1. Lecture 2: Time Series Forecasting
Forecasting and production
Data and demand patterns
Stationary demand forecasting model
Naïve method
Moving average method
Exponential smoothing method
Summary
Readings: Page 68-87
Assignment 1
Part 1: , 4.30, 4.34, 4.43
Part 2: Select one article from SR-2. Write a half page summary (using Win-word) to describe the forecasting procedure of the company and point out what you think is the most important aspect for forecasting in this company
Sep 6
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2. Forecasting and Production
Business planning is based on forecast (and strategy)
Is this new product going to sale?
What is the potential market for this new product?
Will customer accept this new technology?
How much to produce in each period?
Availability of raw materials?
Changes in interest rates, exchange rates, material prices?
Bad forecasts are costly
Sony’s video technology, Apple computer (customer/tech)
IBM’s notebooks (new product sales potential)
Stock-out and markdown can cost more than manufacturing cost (Fisher et al. 1994)
Sep 6
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3. Making and Using Forecasts
Forecasts are usually made by marketing and sales
Forecasts are decision inputs for marketing and production/operations
Forecasting horizon in operation planning
short term product sales forecast in days or weeks for inventory management and production plan (MRP)
intermediate term forecast of sales patterns in weeks or months of product family for labor and resource requirement
long term demand trend forecast in months or years for capacity planning
Sep 6
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4. 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 Obtain, clean and analyze data
Step 5 Make the forecast
Step 6 Monitor the forecast
“The forecast”
Sep 6
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5. Subjective Forecasting Methods
Sales force composites
sales manager aggregates salesmen’s individual sales estimates
could be biased
Consumer survey (market research)
for signals of future trend and shift of preference patterns,
survey and sampling design needs specialist
Executive opinion
no data, expert opinion is the only source of information
interview or consensus meeting
The Delphi method
formal and iterative method of coming up a forecast from
experts’ opinion, a group of experts and a facilitator
Sep 6
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6. Sport Obermeyer, an Example
6 managers look at the new styles and
estimate sales (short life-cycle products)
Forecasts
Member Pandora Parka Entice Jacket
Carolyn 1, ,500
Laura 1,
Tom 1, ,200
Kenny 1,
Wally 1, ,075
Wendy 1, ,425
Average
Std. Dev
1,200
1,200
70.7
627.1
Sep 6
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7. Objective Forecasting Methods
Time Series models
past data contains future demand information and can be used to project future demands
used for operation planning
Associative models
uses explanatory variables to predict the future
Sep 6
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8. Data and Demand Patterns
Data Analysis
We need to know the demand pattern before selecting an appropriate forecasting model
How?
Plot the data to examine the pattern
Example data sets: forecast-s1
Also Figure 3.1 (P. 73)
What are the common patterns?
Why is it important to determine the pattern first?
Sep 6
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9. Demand Patterns Stationary/constant Linear trend Cyclic/seasonal
Cyclic/seasonal with trend
εt : a random fluctuation; a, b: constant;
ct : time-varying coefficient
forecast-s1
Sep 6
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10. Forecasting Model for Stationary Demand
For a stationary demand pattern, there is only one parameter a that we need to estimate from the past demand data
Time series forecasting model
Use Ft to denote the estimate of a made at time t,
i.e., At
Sep 6
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11. Time Series Forecasting
Methods to obtain Ft
Naïve
N-period moving average
Exponential smoothing
Purpose:
To estimate the parameters of the demand model, using past data
To filter out the random element from the past data
Sep 6
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12. Naive Forecasts Ft = At –1 (1) 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.
Ft = At – (1)
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13. Naïve 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
Sep 6
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14. N-Period Moving Average
Select only the recent data
Example 1
Passed demand data:
What is the forecast for period 13? Or for period 15?
With N =3
With N =5
Choice of N: ≥3
forecast-s2
(2)
Sep 6
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15. Exponential Smoothing
Use Ft to denote the estimate of a at period t,
is the smoothing constant
Data:
What is the forecast for period 13?
forecast-s2
(3)
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16. Information Content All past data are used
The weight to the data of i periods old is
It decreases exponentially as the data gets older
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17. Choice of Smoothing Constant 
Between 0 and 1 (why?)
If the demand is stable, choose a small ; if the demand is rapidly increasing or decreasing, choose a large . (why?)
 determines the weight on the most recent data
We should test the forecast model to fit a good 
Usually  is from 0.1 to 0.3
Sep 6
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18. Summary Distinguish three things in time series forecast:
the underlying process
parameter estimation
and forecasting formula/model
Equations (1), (2), (3)
Keys to good forecast
Good data
Right model
Proper balance of forecasting stability and sensitivity to the recent change in data, through selection of N and
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19. Forecast Variations Trend Cycles Irregular variation 90 89 88
Seasonal variations
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20. Review Problems Problem 1 at page 112 Problem 3 at page 113
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21. A Caveat on Forecasts “Man won’t fly for 1000 years”
Wilbur Wright
“No woman in my time will be Prime Minister”
Margaret Thatcher – 1969
“I think there is a world market for about five computers”
Thomas J. Watson
The last slide shows you some of the worst forecasts have ever been made in the history. What do I try to say here? It is important to have planning and rely on scientific methods. However, forecasts are always just forecasts. We will never know what is going to happen for sure in the future. We have to learn to live with uncertainty and inaccurate forecasts. For some people, that is exactly what is making life exciting.
Sep 6
ISMT 162/Stuart Zhu