Modeling Project Organizations: Virtual Prototypes and Virtual Experiments CEE214 Fall 1999 Raymond E. Levitt

Elements of Model Development  Representation  Reasoning  User Interfaces  System Interfaces  Validation

OUTLINE  Background  Why simulate organizations?  Why start with project organizations?  Why use “information processing” as the central modeling framework?  VDT Model Concepts and Evolution  VDT Applications to Date  Using VDT models as “virtual prototypes”  Using VDT models as “virtual experiments”

The Big Ideas 1.Validated analysis tools are central to design; they distinguish real design from trial-and-error experimentation! 2.In the same way that physical-science-based analysis tools help engineers design bridges, airplanes, semiconductors, pharmaceuticals, etc., social-science- based analysis tools can help managers design their organizations systematically 3.A small number of validated organizational analysis tools are already being used by managers to design organizations for projects, programs, and enterprises (SimVision, OrgCon… ) 4.Validated organizational analysis tools also allow researchers to conduct new kinds of virtual computational experiments

Steps in a Formal Design Process START…  Set Design Goals  Design Synthesize: Develop a candidate design solution Analyze: Predict candidate solution’s performance Evaluate: Compare predicted performance vs. goals  Iterate Designs u Relax Design Goals …TERMINATE—Success or Failure?

“Project Org’n Design” vs. Trial and Error 0% 100% Level of Influence Conceptual Design Detailed Design and Implementation Closeout & Operations Expenditure of Funds Expenditure of Funds Outcome Knowledge Project Design Outcome Predictions

Modeling and Simulation of Organizations Bridges the Micro  Macro Theory Gap Organization macro-theory Organization macro-experience Sociology/Economics/ Political Science Organization micro-theory Organization micro-experience Cognitive and Social Psychology Agent micro-behavior Agent- Based Simu- lation Organization micro-theory Organization micro-experience Cognitive and Social Psychology Emergent simulation macro-behavior

Raymond E. Levitt John C. Kunz Department of Civil Engineering Center for Integrated Facility Engineering The Virtual Design Team (VDT): A language and tools for modeling and simulating “virtual organizations” executing work processes

SCOPE: Product Development Organizations? I. Practical Motivation—High Economic Importance  Product lifecycles and market windows are shrinking for many consumer and industrial products,  To accelerate time-to-market, complex, highly interdependent work processes must be executed concurrently  This causes an exponential increase in the amount of needed coordination work and rework  Insufficient “information processing capacity” emerges as a leading cause of “failure” in product development organizations  Extant theory and tools cannot help managers to predict whether, when and where their organizations will fail due to information processing overload

Why Study Product Development Organizations? II. Theoretical Motivation—Perfect fit for IP framework Task Interdependence Goal Incongruency/Ambiguity Task Routineness

Fast Track Projects are Information-Intensive High performance, complex product has high level of inter- dependency between its subsystems Fast-track schedule triggers unplanned coordination and rework for project organization ProcessOrganizationProduct Project team must process large amount of information under extremely tight time constraints

Using an “Information-Processing” Simulation Framework to Design Project Organizations 1.Model work process to determine “IP load” on organization arising from direct work, coordination and rework 2.Use simulation model as “virtual prototype” of real organization executing work process 3.Highlight predicted bottlenecks in IP capacity as likely loci of schedule and quality failures 4.Evaluate alternative “virtual prototypes” of work process and organization to test and select potential managerial interventions

What Affects Frequency of Exceptions for Workers and Managers in Projects?  For Workers:  Task complexity relative to workers’ skills  Task interdependency  Task concurrency  For Managers  Above factors, plus: Organizational span of control Level of decentralization

OVERVIEW OF VDT REPRESENTATION & REASONING

VDT Info-Processing Abstraction Workers Process Information = “Direct Work” TASK/ACTIVITY: (Volume of Information) “ACTOR”: (Information Processor) Coordination & Rework

Exceptions must be Processed by the Organization = “Hidden Work” (Jay Galbraith, 1973) “Exception”

Direct Work is not Total Work! Total Work = Direct Work + Hidden Work (Jay Galbraith, 1973) Project Organization must also Coordinate and Supervise + “Exception” Project Participants Perform Assigned Tasks Direct Work “Hidden Work”

Fast-Tracking Amplifies Hidden Work

REPRESENTATION  CPM to model processing of direct work  Add two additional kinds of task interdependence  Add actor skill levels, application experience  Add organization structure, culture, meetings …

Information Volume from Project Tasks & Dependencies: Direct Work, Communication Work and Rework

Project Team Information Processing Capacity: # Actors, Skill Set, Experience, Structure, Policies

VDT Information Processing Model: Matching IP Capacity to IP Demand

REASONING  Monte-Carlo discrete event simulation of information processing and communication  Used previously to model flow of physical work and materials through a supply chain  In VDT, direct work and hidden work are both simply quanta of information to be processed by humans and information processing/communication tools

Communications to other actors “Out tray” Actor “In tray” Communications from other actors Direct Work VDT Simulates Actors Working and Communicating Simulates: - every actor (team) & activity - work, errors, coordination, waiting, decisions, rework Produces: - “database” of actor and project behaviors/outcomes Simulates: - every actor (team) & activity - work, errors, coordination, waiting, decisions, rework Produces: - “database” of actor and project behaviors/outcomes

Performance Predictions Generated by VDT Backlog Quality Schedule Cost Model Simulation Results

ReasoningRepresentation Usefulness Organization Micro-experience Simulation Micro-behaviors Emergent Simulation Macro-behaviors Organization Micro-theory Organization Macro-experience Validation Elements Organization Macro-theory Ethnography Internal Validity Toy Problems Intellective Experiments Authenticity Generalizability Reproducability Retrospective Prospective with Intervention Natural Validation Trajectory

VDT as “Virtual Prototype” for Organization Design CASE STUDY: The Lockheed Martin Launch Vehicle

Lockheed Martin Launch Vehicle Organization Program Manager SE & I Project Manager Avionics Project Manager Avionics Project Manager Propulsion Project Manager NG & C Project Manager Facilities & SE Engineering Manager Business Development Manager Operations Manager Test Engineering Manager Structures Project Manager

Organization of Avionics PDT STSubTeam PDTProduct Development Team SE & I Systems Engineering and Integration NG & CNavigation, Guidance and Control SE Support Equipment (Actor Name) Actor Not Represented in Avionics Model (Actor Name) (# FTEs) Actor Represented in Avionics Model Power Dist. Panel Sub Team (3) Firing Unit SubTeam (3) Operational Interlocks Sub Team (4) Flight Boxes SubTeam Program Manager SE & I Project Manager Propulsion Project Manager NG & C Project Manager Facilities & SE Engineering Manager Business Development Manager Operations Manager Test Engineering Manager Structures Project Manager Structures Project Manager Total: (15) Off-shelf Department Cables Contractor Packaging Department Electronics Department Flight Boxes Department Processor for Package SubTeam (5) Cables SubTeam (3) Off-shelf SubTeam (5) Packaging SubTeam (1.5) Electronics Parts SubTeam (6) Avionics Project Manager Functional Guidance Project Oversight - Monitoring -Exception Handling

Predecessor - Successor relationship Activity Total Hours (start) milestone (finish) Vehicle Avionics Concept System Integration and Test Identify Parts Required Search for Vendors Procurement Support Prepare Documentation Printed Wiring Board Design Printed Wiring Assembly Enclosure Design Top Assembly Procurement Developing Sub-contracts Teaming Agreements Range Requirements Vehicle Interconnect Layout Define Interfaces Detailed Cable Drw. Fabricate and Test Cables Define Requirements New Engineering (Cables) Reengineering Experiences Production Enhancements Build and Test Flight Units Bread Board And Physical Mockup Applying Existing Applications Offshelf SubTeam (ST) (5) Cables ST (3) Flight Boxes ST (15) ElectronicParts ST (6) Packaging ST (1.5) Total Work Volume: 29,234 hours Simulated Duration: 4,611 hours Project Manager (1) 8 hours 584 hours 848 hours 400 hours 904 hours 344 hours 296 hours 2,056 hours 592 hours 504 hours 840 hours 800 hours 448 hours 504 hours 4,800 hours 2,504 hours 888 hours 752 hours 1,200 hours 8 hours Cables SubTeam (3 FTE) Flight Boxes ST (15 FTE) Electronic Parts SubTeam (6 FTE) Packaging SubTeam (1.5 FTE) Physical Mockup 368 hours Avionics Drawings 2,952 hours Activity Model for Avionics PDT Offshelf SubTeam (5 FTE)

Activity Interdependency Chart for LMLV Avionics PDT Note: Predecessor-successor relationships between activities are not shown in this chart.

Vité Vité Model of LMLV Avionics Team

Communications to other actors “Out tray” Actor “In tray” Communications from other actors Direct Work VDT Simulates Actors Working and Communicating Simulates: - every actor (team) & activity - work, coordination, errors, decisions, rework Produces: - “database” of project behaviors/outcomes

Vité Vité Gantt Chart for LMLV Avionics Team

LMLV Project: Actors’ Backlogs

LMLV Project: Non-Completed Communications

Guiding Managerial Interventions: VDT “What-if Analysis” of LMLV Avionics Design Team 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% Increase Cable Subteam’s capacity from 3-5 engineers Replace Cable Subteam members with 3 more experienced engineers Cost Duration Except's Better Worse

Design Fast-Track Project Organizations: Some Example Applications  Reduced time to market for complex manufacturing facilities  Facilitated roll-out of new wireless telecom infrastructure across multiple regions  Developed best practices template to accelerate factory start-ups  Identified & corrected subcontractor management problem that would have delayed project 4 mo.  Helped to meet ship milestone date required to close sale with largest customer  Aligned goals and accelerated rollout of innovative consumer product by 3 mo.  Identified and mitigated critical quality risks to accelerate rollout of new server product  Helped to define scope, schedule and organization for strategic IT projects

Chronology of VDT  SimVision ® Steps in the Maturation of a COM&S Framework Research at Stanford U. by Levitt, Jin, Kunz, et. al. Research at Stanford U. by Levitt, Jin, Kunz, et. al. Concept/methodology development Simulation tool development 30+ validating case studies Mature technology Exclusive License Commercial Product Development Vité Corp. Formed to Commercialize Technology Consulting & Analysis Product Offerings Ongoing VDT Research

Maturation of Modeling User Interface

SYSTEM ELEMENTS/ INTERFACES OF VDT/SimVision ® SOFTWARE Vité Simulation Engine Project Database Analysis Tools Model Builder/Viewer XML

Virtual Organizational Experiments

Modeling and Simulation of Organizations Bridges the Micro  Macro Theory Gap Organization macro-theory Organization macro-experience Sociology/Economics/ Political Science Organization micro-theory Organization micro-experience Cognitive and Social Psychology Agent micro-behavior Agent- Based Simu- lation Organization micro-theory Organization micro-experience Cognitive and Social Psychology Emergent simulation macro-behavior

Research Modalities in Engineering Science — (Pre-1960s) Empirical Data Inputs Outputs Physical Scale Models Inputs Outputs Empirical scaling rules Theory Physics Chemistry Biology (generally expressed as sets of linear or differential eq’s.)

Limitations of Physical Scale Models  Costly and time-consuming to build  Required skilled physical model builders (often built by model shop technicians—not scientists)  Slow and costly to modify  Scientists could not adapt models rapidly to react to surprising data or to test new insights  Calibration against real world data took decades  Results needed to be interpreted with care  Many important effects do not scale linearly

Physical Scale Models Inputs Outputs Empirical scaling rules Research Modalities in Engineering Science — (Post-1960s) Empirical Data Inputs Outputs Computational Modeling & Simulation Inputs Outputs Limiting modeling assumptions Theory Physics Chemistry Biology

How CM&S Affected Engineering Science and Practice  Rapidly declining time & cost to build and change models  Two orders of magnitude improvement  “disruptive” changes  Progress of Engineering Science Dramatically Accelerated  Could rapidly modify models to test & refine theory iteratively  “Regress” micro-modeling assumptions against meso/macro data  Engineering practice made huge leaps forward  “Real-time” prediction now feasible for even complex problems  Wider range of mathematically indeterminate problems can be solved  Computational modeling now part of standard BS/MS curricula  Model results still need to be interpreted with great care!  Violations of assumptions can be catastrophic —e.g. Sleipner platform)

Research Modalities in Organizational Science—Pre-1970s Empirical Data from Natural Experiments Micro/Meso/Macro Inputs Micro/Meso/Macro Outputs Span 1 or, at most 2, levels Empirical Data from Synthetic Experiments Micro/Meso Inputs Micro/Meso Outputs Theory Sociology Psychology Economics (usually expressed in words & diagrams; sometimes in mathematical or computational models)

Limitations of Synthetic Experiments  Modest time and cost to design and perform experiments  Validation, calibration against real world data is difficult  Individual motivation and context are very difficult to replicate  Many effects do not scale linearly  Ethical concerns nowadays preclude many kinds of experiments previously conducted on human subjects  No links between micro-behaviors and macro outcomes  Micro inputs and outputs cannot generally be related to, or reconciled with, macro data or even macro-theory  Result: Discipline-Based “Islands of Theorizing”

Empirical Data from Synthetic Experiments Micro/Meso Inputs Micro/Meso Outputs Research Modalities in Organizational Science — Post ~1970 Empirical Data from Natural Experiments Micro/Meso/Macro Inputs Micro/Meso/Macro Outputs Computational Modeling & Simulation Micro/Meso/Macro Inputs Micro/Meso/Macro Outputs Nested models link micro- macro data and theories Theory Psychology Sociology Economics

Computational Virtual Experiments  CM&S of organizations now beginning to be used to replace some kinds of natural or synthetic social experiments  A validated “emulation” model can be viewed as an “organizational test bench” for theorem - proving experiments  CMOT Journal has already published several papers of this type  (Wong & Burton, Carroll & Burton, …)

How Computational Modeling is Affecting Organizational Science and Mgt. Practice  Rapidly declining time and cost to start generating validated predictions for a modeled system u Organization Science poised to make huge leaps forward ÙCan now rapidly modify models to test & refine theory iteratively ÙCan “regress” micro-modeling assumptions against meso/macro data ÙValidated models beginning to serve as “virtual synthetic experiments” u Org. Design practice starting to incorporate CM&S ÙRapid feedback develops “management judgment” by induction  Enabling “flight simulation of alternatives” and “extreme collaboration” u CM&S Entering Mainstream Education and Research ÙComputational modeling & simulation now taught as part of PhD/MS/(BS) ÙMIT launching a Center for Computational Politics u Model results still need to be interpreted with care ÙContingency theory says context matters greatly! ÙDifferences in task, technology, … must be taken into account

Using a Calibrated “Emulation” Model to Conduct Virtual Experiments Indirect Work (Days) LaminarTransition Turbulent Exception Rate

Commercialization of COM&S Tools

Progress of CM&S of Organizations CM&S in Engineering Science CM&S in Organization Science First use by leading edge consultants First taught at MS level in multiple universities Commercial SW — Routinely used in practice 2010

Where is COM&S Going?  Games  From action games to Sims ®  Analysis tools for many kinds of planning  From military, intelligence, to other public agencies (e.g., building plans, health care, transportation)  Commercial (department stores, arenas, …)  Analysis Tools for Corporate Decision Making  From project design to enterprise design  Organizational aspects of M&A evaluation  Organizational aspects of supply chain optimization  Analysis Tools for Personal Decision Making  Evaluating your fit with a prospective employer  Evaluating compatibility with a marriage partner, …

Using VDT as “Virtual Experiment” Example: Explore relationship between centralization of decision making and time taken to complete complex task Use Contingent design and run multiple “virtual experiments” varying centralization for: 1. High Task Uncertainty 1.1 With High Skill for all Actors 1.2 With Low Skill for all Actors 2. Low Task Uncertainty 2.2 With High Skill for all Actors 2.2 With Low Skill for all Actors

Current Research with Undergraduates Simulate Virtual Organizations to Search for the “Edge of Chaos” Figure A. The parallel project with two dependency links; schedule quality ratio for different VFP values. Figure B. The parallel project with six dependency links; schedule quality ratio for different VFP values.

Closing in on an “Organizational Reynolds Number” LaminarTransitionTurbulent 0.2 E / C * Log(r) = 0.25

EVOLUTION OF SCOPE OF VDT

Trajectory of VDT Research Scope Organizational Flexibility LowHigh Predictable Unpredictable Task Predictability Nonroutine Projects Routine Projects Service/ Maintenance Work Communities of Practice

99-03: Lambert/ Buettner Model More Complex Social Behaviors Model More Innovative Tasks Model More Dynamic Organizational Forms VDT Scope Trajectory: Routine Projects to Non-Routine Communities of Practice Model More Effects of Communication/ Collaboration Tools Model More Effects of Communication/ Collaboration Tools 97-01: Miller 96-03: Fridsma/Cheng 95-99: Thomsen/Kish 90-94: Cohen/ Christiansen

… behind the Virtual Design Team  Faculty Collaborators  James March (SU: Ed., Sociology, GSB)  John Kunz (SU: CIFE)  Yan Jin (USC: ME)  Clifford Nass (SU: Comm.)  Richard Burton (Duke: Business)  Martin Fischer (SU: CEE)  Bernardo Huberman (Xerox PARC: Physics)  Peter Glynn (SU: MS&E)  Pam Hinds (SU: MS&E)  Noah Mark (SU: Sociology)  Dianne Bailey (SU: MS&E)  Borge Obel (Odense U: Business School)  Kathleen Carley (CMU: CS)  Nosh Contractor (UIUC: Comm.)  Andrea Hollingshead (UIUC: Psych)  Janet Fulk (USC: Business School)  Peter Monge (USC: Comm.)  Douglass North (Wash. U: Econ., NL)  Steve Barley (SU: MS&E)  John Koza (SU: CS)  Students  Geoff Cohen  Tore Christiansen  Jan Thomsen  Douglas Fridsma  Gaye Oralkan  Yul Kwon  John Chachere  Per Björnsson  William Hewlett, III  Jolin Salazar Kish  Carol Cheng-Cain  Walid Nasrallah  Roxanne Zolin  Monique Lambert  Archis Ghate  Sam Miller  Ray Buettner  Mike Fyall  Alfonso Pulido  Ashwin Mahalingam  Michael Murray  Bijan Khosraviani  Ryan Orr  Tamaki Horii  Laleh Haghshenass

The Real Team Behind the Virtual Design Team: Undergraduate Research Assistants to Date Diane NewmanBS German Studies MS, Ph.D, MIT, Civil & Environmental Eng’g. Professor of Geomicrobiology, Cal Tech Yul KwonBS Symbolic Systems J.D., Yale Law School Enrolled in Ph.D. in Public Policy, Harvard Corporate Attorney, Wilson Sonsini, Goodrich & Rosati William C. Hewlett, IIIBS, Symbolic Systems MS, Computer Science PC game developer Mike FyallSenior, MSE Recruited to Goldman Sachs Summer internships with Vité management consultants Jason GlickmanSenior MSE CoTerminal MS Student, MSE Summer internships with MSD Investments Jason PowersSenior MSE CoTerminal MS Student, MSE Summer internship with Vité management consultants Tarmigan CaseboltSophomore, MSE?

Ongoing Research on Organization Design Institutional Complexity in Global Projects  Existing project organization modeling and simulation tools address engineering projects, whose participants have similar goals, values, culture, norms & technologies Coordination Complexity Production Costs Coordi- nation Costs Institutional Complexity Institutional Costs  Global projects to develop infrastructure, eco-sustainability, health care delivery and education encounter conflicting goals, values, norms, cultures and technologies