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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