1. Lecture 3
Forecasting
CT – Chapter 3

2. Forecast
A statement about the future value of a variable of interest such as demand.
Forecasts affect decisions and activities throughout an organization
Accounting, finance
Human resources
Marketing
Operations
Product / service design

3. Uses of Forecasts Accounting Cost/profit estimates Finance
Cash flow and funding
Human Resources
Hiring/recruiting/training
Marketing
Pricing, promotion, strategy
Operations
Schedules, MRP, workloads
Product/service design
New products and services

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

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. 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

7. Judgmental Forecasts Executive opinions Sales force opinions
Consumer surveys
Outside opinion
Delphi method
Opinions of managers and staff
Achieves a consensus forecast

8. 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

9. Forecast Variations Figure 3.1 Trend Cycles Irregular variation 90 8988
Seasonal variations

10. Smoothing/Averaging Methods
Used in cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects
Purpose of averaging - to smooth out the irregular components of the time series.
Four common smoothing/averaging methods are:
Moving averages
Weighted moving averages
Exponential smoothing

11. Example of Moving Average
Sales of gasoline for the past 12 weeks at your local Chevron (in ‘000 gallons). If the dealer uses a 3-period moving average to forecast sales, what is the forecast for Week 13?
Past Sales
Week Sales Week Sales

12. Management Scientist Solutions
MA(3) for period 4
= ( )/3 = 19
Forecast error for period 3 = Actual – Forecast = 23 – 19 = 4

13. MA(5) versus MA(3)

14. Review of last class Forecasting Smoothing/averaging method
What is a forecast?
Organizational functions that use forecasts
Desirable characteristics of a forecast
Types of forecasts
Types of time series
Smoothing/averaging method
Moving averages
Weighted moving averages
Advantage

15. 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.

16. Exponential Smoothing
Ft+1 = Ft + (At - Ft)
Weighted averaging method based on previous forecast plus a percentage of the forecast error
A-F is the error term,  is the % feedback

17. Picking a Smoothing Constant
 .1
.4
Actual

18. Suitable for time series data that exhibit a long term linear trend
Linear Trend Equation
Suitable for time series data that exhibit a long term linear trend Ft
Ft = a + bt a
Ft = Forecast for period t
t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line t

19. Sale increases every time period @ 1.1 units
Linear Trend Example
Linear trend equation
F11 = (11) = 32.5
Sale increases every time 1.1 units

20. Actual/Forecasted sales
Actual vs Forecast
Linear Trend Example 35 30 25
Actual/Forecasted sales 20
Actual 15
Forecast 10 5 1 2 3 4 5 6 7 8 9 10
Week
F(t) = t

21. Forecasting with Trends and Seasonal Components – An Example
Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times.
Average weekly sales ($) during each of the three seasons
during the past four years are known and given below.
Determine a forecast for the average weekly sales in year 5 for each of the three seasons.
Year
Season

22. Management Scientist Solutions

23. Interpretation of Seasonal Indices
Seasonal index for season 2 (Father’s Day) = 1.236
Means that the sale value of ties during season 2 is 23.6% higher than the average sale value over the year
Seasonal index for season 3 (all other times) = 0.586
Means that the sale value of ties during season 3 is 41.4% lower than the average sale value over the year

24. 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

25. MAD and MSE  Actual  forecast MAD = n MSE = Actual forecast)   n (2   n (

26. Measure of Forecast Accuracy
MSE = Mean Squared Error

27. Forecasting Accuracy Estimates Example 10 of textbook

28. Sources of Forecast errors
Model may be inadequate
Irregular variations
Incorrect use of forecasting technique

29. Characteristics of Forecasts
They are usually wrong
A good forecast is more than a single number
Aggregate forecasts are more accurate
The longer the forecast horizon, the less accurate the forecast will be
Forecasts should not be used to the exclusion of known information

30. 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