Information Flow and Decision-Making in Advanced Vehicle - - PowerPoint PPT Presentation

Information Flow and Decision-Making in Advanced Vehicle Development

Presented by: Presented by: Joseph A. Donndelinger

General Motors Research & Development Center

Engineer Engineer Manufacture Manufacture

SOP

Start

• f

Production

VDR

Verified Data Release Vehicle Program Initiation

DSI

Document of Strategic Intent

Portfolio Plan Development Global Vehicle Development Process

Managed by the Vehicle Line Executives

Advanced Vehicle Development Process

DSI Prep Program Framing Portfolio Planning

VPI

Engineer Engineer Manufacture Manufacture

SOP

Start

• f

Production

VDR

Verified Data Release Vehicle Program Initiation

DSI DSI DSI

Document of Strategic Intent

Portfolio Plan Development Global Vehicle Development Process

Managed by the Vehicle Line Executives

Advanced Vehicle Development Process

DSI Prep Program Framing Portfolio Planning DSI Prep Program Framing Portfolio Planning

VPI

Focus on AVDP

GM’s Vehicle Development Process GM’s Vehicle Development Process

Motivating Questions Motivating Questions

• 1. To what extent can iteration be removed from the

design process?

• 2. What are the triggers for generation, storage, and

distribution of information in product development?

• 3. How can uncertainty be characterized and managed

throughout the execution of the product development process?

• 4. What is the role of the engineer in decision-making?

Approach Approach

• Apply leading-edge methods to model GM’s Advanced

Vehicle Development Process (AVDP)

• Design Structure Matrix (DSM)
• Decision Analysis (DA)
• Analyze models to gain insight into AVDP execution
• Insights from individual models
• Composite insights from both models

Contributors Contributors

DSM Model

GM R&D Center

• Alexandra Elnick
• Datta Kulkarni
• Joe Donndelinger

MIT CIPD

• Ali Yassine
• Alberto Cividanes
• Steven Eppinger
• Daniel Whitney
• William Finch

DA Model

GM R&D Center

• John Cafeo
• Robert Lust
• Joe Donndelinger

Oakland University

• Zissimos Mourelatos

Stanford University

• Ali Abbas
• Apiruk Detwarasiti
• Christopher Han
• John Feland
• Ronald Howard
• Ross Shachter

What is a Design Structure Matrix?

• A Design Structure Matrix (DSM) is a

compact, matrix representation of a system/project.

• The matrix contains a list of all

constituent subsystems/activities and the corresponding information exchange and dependency patterns.

B A X A B X X X X X D E C E D C X F F X X

The Design Structure Matrix (DSM) The Design Structure Matrix (DSM)

Three possible sequences for two tasks:

A B A B A B

Independent (Parallel) Dependent (Series) Interdependent (Coupled)

B A X A B B A A B B A X X A B

In DSM notation:

Information Flows & Task Sequencing Information Flows & Task Sequencing

B A X A B X X X X X D E C E D C X F F X X

Dependent (Series) Independent (Parallel) Interdependent (Coupled)

• Marks along the rows indicate inputs

(i.e. Task E receives inputs from tasks A, B, D and F)

• Marks along the columns indicate outputs

(i.e. Task E provides an output to task F)

Reading the DSM Reading the DSM

• Product Planning
• Marketing
• Finance
• Design Studio
• Program Management

Team

• Quality
• Vehicle Integration

Engineering

• Manufacturing

Engineering

• Packaging Engineering
• Systems Engineering
• Body Engineering
• Chassis Engineering
• Powertrain Engineering

Key Functions Interviewed: Key Functions Interviewed:

Expert Opinion Phase Quick Study Phase Integrated Vehicle Concept Model and O.D. Deliverables Phase 39 tasks 52 tasks 29 tasks Identified 120 tasks from 19 different functions

Track Total Vehicle Issues (TVIE) Update Financial Assessment and Finalize Business Case (Finance) Develop Mainstream Integrated Concept Vehicle Model (VCE) Assess Risks in Performance Requirements (Performance Development) Review Quick Study Deliverables (Review Board)

Track Total Vehicle Issues (TVIE) Input from Systems Engineering to Different Compartments Input from Performance Development to Different Compartments Input from Manufacturing to Different Compartments Feedback Provided by Different Engineering Compartments

Planning Assistant Planning Director Vehicle Program Management Director Manufacturing Engineering Director Option Development Review Board Option Development Team Leadership GPDC Planner Brand/Marketing Vehicle Project Management Manager Total Vehicle Integration Total Manufacturing Integration Option Development Team Compartment Integration: GPDC Finance GPDC Quality Systems Engineering Vehicle Concept Engineering Performance Development Die Manufacturing Design Studio Body Chassis IP/Cockpit Front Passenger/Rear

Team Structure Team Structure

Option Planning Team Typical Section Team Performance Integration Team Compartment Integration Teams Body Front & Chassis IP/Cockpit Passenger/ Rear

Supplier Design Reviews

Design Center Interface

VAPIR

(Vehicle and Process Integration Review)

Team Meetings Team Meetings

Iteration in design process

• Extensive in 2nd phase of AVDP
• Contributing factors:
• Investigations of alternatives to mainstream design
• Coordination of work across disciplines
• Tremendous complexity in subsystem interactions

Generation and distribution of information

• Highly structured - Largely driven by templates
• Centrally stored
• Often reviewed in “town hall” meetings
• Ad-hoc networks arise as needed

Insights from DSM Model (1) Insights from DSM Model (1)

Management of uncertainty

• Allocate all available resources to reducing uncertainty
• More model detail Less perceived uncertainty

Roles in decision-making

• Engineers:
• Develop alternatives
• Provide information
• Managers:
• Make decisions

Insights from DSM Model (2) Insights from DSM Model (2)

The Decision Analysis Cycle The Decision Analysis Cycle

Deterministic Phase Probabilistic Phase Informational Phase Decision Information Gathering Prior Information New Information Gather New Information Act Deterministic Phase Probabilistic Phase Informational Phase Decision Information Gathering Prior Information New Information Gather New Information Act

Matheson, J.E. and Howard, R.A., An Introduction to Decision Analysis (1968), from Readings on The Principles and Applications of Decision Analysis, Strategic Decisions Group, 1989.

D - Decision Node

Multiple decisions non-deterministically affect the value

U - Uncertainty Node

Multiple non-deterministic relationships represent the knowledge of how decisions affect the value

$ - Value Node

Value captures the preference under uncertainty among various prospects

Decision Diagrams Decision Diagrams

D U $ D U $

The Elements of Decision Quality The Elements of Decision Quality

(courtesy of Strategic Decisions Group) (courtesy of Strategic Decisions Group)

Product

Decision Diagram for Design Alternatives (1) Decision Diagram for Design Alternatives (1)

Manufacturing Process Product Design Parameters Mfg Process Parameters Independent Variables

This is how new engineers see the world…

Deterministic Quantities

Cost to Produce Product Manufacturing Process Product Design Parameters Mfg Process Parameters Decision(s) Uncertainties

This is how experienced engineers see the world… Decision Diagram for Design Alternatives (2) Decision Diagram for Design Alternatives (2)

Profit

Customer Perceived Performance Attributes (Styling, noise level, etc…) Cost to Produce Product Attribute Revenue Manufacturing Process Economy Customer Prior Experience Product Design Parameters Market Performance Mfg Process Parameters Competitors Products Decision(s) Uncertainties

Outcome

This is how decision makers see the world… Decision Diagram for Design Alternatives (3) Decision Diagram for Design Alternatives (3)

Target Market Segment Degree

• f Re-Use

NPV

Key Purchase Drivers | Market Segment Sales Volume Material Cost Average Price Competitor Pricing Competitor Offerings Investment Cost Actual SOP Market Research Architecture (new/existing) SOP Target Date Plant Candidate(s) Styling Requirements Performance Requirements Financial Targets Clinic Results Vehicle Dev. Duration

“Product Definition”

Plant Selection Production Capacity Pricing Strategy Design Revisions Actual Product Clinic Preparation Executive Input Union Demands Economic Environment

• Mfg. TP

Safety/ Quality Issues Recall Likelihood Law Suits

A Decision Diagram for the AVDP A Decision Diagram for the AVDP

Iteration in design process

• Occurs naturally in the Decision Analysis Cycle
• Driven by information needs of decision-makers

Generation and distribution of information

• Analytical results supplement the decision-maker’s

State of Information

• Analyses are performed when the results are material

to decisions at hand

Insights from DA Model (1) Insights from DA Model (1)

Management of uncertainty

• Decision-making under uncertainty and risk is

INEVITABLE

• Epistemic uncertainty can be reduced
• Aleatory uncertainty CANNOT be reduced
• Uncertainty must be assessed by the decision-maker

Roles in decision-making

• Engineers develop alternatives and provide information
• Managers assess uncertainties and make decisions

Insights from DA Model (2) Insights from DA Model (2)

An optimal determination of design parameters based upon pre-specified objective functions

• Current Perspective
• Viewed as dimensional adjustments on the

selected design

• Formulate an optimization problem where the
• bjective is to minimize the deviation from the

specifications

• Decision Analytic Perspective
• Formulate an optimization problem where the

single objective is to maximize expected utility instead of deterministic value functions

Engineering Design: Parametric Design Engineering Design: Parametric Design

Selection of a design from a fixed set of alternatives based upon some stated criteria

• Current Perspective
• No creative process involved
• Pick the design from the set that matches the

requirements

• Decision Analytic Perspective
• Pick the optimal design from the set based on the

maximum expected utility criterion

Engineering Design: Design Selection Engineering Design: Design Selection

A creative configuration of entities to construct a system based upon some criteria

• Current Perspective
• Ongoing process where final alternatives are

never completely specified until the very end

• End requirements are cascaded down as

specifications in order to allow effective translation & guidance from abstract to detailed designs

• Decision Analytic Perspective
• This is the focus of this research

Engineering Design: Design Synthesis Engineering Design: Design Synthesis

Problem Characteristics

• Very large alternative space
• Extremely complicated

relationships

• Organizational boundaries
• Time constraints
• Modeling complexity

Solution Characteristics

• Rational decision-making

under uncertainty procedure

• Manageable modeling in

terms of construction and implementation that recognizes the

• rganizational and resource

restrictions

Decision Diagram for Design Synthesis Decision Diagram for Design Synthesis

U U U U U U U U D D D D D D D D D $ U U U U U U U U D D D D D D D D D $

Design Decisions

System-level decisions

Concept, architecture, interface among subsystems, etc

Subsystem-level decisions

Interface among components, etc

Component-level decisions

Components and parts, etc

Fine-tuning decisions

Parametric design decisions

Time

Concept Concept

Number of Design Decisions

Detailed Detailed

Decisions in Design Synthesis Decisions in Design Synthesis

Uncertainties in Design Synthesis Uncertainties in Design Synthesis

Design Uncertainties

How current decisions affect the value

Evaluation of partially specified alternatives

How current decisions affect subsequent decisions

Evaluation of restrictions imposed on later decisions

How further refined designs will look like

Anticipation of various more-detailed designs

How those detailed designs will affect the value

Given detailed design, value is not known

deterministically

Time

Amount of Design Uncertainties

Concept Concept Detailed Detailed

Final Thoughts Final Thoughts

On iteration in the design process Plan for “good iteration” (allow for exploration of design space and reduction of uncertainty) Reduce “bad iteration” (caused by lack of discipline, poor team or process structure, or miscommunication) On generating and distributing information The primary challenge is focusing resources on the subset of information that is relevant and valuable

Final Thoughts Final Thoughts

On characterizing and managing uncertainty As beauty is in the eye of the beholder, so is uncertainty in the eye of the decision-maker On roles in decision-making Engineers develop alternatives and provide information Managers assess uncertainties and make decisions Computers execute instructions