FORECASTING

1. FORECASTING Operations Management Dr. Ron Lembke

2. Demand Management • Coordinate sources of demand for supply chain to run efficiently, deliver on time • Independent Demand • Things demanded by end users • Dependent Demand • Demand known, once demand for end items is known

3. Affecting Demand • Increasing demand • Marketing campaigns • Sales force efforts, cut prices • Changing Timing of demand • Incentives for earlier or later delivery • At capacity, don’t actively pursue more

4. Predicting the Future We know the forecast will be wrong. Try to make the best forecast we can, • Given the time we want to invest • Given the available data • The “Rules” of Forecasting: • The forecast will always be wrong • The farther out you are, the worse your forecast is likely to be. • Aggregate forecasts are more likely to accurate than individual item ones

5. Time Horizons Different decisions require projections about different time periods: • Short-range: who works when, what to make each day (weeks to months) • Medium-range: when to hire, lay off (months to years) • Long-range: where to build plants, enter new markets, products (years to decades)

6. Forecast Impact Finance & Accounting: budget planning Human Resources: hiring, training, laying off employees Capacity: not enough, customers go away angry, too much, costs are too high Supply-Chain Management: bringing in new vendors takes time, and rushing it can lead to quality problems later

7. Qualitative Methods • Sales force composite / Grass Roots • Market Research / Consumer market surveys & interviews • Jury of Executive Opinion / Panel Consensus • Delphi Method • Historical Analogy - DVDs like VCRs • Naïve approach

8. Quantitative Methods Time Series Methods 0. All-Time Average 1. Simple Moving Average 2. Weighted Moving Average 3. Exponential Smoothing 4. Exponential smoothing with trend 5. Linear regression 6. Seasonality, with a Trend Causal Methods Linear Regression

9. The Human Element • Colbert says you have more nerve endings in your gut than in your brain • Limited ability to include factors • Can’t include everything • More factors = more parameters = more places to make potential errors • If it feels really wrong to your gut, maybe your gut is right

10. Time Series Forecasting Assume patterns in data will continue, including: Trend (T) Seasonality (S) Cycles (C) Random Variations

11. Evaluating Forecasts How far off is the forecast? What do we do with this information? Forecasts Demands

12. Measuring the Errors • Method 1 forecasts are low, high, etc. • Method 2 forecasts always too low. • Running Sum of Forecast Errors, RSFE • Sum of all periods • Also known as the Bias

13. Evaluating Forecasts Mean Absolute Deviation Mean Squared Error Mean Absolute Percent Error

14. MAD of examples • MAD shows that method 1 is off by a larger amount • Method 2 was biased • However, overall, Method 2 seems preferable

15. Tracking Signal • If >4 or <-4 something is wrong • Top should sum to 0 over time. If not, forecast is biased. 4 Upper Limit 0 Forecast Error -4 Lower Limit Forecast Period

16. Summary • Rules of forecasting • Time Horizons • Discussion of Qualitative Methods • List of Quantitative Methods • Importance of Human instincts • Assessing Forecast Accuracy • MAD, MAPE, MSE, RSFE, Tracking Signal