FORECASTING Operations Management Dr Ron Lembke Demand Management

FORECASTING Operations Management Dr. Ron Lembke

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

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

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: 1. The forecast will always be wrong 2. The farther out you are, the worse your forecast is likely to be. 3. Aggregate forecasts are more likely to accurate than individual item ones

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)

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

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

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

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

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

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

Measuring the Errors Period A-F Method 1 A-F Method 2 1 100 10 2 -100 10 3 100 10 4 -100 10 5 100 10 6 -100 10 7 100 10 8 -100 10 9 100 10 10 -100 10 RSFE 0 100 • 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

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

MAD of examples Period |A-F| Method 1 |A-F| Method 2 1 100 10 2 100 10 3 100 10 4 100 10 5 100 10 6 100 10 7 100 10 8 100 10 9 100 10 10 10 MAD 100 10 • MAD shows that method 1 is off by a larger amount • Method 2 was biased • However, overall, Method 2 seems preferable

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

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