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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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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.
slide4
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)

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 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
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
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