1. Chapter 7 Demand Forecasting in a Supply Chain
“Those who do not remember the past
are condemned to repeat it”
George Santayana ( )
Spanish philosopher, essayist, poet and novelist
Chapter 7 Demand Forecasting in a Supply Chain

2. Chapter 7 Demand Forecasting in a Supply Chain
Moving Average
Ardavan Asef-Vaziri
Based on
Operations management: Stevenson
Operations Management: Jacobs, Chase, and Aquilano
Supply Chain Management: Chopra and Meindl
USC Marshall School of Business Lecture Notes
Chapter 7 Demand Forecasting in a Supply Chain

3. Forecasting Objectives
Introduce the basic concepts of forecasting and its importance within an organization.
Identify several of the more common forecasting methods
Measure and assess the errors that exist in all forecasts

4. Uses of Forecasts
Forecast: a prediction of the future value of a variable
of interest, such as demand.
Accounting
Cost/Profit Estimates
Finance
Cash Flow and Funding
Human Resources
Hiring/Recruiting/Training
Marketing
Pricing, Promotion
MIS
IT/IS Systems, Services
Operations
Production Planning, MRP
Product/Service Design
New Products and Services

5. Types of Forecasting Qualitative Techniques Time Series Analysis
Causal Relationship Forecasting

6. Qualitative Methods
Non-quantitative forecasting techniques based on expert opinions and intuition. Typically used when there are no data available.
Subjective, judgmental
Based on intuition, estimates, and opinions
Expert Opinions
Market Research
Historical Analogies

7. Time Series Methods
Analyzing data by time periods to determine if trends or patterns occur.
Moving average
Exponential smoothing
More sophisticated techniques available

8. Causal Relationship Methods
Relating demand to an underlying factor other than time. (Regression) (Number of socks sold depends on number of running shoes sold.)
Multiple Regression Models

9. Four Basic Characteristics of Forecasts
Forecasts are rarely perfect because of randomness.
Beside the average, we also need a measure of variation, which is called standard deviation
Forecasts are more accurate for groups of items than for individuals.
Forecast accuracy decreases as the time horizon increases.
I see that you will get an A this semester.

10. Time Series Models Models for short term decisions Inventory decisions
Stock levels of Gameboys
Production planning decisions
Staffing decisions
Call center scheduling
Fast food chain

11. Time Series Forecasts Find a relationship between demand and time.

12. Components of an Observation
Observed variable (O) =
Systematic component (S) + Random component (R)
Level (current deseasonalized )
Trend (growth or decline)
Seasonality (predictable seasonal fluctuation)
Systematic component: Expected value of the variable
Random component: The part of the forecast that deviates from the systematic component
Forecast error: difference between forecast and actual demand

13. Time Series Techniques
Naive Forecast
Moving Average
Exponential Smoothing

14. 250 wheels Naive Forecast We sold 250 wheels last week....
Now, next week we should sell.…
250 wheels
F(t+1) = At
At : Actual demand in period t
F(t+1) : Forecast of demand for period t+1
The naive forecast can also serve as an
accuracy standard for other techniques.

15. Moving Average
Three period moving average in period 7 is the average of:
MA73 = (A7+ A6+ A5 )/3
Three period moving average in period t is the average of:
MAt3 = (At+ At-1+ At-2 )/3
Ten period moving average in period t is the average of:
MAt10 = (At+ At-1+ At-2 +At-3+ ….+ At-9 )/10

16. Forecast Using Moving Average
n period moving average in period t is the average of:
MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1 )/n
Forecast for period t+1 is equal to moving average for period t
Ft+1 =MAtn
Ft+1 =MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1 )/n

17. An example for comparison of two Moving Averages
Week
Demand 1
358 2
952 3
623 4
186 5
714 6 53 7
893 8
425 9
535 10 47 11
956 12
257
Let’s develop 3-week and 6-week moving average forecasts for demand in week 13.

18. 3-Period and 6-Period Moving Average
( )/3
( )/6

19. Graphical Comparison
6-week MA is smoother than 3-week MA, which appears to result in better predictions.
How do we measure which one is doing better?

20. How do we measure errors?
Standard Deviation of Error = MAD
Error is assumed to NORMALLY DISTRIBUTED with
A MEAN (AVERAGE) = 0
STANDARD DEVIATION = 1.25* MAD

21. MAD for One Method
But. Compare two or more forecasting techniques only over a period when data is available for all techniques.

22. MAD to Compare Two or More Methods

23. Moving Average Comparison
How many periods should we use for forecasting?
6-week forecast is and MAD is 295
3-week forecast is 420 and MAD is 371.4
6-week MAD is lower than 3-week MAD
Seems we prefer 6-week to 3-week.
So … should we use as many periods as possible?

24. Check a Second Example

25. MA comparison
Note that MAD is now lower for the 3-week MA than for the 6-week MA.
3-week MAD is 93.6
6-week MAD is 171.9
What is going on?

26. Moving Average: Observations
A large number of periods will cause the moving average to respond slowly to changes.
When there is a obvious current trend in the data, using larger number of periods results in a forecast with larger error.
In general, there is a trade-off between using more periods to smooth out random variations and using less periods to more closely follow trends.
Try many different time window sizes, and choose the one with the lowest MAD.

27. Tracking Signal

28. Tracking Signal Are our observations within UCL and LCL?
Is there any systematic error?
Tracking Signal
UCL
Time
LCL

29. Tracking Signal
Tracking Signal
UCL
Time
LCL

30. Tracking Signal
Tracking Signal
UCL
Time
LCL

31. Chapter 7 Demand Forecasting in a Supply Chain
Predictions are usually difficult, especially about the future.
Yogi Berra
The former New York Yankees Catcher
Chapter 7 Demand Forecasting in a Supply Chain