Defining the “New Economy”

1. Producing Prosperity - An Industrial Engineer’s Role in Economic ExpansionDr M C JothishankarAdvanced Manufacturing Technology

2. Defining the “New Economy” • Past 100 years, vitality of US economy was determined by success of “traditional” manufacturing industries - automobiles, steel, oil, and chemicals • Today, information technology, communications and intellectual capital determine success • The driving forces of the new economy are ideas, knowledge, services and higher-order skills • Manufacturing remains important - innovation, adaptation, and reengineering are the watchwords of success for today’s workers and businesses.

3. What Does the New Economy Mean? • To those in information technologies - describes the power of new communication tools • To venture capitalists - hundreds of investment opportunities each day • To corporate leaders - new alliances, partnerships or mergers • To trade advocates - accelarates an integrated, global economy • To educators - lifelong learning opportunities • To the average citizen - numerous opportunities at home and at work, and more connectivity worldwide

4. What are the Characteristics • Products are increasingly non-tangible: knowledge is the major input • Productivity is increasing: deployment of technology driving force • Markets are global and competitive: labor and expertise vs. location and physical structure • Entrepreneurs are spurring economic growth • New partnerships are the wave: co-competition creates a flexible economy

5. How Can an IE Become More Competitive in the New Economy? • IEs must build upon their core strengths and focus on the economic foundations of the New Economy: • Cross functional skills • Access to new technologies on which new products and processes are based • Consortium participation • Collaborative work among: • Industries, academia, government and labor

6. Case Study ITraditional Approach(Part 1)

7. Material Movement &Fleet Management This project aims to study the best possible routing/distribution of mails/material between Rockwell facilities in Cedar Rapids.

8. Facts • Over 30 buildings are served • Seven drivers moved material between buildings • Some stops were delivered 12 times a day • Type of material moved: • Internal mail • Dispatches • “Hot” Dispatches • Security Dispatches • Skids

9. Present Routes

10. Traditional IE Approach • Time study on the routes • Foot prints of the routes • Cost calculations on resources

11. Recommendations • Reduce current seven drivers to four drivers • Establish a “hub” at 120 Mailroom for relay of dispatches between routes • Reduce frequency of visits to a maximum of 8 per day

12. Final

13. Results Total Savings out of this project: $121,000

14. Case Study ISame Problem in New Economy - A Collaborative Approach(Part 2)

15. U. S. Region • Dr. M. C. Jothishankar • Dr. Dennis Bricker • Japan Region • Mr. Tomomitsu Murano, • Mr. Hisaya Watanabe • Dr. Seiichi Kawata THE UNIVERSITY OF IOWA • European Region • Prof. Dr.-Ing. Heinz Wörn • Mr. Daniel Frey • University of Karlsruhe, • Germany

16. Quote to Note “The improvements achieved in one company can be easily be wasted in the subsequent phases of logistics chain.” - Heard on the Street!

17. Objective of GALAXI To develop an optimization and simulation model that will minimize the overall fleet operations cost and most effectively distribute material between different manufacturing plants.

18. Description of the Model • In this manufacturing system, there are a number of factories • Each factory manufactures parts, which are used for assembly of products at the same or another factory • Parts are transferred between factories by using several kinds of vehicles (trucks) on demand

19. P4 P3 P5 P2 P1 P6 P8 P7 Example of Plants

20. Proposed Solution Method • The problem will be modeled as a minimum-cost, multi-period, multi- commodity network flow problem. • One set of variables will specify the routes and schedules for the trucks, while another set of variables will specify the movement of the parts.

21. storage costs & penalties for late delivery shipping costs cost of vehicles Total Cost to be Minimized

22. Constraints • capacity restrictions • conservation of flow for each material • limit on # vehicles of each type • integrality of vehicles

23. Solution Approach • Benders’ Decomposition • Lagrangian Relaxation • Cross-Decomposition • Genetic Algorithm • Simulation

24. Genetic Algorithms Manual Input Truck schedules Benders' Lagrangian Subproblem Subproblem (Solves Lagrangian Relaxation) (Determines Material movement) Lagrangian multipliers Cross Decomposition

25. Deterministic Model USA Cross-Decomposition Method F(Y) Japan Genetic Algorithms Simulation Model Stochastic Model Germany Inter-Relationship Among Models

26. Sample Simulation

27. Expectations of This Project • We hope to reduce our total material movement cost by 30% a savings of almost $ 250,000 annually　 • This software will help the truck schedulers to make better decisions and to reduce the time spent in scheduling • Increased truck utilization

28. Case Study IISetup Reduction(Part 1)

29. Concepts • Look beyond the problem under study - Instead of “Point” solution approach the problem to provide a “System Solution” • Use Re-engineering principles • Involve the users

30. Problem Overview • PCB assembly machines have high pick-and-place rates, but their set-up times are typically very long • PCBs scheduled in Process Center on first-come-first-serve basis • Set-up is changed for every PCB batch • Large set-up times and underutilized resources

31. Setup Details • Kits run per day : 30 • Feeder changes between kits : 40 • Feeder changes per day : 1200 • Time to change a feeder : 30 Seconds • Time to change feeders / month : 1200 x 22 x 0.5 = 220 hours (10 hours a day!)

32. Process Center OperationImprovement Objectives • Set-up time reduction • Scheduling time reduction • Increase machine utilization • Decrease manufacturing lead time • Increase throughput

33. Process Center Optimization Project Approach • PCB manufacturing process reengineering • Development of optimization algorithms • Software development • Simulation studies

34. Process Center Optimization • Cluster PCBs into groups • Sequence the PCB groups to minimize the total set-up time • Optimize assignment of feeder locations to minimize the number of feeder changeovers • Use simulation to evaluate system performance for generated schedules

35. Clustering PCBs • Clustering PCBs into a minimum set of groups such that: • Groups are formed based on similar components • Total number of unique component types should be less than the number of feeders • Within each PCB group, no set-up is necessary when changing from one PCB type to another

36. Printed Circuit Board Component 1 2 3 4 5 6 7 8 9 a 1 1 b 1 1 1 c 1 1 d 1 1 1 1 e 1 1 1 1 f 1 1 g 1 1 1 1 1 h 1 1 Clustering PCBs - Example (Before)

37. Clustering PCBs - Example (After) PCB Groups: (4,1,7) (9,8) (2,5) (6,3) Printed Circuit Board Component 4 1 7 9 8 2 5 6 3 g 1 1 1 1 1 f 1 1 a 1 1 b 1 1 1 d 1 1 1 1 e 1 1 1 1 c 1 1 h 1 1

38. Select work orders to be scheduled for production Balance workload on assembly lines Split assembly components between machines Group similar assemblies into families Generate family and assembly sequences Generate machine placement programs Optel Procedure

39. Production Plan Part Inventory Process Center Data O P T E L PCB Design Data Schedule Interacting Elements

40. Case Study IIManufacturing Optimization and Execution System (MOES) for a PCB Assembly Plant(Part 2)

41. Production Scheduling OPTEL Machine Optimization PDM Assembly Data Modeling Shop Floor Setup Verification ERP/ MRP Material Management Plant Process Monitor Plant Data Manager Present Optel Framework

42. Benefits of Using Optel • Setup time reduction: 70% • Increase in machine utilization: 20% • Increase in component placement: 60% • Reduction in machine programming time: 95% • Eliminated night shift and week end operations

43. Results Total Savings out of this OPTEL project: $1.5 M/Year/site

44. Case Study IIIE-Manufacturing

45. Partners of this E-Manufacturing Project

46. Virtual Factory ANY PRODUCT ANY TIME ANY WHERE

47. Vision Our vision is E-manufacturing where we have • Seamless, scalable and robust evolution of products from design to manufacturing • Computer tools (such as simulators, rule-bases, visualizing environments) to rapidly plan, validate and deploy manufacturing instructions • Flexible manufacturing systems for simultaneous production of multiple products and minimum system change over

48. Design Release Physical Constraints and Functions Detailed Design MFG. Review (DFM Checks) Trial&Error Production Present flow

49. Physical Constraints and Functions Engineering Design Computer Integrated Manufacturing Design For Manufacturing Virtual Environment Enterprise Resource Planning Optel - Manufacturing Execution System Design Release Production Proposed Flow

50. Rules Designs Resources Rule Inference Engines Manufacturing Execution Systems Simulator Process Planners Visualizers Process Plans Manufacturing Analysis Virtual Products Machine Programs Virtual Environment