FORECASTING - PowerPoint PPT Presentation

View by Category
About This Presentation
Title:

FORECASTING

Description:

chapter 3 forecasting – PowerPoint PPT presentation

Number of Views:60
Avg rating:3.0/5.0
Slides: 20
Provided by: Ralp144
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: FORECASTING


1
FORECASTING
2
FORECASTING
  • Forecasts serve as a basis for planning--capacity,
    budgeting, sales, production, inventory,
    personnel
  • Successful forecasting requires a skillful
    blending of both art and science
  • Two uses of forecasts
  • Planning the system--Long Range
  • Planning the use of the system--Short Range

3
Forecasting
  • Assumes causal system
  • past gt future
  • Forecasts rarely perfect because of randomness
  • Forecasts more accurate forgroups vs.
    individuals
  • Forecast accuracy decreases as time horizon
    increases

I see that you willget an A this semester.
4
Elements of a Good Forecast
Timely
Accurate
Reliable
Written
Easy to use
Meaningful
5
Steps in the Forecasting Process
The forecast
6
APPROACHES TO FORECASTING
  • QUALITATIVE--based on subjective inputs, soft
    data
  • judgmental forecasts, opinions, hunches,
    experience, etc.
  • QUANTITATIVE--based on historical data
  • --project past experience into the future
  • --uncover relationships between variables that
    can be used to predict the future

7
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

8
Judgmental Forecasts
  • Executive opinions
  • Sales force composite
  • Consumer surveys
  • Outside opinion
  • Opinions of managers and staff
  • Delphi technique

9
QUANTITATIVE FORECASTS
  • Time-Series techniques
  • --Naïve
  • --Moving Average models
  • --Exponential Smoothing models
  • --Classical Decomposition
  • --Box-Jenkins ARIMA models
  • --Neural Networks

10
QUANTITATIVE FORECASTS
  • Causal or Associative techniques
  • --Simple linear regression
  • --Multiple linear regression
  • --Nonlinear regression

11
FORECASTING DATA
  • time-series
  • --time-ordered sequence of observations taken
    at regular intervals over a period of time
  • Annual, Quarterly, Monthly, Weekly,
  • Daily, Hourly, etc.

12
UNDERLYING BEHAVIOR
  • Trend - long-term movement in data
  • Seasonality - short-term, regular, periodic
    variations in data
  • Cycles - wave-like variations of more than one
    years duration
  • Irregular variations - caused by unusual
    circumstances
  • Random variations - caused by chance

13
Forecast Variations
Irregularvariation
Trend
Cycles
90
89
88
Seasonal variations
14
Naive Forecasts
Uh, give me a minute.... We sold 250 wheels
last week.... Now, next week we should
sell. the latest observation in a sequence is
used as the forecast for the next period Ft
At-1
15
Simple Moving Average
16
Exponential Smoothing
Ft Ft-1 a(At-1 - Ft-1)
  • 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.

17
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
  • Tracking Signal
  • - Ratio of cumulative error and MAD

18
MAD,MSE, MAPE
Actual
-
forecast
?
X 100
MAPE

Actual
n
19
Tracking Signal
About PowerShow.com