rt332: measuring progress and productivity in model-based engineering
TRANSCRIPT
RT332: Measuring Progress and Productivity
in Model-based Engineering
NAUM 2017: 01/31/2017
Progression in Model-driven Engineering Process
RT332 Research Team
Chair: Derwin Cartmel, Day & Zimmermann
Co-Chair: John Moncrief, Chevron
PI: Mani Golparvar-Fard, University of Illinois
Co-PI: Jesus M. de la Garza, Virginia Tech
Co-PI: Martin Fischer, Stanford University
GRA: Gustavo Garcia, University of Illinois
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Research Question
How can we accurately measure progress and productivity
in a model-driven approach to engineering, without imposing
additional work or taking away from actual productivity?
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MMI 200 MMI 300 MMI 400 MMI 500
Model Snapshots from http://mep.trimble.com/services/3d-modeling-service
Purpose and Mission
Primary purpose
• Establish procedures and define metrics by which project
stakeholders can reliably measure progress and productivity in
a model-driven approach to engineering.
Mission
• Conduct research to enable the industry to adopt best
productivity measurement practices and tools.
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Objectives
Create and validate a “guideline” that is adoptable and adaptable for measuring progress and productivity in a model-driven approach to engineering.
I. Define and differentiate progress and productivity;
II. Standardize Model Maturity Indexes (MMIs) w.r.t reliable output for each engineering discipline and project lifecycle phase for both green and brownfield projects;
III. Investigate current best practices in measuring progress and productivity in industrial sector and contrast them with existing practices in commercial building sector; and
IV. Develop recommendations for implementation of the guideline.
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Expected Products of Research
a. Module Maturity Index (MMI) Definitions per model discipline
b. A toolkit standardizing measurement of progress, productivity, and risk
per each Engineering discipline per each WBS location, considering both
progress within the discipline and interdisciplinary relationship.
c. A Model Execution Plan based on MMI definitions
d. Recommendations for implementation.
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Research Overview
Research Methodology
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1- MMI Definitions
• Create Model Maturity Definitions per model discipline
• Specific to Industrial Process Sector but applicable to both greenfield and brownfield projects
• Validate MMI Definitions
2- Dependency Matrix
• Explore, formalize, and validate dependencies among model disciplines in the context of model-based engineering workflows
3- Model MRI Toolkit
• Transform definitions into a toolkit to measure progress and productivity based on model disciplines and WBS Locations.
• Validate M MRI Toolkit
4. Model Execution Plan
• Draft a guideline together with a Model Execution Plan template
• to plan, monitor and control model-based engineering process
• Applicable to daily practices and model review sessions
5- Research Products
• MMI definitions
• MMRI Toolkit
• Model Execution Plan
• Real-World demo examples
• Adoptable and adaptable Guideline for implementation
Create and validate a standardized set of definitions to measure progress and
productivity of model-based engineering.
1. MMI Definitions
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Model Maturity Index (MMI) Definitions
for 12 Engineering DisciplinesValidation through Three (3) Charrettes
Accurate Definition for Discrete MMI Levels (typically 100, 200, 300, 350, 400, 500-600)
1. Piping
2. Layout
3. Equipment
4. Structural
5. Civil
6. Foundation
7. Instrumentation
8. Electrical
9. HVAC
10. Buildings
11. FP
• P&IDs
1. MMI Definitions (Cont’d)
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Glossary (with order of precedence in model maturity levels)
● Preliminary: Initial project data is available but has not yet been reviewed or coordinated internally
and externally. For example, project data could be informal data from previous projects.
● Design-specified: Data taken from project specifications. For example, the pipe size is calculated
based on project flow rates; materials have been selected based on process conditions; and
equipment sizing based on project specifications. For example, cut sheets are submitted with
budgetary or Purchase Order (PO) level quotations.
● Confirmed: Project data has been reviewed and accepted by all required parties internally per
project requirements and includes routine feedback from clients/third-parties. For example,
designer has coordinated the design with other required disciplines. Note: Vendor data does not
have “confirmed” status.
● Approved: Project data has been reviewed and accepted by all required parties internally and
externally per project requirements (internally: design teams; externally: client, third parties,
vendors, constructors, operators)
Example: Piping - MMI Levels
1. MMI Definitions (Cont’d)
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2. Dependency Matrix
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Explore the inter-disciplinary relationships in model-based engineering and account
for Quality of data in model maturity
2. Validation of MMI Definitions and Matrix
14Consent form scenarios (snapshots, details, videos) feedback form per group
20-30 participants, typically three disciplines in one meeting
Model Maturity Risk Index (M-MRI) Toolkit to quantitatively benchmark and track
progress (and productivity) in model-based engineering based on quality of data and
interdisciplinary relationships
Tracks Progress (and productivity)
per discipline
per WBS Location
based on units of measurement
Accounts for scope of work
and difficulty of engineering
in each WBS location
3. M-MRI Toolkit
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3. M-MRI Toolkit
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Intuitively tracks progress in model-based engineering per discipline, per WBS location
• Provides insight on maturity to the next MMI level
• Offers control feedbacks on whether the bottlenecks are within the discipline or are the impact of
other disciplines
Discipline-specific questions with risk to next MMI level
Feedback on maturity of other disciplines
3. M-MRI Toolkit
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Intuitively tracks progress in model-based engineering
• Per discipline, per WBS location
• Provides insight on maturity to the next MMI level
• Offers control feedbacks on whether the bottlenecks are within the discipline or are the impact of
other disciplines
to next MMI level
Several charrettes with Eng, modeling, and project controls experts
Per model discipline
3. Validation of the M-MRI Toolkit
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Engineering scenarios and models prepared
Feedback was captured and incorporated in the toolkit
4. Model Execution Plan (ModelXP)
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Appendix to existing ModelXPs
Define expectations on MMI levels per engineering milestone and maturity of deliverables
Clarify responsibilities
Establish units of measurement and impact factors for WBS Locations
Account for model-based engineering schedule
4.5 Survey
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Explore current best practices and establish guidelines that are adoptable and
adaptable
Google Forms Testing Phase
5. Research Products
MMI definitions
Model MRI Toolkit
Model Execution Plan
Real-World demo examples
Adoptable and adaptable Guideline for implementation of the toolkit and execution plan
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Extract Modeling
Information
Automate Progress
Monitoring
Color Code Models
Create Productivity
Plug-Ins
6. Collaboration With AVEVA
MMI 300 MMI 400 MMI 500MMI 200
RT332: Measuring the Progress and Productivity
of Model-based Engineering
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