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OMForecasting and Demand Planning
11
COLLIER/EVANS
5
Copyright ©2016 Cengage Learning. Al l Rights Reserved. May not be scanned, copied or dupl icated, or posted to a publ icly accessible website, in whole or in part.
LEARNING OUTCOMES
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1 Describe the importance of forecasting to the value chain
2 Explain basic concepts of forecasting and time series
3 Explain how to apply simple moving average and exponential smoothing models
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LEARNING OUTCOMES (continued)
4 Describe how to apply regression as a forecasting approach
5 Explain the role of judgment in forecasting
6 Describe how statistical and judgmental forecasting techniques are applied in practice
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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
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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
6Copyright ©2016 Cengage Learning. Al l Rights Reserved. May not be scanned, copied or dupl icated, or posted to a publ icly accessible website, in whole or in part. OM5 | CH11
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
7Copyright ©2016 Cengage Learning. Al l Rights Reserved. May not be scanned, copied or dupl icated, or posted to a publ icly accessible website, in whole or in part. OM5 | CH11
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
8Copyright ©2016 Cengage Learning. Al l Rights Reserved. May not be scanned, copied or dupl icated, or posted to a publ icly accessible website, in whole or in part. OM5 | CH11
Components of DemandD
eman
d f
or
pro
du
ct o
r se
rvic
e
| | | |1 2 3 4
Time (years)
Average demand over 4 years
Trend component
Actual demand line
Random variation
Seasonal peaks
9Copyright ©2016 Cengage Learning. Al l Rights Reserved. May not be scanned, copied or dupl icated, or posted to a publ icly accessible website, in whole or in part. OM5| CH11
Exhibit
11.2 Example Linear and Nonlinear Trend Patterns
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Exhibit
11.3 Seasonal Pattern of Home Natural Gas Usage
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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
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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
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© 2011 Pearson Education, Inc. publishing as Prentice Hall
January 10February 12March 13April 16May 19June 23July 26
Actual 3-MonthMonth Shed Sales Moving Average
(12 + 13 + 16)/3 = 13 2/3
(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3
Moving Average Example
(10 + 12 + 13)/3 = 11 2/3
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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.
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January 10February 12March 13April 16May 19June 23July 26
Actual 3-Month WeightedMonth 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
Weighted Moving Average
101213
[(3 x 13) + (2 x 12) + (10)]/6 = 121/6
Weights Applied Period
3 Last month2 Two months ago1 Three months ago
6 Sum of weights
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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
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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
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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
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Exhibit
11.12 Format Trendline Dialog Box
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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
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Common Measures of Error
Mean Absolute Deviation (MAD)
MAD =∑ |Actual - Forecast|
n
Mean Squared Error (MSE)
MSE =∑ (Forecast Errors)2
n
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MAD working
ONE FORECAST(F) ACTUAL(A) F-A |F-A|JAN 10 12 -2 2FEB 12 13 -1 1
MAR 13 11 2 2APR 16 15 1 1MAY 19 22 -3 3JUN 23 18 5 5JUL 26 26 0 0
AUG 18 20 -2 2SEP 16 17 -1 1OCT 12 13 -1 1NOV 10 9 1 1DEC 14 12 2 2
21MAD = 1.75
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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
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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
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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
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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
SUMMARY
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• 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
KEY TERMS
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• Bias• Cyclical patterns• Forecast error• Forecasting• Grass roots forecasting• Irregular variation• Judgmental forecasting• Moving average (MA) forecast• Multiple linear regression model• Planning horizon
KEY TERMS
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• Random variation• Regression analysis• Seasonal patterns• Single exponential smoothing• Statistical forecasting• The Delphi method• Time bucket• Time series• Trend
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