Design for Harmony:
An Exploration of Enterprise and Product
Architecture Tradespace Dynamics
Christopher J. Roberts, Ph.D. Student, Engineering Systems Division
Advisors: Prof. Deborah Nightingale, Dr. Donna Rhodes
Enterprise Architecting is the proactive process of designing and evolving the desired future state of the enterprise. It encompasses choices about the fundamental business models employed and the strategic responses available to dynamic changes in context. Similarly, product architectures govern the design and evolution of products.
Product development enterprises experience dynamic coupling between their enterprise and product architectures that leads to emergent behaviors often manifested as “ility” properties (flexibility, rigidity, etc.), for better or worse. Better understanding of enterprise-product architectural coupling and interactions with the dynamic context in which they are embedded will allow the design of symbiotic enterprise-product architectures that deliver sustainable stakeholder value.
The goals of this research are to develop core theory & methodology, create a robust dataset, apply innovative modeling & analysis, and impact the policy & practice of enterprise and product architecting in dynamic contexts.
1. What are the dominant enterprise & product variables that give rise to dynamic coupling and emergent behavior?
2. What are the dominant contextual factors (i.e. political, market, etc.) that influence architectural co-evolution?
3. How can the dynamic relationships between the enterprise-product architectures and contextual factors be managed?
Motivation / Problem Statement
Research Design
Defining Survivability
Key Questions
Expected Contributions
Literature Review
Timeline
Christopher Roberts
cjr@mit.edu
Dynamic Tradespace Exploration
Advancement of enterprise and product architecting core theory
• Extension of dynamic tradespace exploration to include enterprise architecture and contextual factors
• Development of dynamic complexity metrics Systematic Observation and Documentation
• Collection of a robust empirical dataset through case studies, field work, probabilistic modeling and statistical analysis
Innovative modeling and analysis
• New engineering system representation and visualization schemas Impact to policy and practice
• Enable more effective
enterprise and technology strategic planning and
governance
Technology
Management
Policy
This research is conducted with an iterative concurrent
process using qualitative and quantitative methods
. .
Engineering Systems Fundamentals & Research Publications
Product Development & Systems Architecture
Product Planning & Acquisition Strategy Lean Enterprises & Enterprise Architecting
Dynamics of Innovation
Complexity Theory
1. Knowledge Capture and Synthesis (Descriptive) 2. Theory Development (Normative) 3. Computer Experiments (Theory Evaluation) 4. Case Applications (Prescriptive) Existing Theory, Empirical Data Literature Review, Field Interviews, Data Collection Methods Methods External Validation Internal Validation Analysis Techniques, Empirical Data
Fall 2007 Spring 2008 Fall 2008 Spring 2009 Fall 2009 Spring 2010 Fall 2010 Spring 2011 Program Start, Coursework, Research Exploration Coursework, Research Definition, Literature Review Coursework, Research Substantiation & Proposal, General Exams Data Gathering, Theory Development, Conference Publications Data & Theory Refinement, Computer Simulations Computer Simulations, Case Applications, Conference Publications Synthesis & Refinement, Impact & Outreach Activities Dissertation, Journal Publications
Enterprise Architecting1 is used to provide
a data gathering and modeling framework to capture the dynamic complexity of the
enterprise component interactions
Dynamic MATE4 is used to explore the
dynamic complexity of the joint enterprise-product architecture tradespace under variable contextual scenarios and time scales
+
Enterprise and Product Architectures are Dynamically Coupled
Candidate Methods
Qualitative Methods
• Enterprise Architecting1
• Enterprise Value Stream Mapping Analysis2
• Complex, Large-scale,
Interconnected, Open, Socio-technical (CLIOS) Process3
• Case studies & analysis
• Field interviews & ethnography
1 Nightingale, Deborah and Donna Rhodes. (2008). ESD.38 Enterprise Architecting Graduate Course. MIT ESD.
2MIT Lean Advancement Initiative. Lean.mit.edu
3 Sussman, Joseph et al. (2008). “The ‘CLIOS Process:’ A Users Guide.”
Quantitative Methods
• Dynamic Multi-Attribute Tradespace Exploration (MATE)4
• Statistical inference from empirical datasets
• System Dynamics
• Complexity methods
championed at the Santa Fe Institute, including
non-equilibrium statistical physics and network & scaling theories
4Ross, A. (2006). "Managing Unarticulated Value: Changeability in Multi-Attribute Tradespace Exploration." Doctoral dissertation, MIT ESD. Available at seari.mit.edu
5 Santa Fe Institute. www.santafe.edu
Tradespace analysis allows identification of symbiotic
enterprise-product architectures with desirable “ility” properties
• Conceptualization of dynamic complexity in enterprise and product architectures
• Metrics for component dynamic complexity to be used in
tradespace exploration modeling
• Prescriptions for improved enterprise strategic
planning and governance Findings
