Module: Forecasting Operations Management as a Competitive Weapon.

transcript

Module:

Forecasting

Operations Management as a Competitive Weapon

2Module: Forecasting

Learning ObjectivesAt the end of this module, each student will be able

1. Describe forecasting

2. Describe time series

3. Explain forecast selection and monitoring

1. What is Forecasting?Process of predicting

a future eventUnderlying basis of

all business decisions

Company needsOperations needs

Sales will be $200 Million!

Major Demand Components

Average demand for the period Trend Cyclical Seasonal Random

Realities of Forecasting

Forecasts are seldom perfect Most forecasting methods assume

that there is some underlying stability in the system

Both product family and aggregated product forecasts are more accurate than individual product forecasts

Forecast based only on past values Assumes that factors influencing past,

present, & future will continue

Example:Year: 1999 2000 2001 2002 2003

2004Sales: 78.7 63.5 89.7 93.2 92.1 ?

2. Time Series

Form of weighted moving average Weights decline exponentially Most recent data weighted most

Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen

Involves little record keeping of past data

Exponential Smoothing Method

You’re organizing a Kwanza meeting. You want to forecast attendance for 2004 using exponential smoothing ( = .10). The 2003 forecast was 175, actual was 190..

Exponential Smoothing Example

Exponential Smoothing Solution

Ft = Ft-1 + · (At-1 - Ft-1)

F2004 = F2003 + · (A2003 – F2003) = 175 + 0.10 (180 – 175) = 175 + 0.10 (5) = 175.5

You want to achieve: Low forecast error pattern Low forecast error size

3. Forecasting Selection Guidelines

Desired Pattern

Time (Years)

Trend Not Fully Accounted for

Pattern of Forecast Error

Forecast Error Equations

nForecast errors

Mean Squared Error

MADA F

nForecast errors

Mean Absolute Deviation

You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with two models. Which model should you use?

Actual Model 1 Model 2 Year Sales Forecast Forecast

1 10 6 102 10 13 103 20 20 194 20 27 205 40 34 38

Selecting a Forecasting Model

Forecasting Model SelectionModel 1

Year Actual Forecast Error Error2 |Error|1 10 6 4 16 4 MAD2 10 13 -3 9 3 43 20 20 0 0 04 20 27 -7 49 7 MSE5 40 34 6 36 6 22

0 110 20Model 2

Year Actual Forecast Error Error2 |Error|1 10 10 0 0 0 MAD2 10 10 0 0 0 0.63 20 19 1 1 14 20 20 0 0 0 MSE5 40 38 2 4 2 1

0 110 20Model 2

Measures how well forecast is predicting actual values

Is my forecast tool out of control?

Tracking Signal

Tracking Signal ComputationMonth Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth

1 90 1002 95 1003 115 1004 100 1005 125 1006 140 100

Error = Actual-Forecast

Tracking Signal Computation

RSFE = (Error)

Month Actual Forecast Error RSFE |Error| RSAE MADMonth TSMonth

1 90 100 -102 95 100 -53 115 100 154 100 100 05 125 100 256 140 100 40

|Error| = ABS(Error)

1 90 100 -10 -102 95 100 -5 -153 115 100 15 04 100 100 0 05 125 100 25 256 140 100 40 65

1 90 100 -10 -10 102 95 100 -5 -15 53 115 100 15 0 154 100 100 0 0 05 125 100 25 25 256 140 100 40 65 40

RSAE = (|Error|)

MADMonth = RSAE / Month

1 90 100 -10 -10 10 102 95 100 -5 -15 5 153 115 100 15 0 15 304 100 100 0 0 0 305 125 100 25 25 25 556 140 100 40 65 40 95

TSMonth = RSFE / MADMonth

1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 15 0 15 30 10.04 100 100 0 0 0 30 7.55 125 100 25 25 25 55 11.06 140 100 40 65 40 95 15.8

1 90 100 -10 -10 10 10 10.0 -1.02 95 100 -5 -15 5 15 7.5 -2.03 115 100 15 0 15 30 10.0 0.04 100 100 0 0 0 30 7.5 0.05 125 100 25 25 25 55 11.0 2.36 140 100 40 65 40 95 15.8 4.1

Out of control, > 3

Module: Forecasting Operations Management as a Competitive Weapon.

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