SCEA International Conference and Educational Workshop June, 2004

1. SCEA International Conference and Educational Workshop June, 2004

2. We Are Vought

3. Vought History The Legacy of the Vought Name Dates Back to 1917 When Chance Vought Co-founded the Lewis & Vought Corp (LTV). 1992, LTV Sold Its Aircraft Division Assets to The Carlyle Group and Northrop Corp, Creating a New Company Named Vought Aircraft Company. 1994, Northrop, Concurrently With Its Purchase of Grumman Aerospace, Acquired Vought Aircraft Company From Carlyle 2000, The Carlyle Group Purchased Northrop Grumman's Aerostructures Business Group, Creating a New Company. The New Business Uses the Heritage Name Vought Aircraft Industries, Inc. And Remains Based in Dallas. 2003, Vought Acquired The Aerostructures Corporation; Currently in Transition to Integrate Operations Today, Vought Aircraft Industries Is a Major Subcontractor on Many Commercial and Military Aircraft Programs.

7. Systems Engineering Cost Estimating Systems Engineering Cost Estimating Apply on Expert Systems and Math Models Provide ROM Estimates When Detail Design Is Not Available Establish Design to Cost Targets Independent Estimating Based on Parameters Decision Support Aid in Bid/No Bid Decision-Making Process Analyze Sensitivity of Program/Design Factors Evaluate Technical/Cost Relationships to Determine Key Cost Drivers Analyze Competitive Cost Data Develop Market Price Benchmarks Optimize Design for System Engineering Trade Studies

8. Trade Study Methodology

9. Creating Your Expert System

15. Building an Expert System Eight Steps Identified to Build an Expert System 1. Expert Systems Product Definition 2. Available History 3. Experts Identification 4. The Knowledge Engineer 5. Key Characteristics Determination 6. Fuzzy Logic 7. Decision Analysis Methodology 8. Innovative Concepts Incorporation

17. Determine What Historical Data Is Available In-house Programs Standards Industry Data Government Data Normalize Historical Data Hours Per Pound Dollars Per Pound Component Per Square Foot Support Ratios Economic Adjustments Derive Company Cost Estimating Relationships (CER�s)

18. Who Is an Expert? Senior Estimator Manufacturing Lead Person Shop Manager Senior Manufacturing Engineer Senior Design Engineer

19. Natural Language Processing Interfaces With Knowledge and Reasoning Problem Use of Natural Language Presumes Understanding by the Listener, Not Simple Decoding Examples: �British Left Waffles on Falklands� �Kicking Babies Considered Healthy� Translation: �The Spirit Is Willing, but the Flesh Is Weak� �The Vodka Is Fine, but the Meat Is Rotten�

20. Building an Expert System What Is a �Knowledge Engineer?� A Person Who Designs the Logic Paths in an Expert System May Not Be an �Expert� in the Subject Matter of That Particular Expert System A Person Who Understands the Decision/logic Process of Reaching a Conclusion A Person That Can Interpret the Logic Process Used by the �Experts�

21. Building an Expert System What Are �Key Characteristics� Key Characteristics Are the Properties of an Item That an Expert Uses to Estimate Weight Fastener Count Schedule Size Materials Power Speed Surface Contour Part Count Manufacturing Technology Environment Who Identifies the �Key Characteristics� Used? Identified by the Experts in Each Functional Department How Are �Key Characteristics� Used? Knowledge Engineers Use the Key Characteristics and Fuzzy Logic to Construct a Relationship Between the Key Characteristics and the Relevant Historical Data

22. What is Fuzzy Logic? Fuzzy Logic Is a Calculus of Compatibility. Unlike Probability, Which Is Based on Frequency Distribution in a Random Population, Fuzzy Logic Deals With Describing the Characteristics of Properties. Fuzzy Logic Describes Properties That Have Continuously Varying Values by Associating Partitions of These Values With Semantic Label Bill Is Tall Tom Is Short A One Pound Part Is Average A Small Part Has a Value of Less Than 0.8 Lb. A Heavy Part Has a Value of over 5.5 Lb. Fuzziness Is a Measure of How Well an Instance (Value) Conforms to a Semantic Ideal or Concept Building an Expert System

23. Boolean Logic

24. Why Fuzzy? Most Modes of Human Reasoning and Common Sense Reasoning Are Approximate in Nature Approximation of Data Incompleteness of Data Uncertainty of Knowledge Is a Statement Absolutely True and Auditable? Imprecision of Knowledge Inflation: 3.8% Versus Low Rate Fuzzy Logic Handles Partial Truth Value Between �Completely True� and �Completely False�

25. Incorporates Objective and Subjective Selection Criteria in a Structured Approach Considers Relative Importance of Criteria in Determining the Worth of the Alternatives Overall Performance Is Summation of Weighted Utility Value of Each Criteria Output Is a Single Value That Represents the Relative Worth of an Alternative Building an Expert System

26. Multi-Attribute Utility Analysis Management Science and Systems Engineering Tool Systematic Repeatable Accountable/Traceable Flexible Fast Concept Originally Applied by Economists and Market Researchers Also known as Hierarchical Analytical Process

27. The Hierarchical Analytical Process*

28. Weighting Factors Establish Weighting Factors for Each Step of the Hierarchy Determine the Value (or Utility) of Each Contributor to Cost The Sum of the Utilities Must Equal �1� Compute a Relative Adjustment Factor

29. Some Multi-Attributes Contained in an Expert System

30. Building an Expert System Value/Producibility Engineering Design/Build Teams Design to Cost 2-D and 3-D Computerized Modeling Advanced Tooling Philosophy Determinate Assembly Automated Factory Integrated Design/Manufacturing Data Base New Materials Advanced Processes �Can Do� Attitude

31. Benefits From Establishing an �Expert System� Gives a Consistent Starting Point for All Estimates Captures the Thought Process of Senior Estimators Helps Train New Personnel Helps Retain the Experiences of the �Expert� When They Retire or Leave the Company Captures Rules to Be Applied Questions to Be Asked Default Values Puts Discipline in the Estimating System. Helps Support �Estimator's Opinion� for Proposal Justification

32. Top Down Parametric Model Used to Estimate Total Recurring Production Costs by Function Cost Drivers Include: Weight and Material Mix T1 Man-hours by Material Type Multiple Slope Improvement Curves for Labor and Material Programmatics Economics T1 Cost and Improvements Curves for Avionics Equipment Raw Materials

33. Role of Top-Down Recurring Cost Model

34. Expert Systems and Process Modeling Apply Knowledge Based Expert System Techniques to Design for Manufacturing and Assembly (DFMA) Models DFMA Models Are Based on Rule Building Process Models Help Retain Expert Knowledge

35. Process Based Cost Prediction Tool Design for Manufacturing and Assembly DFMA

36. Process Based Cost Model Vought Applies the Boothroyd Dewhurst Inc. Design for Manufacturing and Assembly (DFMA) Software Suite of Design for Assembly (DFA) and Design for Manufacturing (DFM) Concurrent Costing Models From For Use in Performing Cost Trade Studies Design for Assembly (DFA) Involves Step-by-step Assessment of Manufacturability Issues Design for Assembly Is a Systematic Procedure Used to Reduce Overall Product Cost Through Design Simplification. User Can Develop Relationships Based on Historical Data

38. Metallic Processes Metallic Product Analysis Is Generated With Current Levels of Process Information Evaluate Differences in Configuration Concepts Metallic Component Assembly Is Mature and Well Understood

39. Composite Processes Vought Aircraft Has a Wide Range of Programs That Employ Composite Material Application Composite Product Analysis Is Generated With Current Levels of Process Information Vought Derived Equations are proprietary Models Vought�s Current Composite Manufacturing Processes Data Concerning New Processes Developed From Synthesized From Simulations

41. In Conclusion