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Grand Challenges for Industrial Engineering

Grand Challenges for Industrial Engineering. Leon McGinnis Eugene C. Gwaltney Professor of Manufacturing Systems Georgia Tech January 9, 2001. Agenda. What’s a grand challenge? What’s industrial engineering? Are there any IE grand challenges? Some modest first steps.

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Grand Challenges for Industrial Engineering

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  1. Grand Challenges for Industrial Engineering Leon McGinnis Eugene C. Gwaltney Professor of Manufacturing Systems Georgia Tech January 9, 2001

  2. Agenda • What’s a grand challenge? • What’s industrial engineering? • Are there any IE grand challenges? • Some modest first steps ASU, 10/16/2002

  3. What’s a “Grand Challenge”?

  4. The HyperSoar is a B-52 sized hypersonic bomber/recon aircraft concept under investigation by US DoE's Lawrence Livermore National Laboratory and University of Maryland. If developed, it would have the capability to take-off from the US and deliver its goods anywhere in the world in a couple of hours without refueling. It's speed(Mach 10) and altitude will make it near impossible to track it, much less bring it down. http://www.geocities.com/spacetransport/hypersoar.html ASU, 10/16/2002

  5. GM has spent over $1 billion to develop a concept car designed around fuel cells for energy and a “drive by wire” control system ASU, 10/16/2002

  6. High Performance Computing and Communication The Grand Challenge problems have the following in common: • They are questions to which many scientists and engineers would like to know answers, not just esoterica. • They are difficult; we don't know how to do them right now. • We think they can be done by computer, but we think current computers are not "powerful" enough. John L. GUSTAFSON http://www.scl.ameslab.gov/Publications/GrandChallenge/Paradigm.html ASU, 10/16/2002

  7. The CPGE and the CSM are in the third year of a DOE Grand Challenge research project entitled Parallel Algorithms for Modeling Flow in Permeable Media. The objective of this work is to investigate the use of high-performance parallel processing as a tool to simulate the behavior of petroleum reservoirs and groundwater aquifers. The two research areas addressed in this project are enhanced oil recovery and the removal of contaminants from fresh water aquifers. Two parallel, special purpose reservoir simulation codes and one parallel ground water code (UTCHEM, UTCOMP, RParSim) have been developed, tested and applied to field problems. http://www.pe.utexas.edu/HPCC/hpcc.html ASU, 10/16/2002

  8. A High Performance Computational Engine for the Analysis of Genomes Science Area: Genome Informatics This project will build a high performance, computational genome analysis tool capable of meeting the needs of the next phase of the Human Genome project. This tool will impact not only the Human Genome project, but also the effectiveness of human and microorganism genome centers and related developments in structural biology, genetic medicine, biotechnology, pharmaceuticals, and bioremediation. http://www.nersc.gov/research/GC/gcnersc.html ASU, 10/16/2002

  9. CRA Conference on "Grand Research Challenges" in Computer Science and Engineering June 23-26, 2002 Airlie House, Warrenton, Virginia The participants selected five Grand Challenges for special consideration: Systems you can count on (137 KB PDF) A teacher for every learner (24 KB PDF) 911.net (ubiquitous information systems) (21 KB PDF) Augmented cognition (166 KB PDF) Conquering complexity (81 KB PDF) http://www.cra.org/Activities/grand.challenges/index.html ASU, 10/16/2002

  10. Testimony on the FY 2003 Budget Request for theNational Science Foundation • Tom Manteuffel, Ph.D.President, Society for Industrial and Applied Mathematics • Selected Grand Challenge Problems • Health Sciences: Designing effective new drugs requires the capability to model proteins. Known as proteomics, this research depends heavily on mathematical formulations and computational techniques. • Information Sciences: Many methods to assure reliability, security, and trust in computing infrastructures, such as the computer internet (Web), have their roots in some of the more complex "pure" areas of mathematics. • Homeland Security: Prevention, detection and response technologies, ranging from distributed pathogen identification, data management and data mining, and simulation and risk assessment, to signals analyses and reconnaissance, all involve complex mathematics and computation. • Medical Imaging and Visualization: Techniques for enhancing perception for various technologies, such as, magnetic resonance imaging, computerized axial tomography, and positron emission tomography are derived using deep mathematics and complex computations. • Geophysical Sciences: Understanding the effects on our environment, oceans, atmosphere, and global climate, is a case-study of complex mathematical interactions. • Engineering Sciences: Predicting the onset and effects of earthquakes and other shocks is only possible through massive computational models linked to empirical data. ASU, 10/16/2002

  11. Grand Challenge • Fundamental Intellectual Problem(s) • Scope and Scale • Inevitable Impact • Research process • Discipline • Society ASU, 10/16/2002

  12. In one of his first interviews since becoming Chairman of The Engineering and Technology Board in January, Sir Peter Williams talks about the critical need to redefine engineering in the 21st century, citing Bio-Pharmaceuticals as one of the leading, new technology areas. He says: ‘Such areas have probably attracted people because they deal with some of the great issues of the 20th Century. Where at the turn of the century these were physics and engineering based, many are now around DNA, the human genome, cloning and stem-cells. All these huge issues are in the national culture and media all the time. Traditional engineering doesn’t have these grand challenge issues now. We’re up against the sexiness of the IT world and the grand challenge aspect’. http://216.239.35.100/search?q=cache:pfsR7mlT6awC:www.etechb.co.uk/media_centre/bulletin/+%22grand+challenge%22+engineering&hl=en&ie=UTF-8 ASU, 10/16/2002

  13. Sales & Marketing Technical Operations Customers Suppliers Strategic Planning; Process & System Development Business & Product Development IE Systems? ASU, 10/16/2002

  14. Physical Logical machine tools operators tooling workpiece speeds & feeds operation sequence control limits control I/F Conversion container conveyor vehicle operator inventory process routes sequence/schedule prod’n/inventory plan Flow Sales & Marketing Customers Suppliers Strategic Planning; Process & System Development Business & Product Development IE’s “Natural” Domain ASU, 10/16/2002

  15. IE Research Domain Physical Logical machine tools operators tooling workpiece speeds & feeds operation sequence control limits control I/F Conversion container conveyor vehicle operator inventory process routes sequence/schedule prod’n/inventory plan Flow ASU, 10/16/2002

  16. The Big Picture Profitability Cost Revenue Design Global Economy Investment Speed,Flexibility Marketing Resource Efficiency ASU, 10/16/2002

  17. Are there any IE “grand challenges”?

  18. The vision for 2020 and beyond described in Chapter 1 suggests considerable changes in the manufacturing enterprise. The social and political environment, the needs of the marketplace, and opportunities created by technological breakthroughs will drive these changes. Moving from the current status of manufacturing to manufacturing in 2020 will present major challenges, which the committee defines as "grand challenges" or fundamental goals, that would make realization of the vision possible. The six grand challenges are listed below: • • achieve concurrency in all operations • • integrate human and technical resources to enhance workforce performance and satisfaction • • instantaneously transform information gathered from a vast array of sources into useful knowledge for making effective decisions • • reduce production waste and product environmental impact to "near zero" • • reconfigure manufacturing enterprises rapidly in response to changing needs and opportunities • • develop innovative manufacturing processes and products with a focus on decreasing dimensional scale http://www.nap.edu/readingroom/books/visionary/ch2.html ASU, 10/16/2002

  19. Ten Strategic Technologies • adaptable, integrated equipment, processes, and systems that can be readily reconfigured • manufacturing processes that minimize waste and energy consumption • innovative processes for designing and manufacturing new materials and components • biotechnology for manufacturing • system synthesis, modeling, and simulation for all manufacturing operations • technologies to convert information into knowledge for effective decision making • product and process design methods that address a broad range of product requirements • enhanced human-machine interfaces • new educational and training methods that enable the rapid assimilation of knowledge • software for intelligent collaboration systems ASU, 10/16/2002

  20. Research Recommendations • understanding the effect of human psychology and social sciences on decision-making processes in the design, planning, and operation of manufacturing processes • managing and using information to make intelligent decisions among a vast array of alternatives • adapting and reconfiguring manufacturing processes rapidly for the production of diverse, customized products • adapting and reconfiguring manufacturing enterprises to enable the formation of complex alliances with other organizations • developing concurrent engineering tools that facilitate cross-disciplinary and enterprise-wide involvement in the conceptualization, design, and production of products and services to reduce time-to-market and improve quality • developing educational and training technologies based on learning theory and the cognitive and linguistic sciences to enhance interactive distance learning • optimizing the use of human intelligence to complement the application and implementation of new technology • understanding the effects of new technologies on the manufacturing workforce, work environment, and the surrounding community • developing business and engineering tools that are transparent to differences in skills, education, status, language, and culture to bridge international and organizational boundaries ASU, 10/16/2002

  21. ASU, 10/16/2002

  22. Grand IE Challenge #1 Develop a theory and engineering tools for measuring operational performance that will permit clear-cut “system” assessment of change (improvement or degradation) in comparing one system over time, or different systems over a given time period. ASU, 10/16/2002

  23. Grand IE Challenge #1 • Today: • Single-factor productivity metrics • Activity based costing (depends on costs) • Total Factor Productivity (coefficients?) • Tomorrow: • Comprehensive model • Single index ASU, 10/16/2002

  24. Data Envelopment Analysis Resources Services Activities OUTPUTS INPUTS ONE PERFORMANCE INDEX ASU, 10/16/2002

  25. System efficiency of warehouse B is the ratio OA OB O A Production/Output B Resource/Input Internet-based Data Envelopment Analysis (iDEAS-W) Over the internet Html documents Database Solver GT Server At your site ASU, 10/16/2002

  26. iDEAS-W • Web-based self-assessment tool • ~200 users • Next generation tool 1Q2003 • www.isye.gatech.edu/ideas ASU, 10/16/2002

  27. Grand IE Challenge #2 Develop a theory and engineering tools for comprehensively describing flow systems, both their structural and behavioral attributes. ASU, 10/16/2002

  28. Hardware Description Language -- VHDL code for AND-OR-INVERT gate library IEEE; use IEEE.STD_LOGIC_1164.all; entity AOI is port ( A, B, C, D: in STD_LOGIC; F : out STD_LOGIC ); end AOI; architecture V1 of AOI is begin F <= not ((A and B) or (C and D)); end V1; -- end of VHDL code ASU, 10/16/2002

  29. Inspection Order picking Receiving Shipping Bulk storage Carton storageand picking Order sort/ accumulate Warehouse Analysis, Design & Engineering System (WADES) Decision Theory Design Environment Analytic Models Decision User Support Interface System Database Reference Model ASU, 10/16/2002

  30. Elements of a Warehouse DL • Objects • Product • Equipment • Space • Orders • Instructions • Activities • Receiving & Shipping • Put-away • Replenishment • Order Picking • Accumulation/Sortation The challenge is to create a unified model that incorporates all the important objects and activities ASU, 10/16/2002

  31. Grand IE Challenge #3 Develop a design theory and computational tools for engineering design of flow systems. ASU, 10/16/2002

  32. Data Expert Warehouse Design Functional Space • Functional requirements • Functional cost Profiling • Systems Specification and Optimization • Sizing and configuration • Operating policy selection Architecture Function Flow Network Major focus on client data and its analysis. ASU, 10/16/2002

  33. Grand IE Challenge #4 Develop a theory and computational tools for high-fidelity emulation of proposed flow system designs. ASU, 10/16/2002

  34. Dilemma Discrete Event Transaction System Discrete Event Control System Model ASU, 10/16/2002

  35. The fidelity of models of large-scale complex flow systems (like wafer fabs) is hostage to our ability to capture the behavior of “non-observable” processes, such as legacy software! ASU, 10/16/2002

  36. Resolution Discrete Event Transaction System Discrete Event Control System Model ASU, 10/16/2002

  37. Four Grand Challenges Operational Performance Measurement System Representation Emulation System Design ASU, 10/16/2002

  38. Do These Pass the Test? • Fundamental Intellectual Problem(s) • Scope and Scale • Inevitable Impact • Research process • Discipline • Society ASU, 10/16/2002

  39. Why is this important? ASU, 10/16/2002

  40. Your Turn!

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