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Describe the importance of forecasting to the value chain Explain basic concepts of forecasting and time series Explain how to apply simple moving average and exponential smoothing models

Describe how to apply regression as a forecasting approach Explain the role of judgment in forecasting Describe how statistical and judgmental forecasting techniques are applied in practice

Forecasting and Demand Planning Process of projecting the values of one or more variables into the future Forecasting Enables companies to integrate planning information from different departments or organizations into a single demand plan Demand planning

Basic Concepts in Forecasting Forecast planning horizon Planning horizon: Length of time on which a forecast is based Spans from short-range forecasts with a planning horizon of under 3 months to long-range forecasts of 1 to 10 years Time bucket: Unit of measure for the time period used in a forecast

Data Patterns in Time Series Time series: Set of observations measured at successive points in time or over successive periods of time Characteristics Trend: Underlying pattern of growth or decline in a time series Seasonal patterns: Characterized by repeatable periods of ups and downs over short periods of time

Data Patterns in Time Series Cyclical patterns: Regular patterns in a data series that take place over long periods of time Random variation: Unexplained deviation of a time series from a predictable pattern Irregular variation: One-time variation that is explainable

Average demand over 4 years Components of Demand Trend component Demand for product or service | | | | 1 2 3 4 Time (years) Seasonal peaks Actual demand line Average demand over 4 years Random variation

11.2 Example Linear and Nonlinear Trend Patterns

11.3 Seasonal Pattern of Home Natural Gas Usage

Statistical Forecasting Models Statistical forecasting: Based on the assumption that the future will be an extrapolation of the past Methods Time-series - Extrapolates historical time-series data Regression - Extrapolates historical time-series data and includes other potentially causal factors that influence the behavior of time series

Simple Moving Average (MA) Moving average (MA) forecast: Average of the most recent k observations in a time series Ft+1 = ∑(most recent k observations)/k = (At + At–1 + At–2 1 ... 1 At–k+1)/k MA methods work best for short planning horizons when there is no major trend, seasonal, or business cycle pattern As the value of k increases, the forecast reacts slowly to recent changes in the time series data

Moving Average Example January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average (10 + 12 + 13)/3 = 11 2/3 (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3 © 2011 Pearson Education, Inc. publishing as Prentice Hall

Weighted Moving Average (WMA) If we think there is a trend in the data, such as increasing / decreasing – then using a WMA is recommended to show the trend better than a MA. Process is similar, but data points are weighted so that most recent have more impact.

Weighted Moving Average Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Weighted Month Shed Sales Moving Average [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6

Single Exponential Smoothing Forecasting technique that uses a weighted average of past time-series values To forecast the value of the time series in the next period Ft+1 = αAt + (1 – α)Ft = Ft + α(At – Ft) Where, α is called the smoothing constant

Regression as a Forecasting Approach Regression analysis: Method for building a statistical model that defines a relationship between numerical variables, such as: Single dependent One or more independent Yt = a + bt Simple linear regression finds the best values of a and b using the method of least squares

Excel’s Add Trendline Option Excel provides a tool to find the best-fitting regression model for a time series by selecting the add trendline option from the chart menu

11.12 Format Trendline Dialog Box

Forecast Errors and Accuracy Forecast error: Difference between the observed value of the time series and the forecast, or At - Ft Mean square error (MSE) MSE = Σ(At - Ft)2/T Influenced more by large forecasts errors than by small errors Mean absolute deviation error (MAD) MAD = Σ|At - Ft|/T

Common Measures of Error Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n

MAD working ONE FORECAST(F) ACTUAL(A) F-A |F-A| JAN 10 12 -2 2 FEB 13 -1 1 MAR 11 APR 16 15 MAY 19 22 -3 3 JUN 23 18 5 JUL 26 AUG 20 SEP 17 OCT NOV 9 DEC 14 21 MAD = 1.75

Forecast Errors and Accuracy Mean absolute percentage error (MAPE) MAPE = Σ|(At - Ft)/At|x100/T Measurement scale factor in MAPE eliminated by dividing the absolute error by time-series data value, making it easier to interpret

Causal Forecasting with Multiple Regression Multiple linear regression model: Has more than one independent variable Other independent variables that influence the time series Economic indexes Demographic factors

Judgmental Forecasting Relies upon opinions and expertise of people in developing forecasts Approaches Grass Roots forecasting: Asking those who are close to the end consumer about the customer’s purchasing plans The Delphi method: Forecasting by expert opinion by gathering judgments and opinions of key personnel Based on the experience and knowledge of the situation

Forecasting in Practice Managers use a variety of judgmental and quantitative forecasting techniques Statistical forecasts are adjusted to account for qualitative factors Tracking signal - Provides a method for monitoring a forecast by quantifying bias Tracking signal = Σ(At – Ft)/MAD Tracking signals between plus and minus 4 indicate an adequate forecasting model

Process of projecting the values of one or more variables into the future is known as forecasting Statistical forecasting and regression analysis are methods used for forecasting

Bias Cyclical patterns Forecast error Forecasting Grass roots forecasting Irregular variation Judgmental forecasting Moving average (MA) forecast Multiple linear regression model Planning horizon

Random variation Regression analysis Seasonal patterns Single exponential smoothing Statistical forecasting The Delphi method Time bucket Time series Trend