The document discusses a research project on measuring progress and productivity in model-driven engineering, led by a team of experts from various universities and industry. It outlines objectives to create guidelines and standardized definitions for model maturity indexes across multiple engineering disciplines to aid in measuring productivity without additional workload. Expected outcomes include a module maturity index for each discipline, a toolkit for assessment, and a model execution plan to facilitate the implementation of best practices in the engineering sector.

2. RT332: Measuring Progress and Productivity
in Model-based Engineering
NAUM 2017: 01/31/2017
Progression in Model-driven Engineering Process

3. 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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4. 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

5. 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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6. 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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7. 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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8. 8
Research Overview

9. 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

10.  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 Disciplines
Validation throughThree (3) Charrettes

11. 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)

12. Example: Piping - MMI Levels
1. MMI Definitions (Cont’d)
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13. 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

14. 2. Validation of MMI Definitions and Matrix
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Consent form scenarios (snapshots, details, videos) feedback form per group
20-30 participants, typically three disciplines in one meeting

15.  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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16. 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

17. 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

18.  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

19. 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

20. 4.5 Survey
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 Explore current best practices and establish guidelines that are adoptable and
adaptable
Google Forms Testing Phase

21. 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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22. 22
Extract
Modeling
Information
Automate
Progress
Monitoring
Color Code
Models
Create
Productivity
Plug-Ins
6. Collaboration With AVEVA

23. MMI 300 MMI 400 MMI 500MMI 200
RT332: Measuring the Progress and Productivity
of Model-based Engineering

24. 24
Survey

25. Turner & Townsend
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