05 - Technological & Quantitative Forecasting.ppt

8/14/2019 05 - Technological & Quantitative Forecasting.ppt

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Technological Forecasting

Learning Objectives

List the elements of a good forecast. Outline the steps in the forecasting process.

Describe at least three qualitative

forecasting techniques and the advantagesand disadvantages of each.

Compare and contrast qualitative and

quantitative approaches to forecasting.


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Learning Objectives

Briefly describe averaging techniques, trendand seasonal techniques, and regression

analysis, and solve typical problems.

Describe two measures of forecastaccuracy.

Describe two ways of evaluating and

controlling forecasts. Identify the major factors to consider when

choosing a forecasting technique.


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FORECAST: A statement about the future value of a

variable of interest such as demand.

Forecasting is used to make informeddecisions.

Long-range

Short-range


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Forecasts

Forecasts affect decisions and activitiesthroughout an organization

Accounting, finance

Human resources Marketing

MIS

Operations Product / service design


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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

Uses of Forecasts


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Assumes causal systempast ==> future

Forecasts rarely perfect because of

randomness Forecasts more accurate for

groups vs. individuals

Forecast accuracy decreasesas time horizon increases

I see that you will

get an A this semester.

Features of Forecasts


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Elements of a Good Forecast

Timely

AccurateReliable

Written


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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 Obtain, clean and analyze data

Step 5 Make the forecast

Step 6 Monitor the forecast

The forecast


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Types of Forecasts

Judgmental- uses subjective inputs

Time series - uses historical dataassuming the future will be like thepast

Associative models - usesexplanatory variables to predict the

future


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Judgmental Forecasts

Executive opinions

Sales force opinions

Consumer surveys

Outside opinion

Delphi method

Opinions of managers and staffAchieves a consensus forecast


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Time Series Forecasts

Trend- long-term movement in data

Seasonality- short-term regular

variations in data

Cyclewavelike variations of more thanone years duration

Irregular variations - caused by unusual

circumstances Random variations - caused by chance


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Forecast Variations

Trend

Irregular

variatio

n

Seasonal variations

90

89

88

Figure 3.1

Cycles


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Naive Forecasts

Uh, give me a minute....We sold 250 wheels last

week.... Now, next week

we should sell....

The forecast for any period equalsthe previous periods actual value.


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Simple to use Virtually no cost

Quick and easy to prepare

Data analysis is nonexistent Easily understandable

Cannot provide high accuracy

Can be a standard for accuracy

Nave Forecasts


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Stable time series data F(t) = A(t-1)

Seasonal variations

F(t) = A(t-n)

Data with trends

F(t) = A(t-1) + (A(t-1)A(t-2))

Uses for Nave Forecasts


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Techniques for Averaging

Moving average

Weighted moving average

Exponential smoothing


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Moving Averages

Moving averageA technique that averages anumber of recent actual values, updated asnew values become available.

Weighted moving averageMore recentvalues in a series are given more weight in

computing the forecast. Ft= WMAn= wnAt-n+ wn-1At-2+ w1At-1 or: Ft+1 =wtAt & wt= 1.0 where t = (1,n)

Ft= MAn= n

At-n

+ A

t-2+ A

t-1


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Simple Moving Average

35

37

3941

43

45

47

1 2 3 4 5 6 7 8 9 10 11 12

Actual

MA3

MA5

Ft= MAn= n

At-n+ At-2+ At-1


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Exponential Smoothing

Premise--The most recent observationsmight have the highest predictive value.

Therefore, we should give more weight to

the more recent time periods whenforecasting.

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


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Weighted averaging method based on

previous forecast plus a percentage of the

forecast error

A-F is the error term, is the % feedback

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


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Period Actual Alpha = 0.1 Error Alpha = 0.4 Error

1 42

2 40 42 -2.00 42 -2

3 43 41.8 1.20 41.2 1.8

4 40 41.92 -1.92 41.92 -1.92

5 41 41.73 -0.73 41.15 -0.15

6 39 41.66 -2.66 41.09 -2.09

7 46 41.39 4.61 40.25 5.75

8 44 41.85 2.15 42.55 1.45

9 45 42.07 2.93 43.13 1.87

10 38 42.36 -4.36 43.88 -5.8811 40 41.92 -1.92 41.53 -1.53

12 41.73 40.92

Example 3 - Exponential Smoothing


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Picking a Smoothing Constant

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

and .1

.4

Actual


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Common Nonlinear Trends

Parabolic

Exponential

Growth

Figure 3.5


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Linear Trend Equation

Ft= Forecast for period t

t = Specified number of time periods

a = Value of Ftat t = 0 b = Slope of the line

Ft= a + bt

0 1 2 3 4 5 t

Ft


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Calculating a and b

b =n (ty) - t y

n t2 - ( t)2

a =y - b t

n


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Linear Trend Equation Example

t yWeek t Sales ty

1 1 150 150

2 4 157 314

3 9 162 4864 16 166 664

5 25 177 885

t = 15 t2 = 55 y = 812 ty = 2499

( t)2 = 225


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Linear Trend Calculation

y = 143.5 + 6.3t

a =812 - 6.3(15)

5=

b = 5 (2499) - 15(812)5(55) - 225

= 12495 -12180275 - 225

= 6.3

143.5


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Techniques for Seasonality

Seasonal variations Regularly repeating movements in series values

that can be tied to recurring events.

Seasonal relative Percentage of average or trend

Centered moving average

A moving average positioned at the center of thedata that were used to compute it.


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Associative Forecasting

Predictor variables - used to predict valuesof variable interest

Regression- technique for fitting a line to a

set of points

Least squares line - minimizes sum of

squared deviations around the line


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Linear Model Seems Reasonable

A straight line is fitted to a set of sample points.

0

10

20

30

40

50

0 5 10 15 20 25

X Y7 15

2 10

6 13

4 15

14 2515 27

16 24

12 20

14 27

20 4415 34

7 17

Computedrelationship


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Linear Regression Assumptions

Variations around the line are random

Deviations around the line normally

distributed

Predictions are being made only within therange of observed values

For best results:

Always plot the data to verify linearity Check for data being time-dependent

Small correlation may imply that other variables

are important


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Forecast Accuracy

Error - difference between actual value andpredicted value

Mean Absolute Deviation (MAD)

Average absolute error

Mean Squared Error (MSE)

Average of squared error

Mean Absolute Percent Error (MAPE) Average absolute percent error


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MAD, MSE, and MAPE

MAD =Actual forecast

n

MSE = Actual forecast)

-1

2

n

(

MAPE =Actual forecas

t

n

/ Actual*100)(


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MAD, MSE and MAPE

MAD Easy to compute

Weights errors linearly

MSE Squares error

More weight to large errors

MAPE Puts errors in perspective


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Controlling the Forecast

Control chartA visual tool for monitoring forecast errors

Used to detect non-randomness in errors

Forecasting errors are in control ifAll errors are within the control limits

No patterns, such as trends or cycles, are

present


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Tracking Signal

Tracking signal = (Actual-forecast)MAD

Tracking signalRatio of cumulative error to MAD

BiasPersistent tendency for forecasts to be

Greater or less than actual values.

Choosing a Forecasting


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Choosing a ForecastingTechnique

No single technique works in everysituation

Two most important factors

CostAccuracy

Other factors include the availability of:

Historical data Computers

Time needed to gather and analyze the data

Forecast horizon


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Operations Strategy

Forecasts are the basis for many decisions

Work to improve short-term forecasts

Accurate short-term forecasts improve

Profits Lower inventory levels

Reduce inventory shortages

Improve customer service levels

Enhance forecasting credibility


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Supply Chain Forecasts

Sharing forecasts with supply can Improve forecast quality in the supply chain

Lower costs

Shorter lead times Gazing at the Crystal Ball (reading in text)

E ti l S thi


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Li T d E ti


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Linear Trend Equation

Si l Li R i


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Simple Linear Regression

05 - Technological & Quantitative Forecasting.ppt

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