1. Systems Realization Laboratory Information Economics in Design Chris Paredis The Systems Realization Laboratory PLM Center of Excellence G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology www.srl.gatech.edu www.marc.gatech.edu/plm

2. Systems Realization Laboratory What is Information Economics?  Economics Study of the production, distribution and consumption of goods and services, and the management of these processes Study of how people choose to allocate scarce resources to satisfy competing uses or wants A study of choice  Design Transformation of information from requirements to product description  Information Economics in Design Which information should be created to support design decisions? What is the value of information? What is the cost of information? How can one generate more valuable information at a lower cost?

3. Systems Realization Laboratory Foundations of Information Economics  Some history Daniel Bernoulli (1738) – Expected utility Knight (1921) – Risk and uncertainty in economics von Neumann & Morgenstern (1944) – Utility theory Marschak (1950s) – Economics of organization and information Renewed interest in the context of Information Systems (1990s)  Value of information = the difference in the expected value of a decision made with or without considering the information

4. Systems Realization Laboratory Overview of Presentation  Context What is Information Economics? Information and Knowledge in Product Development  Examples of Information Economics in the SRL Related to Information How should one represent information and uncertainty? How should one use uncertain information to make decisions? How should one compute with uncertain information? Which information should one gather? Which models should one use? Related to Knowledge How should one represent knowledge, models? How should one manage knowledge, models? How should one design the design process?

5. Systems Realization Laboratory Product Development: A Decision-Based Perspective Concept Developme nt Design Production & Testing Sales & Distribution Maintenanc e & Support Portfolio Planning Decisions Evaluate Alternatives Generate Alternatives Select Alternative KnowledgeInformation Generic Decision Process

6. Systems Realization Laboratory R R R Information-Driven Product Development R R R R R R R DesignersSuppliers R R R R R R R R R R R R RR R R R R ManufacturingAnalysts Implicit Not Computer- interpretable Not Interoperable Coarse-grained PDM CAD1 CAD2 FEM Process Planning R

7. Systems Realization Laboratory A Process Perspective Process = Order in which Relationships are Applied Product Perspective Process Perspective

8. Systems Realization Laboratory Product Lifecycle Management Framework

9. Systems Realization Laboratory Research Issues  We need to develop a deeper understanding of the structure of the PLM information graph Which concepts & relationships?  Ontologies How to represent information and knowledge?  uncertainty, context, … How to reconcile multiple ontologies?  interoperability Reusable patterns?  Knowledge Repositories  We need methods for managing the PLM information graph (creating, sharing, modifying,…) Which tools to create and modify info?  maps to stakeholders In which order to build the graph?  concurrent engineering  How to coordinate among multiple stakeholders?  How to maintain consistency?  How to propagate changes? How to maintain, retrieve and apply reusable knowledge templates?

10. Systems Realization Laboratory Research Issues  We need an IT infrastructure for distributed computation and collaboration support How to integrate multiple simulation, analysis, and optimization tools in a distributed fashion?  Interoperability, security, load balancing, … How to provide geographically distributed decision makers with relevant information – in real-time? Overall Theme How can one design better at a lower cost? Guiding Principle Maximize net value of decisions about both product and process Increase the value – Decrease the cost

11. Systems Realization Laboratory Overview of Presentation  Context What is Information Economics? Information and Knowledge in Product Development  Examples of Information Economics in the SRL Related to Information How should one represent information and uncertainty? How should one use uncertain information to make decisions? How should one compute with uncertain information? Which information should one gather? Which models should one use? Related to Knowledge How should one represent knowledge, models? How should one manage knowledge, models? How should one design the design process?

12. Systems Realization Laboratory How should one represent information and uncertainty? (Jason Aughenbaugh, Scott Duncan)  Aleatory uncertainty Inherently random – irreducible Best represented as probability distribution Examples:  Manufacturing variability  Epistemic uncertainty Due to a lack of knowledge Best represented as interval Examples:  Error due to model approximation  Future design decisions Choose the representation that results in best design decisions

13. Systems Realization Laboratory  Combines probability distributions and intervals  P-box: Upper and Lower bound on all plausible CDF's  Generalization of both intervals and probability distributions Probability Bounds Analysis – P-boxes (introduced by Ferson and Ginzberg, 1996) Interval [0,1] Normal( [0,1],1) To judge the value of the representation, one needs to relate it to decisions

14. Systems Realization Laboratory How should one make decision with P-boxes? (Jason Aughenbaugh, Steve Rekuc)  Expected Utility = Interval !! Maps to set-based design Eliminate only the dominated designs  Acknowledging ignorance results in better decisions !  Characterize difference in performance Many sources of uncertainty are 'shared' Taking dependence into account reduces uncertainty in the difference in performance Diff in Expected Utility DV UB LB DV Expected Utility UB LB Conservative Solution Make better decisions with the same information

15. Systems Realization Laboratory Which information to gather or models to use? (Jay Ling)  If epistemic uncertainty is too large to make a decision Gather more information Perform additional simulations (model = information source)  Perform the action that yields the most bang for your buck  Satisficing solution When making a better decision costs more than it is worth Optimal in terms of Information Economics DV Expected Utility UB LB DV Expected Utility Gather additional information most efficiently

16. Systems Realization Laboratory Overview of Presentation  Context What is Information Economics? Information and Knowledge in Product Development  Examples of Information Economics in the SRL Related to Information How should one represent information and uncertainty? How should one use uncertain information to make decisions? How should one compute with uncertain information? Which information should one gather? Which models should one use? Related to Knowledge How should one represent knowledge, models? How should one manage knowledge, models? How should one design the design process?

17. Systems Realization Laboratory How should one representing uncertain knowledge? (Rich Malak) Strain Stress 0 σ UB Strain Stress 0 σ UB with Applicability Domain Epistemic Uncertainty Goal: Enable sharing and reuse of models – Amortize costs

18. Systems Realization Laboratory Reusable and Composable Models (Manas Bajaj, Greg Mocko, Nsikan Udoyen)  Common associations between geometry and analyses/ simulations  Common patterns between CAD description and simulation models Recurring Pattern Mass Material Has Behavior Has Form Motor Form Has Shape Has Material Geometry Has Mass Parameter Has Energy Port Port Mass Equation Information Graph Reusable Patterns? Enable reuse of models – Amortize costs

19. Systems Realization Laboratory Port-Based Abstraction – Knowledge Templates  Port Location of intended interaction Exchange of energy, material, signal  Abstraction becomes container for associated models Rotor Port Stator Port Electrical Connector Model 1 Behavioral Models Model 1 CAD ModelsCost Models … Store knowledge in modular, reusable templates – Amortize costs

20. Systems Realization Laboratory Goals Preferences Variables Parameters Constraints Response Objective Analysis Driver Goals Preferences Variables Parameters Constraints Response Objective Analysis Driver Pressure VesselSpring Reusable, Declarative Decision Templates (Marco Fernandez, Jitesh Panchal)

21. Systems Realization Laboratory Summary  Information Economics A framework for making decisions about design  Applies to many of the problems we are working on in SRL  Can serve as a guide for new research directions Which information costs dominate? How can we reduce the costs? How can we improve value? Questions? Comments?