1. Chapter 9 Forecasting Copyright 2015 Health Administration Press

2. After mastering this material, students will be able to  articulate the value of a good forecast,  describe the attributes of a good forecast,  apply demand theory to forecasts, and  use basic forecasting tools appropriately. 2Copyright 2015 Health Administration Press

3. Forecasting: Using the past to predict the future.  Most business decisions rely on forecasts.  Key forecasts are – future demand for products, and – future price of inputs.  The goal is to support decision making.  “What if” forecasts can be helpful. 3Copyright 2015 Health Administration Press

4. Why forecast  quantity demanded?  input prices? 4Copyright 2015 Health Administration Press

5. Good forecasts should be  easy to understand,  transparent,  easy to modify,  accurate, and  precise. 5Copyright 2015 Health Administration Press

6. Understandability  Ideally the methods are simple – so decision makers can see flaws, – so hidden assumptions will be obvious, and – so that nobody will be “snowed.”  It’s the job of the analyst to make forecasts look simple. 6Copyright 2015 Health Administration Press

7. Transparency  Transparency is closely tied to simplicity.  The audience needs to know what you did with – the approach, – the data, – and so forth.  You want decision makers to ask hard questions. 7Copyright 2015 Health Administration Press

8. Easy Modification  How will a forecast change – if a key assumption changes? – if key data change?  Example – You forecast sales growth of 8 percent with an assumption that the economy grows 3 percent. – What happens if the economy grows 2 percent? 8Copyright 2015 Health Administration Press

9. Accuracy and Precision  Since all forecasts err, there’s a tradeoff. – Accuracy: Forecast interval includes true value – Precision: Forecast interval is very small 9Copyright 2015 Health Administration Press

10. Accuracy and Precision  Example of an accurate, imprecise forecast: – Sales will increase by 0–10 percent. – If sales increase 1 percent, the forecast is accurate.  Example of a precise, inaccurate forecast: – Sales will increase 2 percent. – If sales increase 1 percent, the forecast is inaccurate. 10Copyright 2015 Health Administration Press

11. Many Ways to Forecast  Qualitative – Delphi – Focus groups – Expert judgment 11Copyright 2015 Health Administration Press  Quantitative – Simple extrapolation – Moving averages – Statistical techniques

12. Qualitative Forecast: Delphi  Delphi – Forecast is developed by a panel of experts. – Experts anonymously answer questions. – Responses are fed back to panel. – Members may then change their original responses. Very time consuming and expensive New groupware makes this much easier 12Copyright 2015 Health Administration Press

13. Qualitative Forecast: Market Research  Market research – Panels – Questionnaires – Surveys 13Copyright 2015 Health Administration Press

14. Qualitative Forecast: Expert Judgment  Expert judgment by – management, – sales force, and – other knowledgeable persons. 14Copyright 2015 Health Administration Press

15. Quantitative Forecast: Simple Extrapolation  Simple extrapolation – Percentage adjustment – Moving average 15Copyright 2015 Health Administration Press

16. Quantitative Forecasts: Model-Based Methods  Model-based methods – Trend and seasonal decomposition – Time series methods (e.g., ARIMA Models) – Multiple regression using leading indicators 16Copyright 2015 Health Administration Press

17. Simple Extrapolation  Example: Sales will increase by 2 percent – What’s good about this forecast? – What’s bad about it? 17Copyright 2015 Health Administration Press

18. Moving Average  Example: Sales will equal (Q t-1 + Q t-2 ) / 2 – What’s good about this forecast? – What’s bad about it? 18Copyright 2015 Health Administration Press

19. Weighted Average  Example: Sales will equal (α t-1 Q t-1 ) + (α t-2 Q t-2 ) – What’s good about this forecast? – What’s bad about it? 19Copyright 2015 Health Administration Press

20. Regression-Based Forecast  Example: Sales will equal β 1 X + β 2 Y + β 3 Z (weights come from statistical analysis) – What’s good about this forecast? – What’s bad about it? 20Copyright 2015 Health Administration Press

21. A Moving Average Forecast 21Copyright 2015 Health Administration Press

22. A Regression Forecast = 207.22 + 2.21 × Time 22Copyright 2015 Health Administration Press

23. HOW FORECASTS GO WRONG 23Copyright 2015 Health Administration Press

24. Laws of Forecasting  The future will be like the past. – Changes can make our forecasts terrible.  Forecasts will be wrong. – This is true even if the future is like the past. – This is more true if the data are noisy. 24Copyright 2015 Health Administration Press

25. Implications  Forecasts identify likely outcomes. – Some will be more likely than others. – Even unlikely outcomes may occur.  The goals are to – support decision making, and – reduce uncertainty. 25Copyright 2015 Health Administration Press

26. Who would predict this? 26Copyright 2015 Health Administration Press

27. We need to forecast  future values, and  to understand how accurate our forecasts are. 27Copyright 2015 Health Administration Press

28. Why is it crucial to estimate forecast error?  Q1 estimate = 2,400 resident days. – Physical capacity is 2,800 resident days. – Nursing capacity varies with staffing.  Hiring lead time is 30 days.  How are these two estimates different? – 2,400 ± 200 (with 95 percent confidence) – 2,400 ± 900 (with 95 percent confidence) 28Copyright 2015 Health Administration Press

29. The Forecasting Process 1.Set the goal. 2.Choose a reasonable model. – Collect data. – Analyze data. 3.Share the results with decision makers. 4.Revise the forecast as new data arrive. 29Copyright 2015 Health Administration Press

30. Advice for Forecasters  You will be wrong. – Be able to explain why. – Be humble. – Track factors that affect your forecasts.  The goal is to support decision making.  Forecasting is not an end in itself. 30Copyright 2015 Health Administration Press

31. CONCLUSIONS 31Copyright 2015 Health Administration Press

32. Forecasts are  important,  just tools, and  always wrong. 32Copyright 2015 Health Administration Press

33. Forecasts  combine judgment and history, and  should be – consistent with demand theory, – explicit about their assumptions, and – explicit about how precise they are. 33Copyright 2015 Health Administration Press

34. Good forecasts should be  easy to understand,  transparent,  easy to modify,  accurate, and  precise. Unfortunately, these goals often conflict. 34Copyright 2015 Health Administration Press

35. Many Forecasting Techniques  Qualitative – Delphi – Focus groups – Expert judgment 35Copyright 2015 Health Administration Press  Quantitative – Extrapolation – Moving averages – Regression models