Forecasting Chapter 11

Chapter 11 Outline A Forecasting Framework Qualitative Forecasting Methods Time-Series Forecasting Moving Average Exponential Smoothing Forecast Errors Advanced Time-Series Forecasting Causal Forecasting Methods Selecting a Forecasting Method Collaborative Planning, Forecasting and Replenishment

A Forecasting Framework Focus of the chapter is on the forecasting of demand for output from the operations function. Demand may differ from sales Difference between forecasting and planning Forecasting: what we think will happen Planning: what we think should happen Forecasting application in various decision areas of operations (capacity planning, inventory management, others) Forecasting uses and methods (See Table 11.1)

Use of Forecasting: Operations Decisions

Use of Forecasting: Marketing & Finance

‘Qualitative’ Forecasting Methods Based upon managerial judgment when there is a lack of data. No specific model. Major methods: Delphi Technique Market Surveys Life-cycles Analogy Informed Judgment (naïve models)

Time-Series Forecasting Components of time-series data: Trend—general direction (up or down) Seasonality—short term recurring cycles Cycle—long term business cycle Error (random or irregular component) “Decomposition” of time-series Data are broken into the four components Moving Averages Exponential Smoothing

Moving Average Assumes no trend, seasonal or cyclical components. Simple Moving Average: Weighted Moving Average:

Moving Average Period Actual Demand Forecast 1 10 2 18 3 29 4 19 Compute three period moving average (number of periods is the decision of the forecaster) Period Actual Demand Forecast 1 10 2 18 3 29 4 19 (10+18+29)/3 = 19 Period 5 will be (18+29+actual for period 4)/3

Note: The more periods, the smoother the forecast. Time-Series Data Plot Note: The more periods, the smoother the forecast.

Exponential Smoothing The new average is computed from the old average: The value of the smoothing constant () is a choice. It determines how much the calculation smooths out the random variations. Its value can be set between zero (0) and one (1). Normally it is in the 0.1 to 0.2 range.

Simple Exponential Smoothing The forecast: F=forecast of demand (both this period and next) D = actual demand (this period) t = time period No trend, cyclical or seasonal components. Note: we are adjusting Ft to get Ft+1

Exponential Smoothing-calculation Facts: September forecast for sales was 15 September actual sales were 13 Alpha ( α) is 0.2 What is the forecast for October? Calculation October Forecast = September forecast + α(September actual-September forecast) =15+0.2(13-15)=15+0.2(-2)=15-0.4=14.6

Forecast Errors Cumulative Sum of Forecast Error (CFE) and Mean Error (ME) Mean Square Error (MSE) Mean Absolute Deviation (MAD)—measure of deviation in units. Mean Absolute Percentage Error (MAPE) Tracking Signal (TS)—relative measure of bias

Forecast Errors: Formulas Cumulative sum of Forecast Errors Mean Absolute Percentage Error Mean Square Error Tracking Signal Mean Error Mean Absolute Deviation

Tracking Signal Analogous to control charts in quality control, viz. if there is no bias, its values should fluctuate around zero. Is a relative measure, i.e. the numbers mean the same for any forecast.

Advanced Time-Series Forecasting Adaptive exponential smoothing Smoothing coefficient () is varied Box-Jenkins method Requires about 60 periods of past data

Time Series vs. Causal Models Time series compares data being forecast over time, i.e. Time is the independent variable or x- axis or x-variable. Causal models compare data being forecast against some other data set which the forecaster may think is a cause of the forecasted data, e.g. population size causes newspaper sales.

Causal Forecasting Models The general regression model: Other forms of causal model: Econometric Input-output Simulation models

Example of Time Series Model Yt = a + b(t) Dt = actual sales Ft = forecasted sales t = time period (e.g. year) F7 = 117.87 + 1.66 (7) = 129.47 = sales forecast for next year

Example of Causal Model Yt = a + b(t) Dt = actual sales in year t Ft = forecasted sales It = median family income (000’s) F7 = 38.23 + 2.397 (7) = 128.34 = sales forecast for next year (year 7)

Selecting a Forecasting Method User and system sophistication People reluctant to use what they don’t understand Time and resources available When is forecast needed? What is value of forecast? Use or decision characteristics, e.g. horizon Data availability and quality Data pattern Don’t force the data to fit the model!

Forecast Horizons and Forecast Accuracy The longer the forecast horizon, the less accurate the forecast Long lead times require long forecast horizons Lean, responsive companies have the goal of decreasing lead times so they are shorter than the forecast horizon

Collaborative Planning, Forecasting and Replenishment (CPFR) Aim is to achieve more accurate forecasts Share information in the supply chain with customers and suppliers. Compare forecasts If discrepancy, look for reason Agree on consensus forecast Works best in BtoB with few customers

Summary A Forecasting Framework Qualitative Forecasting Methods Time-Series Forecasting Moving Average Exponential Smoothing Forecast Errors Advanced Time-Series Forecasting Causal Forecasting Methods Selecting a Forecasting Method Collaborative Planning, Forecasting and Replenishment

End of Chapter Eleven