operation Management

1. Demand Management
Processing,
Influencing, &
Anticipating
Demand
M ake S to r e B uy M ove Sell
B uy M ove M ake Sell B uy M ake
M ove M ove Sell S to r e
McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Managing the sell side of a business
Supplier Supply-Demand Management Customer
Relationship "Make, Move, Store" Relationship
Management Management
"Buy" Plant "Sell"
Plant Warehouse
Customers
Suppliers
Plant
4-2

3. Key questions
1. What is the scope of demand management?
2. What does order processing involve; why is it an important area for management
attention?
3. What is customer profit potential, & how is it relevant for influencing demand?
4. What are 5 alternatives for improving forecast accuracy, what do they mean, & how
can they be applied?
5. How do the tactics of part standardization & postponement of form or place help
improve forecast accuracy?
6. What is the difference between long term & short term forecasting?
7. What are 4 long term forecasting methods; what are the risks of
salesperson/customer input?
8. What are the components of demand, & which component is not forecasted?
9. How do the moving average, Winters, & focus forecasting methods work?
10. What is the role of the number of periods in the moving average method, & the
smoothing parameters in the Winters method?
11. What is the purpose of filtering, & why is it important for computer-based
forecasting?
12. What do the following principles of nature mean & how are they relevant for
demand management? (1) law of large numbers, (2) trumpet of doom, (3) recency
effect, (4) hockey stick effect, (5) Pareto phenomenon
13. What are the managerial insights from the chapter? 4-3

4. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
4-4

5. Scope of demand management
• So what is demand management?
 Concerned with processing, influencing,
and anticipating demand
• We’ll begin with processing demand or,
in more common terms, order
processing or order fulfillment
4-5

6. Processing Demand
Order processing
• Order processing is usually viewed to span
order booking to order shipment
• Example steps?
 Customer validation, order entry, credit checking,
pricing, design changes, availability checks, delivery
time estimation, notification of shipment, notification
of delays
4-6

7. Processing Demand
CUSTOMER ORDER ENTRY AND
CHECKING ER
Customer Validation P
Credit Control Operations…
ORDER
RETURNS INTERRUPTION
ORDER
PICKING AND
ASSEMBLY
CUSTOMER SERVICE
SHIPPING
INVOICING
4-7

8. Processing Demand
Characteristics
• Can be a complex & time consuming process
dealing largely with information flow
 Susceptible to ad hoc modifications over time in
response to problems (e.g., extra credit check added
due to expensive nonpaying customer a few years
ago)
• A major customer contact point with
organization
→ Can significantly impact customer perceptions
• IT advances & high customer impact
4-8

9. Processing Demand
Example 1
Benetton
• Electronic loop linking sales agent, factory, & warehouse
• If not available, measurements transferred to knitting
machine for production
• Benetton uses a single warehouse
 Staffed by 8 people & about 230,000 pieces shipped daily
4-9

10. Processing Demand
Example 2
K-Mart and MasterLock
• Policy for mistake in shipment or invoice
 Strike 1: $10,000, Strike 2: $50,000, Strike 3: lose
business
4-10

11. Processing Demand
Example 3 – customer tools
• Amazon online order tracking
4-11

12. Processing Demand
Example 4 – customer tools
• UPS online order tracking
4-12

13. Processing Demand
Example 4 – continued
• UPS online tools
4-13

14. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
4-14

15. Influencing Demand
Measure customer profit potential
A simple idea
• Some customers are more profitable than others
• Advancing technologies → more practical to estimate profit
potential of individual customers
• Can guide efforts/investments for customer retention &
acquisition . . . investments to influence demand
• E.g.,
 Electronics manufacturer: reviews historical customer profit before
sending service contract renewal
 Wireless phone firm: churn scores & lifetime value estimates
influence # of customer contacts & attractiveness of offerings
4-15

16. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
4-16

17. Forecasting Alternatives
Motivating example 1
Sunbeam
Improved forecasting led to 45% reduction in
inventory
 Included estimates from top 200 customers
4-17

18. Forecasting Alternatives
Motivating example 2
Apple
A history of problems forecasting demand
Many components sourced from 1 supplier -
accurate forecasts are critical
Over $1 billion in unfilled orders during the
crucial holiday season. The CEO (Spindler)
ousted a few months later
4-18

19. Forecasting Alternatives
Motivating example 3
IBM
Badly misjudged demand in PC business in 1996
– went from being profitable in 1995 to a $200
million loss through 1st half of 1996
4-19

20. Forecasting Alternatives
Motivating example 4
Christmas 1999 & e-commerce takes
off
Large unanticipated increase in Internet orders –
didn’t ship on time
E.g., Many Toys ‘R Us Christmas orders not delivered
until March – “I will never buy online again”
4-20

21. Forecasting Alternatives
Improvement alternatives
• Change the forecasting method
 Collect more or different data
 Analyze the information differently
• E.g., involve more people, new forecasting software, spend more
time manually reviewing, focus groups etc.
• Change operations or operating policies
 Introduce early warning mechanisms
 Take advantage of the law of large numbers
 Reduce information delays & leadtimes (trumpet of doom)
4-21

22. Forecasting Alternatives
Early warning
• Change policies so that some (or more)
customers provide earlier commitment of
future demand, e.g.,
 Early bird program for builder markets – discount for
60-day advance order
 Invite large buyers to Aspen in February to view next
year’s skiwear line, & encourage orders
• “Commitment” ≠ asking customers how much
they are likely to buy next quarter
4-22

23. Forecasting Alternatives
Law of large numbers
Principle of Nature
• As volume increases, relative variability decreases
 Postponement in form or place, e.g.,
• Dell – configure your own PC
• From full product line at 12 regional DCs to full product line at a
single super DC, with 10% of product line stocked at 11 regional
DCs (i.e., fast movers that account for 70% of sales)
 Part standardization, e.g.,
• Arby’s sandwich wrappers; plastic lids with push down drink
indicator
• Intel Pentium processors all the same size
- 2.8 GHz tests out below 2.8 spec can be sold as a 2.66 GHz chip (“down- 4-23

24. Forecasting Alternatives
Trumpet of doom
Principle of Nature F o re c a s t E rro r R a n g e o ve r T im e
• As forecast horizon P e rc e n tag e
increases, accuracy F o rec ast 0
E rro r
decreases, e.g.,
0 T im e U n til F o re c as t E ve n t
 Reduce production & delivery leadtimes
• Dell pick-to-light system for assembly
 Reduce information delays
• EDI transmission of daily consumer demand up
through multiple levels in the supply chain
4-24

25. Forecasting Alternatives
Reduce demand volatility
2 Principles of Nature
• Beware of product proliferation
 Pareto analysis – separating the important few from the trivial many
 Periodic length of line analysis to critically assess whether to continually
offer “slow movers”
 Principle of Nature: Pareto phenomenon – the lion’s share of an aggregate
measure is determined by relatively few factors
• E.g., “the 80-20 rule” – 80% of demand is due to 20% of product line
• Beware of perverse cycle of promotions – customers wait for
sale before buying, thereby forcing a sale
 A step further – dynamic pricing to stabilize demand & align with supply
• Reduce the hockey stick effect… 4-25

26. Forecasting Alternatives
Hockey stick effect
Principle of Nature
• Volume tends to pick up towards the end
of a reporting period . . . why?
• Look for ways to lessen the effect –
contributes to demand volatility,
inefficiency, poor service
Jan Feb
4-26

27. Forecasting Alternatives
Channel stuffing
One contributor to the hockey stick effect
Lots of sales booked near the end of a quarter,
then sales drop off at the start of the next
quarter
E.g.,
 A large brewer offered a vacation to the salesperson
in each region who sold the most beer to stores over
a 3 month period
 One winner was able to convince a few stores to free
up backroom space and fill it entirely with beer
4-27

28. Forecasting Alternatives
Improvement alternatives
• We’re about to focus on
methods for predicting short pork bellies
demand
• But, important to remember . . . many creative ways to
improve forecast accuracy that have nothing to do with
method
– E.g., early warning incentives, law of large numbers, trumpet of
doom, reduce demand volatility
4-28

29. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
4-29

30. Long Term Forecasting
Characteristics of long term forecasts
• Single or multi-year horizon
• Monthly or annual time bucket
• Aggregate units
 Input to “long term” decisions
• Accuracy generally more important than short term
forecasts . . . why?
• Tend to use expensive & time consuming methods . . .
due to the preceding point & due to a PON . . . which is?
4-30

31. Long Term Forecasting
Recency effect
Principle of Nature
Humans tend to overreact to (or be overly
influenced by) recent events
E.g.,
Hughes Electronics Corp. developed an artificial
intelligence based financial trading system. The
developers did this by encoding the wisdom of
Christine Downton, a successful portfolio manager.
One motivation for creating the system is that it is
immune to the recency effect, i.e., humans tend to
get overly fixated on the most recent information.
4-31

32. Long Term Forecasting
Some alternative methods
• Judgment
• Salesperson & customer input
 Great information source, but beware of bias potential
& recency effect = humans tend to be overly
influenced by recent events
• Outside services
• Causal methods . . . examples?
4-32

33. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
 Characteristics
 Components of demand
 Moving average
 Winters method
 Focus forecasting
 Filtering
4-33

34. Short Term Forecasting
Long term/short term characteristics
Long term forecasts Short term forecasts
 Single or multi-year horizon  Weekly or monthly horizon
 Monthly or annual time bucket  Daily & weekly time bucket
 Aggregate units (e.g., product/  Detailed units (e.g., SKU)
service categories)
 Input to “short term” decisions
 Input to “long term” decisions
 Inexpensive & quick methods
 Expensive & time consuming
methods • Accuracy importance
• Accuracy importance • Trumpet of doom
• Trumpet of doom
Could argue using 2 different principles of nature that it’s [easier?/harder?] to be
accurate with short term forecasting than with long term forecasting
4-34

35. Definition of the Forecasting Process
• The Art and Science of Predicting Future
Events
 Forecasting vs. Predicting
 Based on Past Data
 Economic vs. Demand Forecasting
4-35

36. Elements of Demand Forecasting
• Dynamic in Nature
• Consider Uncertainty (Stochastic)
• Rely on Information contained in Past
Data
• Applied to various time horizons
 short term
 medium term forecasts
 long term forecasts
4-36

37. Steps in the Forecasting Process
• Determine the Use of the Forecast
• Select the Items to be Forecasted
• Determine a Suitable Time Horizon
• Select an appropriate Set of Forecasting Models
• Gather Relevant Data
• Conduct the Analysis
• Validate the Model - Assess its Accuracy
• Make the Forecast
• Implement the Results
4-37

38. Independent Demand:
What a firm can do to manage it?
• Can take an active role to influence
demand
FORECASTING
• Can take a passive role and simply
respond to demand
4-38

39. Types of Forecasts
• Qualitative (Judgmental)
• Quantitative
 Time Series Analysis
 Causal Relationships
 Simulation
4-39

40. Qualitative Methods
Executive Judgment Grass Roots
Qualitative Market Research
Historical analogy
Methods
Delphi Method Panel Consensus
4-40

41. Delphi Method
1. Choose the experts to participate representing a
variety of knowledgeable people in different
areas
2. Through a questionnaire (or E-mail), obtain
forecasts (and any premises or qualifications for
the forecasts) from all participants
3. Summarize the results and redistribute them to
the participants along with appropriate new
questions
4. Summarize again, refining forecasts and
conditions, and again develop new questions
5. Repeat Step 4 as necessary and distribute the
final results to all participants
4-41

42. Quantitative Forecasting Models
• Both Pattern Based and Correlational
Models rest on the assumption that the
relationships of the past will continue
into the Future
• Both can Mathematically Characterize the
Probabilistic Nature of the Forecast
• Both Use Information from Relevant
Time Frames
4-42

43. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
 Characteristics
 Components of demand
 Moving average
 Winters method
 Focus forecasting
 Filtering
4-43

44. Components of Demand
• Average demand for a period of
time
• Trend
• Seasonal element
• Cyclical elements
• Random variation
• Autocorrelation
4-44

45. Pattern Based Analyses
• Definition
 Identifying an underlying pattern in historical
data, describe it in mathematical terms, and
then extrapolate it into the future
• Uses a “Time Series” of Past Data
4-45

46. Time Series Variation
• Time Series of Demand Data Typically
Contain Four Components of Variation
About the Mean or Average
• Pattern Based Forecasting Needs to
Mathematically Characterize Each of
these
4-46

47. Finding Components of Demand
Seasonal variation
Seasonal variation
x
x x Linear
x x Linear
x x
x x Trend
Trend
Sales
x x x
x
x
xx
x xx x x
x
x
Average
x x x
x x x x Average
x x x xxx x
x x
x xxxx
x
x x
1 2 3 4
Year
4-47

48. Time Series Analysis
• Time series forecasting models try to
predict the future based on past data
• You can pick models based on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel
4-48

49. Simple Moving Average Formula
• The simple moving average model assumes
an average is a good estimator of future
behavior
• The formula for the simple moving average
is:
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
n
Ft = Forecast for the coming period
n = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n”
periods
4-49

50. Simple Moving Average Problem (1)
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
Week Demand n
1 650 Question: What are the 3-
Question: What are the 3-
2 678 week and 6-week moving
week and 6-week moving
3 720 average forecasts for
average forecasts for
4 785 demand?
demand?
5 859
Assume you only have 3
Assume you only have 3
6 920
7 850
weeks and 6 weeks of
weeks and 6 weeks of
8 758
actual demand data for the
actual demand data for the
9 892 respective forecasts
respective forecasts
10 920
11 789
12 844
4-50

51. Calculating the moving averages gives us:
Week Demand 3-Week 6-Week
1 650 F4=(650+678+720)/3
2 678
=682.67
3 720 F7=(650+678+720
4 785 682.67 +785+859+920)/6
5 859 727.67
=768.67
6 920 788.00
7 850 854.67 768.67
8 758 876.33 802.00
9 892 842.67 815.33
10 920 833.33 844.00
11 789 856.67 866.50
12 844 867.00 854.83
4-51

52. Plotting the moving averages and comparing
Plotting the moving averages and comparing
them shows how the lines smooth out to reveal
them shows how the lines smooth out to reveal
the overall upward trend in this example
the overall upward trend in this example
1000
900
Demand
800
Demand
3-Week
700
6-Week
600
500 Note how the
Note how the
1 2 3 4 5 6 7 8 9 10 11 12 3-Week is
3-Week is
Week smoother than
smoother than
the Demand,
the Demand,
and 6-Week is
and 6-Week is
even smoother
even smoother
4-52

53. Simple Moving Average Problem (2) Data
Question: What is the 3
Question: What is the 3
week moving average
week moving average
forecast for this data?
forecast for this data?
Week Demand
1 820 Assume you only have
Assume you only have
3 weeks and 5 weeks
3 weeks and 5 weeks
2 775 of actual demand
of actual demand
3 680 data for the
data for the
4 655 respective forecasts
respective forecasts
5 620
6 600
7 575
4-53

54. Simple Moving Average Problem (2) Solution
Week Demand 3-Week 5-Week
1 820 F4=(820+775+680)/3
2 775 =758.33
3 680 F6=(820+775+680
+655+620)/5
4 655 758.33 =710.00
5 620 703.33
6 600 651.67 710.00
7 575 625.00 666.00
4-54

55. Weighted Moving Average Formula
While the moving average formula implies an equal
While the moving average formula implies an equal
weight being placed on each value that is being
weight being placed on each value that is being
averaged, the weighted moving average permits an
averaged, the weighted moving average permits an
unequal weighting on prior time periods
unequal weighting on prior time periods
The formula for the moving average is:
The formula for the moving average is:
Ft = w1A t-1 + w 2 A t-2 + w 3A t-3 +...+w n A t-n
n
wt = weight given to time period “t”
wt = weight given to time period “t”
occurrence (weights must add to one)
∑w i =1
occurrence (weights must add to one) i=1
4-55

56. Weighted Moving Average Problem (1) Data
Question: Given the weekly demand and weights, what is
Question: Given the weekly demand and weights, what is
the forecast for the 4th period or Week 4?
the forecast for the 4th period or Week 4?
Week Demand Weights:
1 650
2 678 t-1 .5
3 720 t-2 .3
4 t-3 .2
Note that the weights place more emphasis on the
Note that the weights place more emphasis on the
most recent data, that is time period “t-1”
most recent data, that is time period “t-1”
4-56

57. Weighted Moving Average Problem (1) Solution
Week Demand Forecast
1 650
2 678
3 720
4 693.4
F4 = 0.5(720)+0.3(678)+0.2(650)=693.4
4-57

58. Weighted Moving Average Problem (2) Data
Question: Given the weekly demand information and
Question: Given the weekly demand information and
weights, what is the weighted moving average forecast
weights, what is the weighted moving average forecast
of the 5th period or week?
of the 5th period or week?
Week Demand Weights:
1 820 t-1 .7
2 775 t-2 .2
3 680
t-3 .1
4 655
4-58

59. Weighted Moving Average Problem (2) Solution
Week Demand Forecast
1 820
2 775
3 680
4 655
5 672
F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672
4-59

60. Short Term Forecasting – Moving Average and Weighted Moving Average
Some pros/cons
1. Simple (+)
2. Designated weights of history (-)
3. History cut-off beyond m periods (-)
4-60

61. Exponential Smoothing Model
Ftt = Ft-1 + α(At-1 - Ft-1)
F = Ft-1 + α(At-1 - Ft-1)
Where :
Ft = Forcast value for the coming t time period
Ft - 1 = Forecast value in 1 past time period
At - 1 = Actual occurance in the past t time period
α = Alpha smoothing constant
• 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
4-61

62. Exponential Smoothing Problem (1) Data
Question: Given the
Question: Given the
Week Demand weekly demand data,
weekly demand data,
1 820 what are the
what are the
exponential smoothing
exponential smoothing
2 775 forecasts for periods 2-
forecasts for periods 2-
3 680 10 using α=0.10 and
10 using α=0.10 and
4 655 α=0.60?
α=0.60?
Assume F1=D11
Assume F1=D
5 750
6 802
7 798
8 689
9 775
10
4-62

63. Answer: The respective alphas columns denote the forecast
values. Note that you can only forecast one time period into
the future.
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
4-63

64. Exponential Smoothing Problem (1) Plotting
Note how that the smaller alpha results in a smoother line
Note how that the smaller alpha results in a smoother line
in this example
in this example
900
800 Demand
Demand
700 0.1
600 0.6
500
1 2 3 4 5 6 7 8 9 10
Week
4-64

65. Exponential Smoothing Problem (2) Data
Question: What are
Question: What are
Week Demand
the exponential
the exponential
1 820
smoothing forecasts
smoothing forecasts
2 775 for periods 2-5 using
for periods 2-5 using
3 680 a =0.5?
a =0.5?
4 655
5
Assume F11=D11
Assume F =D
4-65

66. Exponential Smoothing Problem (2) Solution
F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75
Week Demand 0.5
1 820 820.00
2 775 820.00
3 680 797.50
4 655 738.75
5 696.88
4-66

67. Seasonal Adjustments
• Applied to Moving Averages and Time
Series Regression
• First, Calculate a Seasonal Index (SI)
Factor for Each Relevant Time Period
(day, week, month, quarter)
• Each Seasonal Period’s SI is
Calculated by Averaging the Ratio of
its Actual Demand to the Forecast
Demand for all Corresponding Periods
4-67

68. Seasonal Adjustments
• Forecast for Future Periods is Calculated
by Multiplying the Unadjusted Moving
Average or Time Series Forecast for a
given Period by the Corresponding
Seasonal Index for that Period
• i.e. if the SMA forecast for the month of
March is 27 and the SI for March is
1.125, then
• Emar = 27*1.125 = 30.375
4-68

69. Seasonal Adjustment Example
Seasonal Adjustments
Sales Demand
Monthly Overall SI Adjusted
Month 1993 1994 Seasonal Index
Average Average Forecast
Jan 80 100 90.00 94.00 0.96 86.17
Feb 75 85 80.00 94.00 0.85 68.09
Mar 80 90 85.00 94.00 0.90 76.86
Apr 90 110 100.00 94.00 1.06 106.38
May 115 131 123.00 94.00 1.31 160.95
Jun 110 120 115.00 94.00 1.22 140.69
Jul 100 110 105.00 94.00 1.12 117.29
Aug 90 110 100.00 94.00 1.06 106.38
Sep 85 95 90.00 94.00 0.96 86.17
Oct 75 85 80.00 94.00 0.85 68.09
Nov 75 85 80.00 94.00 0.85 68.09
Dec 80 80 80.00 94.00 0.85 68.09
Average 87.92 100.08
Expected Demand for 1995 = 1153.23
4-69

70. Seasonal Adjustments
Example Graph
Seasonal Adjusted Forecasting 1993
1994
170
SI Adjusted
Forecast
150
Overall
Average
130
110
90
70
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
4-70

71. Evaluating Forecast Accuracy
• Use of Residuals Analyses
 Residuals are the Difference Between the
Forecast and the Actual Demand for a Given
Period
• Assessed by Several Measures
 Mean Absolute Deviation - MAD
 Mean Squared Error - MSE
 Tracking Signal
4-71

72. The MAD Statistic to Determine
Forecasting Error
n
1 MAD ≈ 0.8 standard deviation
∑A
t=1
t - Ft
1 standard deviation ≈ 1.25 MAD
MAD =
n
• The ideal MAD is zero which would
mean there is no forecasting error
• The larger the MAD, the less the
accurate the resulting model
4-72

73. MAD Problem Data
Question: What is the MAD value given
Question: What is the MAD value given
the forecast values in the table below?
the forecast values in the table below?
Month Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
4-73

74. MAD Problem Solution
Month Sales Forecast Abs Error
1 220 n/a
2 250 255 5
3 210 205 5
4 300 320 20
5 325 315 10
40
n
Note that by itself, the MAD
∑A
t=1
t - Ft
40
Note that by itself, the MAD
only lets us know the mean
only lets us know the mean
MAD = = = 10 error in a set of forecasts
error in a set of forecasts
n 4
4-74

75. Evaluating Forecast Accuracy
Mean Absolute Deviation - MAD
• Exponentially Smoothed MAD
 MADt = αMAD|Dt - Forecastt| + (1- αMAD)MADt-1
4-75

76. Evaluating Forecast Accuracy
Mean Squared Error - MSE
• MSE = (Σ(Di - Forecasti)2)/n
Time Time
Actual Squared
Period Series Series
Demand Error
Forecast Residual
1 12 12.16 -0.16 0.03
2 13 12.13 0.87 0.76
3 10 12.09 -2.09 4.39
4 11 12.06 -1.06 1.13
5 10 12.03 -2.03 4.12
6 14 12.00 2.00 4.01
7 16 11.97 4.03 16.28
8 15 11.93 3.07 9.40
9 13 11.90 1.10 1.21
10 8 11.87 -3.87 14.97
11 10 11.84 -1.84 3.37
12 12 11.80 0.20 0.04
13 9 11.77 -2.77 7.69
14 13 11.74 1.26 1.59
15 13 11.71 1.29 1.67
MSE = 4.71
RMSE = 2.17
4-76

77. Tracking Signal Formula
• The Tracking Signal or TS is a measure that
indicates whether the forecast average is
keeping pace with any genuine upward or
downward changes in demand.
• Depending on the number of MAD’s selected, the
TS can be used like a quality control chart
indicating when the model is generating too
much error in its forecasts.
• The TS formula is:
RSFE Running sum of forecast errors
TS = =
MAD Mean absolute deviation
4-77

78. Evaluating Forecast Accuracy
Tracking Signal
• Tracking Signal = Running Sum of
Forecast Error / MAD = RSFE/MAD
Time Time
Actual Tracking
Period Series Series RSFE MAD
Demand Signal
Forecast Residual
1 12 12.16 -0.16 -0.16 0.03 -5.00
2 13 12.13 0.87 0.72 0.20 3.58
3 10 12.09 -2.09 -1.38 0.58 -2.38
4 11 12.06 -1.06 -2.44 0.68 -3.61
5 10 12.03 -2.03 -4.47 0.95 -4.72
6 14 12.00 2.00 -2.47 1.16 -2.13
7 16 11.97 4.03 1.57 1.73 0.90
8 15 11.93 3.07 4.63 2.00 2.32
9 13 11.90 1.10 5.73 1.82 3.15
10 8 11.87 -3.87 1.86 2.23 0.84
11 10 11.84 -1.84 0.03 2.15 0.01
12 12 11.80 0.20 0.22 1.76 0.13
13 9 11.77 -2.77 -2.55 1.96 -1.30
14 13 11.74 1.26 -1.29 1.82 -0.71
15 13 11.71 1.29 0.00 1.72 0.00
4-78

79. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
 Characteristics
 Components of demand
 Moving average
 Winters method
 Focus forecasting
 Filtering 4-79

80. Short Term Forecasting – Winters
Old man winters
Winters method used to forecast one period into the future
See how method detects patterns & adapts to market changes over
time
Old Man Winters in Action
600.00
500.00
400.00
Volum e
Actual
300.00
Forecast
200.00
100.00
0.00
0 20 40 60 80 100
Tim e
4-80

81. Short Term Forecasting – Winters
Key to Winters method
• Winters is an exponential smoothing
method
• Smoothing is based on a key idea
 For each component (which are?), a portion
of difference between estimate & actual is
due to randomness & certain portion due
to real change
4-81

82. Short Term Forecasting – Winters
Smoothing in action...
• New estimate = old estimate + (some
percentage)(error)
• Smoothes out peaks & valleys (i.e.,
randomness) of actual 4-82

83. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
 Characteristics
 Components of demand
 Moving average
 Winters method
 Focus forecasting
 Filtering
4-83

84. Short Term Forecasting – Focus
Bernie’s insight…
…or what is focus forecasting?
• An intuitive & successful idea
• Regularly use a # of different methods to
generate forecasts
• Maintain historical accuracy information on each
method
• Use the most accurate method to generate
“official” forecasts
4-84

85. Short Term Forecasting – Focus
Advertisement
appearing in
APICS The
Performance
Advantage
4-85

86. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
 Characteristics
 Components of demand
 Moving average
 Winters method
 Focus forecasting
 Filtering 4-86

87. Short Term Forecasting – Filtering
Two types of filters
• An important feature of computer-based forecasting
systems
 Large amounts of data – impractical to manually review all
1. For data input errors (e.g., typos, scanner errors)
 If |“actual” - forecast| > limit, then report
2. For unacceptable forecast errors (e.g., warranting
management attention)
 If average absolute error > limit, then report
4-87

88. Road map
• Processing Demand
• Influencing Demand
• How to Improve Forecast Accuracy
• Long Term Forecasting
• Short Term Forecasting
• Dependent Demand
• Correlational Forecasting
• Summary
4-88

89. Demand Management
Bill of Materials (BOM)
Independent Demand:
Finished Goods
A Dependent Demand:
Raw Materials,
Component parts,
Sub-assemblies, etc.
B(4) C(2)
D(2) E(1) D(3) F(2)
4-89

90. Web-Based Forecasting: CPFR
• Collaborative Planning, Forecasting, and
Replenishment (CPFR) a Web-based tool used to
coordinate demand forecasting, production and
purchase planning, and inventory replenishment
between supply chain trading partners.
• Used to integrate the multi-tier or n-Tier supply
chain, including manufacturers, distributors and
retailers.
• CPFR’s objective is to exchange selected internal
information to provide for a reliable, longer term
future views of demand in the supply chain.
• CPFR uses a cyclic and iterative approach to
derive consensus forecasts.
4-90

91. Web-Based Forecasting:
Steps in CPFR
1. Creation of a front-end partnership
agreement
2. Joint business planning
3. Development of demand forecasts
4. Sharing forecasts
5. Inventory replenishment
4-91

92. Correlational Forecasting
• Assumes an Outcome is Dependent an
Existing Relationship Between the
Demand Variable and Some other
Independent Variable(s)
 Demand Variable is Dependent Variable
 Other Related Variables are Independent
Variables
 Generally Expressed as a Multiple Linear
Regression Model
• Y = β0 + β1 X1+ β2 X2+ β2 X2+ . . . βnXn+ εi
4-92

93. Simple Linear Regression Model
The simple linear regression
The simple linear regression Y
model seeks to fit a line
model seeks to fit a line
through various data over
through various data over
time a
time
0 1 2 3 4 5 x (Time)
Yt = a + bx Is the linear regression model
Is the linear regression model
- Yt is the regressed forecast value or dependent
variable in the model
-a is the intercept value of the the regression line, and
- b is similar to the slope of the regression line.
- 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.
4-93

94. Simple Linear Regression Formulas for
Calculating “a” and “b”
a = y - bx
∑ xy - n(y)(x)
b= 2 2
∑ x - n(x )
4-94

95. Simple Linear Regression Problem Data
Question: Given the data below, what is the simple linear
Question: Given the data below, what is the simple linear
regression model that can be used to predict sales in future
regression model that can be used to predict sales in future
weeks?
weeks?
Week Sales
1 150
2 157
3 162
4 166
5 177
4-95

96. Answer: First, using the linear regression formulas, we
Answer: First, using the linear regression formulas, we
can compute “a” and “b”
can compute “a” and “b”
Week Week*Week Sales Week*Sales
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885
3 55 162.4 2499
Average Sum Average Sum
b=
∑xy - n( y)(x) = 2499 - 5(162.4)(3) = 63 = 6.3
∑x - n(x )
2 2
55 − 5(9 ) 10
a = y - bx = 162.4 - (6.3)(3) = 143.5
4-96

97. 97
The resulting regression model
is: Yt = 143.5 + 6.3x
Now if we plot the regression generated forecasts against the
actual sales we obtain the following chart:
180
175
170
165
160 Sales
Sales
155 Forecast
150
145
140
135
1 2 3 4 5
Period
4-97

98. Statistical Assumptions of Multiple Linear
Regression
• The Error Term (the residual εi) is
Normally Distributed
• There is no Serial Correlation Among
Error Terms
• Magnitude of the Error Term is
Independent of the Size of Any of the
Independent Variables - Xi
• Assumptions Can be Tested Through
Analyses of the Residuals - εi
4-98

99. Major Statistical Problems of Multiple
Linear Regression
• Multicolinarity
• Use of Time-Lagged Independent
Variables
• Both of These Problems Result in Models
with Potentially Valid Predictions, but the
Reliability of the β Coefficients is
Questionable
4-99

100. Demand Management
The End
Processing,
Influencing, &
Anticipating
Demand
M ake S to r e B uy M ove Sell
B uy M ove M ake Sell B uy M ake
M ove M ove Sell S to r e
McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved.