This is the second course in a two-course sequence on modeling and analysis of production and logistics systems for graduate students. Emphasis will be on developing a framework for organizing tools and research results in this field and on modeling techniques necessary to conduct production system research. Specific topics to be covered include: Control and design of single-stage production systems such as produce-to-order and produce-to-stock systems, control and design of multiple-stage production systems includding flow lines, transfer lines and push and pull systems such as MRP, JIT and CONWIP, analysis of multiple-stage production systems based on the queueing network and other stochastic modeling techniques, introduction to deterministic and stochastic scheduling for single and multiple stage problems such as flow shop and job shop scheduling.

This course is for dual degree MBA students at Kellogg School of Management. The course is on the basic concepts and techniques of operations management. The foundation of the course is a system fondumantal pronciples that relate the various measures of manufacturing system performance, such as throughput, cycle time, work-in-process, variability, and quality, in a consistent manner and provide a framework for evaluating classical operations management techniques as well as evolving new strategies. Topics include operations strategy, basic factory dynamics, process flow analysis Benchmarking of performance, tools to acheive lean operations, inventory management with certain and uncertain demand, quality management, process capability, statistical process control, six sigma. Analytical tools used include probability and statistics, queueing models, and computer simulation. Both concepts and methods are examined via exercises and case studies.

This is the second course on a sequence of courses on operations management. The course is for dual degree MBA students at Kellogg School of Management. The focus of this second course is to show how key operations management techniques such as Regression, Linear Programming, Integer Programming, and Decision Analysis can be used to solve problems faced in managing a production facility, including forecasting, shop floor control, scheduling, aggregate planning, workforce management, project scheduling, operations scheduling, facilities layout and location, design and control of flexible production resources (e.g., agile workforce and flexible plants), etc. Both concepts and methods are examined via exercises and case studies.

This course is for MEM professional master program, i.e., Master of Engineering Management. The course is aimed at providing students an understanding of how various business situations are modeled and optimized effectively using mathematical modeling and quantitative techniques. Examples of the techniques covered in this course are time-series analysis, regression, optimization (linear, nonlinear, and discrete), probabilistic modeling, decision analysis, and simulation. Application areas include forecasting, finance, operations, production and logistics. Students will learn through examples, cases, and use of software.

This is a graduate level course that covers topics in reliability engineering, optimal maintenance and replacement policies and production control of manufacturing systems with limited repair resources. Specifically, it includes: Introduction to reliability engineering, Catastrophic failure models and reliability functions, Applications of probability distribution functions in reliability evaluation, Combinatorial aspects of system reliability, Reliability evaluation of engineering systems using Markov models, Reliability testing, Transfer lines, Optimal preventive maintenance policies, Optimal replacement policies, Optimal integrated production and maintenance policies in production systems subject to failures.