative to Actual Data

4. 13 – 4 n X 2 - ( X) 2 n XY - X Y b = a = Y-bar – b*X-bar

5. 13 – 5 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. |||||| 051015202530 Week 450 – 430 – 410 – 390 – 370 – 350 – Patient arrivals Time Series Methods Figure 13.4 – Weekly Patient Arrivals at a Medical Clinic

6. 13 – 6 Comparison of 3- and 6-Week MA Forecasts Week Patient Arrivals Actual patient arrivals 3-week moving average forecast 6-week moving average forecast

7. 13 – 7 Comparison of different alpha for Exponential Smoothing Forecasts

8. 13 – 8 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. |||||||||||||||| 0123456789101112131415 80 – 70 – 60 – 50 – 40 – 30 – Patient arrivals Week Actual blood test requests Trend-adjusted forecast Using Trend-Adjusted Exponential Smoothing Figure 13.5 – Trend-Adjusted Forecast for Medanalysis

9. 13 – 9 YearQuarterDemandStep 1. CMAStep 2. D/CMA Step 3. Index for each season Step 4. Assume Demand 2011 = 48 2008Spring 6 Summer4 Fall8 Winter6 2009Spring8 Summer6 Fall10 Winter8 2010Spring10 Summer8 Fall12 Winter10

10. 13 – 10 Comparison of Seasonal Patterns Multiplicative patternAdditive pattern

11. 13 – 11 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. CFE =  E t Measures of Forecast Error  ( E t – E ) 2 n – 1  = Et2nEt2n MSE = |Et |n|Et |n MAD = (  | E t |/ D t ) (100) n MAPE = E = CFE n

12. 13 – 12 % of area of normal probability distribution within control limits of the tracking signal Control Limit SpreadEquivalentPercentage of Area (number of MAD)Number of  within Control Limits 57.62 76.98 89.04 95.44 98.36 99.48 99.86 ± 0.80 ± 1.20 ± 1.60 ± 2.00 ± 2.40 ± 2.80 ± 3.20 ± 1.0 ± 1.5 ± 2.0 ± 2.5 ± 3.0 ± 3.5 ± 4.0 Forecast Error Ranges Forecasts stated as a single value can be less useful because they do not indicate the range of likely errors. A better approach can be to provide the manager with a forecasted value and an error range.

13. 13 – 13 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting Principles TABLE 13.2 | SOME PRINCIPLES FOR THE FORECASTING PROCESS  Better processes yield better forecasts  Demand forecasting is being done in virtually every company, either formally or informally. The challenge is to do it well—better than the competition  Better forecasts result in better customer service and lower costs, as well as better relationships with suppliers and customers  The forecast can and must make sense based on the big picture, economic outlook, market share, and so on  The best way to improve forecast accuracy is to focus on reducing forecast error  Bias is the worst kind of forecast error; strive for zero bias  Whenever possible, forecast at more aggregate levels. Forecast in detail only where necessary  Far more can be gained by people collaborating and communicating well than by using the most advanced forecasting technique or model