1. Forecasting
K.Prasanthi

2. Forecasting Predict the next number in the pattern:

3. Forecasting 3.7 12.5 9.0 Predict the next number in the pattern:
Process of predicting a future event based on historical data
3.7
12.5
9.0

4. What is Forecasting?
Process of predicting a future event based on historical data
Educated Guessing
On the basis of all business decisions
Production
Inventory
Personnel
Facilities

5. Importance of Forecasting
Departments throughout the organization depend on forecasts to formulate and execute their plans.
Finance needs forecasts to project cash flows and capital requirements.
Human resources need forecasts to anticipate hiring needs.
Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc.

6. Medium-range forecast
Period of forecasting
Short-range forecast
Usually < 3 months
Job scheduling, worker assignments
Medium-range forecast
3 months to 2 years
Sales/production planning
Long-range forecast
> 2 years
New product planning
Detailed
use of
system
At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons.
Design
of system

7. Qualitative Forecasting Methods
Models
Sales
Force
Composite
Delphi
Method
Executive
Judgement
Market
Research/
Survey
A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models?
Smoothing

8. Qualitative Methods Briefly, the qualitative methods are:
Executive Judgment: Opinion of a group of high level experts or managers
Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.
Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services. .

9. Qualitative Methods
Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization.
Typically, the procedure consists of the following steps:
Each expert in the group makes his/her own forecasts in form of statements
The coordinator collects all group statements and summarizes them
The coordinator provides this summary and gives another set of questions to each
group member including feedback as to the input of other experts.
The above steps are repeated until a consensus is reached. .

10. Quantitative Forecasting Methods
Regression
Time Series
Models
Models
A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models?
2. Moving
3. Exponential
1. Naive
Average
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

11. Quantitative Forecasting Methods
Time Series
Models
Models
2. Moving
3. Exponential
1. Naive
Average
Smoothing
A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models?
a) simple
b) weighted
a) level
b) trend
c) seasonality

12. Try to predict the future based on past data
Time Series Models
Try to predict the future based on past data
Assume that factors influencing the past will continue to influence the future 14

13. Demand in next period is the same as demand in most recent period
1. Naive Approach
Demand in next period is the same as demand in most recent period
May sales = 48 →
Usually not good
June forecast = 48
This slide introduces the naïve approach. Subsequent slides introduce other methodologies.

14. 2a. Simple Moving Average
Assumes an average is a good estimator of future behavior
Used if little or no trend
Used for smoothing
Ft+1 = Forecast for the upcoming period, t+1
n = Number of periods to be averaged
A t = Actual occurrence in period t 15

15. 2a. Simple Moving Average
You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.
Sales
(000)
Month 1 4 2 6 3 5 4 ? 5 ? 6 ?

16. 2a. Simple Moving Average
You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.
Sales
(000)
Moving Average
Month
(n=3) 1 4 NA 2 6 NA 3 5 NA 4 ?
(4+6+5)/3=5 5 ? 6 ?

17. What if ipod sales were actually 3 in month 4
2a. Simple Moving Average
What if ipod sales were actually 3 in month 4
Sales
(000)
Moving Average
Month
(n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 ? 5 5 ? 6 ?

18. 2a. Simple Moving Average
Forecast for Month 5?
Sales
(000)
Moving Average
Month
(n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 5 ?
(6+5+3)/3=4.667 6 ?

19. Actual Demand for Month 5 = 7
2a. Simple Moving Average
Actual Demand for Month 5 = 7
Sales
(000)
Moving Average
Month
(n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 5 ? 7
4.667 6 ?

20. 2a. Simple Moving Average
Forecast for Month 6?
Sales
(000)
Moving Average
Month
(n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 5 7
4.667 6
(5+3+7)/3=5 ?

21. 2b. Weighted Moving Average
Gives more emphasis to recent data
Weights
decrease for older data
sum to 1.0
Simple moving
average models
weight all previous
periods equally
This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights.

22. 2b. Weighted Moving Average: 3/6, 2/6, 1/6
Month
Sales
Weighted
Moving Average
(000) 1 4 NA 2 6 NA 3 5 NA 4 ?
31/6 = 5.167 5 ? 6 ?

23. 2b. Weighted Moving Average: 3/6, 2/6, 1/6
Month
Sales
Weighted
Moving Average
(000) 1 4 NA 2 6 NA 3 5 NA 4 3
31/6 = 5.167 5 7
25/6 = 4.167 6
32/6 = 5.333

24. 3a. Exponential Smoothing
Assumes the most recent observations have the highest predictive value
gives more weight to recent time periods
Ft+1 = Ft + a(At - Ft) et
Need initial
forecast Ft
to start.
Ft+1 = Forecast value for time t+1
At = Actual value at time t
 = Smoothing constant 24

25. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai
Given the weekly demand
data what are the exponential
smoothing forecasts for
periods 2-10 using a=0.10?
Assume F1=D1 25

26. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
F2 = F1+ a(A1–F1)
=820+.1(820–820)
=820 26

27. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
F3 = F2+ a(A2–F2)
=820+.1(775–820)
=815.5 26

28. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
This process continues
through week 10 26

29. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
a =
What if the
a constant equals 0.6 26

30. 3a. Exponential Smoothing – Example 2
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
a =
What if the
a constant equals 0.6 26

31. 3a. Exponential Smoothing – Example 3
Company A, a personal computer producer purchases generic parts and assembles them to final product. Even though most of the orders require customization, they have many common components. Thus, managers of Company A need a good forecast of demand so that they can purchase computer parts accordingly to minimize inventory cost while meeting acceptable service level. Demand data for its computers for the past 5 months is given in the following table. 26

32. 3a. Exponential Smoothing – Example 3
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
a =
What if the
a constant equals 0.5 26

33. 3a. Exponential Smoothing
How to choose α
depends on the emphasis you want to place on the most recent data
Increasing α makes forecast more sensitive to recent data
These points should have been brought out in the example, but can be summarized here.

34. Forecast Effects of Smoothing Constant 
Ft+1 = Ft +  (At - Ft) or
Ft+1 =  At + (1- ) At (1- )2At w1 w2 w3
Weights
Prior Period 
2 periods ago
(1 - )
3 periods ago
(1 - )2 =
= 0.10
= 0.90
This slide illustrates the decrease in magnitude of the smoothing constant. In the Power Point presentation, the several previous slides show the steps leading to this slide.
10% 9%
8.1%
90% 9%
0.9%

35. To Use a Forecasting Method
Collect historical data
Select a model
Moving average methods
Select n (number of periods)
For weighted moving average: select weights
Exponential smoothing
Select 
Selections should produce a good forecast
This slide introduces the exponential smoothing method of time series forecasting. The following slide contains the equations, and an example follows.
…but what is a good forecast?

36. A Good Forecast Has a small error Error = Demand - Forecast
This slide indicates one method of selecting .

37. Measures of Forecast Erroret
MAD = Mean Absolute Deviation
MSE = Mean Squared Error
This slide illustrates the equations for two measures of forecast error. Students might be asked if there is an occasion when one method might be preferred over the other.
RMSE = Root Mean Squared Error
Ideal values =0 (i.e., no forecasting error)

38. What is the MAD value given the forecast values in the table below?
MAD Example
= 40 4
=10
What is the MAD value given the forecast values in the table below? At Ft
|At – Ft|
Month
Sales
Forecast 1
220
n/a 2
250
255 5 5 3
210
205 4
300
320 20 5
325
315 10
= 40 31

39. MSE/RMSE Example What is the MSE value? √137.5 =11.73 Month Sales
= 550 4
=137.5
What is the MSE value?
RMSE =
√137.5
=11.73 At Ft
|At – Ft|
(At – Ft)2
Month
Sales
Forecast 1
220
n/a 2
250
255 5 25 5 25 3
210
205 4
300
320 20
400 5
325
315 10
100
= 550 31

40. Measures of Error 84 = 14 6 1,446 = 241 6 -10 84 1,446 = SQRT(241)
1. Mean Absolute Deviation (MAD) t At Ft et
|et|
et2
Jan
120
100 20
400
Feb 90
106
256
Mar
101
102
April 91
May
115 98
June 83
103 84
= 14 6
-16 16
2a. Mean Squared Error (MSE) 1 -1 1
-10 10
100
1,446
= 241 17 17
289 6
-20 20
400
2b. Root Mean Squared Error (RMSE)
-10 84
1,446
An accurate forecasting system will have small MAD, MSE and RMSE; ideally equal to zero. A large error may indicate that either the forecasting method used or the parameters such as α used in the method are wrong.
Note: In the above, n is the number of periods, which is 6 in our example
= SQRT(241)
=15.52

41. Forecast Bias
How can we tell if a forecast has a positive or negative bias?
TS = Tracking Signal
Good tracking signal has low values
MAD 30

42. Quantitative Forecasting Methods
Time Series
Regression
Models
Models
A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models?
2. Moving
3. Exponential
1. Naive
Average
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

43. Exponential Smoothing (continued)
We looked at using exponential smoothing to forecast demand with only random variations
Ft+1 = Ft + a (At - Ft)
Ft+1 = Ft + a At – a Ft
Ft+1 = a At + (1-a) Ft 24

44. Exponential Smoothing (continued)
We looked at using exponential smoothing to forecast demand with only random variations
What if demand varies due to randomness and trend?
What if we have trend and seasonality in the data? 24

45. Regression Analysis as a Method for Forecasting
Regression analysis takes advantage of the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable.
Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related
If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles.
The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line.

46. Formulas
y = a + b x
where,

47. Regression – Example
y = a + b X 26

48. General Guiding Principles for Forecasting
1. Forecasts are more accurate for larger groups of items.
2. Forecasts are more accurate for shorter periods of time.
3. Every forecast should include an estimate of error.
Before applying any forecasting method, the total system should be understood.
Before applying any forecasting method, the method should be tested and evaluated.
6. Be aware of people; they can prove you wrong very easily in forecasting

49. FOR JULY 2nd MONDAY READ THE CHAPTERS ON Forecasting
Product and service design