02 - Technological Forecasting.ppt

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Technology 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 advantages and disadvantages of each.

 _{Compare and contrast qualitative and}

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Learning Objectives

 _{Briefly describe averaging techniques,}

trend and seasonal techniques, and regression analysis, and solve typical problems.

 _{Describe two measures of forecast}

accuracy.

 _{Describe two ways of evaluating and}

controlling forecasts.

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FORECAST:

 A statement about the future value of a variable of interest such as demand.

 Forecasting is used to make informed

decisions.

 Long-range

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Forecasts

 _{Forecasts affect decisions and activities}

throughout an organization

 Accounting, finance

 Human resources

 Marketing

 MIS

 Operations

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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

Uses of Forecasts

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 Assumes causal system

past ==> 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.

Features of Forecasts

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Elements of a Good Forecast

Timely

Accurate Reliable

anin

gful _Written

sy to

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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 technique

Step 4 Obtain, clean and analyze data Step 5 Make the forecast

Step 6 Monitor the forecast

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Types of Forecasts

 _{Judgmental - uses subjective inputs}

 _{Time series - uses historical data}

assuming the future will be like the past

 _{Associative models - uses explanatory}

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Judgmental Forecasts

 _{Executive opinions}  _{Sales force opinions}  _{Consumer surveys}  _{Outside opinion}

 Delphi method

 Opinions of managers and staff

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Time Series Forecasts

 _{Trend - long-term movement in data}  _{Seasonality - short-term regular}

variations in data

 _{Cycle – wavelike variations of more than}

one year’s duration

 _{Irregular variations - caused by unusual}

circumstances

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Forecast Variations

Trend

Irregular variation

Seasonal variations

90 89 88

Figure 3.1

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Naive Forecasts

Uh, give me a minute....

We sold 250 wheels last

week.... Now, next week

we should sell....

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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}

Naïve 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 Naïve Forecasts

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Techniques for Averaging

 _{Moving average}

 _{Weighted moving average}

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Moving Averages

Moving average – A technique that averages a number of recent actual values, updated as new values become available.

 Weighted moving average – More recent values in a series are given more weight in computing the forecast.

 _F _{= WMA} _{= w} _A _{+ … w} _A _{+ w} _A F_t = MA_n= At-n _n

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Simple Moving Average

35 37 39 41 43 45 47

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

Actual

MA3 MA5

F

_t

= MA

_n

=

n

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Exponential Smoothing

• Premise--The most recent observations might have the highest predictive value.

 Therefore, we should give more weight to

the more recent time periods when forecasting.

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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}

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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 -5.88 11 40 41.92 -1.92 41.53 -1.53

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 D e m a n

d .1

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Common Nonlinear Trends

Parabolic

Exponential

Growth

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Linear Trend Equation

 F_t = Forecast for period t

 t = Specified number of time periods  _{a = Value of F}_t_{at t = 0}

 b = Slope of the line

F_t = a + bt

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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 y

W e e k t2 S a l e s t y

1 1 1 5 0 1 5 0

2 4 1 5 7 3 1 4

3 9 1 6 2 4 8 6

4 1 6 1 6 6 6 6 4

5 2 5 1 7 7 8 8 5

 t = 1 5  t 2 = 5 5  y = 8 1 2  t y = 2 4 9 9

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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 =

12495-12180

275 -225 = 6.3

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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}

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Associative Forecasting

 _{Predictor variables - used to predict values}

of variable interest

 _{Regression - technique for fitting a line to a}

set of points

 _{Least squares line - minimizes sum of}

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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

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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 the

range of observed values

 For best results:

 _{Always plot the data to verify linearity}

 Check for data being time-dependent

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Forecast Accuracy

 Error - difference between actual value and predicted value

 Mean Absolute Deviation (MAD)

 Average absolute error

 Mean Squared Error (MSE)

 Average of squared error

 _{Mean Absolute Percent Error (MAPE)}

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MAD, MSE, and MAPE

MAD =  Actual  forecast

n

MSE = Actual forecast)

-1

2

 

n

(

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MAD, MSE and MAPE

 _MAD

 Easy to compute

 Weights errors linearly

 _MSE

 _{Squares error}

 More weight to large errors

 _MAPE

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Example 10

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 2.28

7 216 217 -1 1 1 0.46

8 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75

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Controlling the Forecast

 _{Control chart}

 A visual tool for monitoring forecast errors

 Used to detect non-randomness in errors

 _{Forecasting errors are in control if}

 _{All errors are within the control limits}

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Sources of Forecast errors

 _{Model may be inadequate}  _{Irregular variations}

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Tracking Signal

Tracking signal = (Actual-forecast)

MAD



•Tracking signal

–Ratio of cumulative error to MAD

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Choosing a Forecasting

Technique

 _{No single technique works in every}

situation

 _{Two most important factors}

 Cost

 Accuracy

 _{Other factors include the availability of:}

 Historical data

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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

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Supply Chain Forecasts

 _{Sharing forecasts with supply can}

 Improve forecast quality in the supply chain

 Lower costs

 Shorter lead times

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Linear Trend Equation

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Simple Linear Regression