1. Forecasting

2. Demand Characteristics
Week
400 –
300 –
200 –
100 –
No. of tables
Continuous demand
M T W Th F M T W Th F
Discrete demand
Independent demand
100 tables
Dependent demand
100 x 1 =
100 tabletops
100 x 4 = 400 table legs

3. Demand Management
Independent demand items are the only items demand for which needs to be forecasted
These items include:
Finished goods and
Spare parts

4. Demand Management Independent Demand (finished goods and spare parts)
B(4)
C(2)
D(2)
E(1)
D(3)
F(2)
Dependent Demand (components) 3

5. Introduction
Demand estimates for independent demand products and services are the starting point for all the other forecasts in POM.
Management teams develop sales forecasts based in part on demand estimates.
Sales forecasts become inputs to both business strategy and production resource forecasts.

6. What is Forecasting?
Process of predicting a future event based on historical data
Educated Guessing
Underlying basis of all business decisions
Production
Inventory
Personnel
Facilities
Sales will be $200 Million!

7. Importance of Forecasting in POM
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.

8. The Effect of Inaccurate Forecasting

9. Product Demand over Time
Demand for product or service
This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor?
Year 1
Year 2
Year 3
Year 4

10. Product Demand over Time
Trend component
Seasonal peaks
Demand for product or service
This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor?
Actual demand line
Random variation
Year 1
Year 2
Year 3
Year 4
Now let’s look at some time series approaches to forecasting…

11. Forms of Forecast Movement

12. Types of Forecasts by Time Horizon
Quantitative
methods
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
Qualitative
Methods

13. Forecasting Methods Time series Regression methods Qualitative
statistical techniques that use historical demand data to predict future demand
Regression methods
attempt to develop a mathematical relationship between demand and factors that cause its behavior
Qualitative
use management judgment, expertise, and opinion to predict future demand

14. Forecasting Approaches
Qualitative Methods (opinion based)
Quantitative Methods
(statistics based)
Used when situation is vague & little data exist
New products
New technology
Involves intuition, experience
e.g forecasting sales on internet
Used when situation is stable & historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
This slide distinguishes between Quantitative and Qualitative forecasting. If you accept the argument that the future is one of perpetual, and perhaps significant change, you may wish to ask students to consider whether quantitative forecasting will ever be sufficient in the future - or will we always need to employ qualitative forecasting also. (Consider Tupperware’s ‘jury of executive opinion.’)

15. Qualitative Forecasting Methods
Models
Executive
judgement
Sales force
survey
Market
research/
survey
Delphi Method
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?

16. Qualitative Methods
Executive committee consensus
Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research

17. Qualitative Methods Briefly, the qualitative methods are:
Executive Judgment: Opinion of a group of high level experts or managers is pooled
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.

18. 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.

19. Quantitative Forecasting Approaches
Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself
Analysis of the past demand pattern provides a good basis for forecasting future demand
Majority of quantitative approaches fall in the category of time series analysis

20. 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

21. 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
A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand
Analysis of the time series identifies patterns
Once the patterns are identified, they can be used to develop a forecast 14

22. Components of Time Series
What’s going on here? x x
Sales 1 2 3 4
Year 6

23. Components of Time Series
Trends are noted by an upward or downward sloping line
Seasonality is a data pattern that repeats itself over the period of one year or less
Cycle is a data pattern that repeats itself... may take years
Irregular variations are jumps in the level of the series due to extraordinary events
Random fluctuation from random variation or unexplained causes

24. Demand in next period is the same as demand in most recent period
1. Simple 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.

25. Moving Average: Naïve Approach
Jan 120
Feb 90
Mar 100
Apr 75
May 110
June 50
July 75
Aug 130
Sept 110
Oct 90
ORDERS
MONTH PER MONTH
Nov
FORECAST

26. Example: Naïve Approach
Jan 120
Feb 90
Mar 100
Apr 75
May 110
June 50
July 75
Aug 130
Sept 110
Oct 90
ORDERS
MONTH PER MONTH -
120 90
100 75
110 50
130
Nov
FORECAST

27. An averaging period (AP) is selected
Simple Moving Average
An averaging period (AP) is selected
Assumes an average is a good estimator of future behavior
It is called a “simple” average because each period used to compute the average is equally weighted

28. Simple Moving Average
The forecast for the next period is the arithmetic average of the AP most recent actual demands
By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response)
By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)

29. 2a. Simple Moving Average
Ft+1 = Forecast for the upcoming period, t+1
n = Number of periods to be averaged
A t = Actual occurrence in period t 15

30. 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 ?

31. 2a. Simple Moving Average
Month
Sales
(000)
Moving Average
(n=3) 1 4 NA 2 6 3 5 ?
(4+6+5)/3=5

32. 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 ?

33. 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 ?

34. 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 ?

35. 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 ?

36. Let’s develop 3-week and 6-week moving average forecasts for demand.
Simple Moving Average
Let’s develop 3-week and 6-week moving average forecasts for demand.
Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

37. Simple Moving Average 16

38. Simple Moving Average 17

39. Weighted Moving Average
This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal
This allows more recent demand data to have a greater effect on the moving average, therefore the forecast
. . . more

40. Weighted Moving Average
The weights must add to 1.0 and generally decrease in value with the age of the data
The distribution of the weights determine impulse response of the forecast

41. Weighted Moving Average
Adjusts moving average method to more closely reflect data fluctuations
WMAn =
i = 1 
Wi Di
where
Wi = the weight for period i, between 0 and 100 percent
 Wi = 1.00 n

42. Weighted Moving Average
Determine the 3-period weighted moving average forecast for period 4
Weights (adding up to 1.0):
t-1: .5
t-2: .3
t-3: .2 20

43. Solution 21

44. Weighted Moving Average Example
MONTH WEIGHT DATA
August 17% 130
September 33% 110
October 50% 90 3
i = 1 
Wi Di
November Forecast

45. Weighted Moving Average Example
MONTH WEIGHT DATA
August 17% 130
September 33% 110
October 50% 90 3
i = 1 
Wi Di
= (0.50)(90) + (0.33)(110) + (0.17)(130)
= orders
November Forecast

46. Exponential Smoothing
Averaging method
Weights most recent data more strongly
Reacts more to recent changes
Widely used, accurate method
Smoothing constant, α
applied to most recent data

47. 3a. Exponential Smoothing
Assumes the most recent observations have the highest predictive value
gives more weight to recent time periods
Need initial
forecast Ft
to start.
Ft+1 = Ft + a(At - Ft) et
Ft+1 = Forecast value for time t+1
At = Actual value at time t
 = Smoothing constant 24

48. 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

49. 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

50. 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

51. 3a. Exponential Smoothing – Example 1
Ft+1 = Ft + a(At - Ft) i Ai Fi
a =
This process continues
through week 10 26

52. 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

53. 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

54. 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

55. 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

56. 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?

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

58. 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)

59. Mean Absolute Deviation (MAD)

60. 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

61. MAD Example PERIOD DEMAND, At Ft ( =0.3) (At - Ft) |At - Ft|
– –
PERIOD DEMAND, At Ft ( =0.3) (At - Ft) |At - Ft|

62. MAD Calculation
 At - Ft  n
MAD = =
= 4.85
53.39 11

63. 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

64. 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

65. Regression Methods Linear regression Correlation
mathematical technique that relates a dependent variable to an independent variable in the form of a linear equation
Correlation
a measure of the strength of the relationship between independent and dependent variables

66. Simple Linear Regression
Relationship between one independent variable, X, and a dependent variable, Y.
Assumed to be linear (a straight line)
Form: Y = a + bX
Y = dependent variable
X = independent variable
a = y-axis intercept
b = slope of regression line

67. Simple Linear Regression Model
Yt = a + bx Y
x (weeks)
b is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight-forward as our usual notion of slope 35

68. Calculating a and b 36

69. Regression Equation Example
Develop a regression equation to predict sales
based on these five points. 37

70. Regression Equation Example
Slide 70 of 55 38

71. y = 143.5 + 6.3t Regression Equation Example 180 175 170 165 160 Sales
155
Sales
Forecast
150
145
140
135
Period 1 2 3 4 5
Slide 71 of 55 39

72. Linear Regression Example
x y
(WINS) (ATTENDANCE) xy x2
4 36.3
6 40.1
6 41.2
8 53.0
6 44.0
7 45.6
5 39.0
7 47.5

73. Linear Regression Example
y =
b =
a = y - bx
xy - nxy2
x2 - nx2

74. Linear Regression Example
x y
(WINS) (ATTENDANCE) xy x2

75. Linear Regression Example
y = =
b = =
= 4.06
a = y - bx
= (4.06)(6.125) = 49 8
346.9
xy - nxy2
x2 - nx2
(2,167.7) - (8)(6.125)(43.36)
(311) - (8)(6.125)2

76. Linear Regression Example
60,000 –
50,000 –
40,000 –
30,000 –
20,000 –
10,000 –
Linear regression line, y = x
Attendance, y
Attendance forecast for 7 wins
y = (7)
= 46.88, or 46,880
| | | | | | | | | | |
Wins, x

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

78. Linear Regression Example
x y
(WINS) (ATTENDANCE) xy x2

79. Linear Regression Example
y = =
b = =
= 4.06
a = y - bx
= (4.06)(6.125) = 49 8
346.9
xy - nxy2
x2 - nx2
(2,167.7) - (8)(6.125)(43.36)
(311) - (8)(6.125)2