Producing Prosperity - An Industrial Engineer’s Role in Economic Expansion Dr M C Jothishankar Advanced Manufacturing Technology

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

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

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

How Can an IE Become More Competitive in the New Economy? n 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

Case Study I Traditional Approach (Part 1)

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

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

Present Routes

Traditional IE Approach n Time study on the routes n Foot prints of the routes n Cost calculations on resources

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

Final

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

Case Study I Same Problem in New Economy - A Collaborative Approach (Part 2)

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

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!

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.

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

Example of Plants P1 P2 P3 P4 P5 P6 P7 P8

Proposed Solution Method n The problem will be modeled as a minimum-cost, multi-period, multi- commodity network flow problem. n 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.

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

Constraints n capacity restrictions n conservation of flow for each material n limit on # vehicles of each type n integrality of vehicles

Solution Approach n Benders’ Decomposition n Lagrangian Relaxation n Cross-Decomposition n Genetic Algorithm n Simulation

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

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

Sample Simulation

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

Case Study II Setup Reduction (Part 1)

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

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

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

Process Center Operation Improvement Objectives n Set-up time reduction n Scheduling time reduction n Increase machine utilization n Decrease manufacturing lead time n Increase throughput

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

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

Clustering PCBs n 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

Clustering PCBs - Example (Before) Printed Circuit Board Component a11 b111 c11 d1111 e1111 f11 g11111 h11

Clustering PCBs - Example (After) PCB Groups: (4,1,7) (9,8) (2,5) (6,3) Printed Circuit Board Component g11111 f11 a11 b111 d1111 e1111 c11 h11

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

Interacting Elements Productio n Plan Part Inventory Process Center Data Schedule PCB Design Data

Case Study II Manufacturing Optimization and Execution System (MOES) for a PCB Assembly Plant (Part 2)

Present Optel Framework Plant Data Manager ERP/ MRP Setup Verification Material Management Machine Optimization Assembly Data Modeling Plant Process Monitor Shop Floor OPTELOPTEL Production SchedulingPDM

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

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

Case Study III E- Manufacturing

Partners of this E-Manufacturing Project

Virtual Factory ANY PRODUCT ANY TIME ANY WHERE

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

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

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

Virtual Environment Process Planners Simulator Manufacturing Execution Systems DesignsRules Resources Process Plans Virtual Products Machine Programs Manufacturing Analysis Visualizers Rule Inference Engines

Flow Diagram DFM Rule Checker DFM Rule files AP 210 file (3D) AP 210 file (2D) Assembly configuration file Placement Sequence (OPTEL) Visualizer (STEP OIV) Machine library Simulator Design Facts

2D to 3D converter n ECAD translators generate AP 210 files containing 2D Geometry n Simulate 3D view of the assembly board n Converter –Input : AP 210 file (2D geometry) –Extrudes them into solids (Advanced BREP) –Output: AP 210 file (2D + 3D geometry) AP 203 files

2D to 3D converter n AP 203 files –Individual packages –Board n These files then converted into Open Inventor format –Inventor: Graphics package used for rendering

DFM Rule Facility n Subset of Rockwell Collins DFM rules were chosen for implementation. n Interface to the simulator –DFM rules which were violated –Components which violated these rules.

Assembly Data n Establish an assembly usage view –Components Organization by package / part family Part numbers, version Configuration management data Location, orientation Reference Designators n Integrated with OPTEL –Magazine Setup –Optimized placement sequence

Simulator Feeder information Component information Placement Sequence Geometric Models Placement Simulator Virtual Board DFM Rule Check Components violating DFM rules

Work Done n Accepting an AP210 2D design file n Manufacturability analysis n Extracting component information n Generating 3D models of components and assemblies n Generate –“as designed” view – “as simulated” view

Rules of New Economy n Change happens –Anticipate change n Be ready to change quickly and enjoy the change –Adapt to change quickly

“It is not the strongest of the species that survive, not the most intelligent, but the one most responsive to change” Charles Darwin