Strategic Management Technique and Method in Engineering Enterprise
Strategic Engineering
description
Transcript of Strategic Engineering
© Olivier de Weck, Oct 2008 Page 1
Strategic EngineeringStrategic Engineering
Olivier L. de Weck, [email protected]
Associate Professor of Aeronautics and Astronautics and Engineering Systems
October 7, 2008
Designing Systems for an Uncertain FutureDesigning Systems for an Uncertain Future
Version 2
Change Propagation Analysis in Complex SystemsChange Propagation Analysis in Complex Systems
© Olivier de Weck, Oct 2008 Page 2
SystemArchitecture
Integrated Modelingand Simulation
MultidisciplinaryDesign Optimization
Strategic Engineering – “the big picture”
“optimal” design x* at t=to
technology
regulations markets
concept
performance, cost, risk
Design forChangeability
uncertainty
changes
at t=to+t requirements change and x* is no longer optimal
flexibilityreal options
TemporalDimension
Design forCommonality
more than one variant of the system is needed: x1
*, x2, … xn
variety
standardization
SpatialDimension
commonalityplatforms
http://strategic.mit.edu
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F/A-18 Center Barrel Section
Y488Y470.5
Y453Wing
Attachment
74A324001
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F/A-18 Complex System Change
F/A-18 System Level Drawing
OriginalChange
FuselageStiffened
Manufacturing Processes Changed
Flight ControlSoftware Changed
Gross Takeoff Weight
Increased
Center of Gravity Shifted
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Change Propagation Analysis
in Complex Systems
Giffin M., de Weck O., Bounova G., Keller R., Eckert C., Clarkson J., “Change Propagation Analysis in Complex Technical Systems”, DETC2007-34652, ASME 2007 Design Engineering Technical Conferences, DETC2007-34871, Las Vegas, NV, September 4-7, 2007
In Press: ASME Journal of Mechanical Design
Sponsor: Raytheon Integrated Defense Systems
ProblemAddressed
Understanding change propagation patterns in large technicalprojects involving hardware, software and human operators
ScientificContribution
Developed procedure for data-mining of a large change request database (9 years, 41,500 changes) and analyzing change patterns (“motifs”) as well as classification of system components with a Change Propagation Index (CPI)
Outcome, Impact Applied to a large USAF Radar System project at Raytheon. Identified areas that are likely candidates for flexibility infusion
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System Description
Complex Sensor System Complex sensor system,
complex hardware, software, human operators
Derivative of earlier system 9 Year development
46 Areas (“Subsystems”) Hardware Software Program Documentation
System Map (graph) Interconnections between
areas
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Data Set
Change Request Database technical, managerial, procedural track parent, child, siblings by
areas with unique ID number chronologically numbered IDs
Data Mining Procedure Export from DBMS to text file Written into MySQL database
with Perl scripts Equivalent to a MS Word
document with 120,000 pages Sorting, Filtering, Anonymizing Write simplified change request
format (see right side)
ID Number 12345
Date Created Date Last Updated
06-MAR-Y5 10-JAN-Y6
Area Affected 19
Change Magnitude 3
Parent ID 8648
Children ID(s) 15678, 16789
Sibling ID(s) 9728
Submitter eng231
Assignees eng008 eng231 eng018
Associated Individuals Admin_001 Engineer_271
Stage Originated, Defect Reason
[blank], [blank]
Severity [blank]
Completed? 1
Typical Change Request
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Change Networks
Apply Graph Theory to extract networks of connected changes
parent-child changes sibling changes
Most changes are only loosely connected
2-10 related changes
Some large networks emerged
Question: do these networks emerge from a single initial change?
(rank) (connected changes)
1 2579
2 424
3 170
4 87
5 64
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Network plot of largest change network in the dataset, with 2579 associated change requests.
Change Propagation Network
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2302423922
23729
23821
23831
23925
23942
23945
23992
24659 25053
24781
24926
24927
25463
24980
25476
25481
25515
8000
12156
13320
22850
22946
26117
27169
27592
27952
281622860128696
29226
29227
2935329731
29744
29826 30126
27627
28878
28166
28567
2765628528 28428
28009
30148
28067
28186
2852928821
28531
27027
27585
28007
28122 28153
28187 28213
28695
2878828790
28846 2939929538
29547
26331
26333
27023
29711
30548
30143
30344
30465
3046630501
30503
30614
30771
31235
31471
31966
31967
31972
31973
32289
32645
Mapping Changes to affected subsystem areas
Change Propagation Network
System Network Map
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Change Propagation Index (CPI)
Classify each area Absorber, Carrier, Multiplier
( ) ( )
( )ij ij
ij
c parent c siblingp
Ctot j
1
( ) ( )N
out ji totj
C i p C i
Area 1 2 3 4 5 6
1 0.4843 0.0011 0.0136 0.0057 0.0125 0.0023
2 0.0061 0.0000 0.0000 0.0030 0.0000 0.0000
3 0.0173 0.0000 0.1053 0.0050 0.0012 0.0000
4 0.0224 0.0000 0.0112 0.0449 0.0000 0.0000
5 0.0137 0.0000 0.0000 0.0000 0.1262 0.0000
6 0.0417 0.0000 0.0000 0.0000 0.0000 0.0833
DSM Change Propagation Frequency
receiving area
instigating area
A change in Area 1 caused changes in Area 6 with afrequency of 4.17%.
1
( ) ( )N
in ij totj
C i p C j
( ) ( )( )
( ) ( )
out in
out in
C i C iCPI i
C i C i
change propagation probability
totalcompletedchangesin Area j
-1 <= CPI <= +1
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System Area Classification
Areas found to be strong multipliers 16: hardware performance evaluation 25: hardware functional evaluation 5: core data processing logic 32: system evaluation tools 19: common software services 3: graphical user interface (GUI)
Areas found to be perfect reflectors 27, 41: look like perfect absorbers but actually zero changes implemented despite numerous changes proposed = perfect reflectors
CPI Spectrum
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Change Requests Written per Month
0
300
600
900
1200
1500
1 5 9 13
17
21
25
29
33
37
41
45
49
53
57
61
65
69 73 77
81
85
89
93
Month
Nu
mb
er
Wri
tte
nChange Request Generation
[Eckert, Clarkson 2004]
Discovered new changepattern: “inverted ripple”
component design
subsystem design
systemintegrationand test
bug fixes
major milestonesor managementchanges
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Insights Inverse relationship between change magnitude and frequency of occurrence
Large changes are infrequent, small ones are ubiquitous
Many change requests are never implemented Some are rejected, others are ignored (~ 50%)
Changes may form complex networks over time. Most are small (<10 changes), a few large ones exist (beware of these !) Change networks form through coalescence and not necessarily through multi-
step causal change propagation
Changes can propagate between areas that are not direct neighbors in the system DSM (not shown here, but we found this is so)
Subsystems can be classified as: Multipliers CPI > ~0.3 Carriers -0.1<CPI<1.0 Absorbers CPI<-0.3
Reflectors of Change CRI>CAI Acceptors of Change CAI>CRI
Analysis of change database revealed that Real world change processes more complex than expected Industry data tends to be “noisy” Potential for deriving change impact and likelihood for future projects
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Future Work Change Prediction:
How good are our predictions regarding actual versus planned effort? How can change propagation patterns observed on past projects be leveraged for
future design decisions (e.g. modularity, flexibility)
Data Processing: Standardize methods for recording and processing data, tracing large change
networks in greater depth- attempt to reconstruct logic
Staffing and Organization: Analyze effects of staffing on changes and components Patterns based on which personnel/organization work on the changes?
Contractual: Can change propagation be used to write better prime and sub-contracts?
Statistical: Are there critical numbers for change propagation? Limits on the number of
propagation steps? .
CMI-Sponsored Workshop on Engineering ChangeMIT Endicott House, October 30-31, 2008~ 12 firms from various industries (aerospace, auto, printing, construction)
Cambridge-MIT-Institute (CMI)Engineering Change Twin WorkshopsEngineering Change Twin Workshops
Trinity Hall College, UKUniversity of Cambridge
April 7-8, 2008
MIT Endicott House, USAOctober 29-31, 2008
Reasons for Change
Problems discovered during production and operations in the field such as retrofits, recalls ….(melioration)
Customization of product variants for different customers and market segments (globalization)
Infusion of new technologies during product refreshes or major “block” upgrades (innovation)
Cost reduction Initiatives, response to new features introduced by other firms (competition)
New government regulations (e.g. fuel economy standards, no lead in electronics …(compliance)
Others ….?
Workshop Goals
Obtain multi-faceted industry perspective on state-of-the art in engineering change practice
Present academic perspective and recent research advances to industry
Establish a research agenda for the next 5 years Put in place basis for Special Issue of RED* Stimulate interest in follow-up collaboration Establish user community for advanced engineering change methods
and tools
* Research in Engineering Design (RED) Journal
Invited Companies
UK Rolls Royce (A/C
Engines)* Perkins (Diesel)* Volvo (Trucks, Engines)* BAE Systems (Defense)* Bosch (Auto Supplier)* BMW (Cars)* BP (Oil & Gas)* MAN Roland (Printing
Systems) Arup (Construction)
US Xerox (Printing Systems) Ford, GM (Cars and Trucks) Agusta Westland (Helicopters) Boeing (Aircraft) General Mills (Food) Fluor (Construction) Mack (Highway Trucks) Gerber (Textile Machines) NASA (Spacecraft) Raytheon (Defense Systems) Ventana Systems (S/W) Aberdeen Group United Technologies Corp.
*attended April 2008
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Strategic Engineering
Strategic Engineering is the process of designing systems and products in a way that deliberately accounts for customization and future uncertainties such that their lifecycle value is maximized.