Forecasting
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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.

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Forecasting

Forecasting


Learning objectives

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 quantitative approaches to forecasting.


Learning objectives1

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.

  • Identify the major factors to consider when choosing a forecasting technique.


Forecasting

What is FORECAST ?

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

  • Forecasting is used to make informed decisions.

  • Long-range

  • Short-range


Uses of forecasts

Uses of Forecasts

Forecasts affect decisions and activities throughout an organization


Features of forecasts

Features of Forecasts

I see that you willget an A this trimester.

  • Assumes causal systempast ==> future

  • Forecasts rarely perfect because of randomness

  • Forecasts more accurate forgroups vs. individuals

  • Forecast accuracy decreases as time horizon increases


Elements of a good forecast

Elements of a Good Forecast

Timely

Accurate

Reliable

Easy to use

Written

Meaningful


Steps in the forecasting process

Steps in the Forecasting Process

“The forecast”

Step 6 Monitor the forecast

Step 5 Make the forecast

Step 4 Obtain, clean and analyze data

Step 3 Select a forecasting technique

Step 2 Establish a time horizon

Step 1 Determine purpose of forecast


Types of forecasts

Types of Forecasts

  • Judgmental - uses subjective inputs

  • Time series - uses historical data assuming the future will be like the past

  • Associative models - uses explanatory variables to predict the future


Judgmental forecasts

Judgmental Forecasts

  • Executive opinions

  • Sales force opinions

  • Consumer surveys

  • Outside opinion

  • Delphi method

    • An iterative process

    • Opinions of managers and staff

    • Achieves a consensus forecast

    • useful for technological forecasting


Time series forecasts

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

  • Random variations - caused by chance


Forecast variations

Forecast Variations

Irregularvariation

Trend

Cycles

90

89

88

Seasonal variations

Time


Naive forecasts

Naive Forecasts

Uh, give me a minute....

We sold 250 wheels last

week.... Now, next week we should sell....

The forecast for any period equals the previous period’s actual value.


Naive forecasts1

Naive Forecasts

  • 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


Uses for naive forecasts

Uses for Naive Forecasts

  • 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))


Techniques for averaging

Techniques for Averaging

  • Moving average

  • Weighted moving average

  • Exponential smoothing


Moving averages

Moving Averages

At-n+ … At-2 + At-1

Ft = MAn=

n

wnAt-n+ … wn-1At-2 + w1At-1

Ft = WMAn=

wn+wn-1+…+w1

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

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


Simple moving average

Simple Moving Average

At-n+ … At-2 + At-1

Ft = MAn=

n

Actual

MA5

MA3


Exponential smoothing

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.

Ft = Ft-1 + (At-1 - Ft-1)


Exponential smoothing1

Exponential Smoothing

  • 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)


Forecasting

Example 3 - Exponential Smoothing


Picking a smoothing constant

Picking a Smoothing Constant

Actual

.4

 .1


Linear trend equation

Linear Trend Equation

Ft

Ft = a + bt

0 1 2 3 4 5 t

  • Ft = Forecast for period t

  • t = Specified number of time periods

  • a = Value of Ft at t = 0

  • b = Slope of the line


Calculating a and b

Calculating a and b

n

(ty)

-

t

y

b

=

2

2

n

t

-

(

t)

y

-

b

t

a

=

n


Linear trend equation example

Linear Trend Equation Example


Linear trend calculation

Linear Trend Calculation

5 (2499)

-

15(812)

12495

-

12180

b

=

=

=

6.3

5(55)

-

225

275

-

225

812

-

6.3(15)

a

=

=

143.5

5

y = 143.5 + 6.3t


Common nonlinear trends

Common Nonlinear Trends

Parabolic

Exponential

Growth


Techniques for seasonality

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 the data that were used to compute it.


Associative forecasting

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 squared deviations around the line


Linear model seems reasonable

Linear Model Seems Reasonable

Computedrelationship

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


Linear regression assumptions

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

    • Small correlation may imply that other variables are important


Forecast accuracy

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)

    • Average absolute percent error


Mad mse and mape

MAD, MSE, and MAPE

2

(

Actual

forecast)

MSE

=

n

-

1

(

Actual

forecast

/ Actual*100)

MAPE

=

n

Actual

forecast

MAD

=

n


Mad mse and mape1

MAD, MSE and MAPE

  • MAD

    • Easy to compute

    • Weights errors linearly

  • MSE

    • Squares error

    • More weight to large errors

  • MAPE

    • Puts errors in perspective


Example 10

Example 10


Controlling the forecast

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

    • No patterns, such as trends or cycles, are present


Sources of forecast errors

Sources of Forecast errors

  • Model may be inadequate

  • Irregular variations

  • Incorrect use of forecasting technique


Tracking signal

Tracking Signal

(Actual

-

forecast)

Tracking signal

=

MAD

  • Tracking signal

    • Ratio of cumulative error to MAD

Bias – Persistent tendency for forecasts to be

Greater or less than actual values.


Choosing a forecasting technique

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

    • Computers

    • Time needed to gather and analyze the data

    • Forecast horizon


Operations strategy

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


Supply chain forecasts

Supply Chain Forecasts

  • Sharing forecasts with supply chain can

    • Improve forecast quality in the supply chain

    • Lower costs

    • Shorter lead times

  • Gazing at the Crystal Ball, Mini Tab, SPSS


Forecasting

Exponential Smoothing


Forecasting

Linear Trend Equation


Forecasting

Simple Linear Regression


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