Research in Industrial and Systems Engineering

The UW-Madison Department of Industrial and Systems Engineering is a national leader in research funding. At the cutting-edge of industrial engineering and systems research, the department offers state of the art facilities for faculty, staff and students to work in. Our faculty are leaders in their fields, respected nationally and internationally for their research.

Research Specializations

Jeffrey T. Linderoth — Convenor

Oguzhan Alagoz
Vicki Bier
Alberto Del Pia
Ananth Krishnamurthy
James Luedtke
Laura Albert McLay
Leyuan Shi

 

The research and teaching program in decision science/operations research aims to improve the quality of decisions about managing scarce and valuable resources. Such resources include not only financial resources but also those related to the quality of human life, medical treatment, the environment and many other important issues.

 

 

OVERVIEW

Graduate study in decision science and operations research entails developing problem-solving skills that can be used to make and implement decisions as efficiently and effectively as possible. These problem-solving skills involve recognizing and identifying decision problems, as well as generating, evaluating, choosing and implementing solutions to these problems.

Students are encouraged to develop expertise in an applied area as well as to understand the theories and tools. Interests of the faculty in decision science/operations research include:

  • Manufacturing process optimization
  • Supply chain design and optimization
  • Medical decision making
  • Optimization of health-care systems
  • Performance analysis of computer systems
  • Security and critical-infrastructure protection
  • Modeling and simulation in defense analysis

 

EMPLOYMENT PROSPECTS

Decision science and operations research techniques are frequently used in jobs in areas such as:

  • Consulting companies
  • Software companies
  • In-house decision science and operations research groups of major corporations (e.g., airlines, computer companies, telecommunication firms)
  • The public sector (e.g., policy analysis, military operations and logistics)
  • Health care (e.g., patient-flow analysis, continuity of care across providers)
  • Industrial-research laboratories
  • National laboratories

In addition, PhD graduates in decision science/operations research also go on to faculty positions in industrial engineering departments, business schools, and schools of public policy. You can find more information on careers in decision science and operations research at:

 

COMMENTS FROM RECENT GRADUATES

“The Decision Science/Operations Research group provides a strong theoretical foundation, thus enabling students to apply OR principles to a variety of practical situations. … The faculty and department staff members are always willing to help interested students.” — Product-launch analyst at a manufacturer of orthopedic implants

“If you thought courses were tough enough, wait till you figure out the impossibly long list of extra-curricular activities that you can plunge into! UW-Madison is a great place to build new friendships with peers as well as faculty.” — Business analyst at a credit-card company

 

MSIE – DECISION SCIENCE/OPERATIONS RESEARCH SPECIALIZATION

Decision Science and Operations Research Brochure

The MS program with specialization in decision science/operations research is designed to provide both balance and breadth in the student’s understanding of decision science and operations research techniques and applications. To accomplish this, students must take at least two classes in optimization, at least two classes in stochastic processes, at least one class in simulation, and at least one class in the area of organizations, decisions, and implementation issues. The program is rounded out with electives, selected with the approval of the student’s adviser. Flexibility is built into the program to accommodate a wide range of interests and applications.

 

PHD CONCENTRATION AREAS

Doctoral students in decision science/operations research concentrate in one or more of three main areas:

Stochastic Processes and Simulation

Uncertainty pervades practical decision making, since people have to make decisions in a world where consequences are far from certain. Students must be familiar with the intellectual tools for modeling uncertainty and with techniques that can be used to evaluate alternatives in the presence of uncertainty.

Before entering this graduate program, students should have a good mathematical introduction to probability and statistics, including commonly used probability distributions, classical estimation, and hypothesis testing. Students will then augment their previous work in probability with a solid grounding in measure-theoretic probability, stochastic processes and simulation, as well as in related applications areas. They will apply these tools to devise better ways of modeling situations involving uncertainty, and better methods for analyzing the resulting models. Research in this field ranges from mathematical analyses of problems or solution methods to quite applied work in devising and implementing solutions to specific problems.

Optimization

Organizations in industry, government, medical care, and other areas commonly encounter planning scenarios in which using scarce resources as efficiently as possible is crucial to the ability of the organization to perform successfully. In many situations, it is necessary to plan operations in a way that both explicitly evaluates many different alternatives, and rigorously accounts for constraints on resource utilization. Optimization is the area of operations research that deals with devising mathematical models and methods to identify a plan that is as good as possible in specific situations.

Students concentrating in this area should ideally have a solid introduction to calculus and linear algebra, as well as practice in modeling practical problems as mathematical programs. They will be trained to use state-of-the-art methods and software to solve these models; this training will involve instruction in the theoretical underpinnings of how these methods work, as well as significant hands-on experience in using software and interpreting its output. An important component of doctoral-level research in optimization is advancing the state of the art by devising and justifying new methods for effectively carrying out the optimization required in decision procedures. This can involve investigating either the mathematical foundations of these methods, or computational issues in designing these methods to be as effective as possible for specific kinds of problems.

Much of the research and teaching in optimization involves collaboration with the Department of Computer Sciences. More information about optimization activities at UW-Madison can be found at  http://wid.wisc.edu/research/optimization/.

Decision Analysis and Multi-Attribute Utility Theory

The decision process is goal-oriented. There are often multiple, conflicting goals to be met by a single decision. In addition, there is usually uncertainty about the consequences that will result from any given choice. In this area, students learn several kinds of techniques for structuring and facilitating such decision problems.

In particular, decision theory can be viewed as a marriage of utility theory (to express preferences about decision outcomes) and Bayesian statistics (to express uncertainty about decision outcomes). In multi-attribute utility modeling (derived from the idea of utility theory in microeconomics), students learn to construct mathematical functions to measure overall satisfaction with decision outcomes, taking into account both trade-offs among multiple objectives and risk attitude (to account for non-linearity in preferences). Such functions can be useful to guide decision making in complex situations. In addition, Bayesian statistics and subjective probability are used to address uncertainty (even in situations where extensive statistical data are not available), and to assess the value of gathering additional information. Tools such as game theory (to address multi-player decisions), real options (to address market uncertainties), and Markov decision processes can also be useful.

Doctoral research in this area focuses on advancing the state of the art in decision analysis, either through methodological advancements, or by applying the principles of decision analysis to new classes of problems (yielding new knowledge about which solutions are likely to be optimal under particular types of conditions).

 

Recent dissertation titles

    • Bozbay (consultant at a global management-consulting firm), “Large-Scale Supply Chain Optimization via Nested Partitions”
    • Kilinc (post-doctoral researcher in a department of industrial engineering) “Disjunctive Cutting Planes and Algorithms for Convex Mixed Integer Nonlinear Programming.”
    • Krishnamurthy (faculty member in a department of decision sciences and engineering systems), “Performance Analysis of Material Control Strategies for Multi-Stage Multi-Product Manufacturing Systems”
    • -W. Lin (faculty member in a department of business administration), “Designing Incentive Systems for Risk-Informed Regulation”
    • Namazifar (senior analyst for consulting company) “Strong Relaxations and Computations for Multilinear Programming.”
    • Pan (post-doctoral researcher in a department of industrial engineering), “Production Scheduling for Suppliers in Extended Manufacturing Enterprises”
    • Resnick (analyst in a large contract-research company), “Models for Optimizing Component Safety Stock Levels in Large-Scale Assembly Systems”
    • Wang (faculty member in a department of industrial engineering and management), “Guidelines for Risk-Based Inspection Programs Based on Approximate Optimal Surveillance Test Intervals”

Oguzhan Alagoz — Convenor

Pascale Carayon
Jingshan Li
Dharmaraj (“Raj”) Veeramani
Nicole Werner
Douglas Wiegmann

 

In the health systems program, MSIE students learn to apply ISyE tools and approaches to specific health care problems. PhD students are trained to develop innovative transportable solutions to critical health care problems while contributing to advancements in decision science, decision support systems and quality research fields.

 

OVERVIEW

Progress challenges health care managers

Each step toward medical progress creates demands for information management, decision making and quality management. Recent changes in financing health services (HMOs, PPOs, integrated service networks, proprietary involvement) have created enormous incentives to improve the productivity of the health system while maintaining or improving quality. Industrial and systems engineers possess tools to analyze these demands and create systems to help solve these problems. Some significant health care issues addressed by UW-Madison health systems engineers include:

  • Creating models and scales to appraise important health variables including quality of life and care satisfaction.
  • Advancing the principles of continuous quality improvement through careful methodological studies and innovative uses of decision support systems to facilitate implementation of these principles.
  • Developing computer systems to educate people on ways to avoid accidents and illness instead of only treating injuries and diseases after they occur.
  • Providing information to support health care delivery; finding ways to help patients, their family members and clinicians improve delivery of health services through computerized decision support systems.
  • Addressing patient safety, evaluating how practice patterns can be monitored to identify opportunities for improving quality of care; finding ways for hospitals to expedite patient discharge.
  • Evaluating health care management and organization; coordinating the many semi-independent hierarchies across health care organizations to improve health care quality.
  • Clinical decision making and decision support; developing systems to improve the development and acceptance of clinical guidelines.

Since health systems engineering includes many different disciplines, the program welcomes students with other professional interests. The following examples illustrate how health systems engineers analyze and solve problems.

Models facilitate understanding

In many areas of engineering, physical models aid problem identification and solution. In the health system, models are more likely to be mathematical constructs or computer simulations. Manipulating mathematical models allows measurement of validity and effectiveness at relatively low cost and without risk to patients. For example, building a fully-equipped ambulatory surgery center would be too costly a way to determine if one were needed. Instead, health systems engineers create financial, numerical, statistical & simulation models to help health workers understand and eventually solve problems.

A typical model involves a computer simulation for senior health managers who enter their decisions on computers. The results can be computed immediately and reported. Mathematical models can also be used to predict the effectiveness of a new health policy or estimate the efficacy of different treatments for cancer pain.

Human judgments are vital

An overriding concern in health care is the appropriate use of human judgments. Health systems engineers recognize critical health care decisions will be made using the best judgments available. Clinicians must diagnose and treat illness and administrators must factor political considerations into their decisions. Uncertainty is not an exception, but an omnipresent concern. Health systems engineers are trained in decision sciences to capture, quantify and incorporate values, expectations and uncertainty into their analyses. In doing so, the judgments of experts can be more effectively and widely used.

Open lines of communication are essential

Understanding how people solve problems is a basic requirement for health systems engineers, who must apply scientific methods in a value-laden setting. In addition to generalizing users’ needs from sample interviews and observations, health systems engineers must examine users’ statements of need, and stimulate users to consider other ways of solving problems and using information. This interactive design process makes extensive use of computers, but differs significantly from ordinary computer systems analysis.

 

EMPLOYMENT PROSPECTS

Currently, there is a shortage of health systems engineers at PhD and MS levels. The department receives many unsolicited requests from potential employers, and opportunities are likely to increase.

Potential employers for PhD-prepared health systems engineers include academic institutions (health administration programs, public health schools and ISyE departments) and health care delivery organizations, consulting firms, and software design companies. Potential employers for MS-prepared health systems engineers include hospitals, nursing homes and similar institutions, doctors’ offices and clinics, governmental and voluntary agencies, various health-planning agencies, universities and medical centers, research and planning organizations, manufacturers of hospital equipment, pharmaceutical companies, health insurance companies, management consultants, and architectural and construction firms.

 

MSIE – HEALTH SYSTEMS SPECIALIZATION

Health Systems Engineering Brochure

The health systems specialization seeks to train students to look at broad issues in health care, including long-term care, prevention, quality improvement, health care financing, and system evaluation. Effective model building requires strong systems analysis skills. While skill in manipulating statistical and mathematical models is essential to an industrial engineer’s success, the health systems engineer must also be able to initiate resolutions to strategic problems using knowledge of how organizational decisions are made.

 

PHD, HEALTH SYSTEMS SPECIALIZATION

To provide an idea of the scope of health systems engineering, some topics suitable for PhD dissertations are listed below:

  • Design and evaluation of computer systems to help case managers develop community-based plans of care, allowing an elderly person to remain at home and not enter a nursing home.
  • Study of ways to measure patients’ needs for nursing services and design a patient-dependency measurement system to support allocation of nursing resources.
  • Evaluate the impact of a computerized health promotion program on consumer self management.
  • Development and evaluation of a computer system to support diagnosis of an emergency patient’s condition.
  • Development of a model to predict and explain the dissemination of new medical technology.
  • Development and evaluation of a computer-based group decision support system for health policy formation and analysis.
  • Development and evaluation of a model-building process for both predicting and explaining the discontinuance of birth control among teenagers.

Robert Radwin — Convenor

Pascale Carayon
John Lee
Nicole Werner
Douglas Wiegmann

 

Today, both workers and management are concerned about the quality of work lives, ergonomics and occupational safety and health. New developments such as information and communication technologies and specialized work requiring repetitive tasks add up to a need for human factors engineering. By examining, designing, testing and evaluating the workplace and how people interact in it, human factors engineers can create productive, safe and satisfying work environments.

 

 

OVERVIEW

Multidisciplinary approach to multifaceted problems

This program has three sub-specialty areas leading to the MS and PhD degrees in industrial and systems engineering. Specialty areas include sociotechnical systems, ergonomics, and occupational and environmental safety and health.

Sociotechnical Systems

Organizational issues such as management approaches, job design, participative problem solving, psychological stress, job satisfaction, performance effectiveness, product/service quality, and quality of work life are addressed by engineers specializing in sociotechnical methods in system design. These engineers may study interaction between people in complex technical and organizational environments, or address appropriate ways of motivating people to work productively and safely. Examining such areas requires diverse knowledge of technology, social systems and organizational behavior as well as synthesis, design and implementation skills. Sociotechnical engineers are trained in psychology, sociology, business, statistics and engineering science in order to address these problems.

Ergonomics

Ergonomics is the study of the principles of work. Ergonomists are concerned with the complex physical relationships between people, machines, job demands and work methods. A prime emphasis is on preventing musculoskeletal injuries in the workplace. These injuries create significant cost to industry in the form of medical bills, worker’s compensation, reduced productivity and lost time. Prevention of injuries is accomplished by understanding biomechanics and the physiology of work, and through the use of biomechanical models, laboratory simulations, field studies and job analyses.

Ergonomists also consider human reliability, psychomotor capabilities and human characteristics in equipment design, work quality and assessment of skill. An important aspect of equipment design is human-computer interaction.

Human factors engineers are also concerned about providing people with physical and mental impairments access to the workplace through technology and rehabilitation engineering. Engineers concerned with human performance often work in diverse areas including space robotics, aviation systems, rescue operations and manufacturing.

Occupational Safety and Health

Occupational safety and health engineers study accident causation, epidemiology, statistical modeling of injuries, analysis of health records, injury prevention, and legal aspects of occupational safety. They are concerned with environmental factors such as noise, vibration, illumination, radiation and temperature. These engineers work in manufacturing, utilities, chemical processing industries, healthcare industry, construction industry, and government. Occupational and environmental safety and health engineers are trained in public health, epidemiology, statistics and engineering science.

The demand for engineers who can combine a concern for the human component with traditional engineering principles is great. Some examples of work performed by human factors engineers include:

  • Designing work systems, processes and workstations that prevent injuries and cumulative trauma disorders.
  • Designing human-computer interfaces that are logical and user friendly, and reducing operator errors.
  • Managing the implementation of major technological and/or organizational change.
  • Motivating people to work safely.
  • Devising jobs that are satisfying and minimize mental stress.
  • Designing manufacturing systems that maximize quality and productivity while taking human limitations into account.

 

MSIE – HUMAN FACTORS AND ERGONOMICS SPECIALIZATION

Human Factors and Ergonomics Brochure

Human factors and Ergonomics MS students take courses in three areas of concentration. These are ergonomics, occupational safety and health, and sociotechnical systems.

 

PHD, HUMAN FACTORS AND ERGONOMICS SPECIALIZATION

Recent dissertation titles in human factors engineering include the following:

  • A Longitudinal Study of the Process & Content of a Participatory Work Organization Intervention
  • Physical Stress Measurements for Work-Related Musculoskeletal Disorders Using Video-Based Continuous Biomechanical Data Acquisition and Interactive Exposure Analysis
  • A Case-Control Study of Medication Use and Occupational Injury
  • Effect of Work Conditions on VDT Workers’ Health & Productivity: A Longitudinal Intervention Field Study in a Service Organization

 

LABORATORIES AND RESEARCH CENTERS

Center for Quality and Productivity Improvement (Carayon)

It is widely recognized that quality is fundamental to achieving long-term success. A renewed focus on customers and processes sets the stage for continuous improvement for industry, government, educational institutions, healthcare, and businesses. All have benefited from higher quality and productivity as well as reduced time and cost to develop, produce and deliver products and services, and improved safety. Data-based total quality methods are the catalyst to help people achieve these benefits.

To rise to the challenge of the international quality revolution, the CQPI was founded in October of 1985 by Professor George E.P. Box and the late Professor William G. Hunter. Since its inception, CQPI has been at the forefront in the development of new techniques for improving the quality of products and processes. Today, the Center is also at the forefront of methods aimed at improving the quality of work processes, working life, healthcare.

The mission of the Center is to create, integrate, and transfer knowledge to improve the quality and performance of industrial, service, governmental, healthcare, educational, social, and other organizations.

The vision of the Center is to excel in the creation, development, and integration of knowledge through research on theories, concepts, and methodologies of quality and productivity measurement, management and improvement, innovation and organizational change.

Areas of expertise in quality engineering are, quality management, quality improvement in healthcare, safety applications and research, and quality of working life, human factors and ergonomics.

Major research support has come from the National Science Foundation, the Agency for Healthcare Research and Quality, the National Institute for Occupational Safety and Health, the UW Graduate School, the State of Wisconsin, and private industry.

Cognitive Systems Laboratory (Lee)

The Cognitive Systems Laboratory (CSL) focuses on cognitive engineering, where the challenge is to understand and improve the capacity of joint human-technology systems. This research has considered technology insertion in the maritime industry, ground transportation, tele-operation, and process control. A specific example is the distraction potential of in-vehicle information systems, such as cellular telephones and e-mail. Another example is the role of trust and appropriate reliance in the supervisory control of automation, such as unmanned aerial vehicles (UAVs). In each of these examples, the ultimate goal is to develop computational models of human performance and design principles that can support effective and humane use of technology.

The common theme of understanding how technology mediates peoples’ attention integrates CSL’s research across the varied research domains of maritime navigation, process control, and driving. Technology-mediated attention builds upon the basic psychological concepts of attention to understand how technology must be shaped so that people attend to the right thing at the right time and respond appropriately. An understanding of how technology can mediate attention is used to create display and control systems that enable people to work effectively with increasingly sophisticated technology.

Students in the CSL learn how to conduct experiments in microworld and simulator environments. They also learn techniques of computational cognitive engineering to model joint human-technology behavior, estimate the state of the operator, and to enhance data interpretation.

Naturalistic Decision Making and Simulation Laboratory (Wiegmann)

The Naturalistic Decision Making & Simulation Lab covers a broad spectrum of research interests, primarily within the aviation and health care industries. Some recent areas of study include interruptions and distractions during surgery, simulated flight training, and cognitive ergonomics for universal design.

Occupational Ergonomics and Biomechanics Laboratory (Radwin)

Research in the Occupational Ergonomics and Biomechanics Laboratory focuses on health aspects of physical stress in the workplace. This work includes prevention and detection of work related musculoskeletal disorders; developing measurement and analytical methods for assessing exposure to physical stress in the workplace; understanding ergonomic aspects of the design, selection, installation and use of manually operated equipment; and quantifying functional deficits associated with musculoskeletal disorders and peripheral neuropathies.

The lab is equipped with a variety of transducers and instruments for measuring human kinetics and kinematics, optical motion analysis, physiological indices and biopotentials. In addition to an electromagnetic vibration generation and measurement system, occupational activities are simulated for conducting research to better understand how to design jobs and equipment in which people play a significant role, so that human capabilities are maximized, physical stress is minimized, and workload is optimized.

 

RELATED INFORMATION

Jingshan Li — Convenor

Kaibo Liu
Ananth Krishnamurthy
Robert Radwin
Leyuan Shi
Dharmaraj (“Raj”) Veeramani
Xin Wang
Shiyu Zhou

 

The U.S. manufacturing industry today faces high levels of local and international competition. Several factors help define a manufacturing company’s competitiveness including new product development time, production lead time, flexibility in responding to changes in demand volume and variety, quality, price, responsiveness to customer delivery requirements, and use of state-of-the-art materials, processes and technologies. In every case, the company’s ability to respond to these factors depends critically on the capability of its manufacturing organization.

 

OVERVIEW 

Enhancing manufacturing competitiveness

Manufacturing systems engineering researchers at UW-Madison have developed a coherent set of methodologies, computer-aided tools, and experimental testbeds to design, analyze and improve manufacturing systems. These include:

  • An integrated approach to rapid modeling, analysis and simulation of manufacturing systems to answer questions related to equipment and process selection, product lot sizing and flow design.
  • Computerized tools to design and optimize asynchronous automatic product assembly systems.
  • Computer simulation tools to design and evaluate flexible computer-integrated manufacturing cells.
  • System architectures and algorithms for distributed control of large intelligent manufacturing systems.
  • Methodologies and computer-aided tools for product design assessment for manufacturability and assimilability, intelligent process planning and CAD/CAM integration.
  • Methodologies for systems and product design which produce manufacturing systems that are more economical to analyze, control, and operate.
  • State-of-the-art automated manufacturing equipment including a new flexible manufacturing cell, stand-alone CNC machines, robots, CMMs, and PLCs. (See details below in section on laboratory facilities.)

The industrial and systems engineering manufacturing systems specialization is intended to provide the skills and expertise necessary to compete successfully in a manufacturing environment. These skills include knowledge of manufacturing processes and machines and their control, knowledge of the essentials of manufacturing systems design and analysis, and knowledge and “hands-on” experience with modern manufacturing technology. After satisfying the necessary breadth requirements of the program, students may choose to study, in more depth, a number of specialized topics from the approved course offerings to enhance their career readiness.

 

EMPLOYMENT PROSPECTS

Graduates of the manufacturing systems specialization enter industry with the skills and knowledge to apply theory and tools to enable manufacturing firms to become more competitive. The manufacturing systems specialization allows students to integrate coursework from the School of Business, helping them to develop insights into financial and managerial aspects of manufacturing as well as the technological components.

Managers of manufacturing enterprises today are faced with an enormous number of competitive pressures as well as a revolution in philosophy and methodologies for improving manufacturing systems. CAD, CAM, Just-in-Time, Total Quality Control, Design for Assembly, Design for Manufacturability, CIM, FMS, Kaizen, Statistical Process Control, Taguchi Methods, and a host of additional tools and methodologies have all proven to provide substantial improvements in quality, reduction in cost, increased productivity, or improved responsiveness when the concepts are applied correctly in appropriate settings. It is the job of the manufacturing systems engineer to understand and apply these new methodologies to guide the improvement of the manufacturing enterprise.

Specialists who design and operate modern, automated computer-integrated manufacturing systems face a bright future. Generic problems faced by the designers of such systems occur in virtually every manufacturing activity in the country. There is a demand for continuous improvement of product designs and manufacturing systems to help U.S. industries meet intense competition from abroad. This challenge will require a large number of manufacturing systems engineers, both now and well into the next century. This demand will occur in the face of projected declines in the number of U.S. engineers available to fill these positions.

 

MSIE – MANUFACTURING AND PRODUCTION SYSTEMS

Manufacturing and Production Systems Brochure

 

PHD, MANUFACTURING AND PRODUCTION SYSTEMS

Recent dissertation titles include the following:

  • Optimization of Asynchronous Flexible Assembly Systems
  • Economic Feasibility and Performance Modeling of a High-Speed LIM-Based Tool Delivery System for Machining
  • State-Dependent Scheduling for Manufacturing Systems
  • Analytical Queuing Models of Manufacturing Workcells with Consideration of Operator Level and Assignment
  • Design and Analysis of Error Recovery Strategies in Flexible Assembly Systems
  • Performance Analysis & Productivity Improvement of Flexible Assembly Cells

 

LABORATORY FACILITIES AND RESEARCH CENTERS

The manufacturing systems specialization provides students access to state-of-the-art laboratories and computing resources in flexible manufacturing, simulation, CAD/CAM, visualization and systems integration. Students also have access to other campus facilities such as the Computer-Aided Engineering.

The College of Engineering participates in a large number of industrial consortia-organizations of faculty, students and industrial sponsors formed for research in specified technical areas. In addition to these consortia, the manufacturing systems specialization program has strong ties with industry that provide students the opportunity to work on applied research projects and help solve industrial problems.

Flexible Manufacturing Cell Laboratory

This laboratory enables integrated design, manufacturing, inspection, and assembly. It includes CAD/CAM systems, CNC milling and turning centers, an automated storage and retrieval system, a material-handling conveyor and robots, a CMM integrated with a computer-aided inspection system, and an assembly robot having tactile- and vision-sensing capabilities.

Manufacturing Systems Analysis Laboratory

In this laboratory, students and faculty members perform research on new techniques for modeling and analysis of manufacturing systems, and application of these techniques to enable time-based competitive manufacturing. The laboratory consists of several computers equipped with state-of-the-art system analysis tools.

Production and Service Systems Laboratory

In this laboratory, students and faculty members carry out research on developing rigorous engineering theory for modeling, analysis, improvement and control of production, healthcare, and service systems, and applying the derived results in practice. All the problems studied are important issues originated from industry, after abstraction and theoretical derivations, their solutions have been successfully implemented on the factory floor or in hospitals and clinics. The laboratory is equipped with several computers and cutting edge software tools.

UW RFID Laboratory

The UW RFID Laboratory involves a multidisciplinary group of faculty and students who conduct basic and applied research on RFID (radio frequency identification) and related automatic identification and data capture (AIDC) technologies. The laboratory comprises of multiple state-of-the-art testbeds. Our focus is on understanding the true capabilities and limitations of these technologies, and developing strategies and approaches for their successful application in a variety of industries including manufacturing, distribution, transportation, and healthcare.

Center for Quick Response Manufacturing

Quick Response Manufacturing (QRM) is a company-wide strategy to cut lead times in all phases of manufacturing and office operations. It can bring your products to market more quickly and secures your business prospects by helping you compete in a rapidly changing manufacturing  arena. QRM will not only make your firm more attractive to potential customers, it will also increase profitability by reducing non- value-added time, cutting inventory, and increasing return on investment. See qrm.engr.wisc.edu for more information.

Pascale Carayon
Shiyu Zhou
David Zimmerman
Harold J. Steudel
Kaibo Liu

 

Ever increasing international and domestic competition has sparked renewed interest in quality improvement of products and services. This, in conjunction with the greater complexity of modern production and service systems, has created a demand for engineers who can master the technical and managerial tools and concepts needed for the economic implementation of quality systems. To meet this demand, the industrial engineering department developed a new graduate program specialization in quality engineering in September 1991. Today it has more than 25 graduate students involved in classes and research leading to MSIE and PhD degrees in industrial engineering.

 

OVERVIEW 

Quality engineering’s heritage and diversity 

This program is based on more than 25 years of quality research and teaching at UW-Madison in such diverse areas as applied engineering statistics in production, design for quality of life in workplace systems, and quality for health care delivery. This rich heritage is evident today in the broadness of faculty interests and research activities comprising the program. Current research activities encompass such areas as:

  • Design of experiments
  • Applied statistical methods
  • Quality in product design and development
  • Quality assurance systems design & ISO 9000
  • Product and system reliability
  • Quality in health care systems improvement and cost reduction
  • Quality design of work systems and jobs
  • Human environmental design
  • Quality improvement for manufacturing systems design and control

Since the program was established in September 1991, the faculty and research activities associated with it have received support from major sources including the National Science Foundation, Emerson Electric Co., Procter & Gamble, IBM, the state of Wisconsin, UW-Madison Graduate School and many other industrial contributors. In spring 1994, Professors Donald S. Ermer and Harold J. Steudel received named and distinguished professorships in total quality from Procter & Gamble and Emerson Electric Co., respectively. The quality engineering specialization in industrial engineering is well established and growing, and offers students exciting opportunities for a rewarding future.

 

MSIE – QUALITY ENGINEERING SPECIALIZATION

Quality Engineering Brochure

The industrial engineering MS degree with concentration in quality engineering is designed to provide necessary background for professional careers in industry or government. Emphasis will be placed on the foundations of quality improvement, organizational dynamics/change strategies, and business and statistical methods. There is a flexible elective list of courses to enable students to specialize with these skills in manufacturing systems, sociotechnical engineering, health systems, and decision sciences. To complete the MS program, a GPA of 3.20 or above in graduate-level courses and 30 degree credits are required with 15 degree credits in the IE department.

For details of the quality engineering curriculum for the MSIE degree, contact the Department of Industrial Engineering.

 

PHD, QUALITY ENGINEERING SPECIALIZATION

The industrial engineering PhD degree with concentration in quality engineering seeks to qualify students for leadership positions in research, consulting, government and industry as well as for positions on university faculties in industrial engineering, business and related fields.

There is no minimum credit requirement for the PhD degree and the program is flexible, determined by the students and their advisor/major professor. Requirements include independent study, satisfactory performance in the quality exam in two areas, the preliminary exam, and a successful defense of a PhD thesis. Admission and GPA requirements are the same as those specified by the IE department.

The curriculum for the quality engineering specialization is designed to provide students with a balance and breadth of understanding of industrial engineering disciplines that contribute to designing and delivering high-quality products or services safely and efficiently. To accomplish this, courses can be selected from each of four groups:

  • foundation courses
  • organizational dynamics/change strategies and business
  • statistical methods
  • an elective grouping consisting of engineering systems, sociotechnical engineering, and measurement/evaluation.

In the case of the latter grouping and specialization, students may want to sample broadly from these disciplines or specialize in the application of quality principles in one of them. Flexibility is built into the curriculum to accommodate a wide range of interests and application opportunities.

 

EMPLOYMENT PROSPECTS

With quality and productivity improvement now recognized as fundamental to achieving long-term success, today’s business or organization requires people with state-of-the-art knowledge and experience with quality principles and methods. The quality engineering specialization prepares students to help a wide variety of businesses and organizations in developing and implementing quality systems to improve their productivity and competitiveness, and the quality of life.

Managers of today’s organizations and enterprises are faced with an enormous number of competitive pressures as well as a revolution in philosophy and methodologies for improving their systems, whether they be in manufacturing, health care, business agencies or government. In all cases, the demand is for better products and/or services at lower costs.

Total Quality Control, Design for Assembly, Design for Manufacturability, Quality Function Deployment, Kaizen, Statistical Process Control, Taguchi Methods, and a host of additional tools and methodologies have all proven to provide substantial improvements in quality, reduction in cost, increased productivity, or improved responsiveness when the concepts are applied correctly in appropriate settings. It is the job of the quality engineer to understand and apply these new methodologies to guide the improvement of the organization. This job may be done working as a quality engineer or manager in an industrial environment, in a health care organization, in a consulting company, in the education field, in government or in other areas of the service sector. The job opportunities are varied and plentiful.

The need for quality engineering specialists to design and operate more productive systems that improve both competitiveness and the quality of work and life is rapidly increasing as worldwide economic and population growth accelerates. In the United States, there is a demand for continuous improvement of product designs and manufacturing systems to help our industries meet intense competition from abroad. Likewise, needs for improvements in health care delivery and workplace design call for quality professionals who can meet these new demands. This challenge will require a large number of quality systems engineers in industry, business and academia, and this need will exist well into the next century.

 

LABORATORY FACILITIES AND RESEARCH CENTERS

The interdisciplinary curriculum in the quality engineering specialization draws on the sophisticated computer equipment and laboratory resources of the many outstanding departments in the College of Engineering and throughout UW-Madison. For example, the School of Business adds strength in the areas of total quality management and organization design and behavior. The Department of Statistics provides excellent resources for training in the fundamental methodologies necessary to solve problems through data collection and analysis. In the College of Engineering, cutting-edge technologies and equipment, like the advanced coordinate measuring machine and software, allow for hands-on research and experience. Likewise, high technology classrooms and industry-based projects provide opportunities for learned by doing and working with people in teams. Several other facilities provide the opportunity for advanced study, including Computer-Aided Engineering.

Center for Quality and Productivity Improvement

It is widely recognized that quality is fundamental to achieving long-term success. A renewed focus on customers and processes sets the stage for continuous improvement for industry, government, educational institutions, healthcare, and businesses. All have benefited from higher quality and productivity as well as reduced time and cost to develop, produce, deliver products and services, and improve safety. Data-based total quality methods are the catalyst to help people achieve these benefits.

To rise to the challenge of the international quality revolution, the Center for Quality and Productivity Improvement (CQPI) was founded in October of 1985 by Professor George E.P. Box and the late Professor William G. Hunter. Since its inception, CQPI has been at the forefront in the development of new techniques for improving the quality of products and processes. Today, the Center is also at the forefront of methods aimed at improving the quality of work processes, quality of working life, and quality of healthcare.

The mission of the Center is to create, integrate, and transfer knowledge to improve the quality and performance of industrial, service, governmental, healthcare, educational, social, and other organizations.

The vision of the Center is to excel in the creation, development, and integration of knowledge through research on theories, concepts, and methodologies of quality and productivity measurement, management and improvement, innovation and organizational change.

Areas of expertise in quality engineering, quality management, quality improvement in healthcare, safety applications and research, and quality of working life, human factors and ergonomics.

Major research support has come from the National Science Foundation, the Agency for Healthcare Research and Quality, the National Institute for Occupational Safety and Health, the UW Graduate School, the State of Wisconsin, and private industry.

Research Centers & Consortia