Arizona State University - Semiconductor Supply Chain Simulation/Control
University: Arizona State University - Dieter Armbruster, Daniel E. Rivera, Matthias Kawski Intel Sponsor: Karl Kempf, Intel FellowDescription: The purpose of the research is to promote Intel’s understanding of controlling supply chains, and provide control simulation tools to continuously improve that understanding. New Emphasis: Integrate with the Edge to Edge Strategic Design Team to assure the results are as directly useable as possible.
Stanford University/Massachusetts Institute of Technology - "Demand Generation: Product Transitions"
Universities: Stanford University and Massachusetts Institute of Technology (MIT)
Stanford University - Dr. Hau Lee
MIT - Jim Rice and Dr. Jim Hines Intel Sponsor: Mary Murphy-Hoye and Jay Hopman
Description: An interesting phenomenon in the high-technology industry (driven by Moore's Law) is the technology and market transition that takes place from one microprocessor generation to the next. There are deliberate market and SN strategies employed to "move the market" from one generation to the next that require strong coordination between the marketing & sales strategies as well as the manufacturing and supply network responses. (e.g. Intel recently transitioned to Pentium 4 through the marketing "Breakaway" campaign). Some of the interesting dynamics to investigate regarding these market transitions are the way demand manifests itself through these transitions, the way the supply network responds, and how demand and supply network plans transform over time. For example, product transitions may create or contribute to oscillations in production, inventory and demand. Transitions may contribute to the "bull whip" effect, the observation that fluctuations increase in severity as one moves up the chain from consumer to producer. And, of course, among the most important dynamics are those that determine the speed and even the success of a transition.
Operation Modeling
ASU "Data Mining Pilot on Intel Factory Data
University: Arizona State University - John W. Fowler; Gerald T. Mackulak
Intel Sponsor: Mani Janakiram
Description: Evaluating factory data for any hidden patterns using cluster analysis/decision trees/networks and other data mining tools, develop PM prediction models and also determine applicable software and implementation aspects. Identify lot movement patterns that coincide with constraint output/cycle time performance characteristics. Visibility and prediction of estimated cycle time for the critical lots would be beneficial for factory planning. SRC/Sematech "New Approaches for Simulation of Water Fabrication"
University: Arizona State University - John W. Fowler; Gerald T. Mackulak
Intel Sponsor: Mani Janakiram
Description: Constraints exist in the simulation model development process. A useful model requires sufficient detail and accuracy in both the physical and statistical domains. Semiconductor fab modeling presents the additional constraint of short product life cycles resulting in short analysis time cycles and a strong reliance on fast model execution if an analysis is to be useful. Tasks: Automated Job-Driven, Resource-Driven, and Mixed Model Conversion; Goal-Driven and Optimization Methodologies
SRC/Sematech "Scheduling of Semiconductor Wafer Fabrication Facilities"
University: Arizona State University - -John W. Fowler, Univ. of Arkansas/Fayetteville, Scott Mason
Intel Sponsor: Mani Janakiram
Description: Task "Scheduling and Rescheduling Methodologies" is to develop a shifting-bottleneck-based approach for scheduling in a wafer fab, including a means for simplifying fab models by black-boxing non-critical areas of the fab. Task "Subproblem Solution Procedures" will develop subproblem solution procedures for specific toolgroup types and topologies, for incorporation into the shifting bottleneck approach for fab-level scheduling in Task 1. Task "Statistical Operations Control" will develop decision rules that trigger, at an opportunistic time, rescheduling of an operation or operations. These rules will enable an adaptive rescheduling strategy that is based on the current disagreement between actual and predicted schedule performance weighed against the potential improvements gained from rescheduling. Task "Testing, Comparison and Implementation Issues" will develop a testing and development framework and implementation of our scheduling, statistical operational control, and rescheduling methodologies.
SRC/Sematech "Forecasting and Demand Management in the Semiconductor Industry"
University: Cornell University - Robin Roundy
Intel Sponsor: Mani Janakiram
Description: The first aspect of the research aims to improve the accuracy of forecasts when loading semiconductor fabrication facilities, simultaneously reducing the need for human input in forecasting. The researchers forecast 0-6 months into the future, by part-number. The main sources of information for forecasting are historical demands, forecasts from clients, and firm orders received. The relative importance of this information is situation-specific. For example, firm orders received are more effective in forecasting demand for month 1 than for month 6. Historical demand is very useful for mature products, but less so for new products. Manually determining which data to use in forecasting, by part number by forecast horizon, takes time. Their optimization-based approach will automatically adjust as products progress through their life cycles and the business climate changes. Secondly, Semiconductor companies prefer to make to order. However they build some inventory without firm orders in hand when clients press the manufacturer to fill orders on a lead time that is shorter than the manufacturing lead time, or when demand temporarily dips below the fabs productive capacity. Benefits include smoothing out the load on fabs with minimal risk, and to shortening lead times for clients. The main risk is making product that cannot be sold immediately, or must be discounted. The two main sources of uncertainty that the researchers capture are customer demand and the randomness in product yields. To understand the demand-related risks we need an estimate of the variance of the error in demand forecasts, derived from the first aspect of their research.
SRC/Sematech "Demand Data Mining and Planning in Semiconductor Manufacturing Networks"
University: National Taiwan University - Argon Chen
Intel Sponsor: Mani Janakiram
Description: Task "Intelligent Demand Aggregation and Forecast Solutions" - Given the multidimensional natures of demand fluctuation and the complicated manufacturing network, demand planning has become one of the most critical challenges facing semiconductor manufacturers. Demand information propagated over the network is the most unreliable information that plagues the planning quality of the entire supply chain. Successful determinations of where, when, and in what quantities products will be needed are the key to improving a manufacturer#s competitiveness, revenues and profits. The role of demand planning is now very different and becomes crucial for planning an entire manufacturing/supply network. Existing demand planning products, however, are only tools that provide users a friendly slice-and-die computing environment. Though the tools allow users to calculate, view and forecast the demand from different perspectives, the planners have to rely heavily on their own understanding of the market and judgement on the trend. Little intelligent information is extracted from the historical data to assist planners. No existing products are capable of advanced, in-depth analysis, such as data mining and statistical analysis techniques, that turn raw demand data into valuable demand-behavior information and business-intelligent information. The goal of this project is to incorporate the data analysis/mining techniques to develop an intelligent demand aggregation/forecast solution. Task "Integration of Demand Planning and Manufacturing Planning" will develop analysis and planning methodologies for integrating demand planning, product mix planning and tool portfolio planning in semiconductor manufacturing network. It will enhance the robustness of demand modeling and support business planning by integrating capacity allocation of multiple technological generations of product demands, processes and tools.
SRC/Sematech "Resource Driven Simulation Methodolgy"
University: University of California/Berkeley - Lee W. Schruben
Intel Sponsor: Mani Janakiram
Description: A typical semiconductor fab simulation model can be used to predict the impact of system changes such as different queue disciplines and job priorities, etc. A major problem with the use of simulation is the amount of time required to build and execute appropriate models for these predictions. We address the issue of model execution and build time by further developing the R-D technique advocated by Schruben. The "resource-driven" technique is based on modeling the cycles of resource entities used to process jobs within the factory rather than the more common approach of modeling the flow of job entities through the factory. The major advantage of this paradigm is that the model footprint is a function of the numbers of resources, not numbers of jobs - execution does not slow appreciably as the system becomes more congested. As currently developed, model execution speed is sensitive to both job mix and routing complexity, but we have promising ideas for addressing these issues. While some loss of information is anticipated using R-D logic, we expect that decisions based on the simulation study will not change - or that R-D models can be enriched to include job flow information to recover needed information.
SRC/Sematech "Preventive Maintenance in Semiconductor Manufacturing Fabs"
University: Univ. of Maryland - Michael Fu; Univ. of Cincinnati - Emmanuel Fernandez
Intel Sponsor: Mani Janakiram
Description: Research is to develop models, algorithms and a suite of software tools that can be easily employed to obtain close-to-optimal PM schedules for semiconductor manufacturing. The models and algorithms will be general enough so that they can satisfy the needs of any specific fab, and the software tools will cover all of the usual bottleneck tool sets in the fabs. Traditionally, maintenance and production planning have been addressed in isolation. This can lead to a substantial under-utilization of equipment in semiconductor manufacturing fabs, which are characterized by re-entrant flows and high correlation between different tools. In addition to considering stochastic unplanned events such as tool failure or process drift, our approach will explicitly take into consideration production control information (e.g., WIP), and future planned production schedules. Another feature of our proposed approach is the model integration of the interdependence of different PMs in the fab, e.g., consolidation of PM tasks for increasing tool availability. Models and algorithms will be developed to cover the major bottleneck tools in a fab. Comprehensive validation through simulation, and use of real data (reliability, WIP levels, etc.) will be performed. These efforts will be further guided by conducting a comprehensive survey of PM practices in industry during the first year. A deliverable from this phase of the project will an extensive simulation engine, based on commercial software (e.g., ASAP, IBM OSL) that can be used in industry to benchmark current practices and gauge potential gains. Efforts during the second and third year will increasingly concentrate on software tool development and transfer to industry. A clear and focused effort will be made to develop software that is fully compatible with commercial software used in industry, in order to facilitate integration in the form of technology transfer to 3rd party commercial vendors.
Consortia/Programs
American Productivity & Quality Center (APQC) - Impact of Knowledge Management Consortia URL:http://www.apqc.org Intel Sponsor: Ron Dickson Description: An internationally recognized resource for process and performance improvement, APQC helps organizations adapt to rapidly changing environments, build new and better ways to work, and succeed in a competitive marketplace. APQC works with its member organizations to identify best practices, discover effective methods of improvement, broadly disseminate findings, and connect individuals with one another, and the knowledge, training, and tools they need to succeed.
ASTD Benchmarking Forum Consortia URL:http://www.astd.org Intel Sponsor: Ron Dickson Description: Established in 1991, the ASTD Benchmarking Forum is a consortium of private and public sector organizations from around the world. The Benchmarking Forum offers a way to benchmark training, learning, and performance improvement processes, practices, and outcomes, as well as providing access to a worldwide network of high level training professionals. By making both formal and informal benchmarking opportunities available, the Benchmarking Forum offers members a greater understanding of the quantitative dimension of their workplace training and learning initiatives, as well as the shared expertise and experiences of worldwide practitioners.
ASU Construction Research and Education for Advanced Technology Environments Program (CREATE) University: Arizona State University University: Arizona State University - Dr. Allan Chasey Program URL:http://www.eas.asu.edu/~cleanrm/ Intel Sponsor: Art Stout Description: The focus of CREATE is an international organization dedicated to research, education and industry partnerships providing the standard for cleanroom construction. CREATE ‘s mission is to provide leadership in cleanroom research, education and technology transfer to industry.
ASU - Data Mining Pilot on Intel Factory Data University: Arizona State University - Dr. George Runger
Intel Sponsor: Mani Janakiram Description: Work with Dr. George Runger of ASU and his student on evaluating factory data for any hidden patterns using cluster analysis/decision trees/networks and other data mining tools, develop PM prediction models and also determine applicable software and implementation aspects. Identify lot movement patterns that coincide with constraint output/cycle time performance characteristics. Visibility and prediction of estimated cycle time for the critical lots would be beneficial for factory planning.
Corporate University Xchange (CUX) Consortia URL:http://www.corpu.com Intel Sponsor: Ron Dickson Description: CUX is the leading provider of corporate education intelligence in the enterprise learning marketplace, offering research on learning best practices, consulting services, events, publications and membership communities. They provide unique services to the learning industry, such as: CLO Xchange - an exclusive network of Chief Learning Officers, consulting on corporate learning strategy and practices, and customized research including market intelligence on the corporate learning marketplace.
Center eBusiness@MIT
University:Massachusetts Institute of Technology - Glenn Urban, Erik Brynjolfsson Program URL:http://ebusiness.mit.edu/ Intel Sponsor: Mary Murphy-Hoye Description: eBusiness is the practice of using information technologies to increase revenues and cut costs.The MIT Sloan School of Management founded the Center for eBusiness@MIT because they believe that the next phase of leadership in eBusiness lies in charting the contours of eBusiness geography as they will emerge two years from now, or five. We believe that collaboration on rigorous, relevant, unbiased research will yield answers to questions about the way eBusiness really works.
Massachusetts Integrated Supply Chain Management Consortium
University: Massachusetts Institute of Technology- Jim Rice Program URL:http://web.mit.edu/supplychain/research/agenda.html Intel Sponsor: Jim Kellso Description: Supply Chain Management Forum is run by MIT for cross-industry participants. The members' dues are used to fund specific research and for monthly Integrated Supply Chain Management forum meetings for review/interaction with Best in Class Supply Chain companies and University professors' research project analysis and conclusions.
MIT Leaders for Manufacturing Program (LFM)
University: Massachusetts Institute of Technology (Sloan School of Management) Program URL:http://lfmsdm.mit.edu/lfm/index.html Intel Sponsor: Frank See Description: The Leaders for Manufacturing (LFM) Programs is an educational and research partnership involving U.S. manufacturing firms and the Massachusetts Institute of Technology's schools of engineering and management. LFM is an intense, two-year manufacturing leadership development program for experienced manufacturing leaders who wish to pursue dual master's degrees in engineering and management and apply these skills to a long-term career in manufacturing leadership. MIT's Leaders for Manufacturing (LFM) Program is an active partnership of MIT's School of Engineering, Sloan School of Management, and industry. This partnership is dedicated to addressing the broad issues of Big M manufacturing, such as product development, marketing and the supply chain. Through its academic program, its research, and its outreach to other universities around the globe, LFM strives to integrate the total manufacturing enterprise with customers, suppliers, government, and community.
Queen's University KM Forum
University: Queen’s University School of Business Consortia URL:http://business.queensu.ca/kbe/ Intel Sponsor: Charles Seeley Description: The purpose is to bring practicing knowledge managers together to examine topics that are of critical concern to them and their organizations. Via the Forum, members share experiences, learn from their peers, establish valuable networks, and develop practical strategies for creating, implementing, and managing knowledge management programs. Forum topics are selected by the members. Prior to each meeting, members research the topic in their own organizations following an outline prepared by the facilitators. Meetings involve lively discussions and exploration of the topic in depth. The Forum meets three times annually.
Rochester Institute of Technology - Microelectronic Engineering Program
University: Rochester Institute of Technology - Dr. Santosh Kurinec
Program URL: http://www.microe.rit.edu/
Intel Sponsor: Mike R. Wegener
Description: Intel supports the operation of the Microelectronics Engineering Laboratory . Its mission is to prepare engineers specifically for the semiconductor industry. The problem that the program works to address is the inadequate supply of engineers educated in integrated circuit processing.
Center for Integrated Facilities Engineering (CIFE)
University: Stanford University - Martin Fischer and John Kunz Program URL:http://www.stanford.edu/group/CIFE/ Intel Sponsor: Art Stout Description: The CIFE mission is to be the world's premier academic research center for Virtual Design and Construction (VDC) of Architecture Engineering Construction (AEC) industry projects.As an industry affiliates program of the Departments of Civil Engineering and Computer Sciencewithin the School of Engineering at Stanford University, Stanford's Center for Integrated Facilities Engineering (CIFE) attempts to create value for students, faculty, its affiliate members and the Architecture / Engineering / Construction industry as a whole. CIFE will pursue research in the management, legal, and business issues associated with the proper selection and implementation of advanced technologies. The purpose of VDC in research and practice is to visualize and describe projects from the perspective of multiple disciplines; analyze project performance in the computer; make changes to the project design and predict their effects; and to evaluate the engineering and business consequence of design alternatives.
Stanford Global Supply Chain Management Forum University: Stanford University - Dr. Hau Lee and Professor Seungjin Whang Program URL:http://www.stanford.edu/ Intel Sponsor: Mary Murphy-Hoye Description:To compete successfully in today's market place, companies need to manage effectively and efficiently the activities of design, manufacturing, distribution, service and recycling of their products and services to their customers. Supply chain management deals with the management of materials, information and financial flows in a network consisting of suppliers, manufacturers, distributors, and customers. The coordination and integration of these flows within and across companies are critical in effective supply chain management. The Forum brings faculty and students from different schools, departments and disciplines together to work on research projects. These projects focus on problems representative of those found by participating companies. They consist of theoretical and model based research, empirical research, and detailed field-based studies of supply chain problems. Collaboration with industrial researchers is encouraged in order to ensure that projects realistically represent the phenomena faced by companies in designing and managing their supply chains. Special attention is paid to issues associated with global supply chains.
Stanford Integrated Manufacturing Assoc. (AIM) University: Stanford University - Richard Reis, Executive Director Program URL:http://www.stanford.edu/ Intel Sponsor: Kirk Hasserjian Description: AIM is a cooperative venture between Stanford University's Graduate School of Business, School of Engineering and member industrial firms. Its purpose is to develop world-class research and education for manufacturers. Emphasis is given to the management processes and technological innovations necessary to develop and deliver commercially superior products for manufacturing enterprises.
Tauber Manufacturing Institute (TMI) University: University of Michigan - Yavuz Bozer and Ken Kohrs Program URL: http://tmi.umich.edu/ Intel Sponsor: Jim Kellso Description: Applied research is a central component of Tauber Manufacturing Institute, and a channel for the University to apply its capabilities and resources directly to the real world, on-going industrial problems. Through TMI, business and engineering faculty teams and representatives from industry are encouraged to conduct manufacturing-related research. TMI Faculty Fellows are faculty members in business and engineering with a special interest in manufacturing. The goal of the program is to establish long-term relationships between specific faculty teams and industry partners and to source best in class employees for Intel through this relationship and the TMI intern program.. In planning and conducting their research, the Fellows focus on relevant, pressing corporate issues and work toward mutually established goals with their industrial partners. Current and past research efforts include: Developing an integrated product development process; transferring Japanese production methods to U.S. manufacturing firms; implementing a six sigma quality program; analyzing product mix complexity; developing quality feedback mechanisms; analyzing the value chain structure; developing effective tool buy-off decision processes. The goal of the program is to establish long-term relationships between specific faculty teams and industry partners. In planning and conducting their research, the Fellows focus on relevant, pressing corporate issues and work toward mutually established goals with their industrial partners.
National Science Foundation Centers
University of Wisconsin - Milwaukee NSF Center for Intelligent Maintenance Systems
Sponsor Universities: University of Wisconsin - Milwaukee - Dr. Jay Lee and University of Michigan - Professor Jun Ni
Program URLS:http://www.uwm.edu/CEAS//ims/index.htm http://www.engin.umich.edu/relations/corporate/ops/cims.html Intel Sponsors: Kirk D. Smith and Richard A. Tyo
Description:The Center for Intelligent Maintenance Systems was created as a National Science Foundation Industry-University Collaborative Research Center (CRC).The charter of the center is to bring about innovations on internet-based maintenance technologies to enable production systems with near zero breakdown conditions, and serve as a Center of Excellence for creation and dissemination of a systemic body of knowledge in intelligent e-maintenance systems and ultimately to impact next generation smart products/systems.
