Chapter 03 - Forecasting

8/12/2019 Chapter 03 - Forecasting

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Forecasting

Engineering Management MGT3202

Prepared by

Hafsa Binte Mohsin

Faculty of Business Administration


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FORECAST

A statement about the future value of a variable of

interest.

Forecasts are the basis for manage budgeting, capacity, sales,

production and inventory, personnel, purchasing and many

more.

Forecasts play an important role in the planning process

because they enable managers to anticipate the future so they

can plan accordingly.


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FORECAST

Accounting Cost/profit estimates

Finance Cash flow and funding

Human Resources Hiring/recruiting/training

Marketing Pricing, promotion, strategy

MIS IT/IS systems, services

Operations Schedules, MRP, workloads

Product/service design New products and services


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COMMON FEATURES TO ALL FORECASTS

Assumes causal systempast ==> future

Forecasts rarely perfect because of randomness

Forecasts more accurate for groups vs. individuals

Forecast accuracy decreases as time horizon increases

I see that you will

get an A+ this semester


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The forecast should be timely.

The forecast should be accurate and the degree of accuracyshould be stated.

The forecast should be reliable; it should work consistently.

The forecast should be expressed in meaningful units.

The forecast should be in writing.

The forecasting technique should be simple to understandand use.

The forecast should be cost effective. More than one technique should be used

The use of both Qualitative and quantitative techniquesprovide best result

ELEMENTS OF A GOOD FORECAST


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STEPS IN THE FORECASTING PROCESS

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting techniqueStep 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

The forecast


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Judgmental Forecasts Forecasts that use subjective inputs such as opinions from

consumer surveys, sales staff, managers, executives and experts.

Time - series Forecasts

Forecasts that project patterns identified in recent time - series

observations.

Associative Model

Forecasting technique that uses explanatory variables to predictfuture demand.

APPROACHES TO FORECASTING


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A time series is a time ordered sequence of observationstaken at regular intervals; e.g.hourly, daily, weekly,

monthly etc.

Analysis of time series data requires the analyst to identify the

underlying behavior of the series. This can often beaccomplished by merely plotting the data and visually examining

the plot.

Different patterns are

Trend, Seasonality, Cycles, Irregular variations and Random variations.

TIME - SERIES FORECASTS


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Trend- A long term upward or downward movement indata.

Seasonality- Short term regular variations related to the

calendar or time of day. Cycle- Wavelike variations lasting more than one year.

Irregular variation- Caused by unusual circumstances,

not reflective of typical behavior.

Random variations- Residual variations after all otherbehaviors are accounted for.

TIME - SERIES FORECASTS (CONTD.)


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TIME - SERIES FORECASTS (CONTD.)

Trend

Irregular

variation

Seasonal variations

9089

88

Cycles


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Naive Method The forecast for any period equals the previous periods value.

The naive approach can be used with a stable series (variations

around an average), with seasonal variations.

Techniques for Averaging/ Averaging Methods

Simple average

Moving Average

Weighted Moving Average

Exponential Smoothing

FORECASTING METHOD


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Technique that averages a number of recent actual values,

updated as new values become available.

MOVING AVERAGE

MAn =n

Aii = 1n


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More recent values in a series are given more weight in

computing the forecast.

WEIGHTED MOVING AVERAGE

EXPONENTIAL SMOOTHING

Weighted averaging method based on previous forecast plus a

percentage of the forecast error.

Ft= Ft-1 + (At-1 - Ft-1)


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Techniques for Trends

1-13

Trend Equation Ft= a+bt

Trend Adjusted Exponential Smoothing


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Mean Absolute Deviation (MAD)

Average absolute error

Mean Squared Error (MSE)

Average of squared error

Mean Absolute Percent Error (MAPE)

Average absolute percent error

FORECAST ACCURACY

MAD =Actual forecast

n

MSE =Actual forecast)

- 1

2

n

(

MAPE =Actual forecast

n

/ Actual)*100%)((


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EXAMPLE1

Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100

1 217 215 2 2 4 0.92

2 213 216 -3 3 9 1.41

3 216 215 1 1 1 0.46

4 210 214 -4 4 16 1.90

5 213 211 2 2 4 0.94

6 219 214 5 5 25 2.287 216 217 -1 1 1 0.46

8 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD = 2.75

MSE = 10.86

MAPE = 1.28


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Period Actual

(t) (A) Forecast (A-F) |E| E2(|E| /

A)*100

1 42 - - - - -

2 38

3 40

4 39

5 37

6 39

7 -

Forecast

Accuracy(=0.1)

MAD MSE MAPE

EXAMPLE02


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THANK YOU

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Chapter 03 - Forecasting