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2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

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

9. 11.2 Example Linear and Nonlinear Trend Patterns

10. 11.3 Seasonal Pattern of Home Natural Gas Usage

11. 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

12. 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– 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

13. 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
( )/3 = 11 2/3
( )/3 = 13 2/3
( )/3 = 16
( )/3 = 19 1/3
© 2011 Pearson Education, Inc. publishing as Prentice Hall

14. 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.

15. 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

16. 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

17. 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

18. 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

19. 11.12 Format Trendline Dialog Box

20. 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

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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

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

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