OM Forecasting and Demand Planning 11 COLLIER/EVANS 5 Copyright ©2016 Cengage Learning. All Rights...

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

2Copyright ©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

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


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


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


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


Exhibit

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


© 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


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.


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


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


Exhibit

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 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


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

SUMMARY


• 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


• 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


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

OM Forecasting and Demand Planning 11 COLLIER/EVANS 5 Copyright ©2016 Cengage Learning. All Rights...

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