Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.

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Presentation transcript:

Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice gues don’t have to finish last. –What is ahead for the global economy Complete coverage of Chapter 12 An Overview of Demand Tools

Exponential Smoothing with Trend Effects More responsive to changes than exponential smoothing Where: FIT t = forecast including trend for period t F t = “base” forecast for period t from simple model T t = forecast trend component of demand for period t α = base smoothing constant δ = trend smoothing constant 12S–2

Exponential Smoothing with Trend Effects Where: FIT t = 250 units F t = 10 T t = 270 α = 0.20 δ = 0.10 Example 12S-1 12S–3

Determining Trend Factors Figure 12S-1 Where: d t = demand value for period t t = number of periods form origin a = y-axis intercept of line b = slope of line 12S–4

Simple Linear Regression: Time Series Where: d t = actual demand value for period t F t = forecast demand from regression equation in period t t = average of all t values d t = average of all d t values n = number of data points 12S–5

Simple Linear Regression: Time Series Where: d t = 16 t*d = 17,785 t = 8.5 d t = 127 n = 16 Using the data set from Table 12S-1 Example 12S-2 12S–6

Seasonality Seasonal Index: adjustment factor to account for seasonal changes or cycles in demand. A ‘season’ can be any time period. SI = period actual demand / average period demand Average SI = Average of SI data for given time frame 12S–7

Seasonality 1.Gather actual demand data 2.Calculate average period demand 3.Calculate seasonal index (actual/avg) 4.Calculate average seasonal index 5.Determine forecast 6.Apply average SI 7.Calculate seasonally adjusted forecast 12S–8

Forecast Error and Signal Tracking Error MeasureDefinitionFormula Bias = Mean Forecast Error (MFE) The average of the deviations of observed values from the forecasted values Mean Percent Error (MPE) The average forecast error restated as a percentage deviation Mean Absolute Deviation (MAD) The average of the absolute values of the deviations of the observed values from the forecasted values Mean Absolute Percentage Error (MAPE) The MAD adjusted to create a relative metrics that indicate how large errors are relative to the actual demand quantities Mean Squared Error (MSE) A measure of forecast error that enables the user to evaluate the sensitivity of a forecast to the magnitude of the errors Root Mean Squared Error (RMSE) The square root of the MSE, a measure that usually give an approximation of the variance of errors Where d t = Actual Demand at Time t and F t = Forecast at Time t 12S–9

Forecast Error and Signal Tracking Table 12S-6 12S–10

Forecast Error and Signal Tracking Figure 12S-4 Tracking Signal: ratio of running forecast error to MAD 12S–11

Advanced Forecasting Models Summary 1.Enhanced forecasting models can better respond to changing demand 2.Multiple measure for determining forecast error 3.Tracking signals can provide notification when forecast error is changing 12S–12