Learning Outcomes: 1. Identify and respond appropriately to real-life engineering ethics cases relevant to co-op work
Audience: Undergraduate

2. Synthesize and apply appropriate technical education to real world technical work
Audience: Undergraduate

3. Communicate effectively in writing and speaking with a range of audiences in the workplace, including those without disciplinary expertise
Audience: Undergraduate

4. Develop professional and transferable habits like time management skills, collaborative problem-solving skills, and research skills for learning new information
Audience: Undergraduate

Learning Outcomes: 1. Define and explain common industrial engineering terminology
Audience: Undergraduate

2. Give examples of career opportunities in industrial engineering
Audience: Undergraduate

3. List the focus areas in industrial engineering and give a brief explanation of how each area uses industrial engineering principles
Audience: Undergraduate

4. Give examples of problems in each area of industrial engineering
Audience: Undergraduate

5. Identify professional and academic development resources available to industrial engineering students
Audience: Undergraduate

6. Investigate an open-ended industrial engineering problem and think critically about how to solve it
Audience: Undergraduate

7. Analyze small datasets using Microsoft Excel
Audience: Undergraduate

Learning Outcomes: 1. Articulate the importance of statistics in engineering applications
Audience: Undergraduate

2. Summarize and describe data using descriptive statistics and graphical methods
Audience: Undergraduate

3. Perform basic statistical analysis on datasets
Audience: Undergraduate

4. Design simple experiments with data for the purpose of statistical analysis
Audience: Undergraduate

5. Use Excel to describe, analyze, graph, and interpret data
Audience: Undergraduate

6. Apply linear and multiple regression techniques
Audience: Undergraduate

7. Apply experiment and analysis techniques to areas of engineering such as Statistical Process Control
Audience: Undergraduate

8. Apply basic probability concepts such as random variables, independence, and probability distributions
Audience: Undergraduate

Learning Outcomes: 1. Demonstrate knowledge of fundamental industrial database management strategies
Audience: Undergraduate

2. Apply data preprocessing, visualization, and modeling techniques
Audience: Undergraduate

3. Apply industrial database management techniques using structured query language (SQL)
Audience: Undergraduate

4. Use leading programming software to perform data regression analysis
Audience: Undergraduate

5. Create and solve a real-life industrial data analytics problem, and present results effectively to audience
Audience: Undergraduate

Learning Outcomes: 1. Apply discounted cash-flow analysis to evaluate proposed capital investments
Audience: Undergraduate

2. Recognize, formulate, and analyze cash-flow models
Audience: Undergraduate

3. Explain model results to managers and other non-specialist decision makers
Audience: Undergraduate

Learning Outcomes: 1. Describe and apply fundamental principles and methodologies relevant to planning, design, operation, and control of production planning and control systems
Audience: Undergraduate

2. Describe and explain how organizational strategy drives operations management approaches and supply chain decisions
Audience: Undergraduate

3. Develop a portfolio of analytical tools and skills related to production planning and control and operations management
Audience: Undergraduate

4. Recognize situations in production system environments that suggest the use of appropriate quantitative methods to assist in decision-making
Audience: Undergraduate

5. Apply skills and tools to reduce waste and to increase productivity and quality in manufacturing and service organizations
Audience: Undergraduate

Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems
Audience: Undergraduate

2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches
Audience: Undergraduate

3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems
Audience: Undergraduate

4. Apply statistical methods to analyze stochastic behaviors of the systems
Audience: Undergraduate

5. Apply simulation software to model the process and evaluate performance measures of the systems
Audience: Undergraduate

Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems
Audience: Undergraduate

2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches
Audience: Undergraduate

3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems
Audience: Undergraduate

4. Apply statistical methods to analyze stochastic behaviors of the systems
Audience: Undergraduate

5. Apply simulation software to model the process and evaluate performance measures of the systems
Audience: Undergraduate

Learning Outcomes: 1. Write an algebraic formulation of an optimization model that captures the main decision elements of practical problems
Audience: Undergraduate

2. Use an algebraic modeling language to solve an optimization model
Audience: Undergraduate

3. Model logical constraints using binary decision variables
Audience: Undergraduate

4. Understand the basic ideas behind algorithms for solving linear programming and discrete optimization problems
Audience: Undergraduate

Learning Outcomes: 1. Recognize the strengths and limits of human perceptual, cognitive and physical abilities and their implications for system design
Audience: Undergraduate

2. Describe human factors tools, techniques and methods commonly used to design and improve system performance
Audience: Undergraduate

3. Evaluate and recommend work and task designs based on human factors and ergonomic principle
Audience: Undergraduate

4. Define the ethical application of human factors in designing products and processes
Audience: Undergraduate

Learning Outcomes: 1. Recognize the strengths and limits of human perceptual, cognitive and physical abilities and their implications for system design
Audience: Undergraduate

2. Describe human factors tools, techniques and methods commonly used to design and improve system performance
Audience: Undergraduate

3. Evaluate and recommend work and task designs based on human factors and ergonomic principles
Audience: Undergraduate

4. Define the ethical application of human factors in designing products and processes
Audience: Undergraduate

Learning Outcomes: 1. Apply basic industrial engineering design methods
Audience: Undergraduate

2. Use appropriate industrial and systems engineering design tools forestablishing solutions to open-ended problems
Audience: Undergraduate

3. Select the appropriate methods to utilize in order to collect real-world data
Audience: Undergraduate

4. Observe and collect data relevant to an industrial engineering problem
Audience: Undergraduate

Learning Outcomes: 1. Conduct and report on independent industrial engineering research
Audience: Undergraduate

2. Independently develop industrial engineering research questions
Audience: Undergraduate

3. Appropriately utilize online and library resources
Audience: Undergraduate

Learning Outcomes: 1. Apply fundamental knowledge of industrial data analytics concepts, problems, and techniques
Audience: Undergraduate

2. Integrate data analytics techniques with industrial and systems engineering domain knowledge to appropriately formulate problem statements and facilitate decision making
Audience: Undergraduate

3. Implement software programming skills to perform data analysis
Audience: Undergraduate

4. Apply industrial data analytics methods and tools to solve real-world industrial engineering problems
Audience: Undergraduate

Learning Outcomes: 1. Distinguish advantages and disadvantages for a range of manufacturing processes
Audience: Undergraduate

2. Select an appropriate manufacturing process when given information such as part design, material, and production quantity
Audience: Undergraduate

3. Use terminology that relates to manufacturing systems design and analysis
Audience: Undergraduate

4. Examine manufacturing system performance by applying analytical techniques such as line balancing, manufacturing system benchmarking, inventory models, and queuing formulas
Audience: Undergraduate

5. Utilize computer-aided design and manufacturing software to display part geometry and create toolpaths for a CNC program
Audience: Undergraduate

Learning Outcomes: 1. Describe and explain the purpose of the various components that make-up many health systems including public health agencies (e.g., CDC), hospital systems, emergency medical services (e.g., 9-1-1), healthcare providers, and health insurance
Audience: Undergraduate

2. Identify problems within health systems that may be amenable to industrial engineering tools
Audience: Undergraduate

3. Apply industrial systems engineering concepts and tools (e.g., regression, queueing theory, simulation, cost-benefit analysis, and machine learning) to specific problems in healthcare
Audience: Undergraduate

Learning Outcomes: 1. Identify and use the structural properties of combinatorial optimization problems
Audience: Undergraduate

2. Apply algorithms for the solution -exact or approximate- of a combinatorial optimization problem
Audience: Undergraduate

3. Explain why the algorithms studied are correct and understand their running time
Audience: Undergraduate

Learning Outcomes: 1. Employ design thinking, and engage the client in the creative process for problem space exploration and idea generation
Audience: Undergraduate

2. Utilize a structured modeling, analysis and decision‐making framework to design a new system or process, or re‐design an existing one
Audience: Undergraduate

3. Choose, synthesize and effectively utilize appropriate ISyE methods, concepts, engineering standards, and modeling and analysis tools in all facets of the project lifecycle
Audience: Undergraduate

4. Adjust and be flexible with design strategy and adapt it suitably to address unanticipated issues during project execution
Audience: Undergraduate

5. Hone skills in teamwork, oral and written communication, and project management
Audience: Undergraduate

Learning Outcomes: 1. Conduct a literature review
Audience: Undergraduate

2. Identify gaps in existing research
Audience: Undergraduate

3. Describe characteristics of a good research question
Audience: Undergraduate

4. Write a proposal to address an open research question
Audience: Undergraduate

5. Describe some current active research areas in industrial engineering
Audience: Undergraduate

Learning Outcomes: 1. Prepare materials for graduate program applications, such as a personal statement
Audience: Undergraduate

2. List key elements of research ethics and common pitfalls in different types of research
Audience: Undergraduate

3. Make a poster summarizing completed and/or in-progress work
Audience: Undergraduate

4. Give a technical presentation summarizing completed and/or in-progress work
Audience: Undergraduate

5. Critique technical research presentations
Audience: Undergraduate

6. List strategies for maintaining healthy work-life balance in graduate school
Audience: Undergraduate

Learning Outcomes: 1. Conduct and report on independent industrial engineering research
Audience: Undergraduate

2. Independently develop industrial engineering research questions
Audience: Undergraduate

3. Appropriately utilize online and library resources
Audience: Undergraduate

Learning Outcomes: 1. Identify, formulate, and solve facilities layout problems by applying principles of engineering and mathematics
Audience: Both Grad & Undergrad

2. Apply engineering design to produce facilities design solutions that meet specified needs with consideration of productivity, safety, and economic factors
Audience: Both Grad & Undergrad

3. Utilize computer software to study and illustrate the operation of a manufacturing system
Audience: Both Grad & Undergrad

4. Collaborate with a team to develop solutions to engineering problems and communicate findings effectively
Audience: Both Grad & Undergrad

5. Demonstrate ability to lead a facilities planning project integrating quantitative techniques and management tools
Audience: Graduate

Learning Outcomes: 1. Apply statistical process control analysis for measuring and controlling quality
Audience: Undergraduate

2. Recognize, formulate, and analyze univariate continuous and discrete control charts
Audience: Undergraduate

3. Use Minitab to perform basic statistical process control analysis
Audience: Undergraduate

4. Communicate the results of the statistical process control analysis to management and other non-specialist users of engineering analyses
Audience: Undergraduate

5. Recognize, formulate, and analyze advanced continuous control charts
Audience: Graduate

6. Perform process capability and measurement system capability analysis
Audience: Graduate

Learning Outcomes: 1. Describe and explain the nature of and tools for change management and continual process improvement
Audience: Both Grad & Undergrad

2. Experience the role of the industrial and systems engineer as a “manager" of continual process improvement
Audience: Both Grad & Undergrad

3. Work effectively on a team-based experiential project focused on process design, analysis, and resource management and integrating the concepts of continual improvement, customer focus, and teamwork
Audience: Both Grad & Undergrad

4. Implement technical skills and processes, often with a non-technical workforce
Audience: Both Grad & Undergrad

5. Apply problem solving and management/planning tools for effectively defining problems, feasible alternative solutions, and measurable goals in a “real world” environment
Audience: Both Grad & Undergrad

6. Identify the impact of organizational and cultural influences on the planning and implementation of change
Audience: Both Grad & Undergrad

7. Demonstrate ability to lead an industry-based team project integrating contemporary change management frameworks and considering organizational culture
Audience: Graduate

Learning Outcomes: 1. Recognize the types of problems that decision analysis can and cannot solve
Audience: Both Grad & Undergrad

2. Structure decision problems by identifying the relevant values, objectives, attributes, decisions, uncertainties, consequences, and trade-offs
Audience: Both Grad & Undergrad

3. Represent aspects of a decision problem graphically or mathematically
Audience: Both Grad & Undergrad

4. Determine the optimal decision, using mathematical techniques as appropriate
Audience: Both Grad & Undergrad

5. Identify which parameters have the most impact on the result
Audience: Both Grad & Undergrad

6. Explain the results of a decision analysis to managers and other non-specialists
Audience: Both Grad & Undergrad

7. Articulate mathematical foundations of decision problems
Audience: Graduate

Learning Outcomes: 1. Explain how decision sciences are used in the health-care industry
Audience: Both Grad & Undergrad

2. Describe concepts related to medical decision making and cost-effectiveness analysis
Audience: Both Grad & Undergrad

3. Apply technical skills in decision analysis including the creation and evaluation of decision trees, the use of sensitivity analysis, and the use of Markov processes and Monte Carlo simulation
Audience: Both Grad & Undergrad

4. Incorporate specific patient preferences into medical decision models through the use of utility analysis
Audience: Both Grad & Undergrad

5. Identify and apply an advanced decision analytical modeling framework to solve medical decision making problems
Audience: Graduate

Learning Outcomes: 1. Describe and apply the basic elements of quality and environmental management systems, including the requirements and certification process of international standards such as ISO 9001 and ISO 14001 and industry-specific applications of these standards
Audience: Both Grad & Undergrad

2. Apply concepts and tools in the development, implementation, and maintenance of effective quality and environmental management systems in manufacturing and service organizations, including various approaches for management system documentation structure
Audience: Both Grad & Undergrad

3. Describe and explain strategic and competitive considerations in management system implementation and maintenance, including risk-based thinking and continual improvement
Audience: Both Grad & Undergrad

4. Develop and apply auditing techniques and skills
Audience: Both Grad & Undergrad

5. Demonstrate ability to synthesize and apply requirements for quality and environmental management considering organizational strategy, culture and constraints
Audience: Graduate

Learning Outcomes: 1. Identify problems amenable to machine learning and the techniques required to solve those problems (regression vs. classification, regularization, bagging vs. boosting, etc.)
Audience: Both Grad & Undergrad

2. Apply appropriate analytical models to solve problems and improve decision-making using Python
Audience: Both Grad & Undergrad

3. Effectively communicate findings through both oral and written formats
Audience: Both Grad & Undergrad

4. Demonstrate an understanding of how industrial engineering techniques (e.g., optimization) are used to train machine learning models
Audience: Graduate

Learning Outcomes: 1. Engage in topics about "optimization in practice".
Audience: Undergraduate

2. Use and analyze the results of state of the art optimization software.
Audience: Undergraduate

3. Use the GAMS modeling system and Jupyter notebooks (in conjunction with elementary Python) or Julia and JUMP.
Audience: Undergraduate

4. Design good models for realistic applications in engineering and the sciences.
Audience: Undergraduate

5. Develop a "commercial strength" application of optimization technology.
Audience: Undergraduate

Learning Outcomes: 1. Use linear programming to formulate real world decision problems.
Audience: Both Grad & Undergrad

2. Apply algorithms to solve linear programming problems and demonstrate their correctness.
Audience: Both Grad & Undergrad

3. Combine different proving techniques explored in class in an original way to show new results.
Audience: Graduate

Learning Outcomes: 1. Use the theory of linear programming to prove general duality results
Audience: Undergraduate

2. Apply the concept of complementarity
Audience: Undergraduate

3. Analyze and develop algorithms for solving optimization and equilibrium problems
Audience: Undergraduate

4. Apply decomposition methods and other advanced algorithms for the solution of optimization and equilibrium problems
Audience: Undergraduate

5. Understand economic concepts and how they relate to optimization and equilibria
Audience: Undergraduate

6. Extend theory of linear programming into the framework of conic programming
Audience: Undergraduate

Learning Outcomes: 1. Explain the topics involved in cognitive human factors and design implications of these concepts
Audience: Both Grad & Undergrad

2. Describe the interactions between human operators and system components including the environment, technology, and organizations
Audience: Both Grad & Undergrad

3. Discuss different human factors perspectives regarding human error and limitations of human performance, especially as these relate to memory, decision-making, action selection, and workload and stress
Audience: Both Grad & Undergrad

4. Identify barriers and limitations to memory and how these must be accounted for in design of systems and displays
Audience: Both Grad & Undergrad

5. Articulate the role of simulation and modeling in system design involving human operators
Audience: Both Grad & Undergrad

6. Use cognitive engineering analysis methods to complete a detailed analysis of a real incident or accident including outlining each layer and component of system failure and proposing possible redesign solutions
Audience: Graduate

Learning Outcomes: 1. Apply observation and interview skills to understand customer needs and system interactions
Audience: Both Grad & Undergrad

2. Translate observation and interview data into models of customer requirements and system constraints
Audience: Both Grad & Undergrad

3. Communicate model content to the customer and to marketing, engineering, management, and other members of the design team
Audience: Both Grad & Undergrad

4. Translate work models into aesthetically appealing and functional design concepts and prototypes
Audience: Both Grad & Undergrad

5. Evaluate and iterate prototype designs into a system that satisfies customer requirements
Audience: Both Grad & Undergrad

6. Design a user study to resolve a design issue, such as the choice of opt-in or opt-out default
Audience: Graduate

Learning Outcomes: 1. Explain the rules and regulations governing accident investigations in different industries including transportation and healthcare
Audience: Both Grad & Undergrad

2. Describe the standard process for conducting an accident investigation
Audience: Both Grad & Undergrad

3. Describe basic accident investigation tools, methods and techniques
Audience: Both Grad & Undergrad

4. Discuss different human factors perspectives regarding the underlying causes of errors and accidents
Audience: Both Grad & Undergrad

5. Identify different approaches for redesigning systems to improve safety
Audience: Both Grad & Undergrad

6. Utilize accident investigation methods and tools to analyze a major accident to determine causes and corrective actions
Audience: Graduate

Learning Outcomes: 1. Explain Human Factors Engineering theories and methods as they pertain to the evaluation and design of healthcare sociotechnical systems
Audience: Both Grad & Undergrad

2. Select and critically evaluate the utility of key Human Factors Engineering concepts and tools for assessing and modeling healthcare delivery challenges in sociotechnical systems
Audience: Both Grad & Undergrad

3. Identify the impact of changes to the healthcare sociotechnical system at the individual and organizational level
Audience: Both Grad & Undergrad

4. Apply a Human Factors Engineering-based sociotechnical system approach to evaluate and design interaction between users and information technologies
Audience: Both Grad & Undergrad

5. Demonstrate the use of Human Factors Engineering techniques in solving healthcare delivery problems within the organizational, social, and physical contexts in information system design
Audience: Both Grad & Undergrad

6. Demonstrate ability to independently evaluate Human Factors Engineering research in healthcare
Audience: Graduate

Learning Outcomes: 1. Consider human values and behavior in developing and using machine learning models
Audience: Both Grad & Undergrad

2. Apply a human-centered design process to data and algorithm-intensive products
Audience: Both Grad & Undergrad

3. Describe the basic elements of machine learning and their limits
Audience: Both Grad & Undergrad

4. Analyze a case study of machine learning deployed by a company and discuss strengths and limits of the deployment using the concepts of the course.
Audience: Graduate

Learning Outcomes: 1. Evaluate jobs, equipment, tools, products, and environments, in which people play a significant role, for health and safety hazards and the risk of injuries and illnesses
Audience: Both Grad & Undergrad

2. Devise how to reduce or eliminate physical stresses and the risk of injuries and illnesses in jobs, equipment, tools, products, and environments
Audience: Both Grad & Undergrad

3. Design jobs, workspaces and products for a diverse work population, to accommodate the variability of human dimensions strength, endurance, and physical capacity to do work
Audience: Both Grad & Undergrad

4. Design jobs equipment, tools, products, and environments so that human capabilities are maximized, physical stress is minimized, and workload is optimized
Audience: Both Grad & Undergrad

5. Identify fundamental physiological principles and biomechanical theories that are germane to the evaluation, design and reduction or elimination of stresses and strain in jobs, equipment, tools, products, and environments
Audience: Graduate

Learning Outcomes: 1. Describe the importance of ethical data science/engineering
Audience: Both Grad & Undergrad

2. Identify challenges of trustworthy data use in engineering such as fairness, privacy, and robustness
Audience: Both Grad & Undergrad

3. Apply the definitions of trustworthy data engineering to real-world datasets
Audience: Both Grad & Undergrad

4. Analyze the data analysis pipelines and evaluate the trustworthiness of their outcomes
Audience: Both Grad & Undergrad

5. Create proper data analysis pipelines with ethical considerations
Audience: Both Grad & Undergrad

6. Implement cutting-edge techniques to enhance the fairness, privacy, and robustness of data analysis processes
Audience: Graduate

7. Conduct independent research on emerging challenges in ethical data engineering
Audience: Graduate

Learning Outcomes: 1. Correctly apply methods of fault tree, event-tree, data, and uncertainty analysis to evaluate potential risks of engineering systems
Audience: Both Grad & Undergrad

2. Recognize, formulate, and analyze risks of engineered systems
Audience: Both Grad & Undergrad

3. Explain the results of risk analysis to managers and other non-specialist decision-makers
Audience: Graduate

Learning Outcomes: 1. Design experiments for various phases of engineering work, including new product design and development, process development, manufacturing process improvement, and health systems design and improvement
Audience: Both Grad & Undergrad

2. Analyze the results of experiments conducted at different phases of engineering work
Audience: Both Grad & Undergrad

3. Apply data analysis skills in statistical methodologies, graphical displays, and visual and inferential interpretations
Audience: Both Grad & Undergrad

4. Work effectively in an experiential project focused on applying appropriate statistical tools and techniques
Audience: Both Grad & Undergrad

5. Explain the eight phases of Six Sigma and apply these steps to a wide range of actual situations
Audience: Both Grad & Undergrad

6. Demonstrate ability to synthesize applications of statistical methodologies via analysis of current literature and case studies
Audience: Graduate

Learning Outcomes: 1. Identify and describe key theories, concepts, and methods in human factors engineering
Audience: Both Grad & Undergrad

2. Apply key theories, concepts, and methods in human factors engineering, using appropriate tools, processes, and/or software
Audience: Both Grad & Undergrad

3. Apply, analyze, or evaluate advanced theories, concepts, or methods in human factors engineering
Audience: Graduate

Learning Outcomes: 1. Identify and describe key theories, concepts, and methods in engineering analytics and operations research
Audience: Both Grad & Undergrad

2. Apply key theories, concepts, and methods in engineering analytics and operations research, using appropriate tools, processes, and/or software
Audience: Both Grad & Undergrad

3. Apply, analyze, or evaluate advanced theories, concepts, or methods in engineering analytics and operations research
Audience: Graduate

Learning Outcomes: 1. Identify and describe key theories, concepts, and methods in manufacturing and supply chain management
Audience: Both Grad & Undergrad

2. Apply key theories, concepts, and methods in manufacturing and supply chain management, using appropriate tools, processes, and/or software
Audience: Both Grad & Undergrad

3. Apply, analyze, or evaluate advanced theories, concepts, or methods in manufacturing and supply chain management
Audience: Graduate

Learning Outcomes: 1. Identify and formulate manufacturing process and system level problems by applying principles of engineering and mathematics
Audience: Both Grad & Undergrad

2. Design the geometry of a mechanical component and develop computer code needed to fabricate the component
Audience: Both Grad & Undergrad

3. Apply principles of engineering and mathematics to solve process- and system-level problems such as setting process parameters and evaluating the system performance
Audience: Both Grad & Undergrad

4. Demonstrate ability to combine different modeling and analysis methods explored in the class for manufacturing processes and systems to achieve strategies for performance improvement
Audience: Graduate

Learning Outcomes: 1. Identify and describe key theories, concepts, and methods in healthcare systems engineering
Audience: Both Grad & Undergrad

2. Apply key theories, concepts, and methods in healthcare systems engineering, using appropriate tools, processes, and/or software
Audience: Both Grad & Undergrad

3. Apply, analyze, or evaluate advanced theories, concepts, or methods in healthcare systems engineering
Audience: Graduate

Learning Outcomes: 1. Recognize types, sources, and contributors to error within the medication use system.
Audience: Undergraduate

2. Explain the influence of work systems and human factors on the development of safe processes for improving safety within the medication use system.
Audience: Undergraduate

3. Apply tools for identifying, analyzing, and anticipating errors within the medication use system (e.g., error reporting systems, root cause analysis, failure modes and effects analysis) and use these to develop safer processes.
Audience: Undergraduate

4. Describe characteristics of healthcare settings that contribute to improved quality and how pharmacists can influence the characteristics.
Audience: Undergraduate

5. Explain how quality indicators are developed, measured, and monitored in the US healthcare system.
Audience: Undergraduate

6. Describe and apply economic evaluation and pharmacoeconomic principles to evaluate pharmacy programs and drug products.
Audience: Undergraduate

Learning Outcomes: 1. Perform data analysis and propose quality improvement plans
Audience: Both Grad & Undergrad

2. Develop, implement, and interpret advanced control charts for monitoring continuous and discrete quality characteristics and multivariate systems
Audience: Both Grad & Undergrad

3. Implement appropriate data reduction and data processing methods in statistical process control
Audience: Both Grad & Undergrad

4. Apply methods and tools to a real problem-solving experience via a course project
Audience: Graduate

Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to solve manufacturing, service, business, or societal operations problems
Audience: Both Grad & Undergrad

2. Identify, formulate, and solve manufacturing and service operations problems using appropriate data and analytics approaches
Audience: Both Grad & Undergrad

3. Identify opportunities and apply engineering solutions for system productivity and quality improvement
Audience: Both Grad & Undergrad

4. Apply the techniques, skills, and tools for engineering practice, such as modeling, design, simulation, and management
Audience: Both Grad & Undergrad

5. Apply advanced skills and tools for analysis, control and optimization of manufacturing and service systems operations
Audience: Graduate

Learning Outcomes: 1. Work effectively on a team-based experiential project focused on process design, analysis, and resource management and integrating the concepts of continual improvement, customer focus, and teamwork
Audience: Graduate

2. Apply problem solving and management/planning tools for effectively defining problems, feasible alternative solutions, and measurable goals in a real-world environment
Audience: Graduate

3. Demonstrate ability to lead an industry-based team project integrating contemporary change management frameworks and considering organizational culture
Audience: Graduate

4. Apply the basics of Factorial Design of Experiments (DOE) as a statistical tool for continuous process improvement
Audience: Graduate

Learning Outcomes: 1. Identify the major capabilities and limitations of discrete-event simulation for modeling types of systems that industrial engineers commonly encounter
Audience: Both Grad & Undergrad

2. Model and run discrete-event simulation in practical situations
Audience: Both Grad & Undergrad

3. Identify the main assumptions underlying simulation models, and what can happen when these assumptions do not hold
Audience: Both Grad & Undergrad

4. Apply the results of the modeling process to management and other non-specialist users of engineering analyses
Audience: Both Grad & Undergrad

5. Apply experimental design or data analytics for systems comparison and output analysis of the simulation models
Audience: Both Grad & Undergrad

6. Apply simulation input/output techniques to model complicated stochastic systems such as manufacturing production systems and health care systems
Audience: Graduate

Learning Outcomes: 1. Identify and interpret basic concepts of probability, including random variables, and conditional probability, expectation, and variance
Audience: Both Grad & Undergrad

2. Analyze probability models such as the Markov chains, the exponential distribution, Poisson processes, and queuing models
Audience: Both Grad & Undergrad

3. Identify, formulate, and develop solution techniques for problems that can be modeled by stochastic models in various domains (engineering, computer science, supply chains, healthcare systems, operations research)
Audience: Both Grad & Undergrad

4. Recognize how and when to apply each type of probability model
Audience: Both Grad & Undergrad

5. Formulate mathematical formulations and apply proof techniques
Audience: Graduate

Learning Outcomes: 1. Identify decision problems for typical logistics systems
Audience: Both Grad & Undergrad

2. Establish optimization models for typical logistics systems
Audience: Both Grad & Undergrad

3. Use multiple methods to solve logistics system models
Audience: Both Grad & Undergrad

4. Explain the insights of solutions and apply them in realistic situations
Audience: Graduate

Learning Outcomes: 1. Recall and state the formal definitions of the mathematical objects and their properties for stochastic processes (e.g., discrete space Markov chains, Poisson processes, renewal processes, branching processes, etc.).
Audience: Both Grad & Undergrad

2. Use such definitions to argue that a mathematical object does or does not have the condition of being a particular type or having a particular property (e.g., irreducibility, aperiodicity, recurrence, transience, the Markov property, etc.).
Audience: Both Grad & Undergrad

3. Recall and state the standard theorems of stochastic processes. (e.g., laws of large numbers for Markov chains, existence of limiting/stationary distributions, law of large numbers for renewal processes, etc.) and recall the arguments for these theorems and the underlying logic of their proofs.
Audience: Both Grad & Undergrad

4. Construct mathematical arguments related to the above definitions, properties, and theorems, including the construction of examples and counterexamples.
Audience: Both Grad & Undergrad

5. Convey arguments in oral and written forms using English and appropriate mathematical terminology, notation and grammar.
Audience: Both Grad & Undergrad

6. Model simple real life situations by means of discrete-space stochastic processes and calculate probabilities associated with those processes.
Audience: Both Grad & Undergrad

7. Identify applications of course content in current areas of research.
Audience: Graduate

Learning Outcomes: 1. Identify suitable analysis techniques to investigate processes related to manufacturing, planning, engineering or office operations within a manufacturing firm
Audience: Both Grad & Undergrad

2. Perform analysis to describe, predict and analyze behavior of a manufacturing system to meet desired managerial and economic objectives for a real-world or realistic manufacturing systems improvement project/case study
Audience: Both Grad & Undergrad

3. Develop recommendations that will improve manufacturing system performance (e.g. reduce flow time, increase throughput)
Audience: Both Grad & Undergrad

4. Collaborate effectively in teams to develop solutions to engineering problems and communicate findings effectively
Audience: Both Grad & Undergrad

5. Reflect on personal strengths and weaknesses with respect to team leadership and project management
Audience: Graduate

Learning Outcomes: 1. Model a variety of manufacturing problems as stochastic models using Markov Chain and Process theory
Audience: Both Grad & Undergrad

2. Identify the basic assumptions underlying stochastic models and understand what can happen when these assumptions do not hold
Audience: Both Grad & Undergrad

3. Apply queueing theory to model manufacturing systems
Audience: Both Grad & Undergrad

4. Apply the line balancing method for assembly systems design
Audience: Both Grad & Undergrad

5. Perform cost analysis for manufacturing systems
Audience: Both Grad & Undergrad

6. Apply the analytical approaches of performance analysis for manufacturing systems to real industry cases
Audience: Both Grad & Undergrad

7. Apply advanced Markov process method to solve complicated performance evaluation problems encountered in manufacturing production systems
Audience: Graduate

Learning Outcomes: 1. Apply multiple forecasting methods for customer demand
Audience: Undergraduate

2. Identify and apply multiple inventory management models
Audience: Undergraduate

3. Identify the limitation of models and tune them for realistic cases
Audience: Undergraduate

4. Create and present reports for realistic inventory management policy
Audience: Undergraduate

5. Demonstrate ability to lead a team project to optimize realistic supply chain decisions with analytical tools and numerical simulation
Audience: Undergraduate

Learning Outcomes: 1. Approach visualization as a design process, using the critical response process and paper prototypes
Audience: Both Grad & Undergrad

2. Design visualizations for different audiences and different purposes
Audience: Both Grad & Undergrad

3. Use the concepts of abstraction and aggregation to translate raw data into meaningful information
Audience: Both Grad & Undergrad

4. Use the flexibility of the grammar of graphics to craft graphs for effective communication
Audience: Both Grad & Undergrad

5. Develop replicable data analyses and visualizations with R and RShiny
Audience: Both Grad & Undergrad

6. Create specialized data analytic visualizations such as time series decompositions and ROC curves
Audience: Graduate

Learning Outcomes: 1. Apply the work system model
Audience: Both Grad & Undergrad

2. Apply a variety of models and theories of job and organization design to answer questions such as, "What makes for a good job?" and "What makes for a bad job?"
Audience: Both Grad & Undergrad

3. Apply interview and survey methods for analyzing jobs
Audience: Both Grad & Undergrad

4. Identify approaches to implement job redesign
Audience: Both Grad & Undergrad

5. Identify societal trends related to job design
Audience: Both Grad & Undergrad

6. Identify similarities and differences between various models and theories of job and organization design
Audience: Graduate

Learning Outcomes: 1. Explain the access and usability issues that users with physical, sensory, or cognitive impairments due to age or permanent or temporary disability might experience when interacting with everyday products and environmental designs
Audience: Both Grad & Undergrad

2. Apply human factors principles of universal design to describe redesign solutions for common products and environmental designs to improve accessibility for all users
Audience: Both Grad & Undergrad

3. Identify barriers to access for users based on specific disabilities
Audience: Both Grad & Undergrad

4. Articulate common misconceptions and biases related to users with disabilities and use various data sources to discuss the reality of designing for users with disabilities or impairments
Audience: Both Grad & Undergrad

5. Identify usability issues for mass-market products and environmental designs using universal design and basic access principles
Audience: Both Grad & Undergrad

6. Propose methods for improving accessibility and usability using universal design and basic access principles
Audience: Both Grad & Undergrad

7. Articulate how social, institutional, and organizational structures and insufficiently designed systems and environments disadvantage various user groups, with special focus on aging and disabled users
Audience: Graduate

Learning Outcomes: 1. Conduct and report on graduate-level industrial engineering research
Audience: Graduate

2. Independently develop industrial engineering research questions
Audience: Graduate

3. Appropriately utilize online and library resources
Audience: Graduate

Learning Outcomes: 1. Identify and respond appropriately to real-life engineering ethics cases relevant to co-op work
Audience: Graduate

2. Synthesize and apply appropriate technical education to real world technical work
Audience: Graduate

3. Communicate effectively in writing and speaking with a range of audiences in the workplace, including those without disciplinary expertise
Audience: Graduate

4. Develop professional and transferable habits like time management skills, collaborative problem-solving skills, and research skills for learning new information
Audience: Graduate

Learning Outcomes: 1. Understand and communicate the conceptualization and measurement of quality of healthcare and patient safety.
Audience: Graduate

2. Illustrate basic concepts and methods in quality improvement as applied to current issues in healthcare.
Audience: Graduate

3. Demonstrate an understanding of the diverse perspectives that can be used to address quality and safety issues in different healthcare organizations.
Audience: Graduate

Learning Outcomes: 1. Learn the terms, basic capabilities, and limitations of stochastic programming models
Audience: Graduate

2. Formulate stochastic programming models
Audience: Graduate

3. Implement the algorithms used to solve stochastic programming problems
Audience: Graduate

4. Learn principles of decomposition algorithms for solving large-scale optimization problems
Audience: Graduate

Learning Outcomes: 1. Identify basic components, such as the state space, of a dynamic program
Audience: Graduate

2. Formulate and solve dynamic programs under different performance criteria such as finite horizon discounted reward/cost criteria
Audience: Graduate

3. Apply dynamic programming tools and concepts in `traditional' industrial engineering applications such as supply chain, manufacturing, and healthcare
Audience: Graduate

Learning Outcomes: 1. Describe and explain the basics of polyhedral theory, which consists in the study of systems of linear inequalities both from an algebraic and a geometric point of view
Audience: Graduate

2. Define perfect formulations and identify what properties are desirable in an integer programming formulation of a problem
Audience: Graduate

3. Explain how valid inequalities can be used as cutting planes to strengthen integer programming formulations
Audience: Graduate

Learning Outcomes: 1. Demonstrate an ability to formulate, analyze, and independently solve advanced industrial engineering problems
Audience: Graduate

2. Communicate research results in writing and/or technical presentations
Audience: Graduate

Learning Outcomes: 1. Apply advanced operations research tools to solve a variety of industrial engineering problems
Audience: Graduate

2. Analyze rigorously the methods used in operations research
Audience: Graduate

Learning Outcomes: 1. Apply advanced organizational design tools to solve a variety of industrial engineering problems
Audience: Graduate

2. Analyze rigorously the methods used in organizational design
Audience: Graduate

Learning Outcomes: 1. Apply basic concepts of economic analysis to the assessment of medical technologies and healthcare interventions more broadly
Audience: Graduate

2. Examine health outcomes on a range from objective measures of physical systems to subjective preference-based measures of health utility and describe the benefits and limitations of using quality-adjusted life years (QALYs) as a health outcome measure
Audience: Graduate

3. Explain why we seek to obtain estimates of the “opportunity cost” of using health care resources, describe the process of “costing” in economic assessments of medical technologies and identify useful sources of information for obtaining cost information (and their limitations)
Audience: Graduate

4. Describe how primary data from randomized controlled trials and observational studies can be designed to assess medical technologies and explain the advantages and disadvantages of different designs in terms of their internal and external validity and decision-relevance
Audience: Graduate

5. Describe how evidence from secondary data can be integrated using meta-analysis and decision-analytic modeling methods to assess medical technologies and demonstrate basic ability to design and execute simple decision tree and Markov models for cost-effectiveness analysis
Audience: Graduate

Learning Outcomes: 1. Demonstrate an ability to formulate, analyze, and independently solve advanced industrial engineering problems
Audience: Graduate

2. Communicate research results in writing and/or technical presentations
Audience: Graduate

Learning Outcomes: 1. Identify and describe modern challenges in industrial engineering
Audience: Graduate

Learning Outcomes: 1. Demonstrate an ability to formulate, analyze, and independently solve advanced industrial engineering problems
Audience: Graduate

2. Communicate research results in writing and/or technical presentations
Audience: Graduate

Learning Outcomes: 1. Conduct and report on graduate-level industrial engineering research
Audience: Graduate

2. Independently develop industrial engineering research questions
Audience: Graduate

3. Appropriately utilize online and library resources
Audience: Graduate