1. CHAPTER 3
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 ==> 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.

4. Elements of a Good Forecast
Timely
Accurate
Reliable
Written
Easy to use
Meaningful

5. 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 Gather and analyze data
Step 5 Prepare the forecast
Step 6 Monitor the forecast
“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 year’s duration
Irregular variations - caused by unusual circumstances
Random variations - caused by chance

13. Forecast Variations Trend Cycles Irregular variation 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. MAn Ft = n Ai “an average that is repeatedly updated” å
Simple Moving Average
MAn Ft = n
Ai “an average that is
repeatedly updated”
i = 1 å

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.  - forecast Actual n MAD = Actual forecast - å n MSE = Actual
MAD,MSE, & MAPE
MAD =
Actual
forecast - å n
MSE =
Actual
forecast) - 1 2 å n (
Actual -
forecast 
X 100
MAPE =
Actual n

19. å å Tracking Signal Tracking signal = (Actual - forecast) MAD