ISM 270 Service Engineering and Management Lecture 6: Forecasting.
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Transcript of ISM 270 Service Engineering and Management Lecture 6: Forecasting.
Vijay Mehrotra
Director and Associate Professor, San Francisco University
Author of regular column ‘Analyze This!’ in Analytics Magazine
Former CEO of Onward, Inc, which became part of Blue Pumpkin and Advertising.com
Ph.D. Stanford, 1992
Forecasting Models Subjective Models
Delphi Methods Causal Models
Regression Models Time Series Models
Moving AveragesExponential Smoothing
Delphi ForecastingQuestion: In what future election will a woman become president of the united states for
the first time?
Year 1st Round Positive Arguments 2nd Round Negative Arguments 3rd Round
2008
2012
2016
2020
2024
2028
2032
2036
2040
2044
2048
2052
Never
Total
N Period Moving Average
Let : MAT = The N period moving average at the end of period T AT = Actual observation for period T
Then: MAT = (AT + AT-1 + AT-2 + …..+ AT-N+1)/N
Characteristics: Need N observations to make a forecast Very inexpensive and easy to understand Gives equal weight to all observations Does not consider observations older than N periods
Moving Average Example
Saturday Occupancy at a 100-room Hotel
Three-period Saturday Period Occupancy Moving Average Forecast
Aug. 1 1 79 8 2 84 15 3 83 82 22 4 81 83 82 29 5 98 87 83Sept. 5 6 100 93 87 12 7 93
Exponential Smoothing
Let : ST = Smoothed value at end of period T AT = Actual observation for period T FT+1 = Forecast for period T+1
Feedback control nature of exponential smoothing
New value (ST ) = Old value (ST-1 ) + [ observed error ]
S S A S
S A S
F S
T T- T T
T T T
T T
1 1
1
1
1
[ ]
( )or :
Exponential SmoothingHotel Example
Saturday Hotel Occupancy ( =0.5) Actual Smoothed Forecast Period Occupancy Value Forecast ErrorSaturday t At St Ft |At - Ft|Aug. 1 1 79 79.00 8 2 84 81.50 79 5 15 3 83 82.25 82 1 22 4 81 81.63 82 1 29 5 98 89.81 82 16Sept. 5 6 100 94.91 90 10
Mean Absolute Deviation (MAD) = 6.6 Forecast Error (MAD) = ΣlAt – Ftl/n
Exponential SmoothingImplied Weights Given Past Demand
S A S
S A A S
S A A S
S A A S
T T T
T T T T
T T T T
T T T T
( )
( )[ ( ) ]
( )[ ( ) ]
( ) ( )
1
1 1
1 1
1 1
1
1 1 2
1 2
12
2
Substitute for
If continued:
S A A A A ST T T TT T ( ) ( ) ..... ( ) ( )1 1 1 11
22
11 0
Exponential Smoothing Weight Distribution
0
0.1
0.2
0.3
0 1 2 3 4 5
Age of Observation (Period Old)
Wei
ght
0 3.
( ) .1 0 21
( ) .1 01472 ( ) .1 01033
( ) .1 0 0724
( ) .1 0 0505
Relationship Between and N
(exponential smoothing constant) : 0.05 0.1 0.2 0.3 0.4 0.5 0.67 N (periods in moving average) : 39 19 9 5.7 4 3 2
Saturday Hotel Occupancy
Effect of Alpha ( =0.1 vs. =0.5)
7580859095
100105
0 1 2 3 4 5 6Period
Occ
upan
cy
Actual
Forecast
Forecast
( . ) 05
( . ) 01
Estimate the relationship of price and promotion changes to volume
pos_units Intercept Seasonality Holiday Price Change Ad Display
Ad Flag
Display Flag
Base Price
Once estimated separately, all these effects can be combined to
predict volume. This is the model.
POS Price Change Display Ad Holiday Seasonality Intercept
Exponential Smoothing With Trend Adjustment
S A S T
T S S T
F S T
t t t t
t t t t
t t t
( ) ( )( )
( ) ( )
1
11 1
1 1
1
Commuter Airline Load Factor
Week Actual load factor Smoothed value Smoothed trend Forecast Forecast error t At St Tt Ft | At - Ft|
1 31 31.00 0.00 2 40 35.50 1.35 31 9 3 43 39.93 2.27 37 6 4 52 47.10 3.74 42 10 5 49 49.92 3.47 51 2 6 64 58.69 5.06 53 11 7 58 60.88 4.20 64 6 8 68 66.54 4.63 65 3 MAD = 6.7
( . , . ) 05 0 3
Exponential Smoothing with Seasonal Adjustment
S A I S
F S I
IA
SI
t t t L t
t t t L
tt
tt L
( / ) ( )
( )( )
( )
1
1
1
1 1
Ferry Passengers taken to a Resort Island Actual Smoothed Index Forecast ErrorPeriod t At value St It Ft | At - Ft| 2003January 1 1651 ….. 0.837 ….. February 2 1305 ….. 0.662 ….. March 3 1617 ….. 0.820 …..April 4 1721 ….. 0.873 ….. May 5 2015 ….. 1.022 …..June 6 2297 ….. 1.165 ….. July 7 2606 ….. 1.322 ….. August 8 2687 ….. 1.363 ….. September 9 2292 ….. 1.162 …..October 10 1981 ….. 1.005 …..November 11 1696 ….. 0.860 …..December 12 1794 1794.00 0.910 ….. 2004January 13 1806 1866.74 0.876 - - February 14 1731 2016.35 0.721 1236
495March 15 1733 2035.76 0.829 1653 80
( . , . ) 0 2 0 3
More sophisticated forecasting techniques
Nonlinear Regression Data mining Machine Learning Simulation-based
The Nature of Project Management
Characteristics of Projects: purpose, life cycle, interdependencies, uniqueness, and conflict.
Project Management Process: planning (work breakdown structure), scheduling, and controlling.
Selecting the Project Manager: credibility, sensitivity, ability to handle stress, and leadership.
Building the Project Team: Forming, Storming, Norming, and Performing.
Principles of Effective Project Management: direct people individually and as a team, reinforce excitement, keep everyone informed, manage healthy conflict, empower team, encourage risk taking and creativity.
Project Metrics: Cost, Time, Performance
Work Breakdown Structure
1.0 Move the hospital (Project)1.1 Move patients (Task)
1.1.1 Arrange for ambulance (Subtask)1.1.1.1 Prepare patients for move1.1.1.2 Box patients personnel
effects1.2 Move furniture
1.2.1. Contract with moving company•••
Project Management Questions
What activities are required to complete a project and in what sequence?
When should each activity be scheduled to begin and end?
Which activities are critical to completing the project on time?
What is the probability of meeting the project completion due date?
How should resources be allocated to activities?
Example: Planning a Tennis Tournament
What is the earliest / latest each activity can be begin / be completed?
Given the plan, how likely is it that things will run behind schedule?
Tennis Tournament Activities
ID Activity Description Network Immediate Duration Node Predecessor (days)1 Negotiate for Location A - 22 Contact Seeded Players B - 83 Plan Promotion C 1 34 Locate Officials D 3 25 Send RSVP Invitations E 3 106 Sign Player Contracts F 2,3 47 Purchase Balls and Trophies G 4 48 Negotiate Catering H 5,6 19 Prepare Location I 5,7 310 Tournament J 8,9 2
Notation for Critical Path Analysis
Item Symbol Definition
Activity duration t The expected duration of an activity
Early start ES The earliest time an activity can begin if all previous activities are begun at their earliest times
Early finish EF The earliest time an activity can be completed if it is started at its early start time
Late start LS The latest time an activity can begin without delaying the completion of the project
Late finish LF The latest time an activity can be completed if it is started at its latest start time
Total slack TS The amount of time an activity can be delayed without delaying the completion of the project
Scheduling Formulas
ES = EFpredecessor (max) (1)
EF = ES + t (2)
LF = LSsuccessor (min) (3)
LS = LF - t (4)
TS = LF - EF (5)
TS = LS - ES (6) or
Early Start Gantt Chart for Tennis Tournament
ID Activity Days Day of Project Schedule 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20A Negotiate for 2 LocationB Contact Seeded 8 PlayersC Plan Promotion 3
D Locate Officials 2
E Send RSVP 10 InvitationsF Sign Player 4 ContractsG Purchase Balls 4 and TrophiesH Negotiate 1 CateringI Prepare Location 3
J Tournament 2
Personnel Required 2 2 2 2 2 3 3 3 3 3 3 2 1 1 1 2 1 1 1 1
Critical Path ActivitiesActivities with Slack
Resource Leveled Schedule for Tennis Tournament
ID Activity Days Day of Project Schedule 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20A Negotiate for 2 LocationB Contact Seeded 8 PlayersC Plan Promotion 3
D Locate Officials 2
E Send RSVP 10 InvitationsF Sign Player 4 ContractsG Purchase Balls 4 and TrophiesH Negotiate 1 CateringI Prepare Location 3
J Tournament 2
Personnel Required 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 1 1
Critical Path ActivitiesActivities with Slack
Incorporating Uncertainty in Activity times
A M D B
F(D) P(D<A) = .01
P(D>B) = .01
optimistic most pessimistic likely
TIME
Formulas for Beta Distribution of Activity Duration
Expected Duration
DA M B_
4
6
Variance
VB A
6
2
Note: (B - A )= Range or 6
Activity Means and Variances for Tennis Tournament
Activity A M B D V A 1 2 3 11 .111 B 5 8 11 C 2 3 4 D 1 2 3 E 6 9 18 F 2 4 6 G 1 3 11 H 1 1 1 I 2 2 8 J 2 2 2
Uncertainly Analysis
Assumptions1. Use of Beta Distribution and Formulas For D and V2. Activities Statistically Independent3. Central Limit Theorem Applies ( Use “student t” if less than 30 activities on CP) 4. Use of Critical Path Activities Leading Into Event Node
ResultProject Completion Time Distribution is Normal With:
For Critical Path Activities
For Critical Path Activities
D_
2 V
Completion Time Distribution for Tennis Tournament
Critical Path Activities D V A 2 4/36 C 3 4/36 E 10 144/36 I 3 36/36 J 2 0
= 20 188/36 = 5.2 = 2
Question
What is the probability of an overrun if a 24 day completion time is promised?
24
P (Time > 24) = .5 - .4599 = .04 or 4%
Days
2 52 .
ZX
Z
Z
24 20
52175
..
Costs for Hypothetical ProjectC
ost
(0,0)
Schedule with Minimum Total Cost
Duration of Project
Total Cost
Indirect Cost
Opportunity Cost
Direct Cost
Activity Cost-time Tradeoff
C
C*
D* D Activity Duration (Days)
Normal
CrashSlope is cost to expedite per day
Cost
Sometimes opportunity is presented to ‘crash’ a project - Spend lots of money to get ahead of (back on) schedule
Cost-Time Estimates for Tennis
Tournament
Time Estimate Direct Cost Expedite CostActivity Normal Crash Normal Crash Slope A 2 1 5 15 10 B 8 6 22 30 4 C 3 2 10 13 D 2 1 11 17 E 10 6 20 40 F 4 3 8 15 G 4 3 9 10 H 1 1 10 10 I 3 2 8 10 J 2 1 12 20 Total 115
*
*
DD
CCS
Progressive Crashing
Project Activity Direct Indirect Opportunity TotalDuration Crashed Cost Cost Cost Cost 20 Normal 115 45 8 168 19 I* 117 41 6 164 18 37 4 17 33 2 16 29 0 15 25 -2 14 21 -4 13 17 -6 12 A*,B* 166 13 -8 171
Normal Duration After Crashing ActivityProject Paths DurationA-C-D-G-I-J 16A-C-E-I-J 20A-C-E-H-J 18A-C-F-H-J 12B-F-H-J 15
Applying Theory of Constraints to Project Management
Why does activity safety time exist and is subsequently lost?1. The “student syndrome” procrastination phenomena.2. Multi-tasking muddles priorities.3. Dependencies between activities cause delays to accumulate.
The “Critical Chain” is the longest sequence of dependent activities and common (contended) resources.
Measure Project Progress as % of Critical Chain completed. Replacing safety time with buffers
- Feeding buffer (FB) protects the critical chain from delays.- Project buffer (PB) is a safety time added to the end of the critical chain to protect the project completion date.- Resource buffer (RB) ensures that resources (e.g. rental equipment) are available to perform critical chain activities.
Accounting for Resource Contention Using Feeding Buffer
J2
B8
START
A2 C3 D2 G4
E10 I3
F4 H1
FB=7
FB=5
NOTE: E and G cannot be performed simultaneously (same person)
Set feeding buffer (FB) to allow one day total slack
Project duration based on Critical Chain = 24 days
Incorporating Project Buffer
J2
B4
START
A2 C3 D2 G2
E5 I3
F2 H1
FB=2
FB=3
NOTE: Reduce by ½ all activity durations > 3 days to eliminate safety time
Redefine Critical Chain = 17 days
Reset feeding buffer (FB) values
Project buffer (PB) = ½ (Original Critical Chain-Redefined Critical Chain)
PB=4
Sources of Unexpected Problems
Cost Time Performance
Difficulties requiremore resources
Scope of workincreases
Initial bids orestimates were toolow
Reporting was pooror untimely
Budgeting wasinadequate
Corrective controlwas not exercised intime
Price changes ofinputs
Delay owing totechnical difficulties
Initial time estimateswere optimistic
Task sequencingwas incorrect
Required resourcesnot available asneeded
Necessary precedingtasks wereincomplete
Client-generatedchanges
Unforeseengovernmentregulations
Unexpectedtechnical problemsarise
Insufficientresources areavailable
Insurmountabletechnical difficulties
Quality or reliabilityproblems occur
Client requireschanges inspecifications
Complications withfunctional areas
A technologicalbreakthrough occurs