Online Master of Engineering in Engineering Management (M.Eng.EM)

This course presents advanced techniques and analysis designed to permit managers to estimate and use cost information in decision making. Topics include: historical overview of the management accounting process, statistical cost estimation, cost allocation, and uses of cost information in evaluating decisions about pricing, quality, manufacturing processes (e.g., JIT, CIM), investments in new technologies, investment centers, the selection process for capital investments, both tangible and intangible, and how this process is structured and constrained by the time value of money, the source of funds, market demand, and competitive position.

This project-based course exposes students to tools and methodologies useful for forming and managing an effective engineering design team in a business environment. Topics covered will include personality profiles for creating teams with balanced diversity; computational tools for project coordination and management; real-time electronic documentation as a critical design process variable; and methods for refining project requirements to ensure that the team addresses the right problem with the right solution.

This course brings a strong modeling orientation to bear on the process of obtaining and utilizing resources to produce and deliver useful goods and services so as to meet the goals of the organization. Decision-oriented models such as linear programming, inventory control, and forecasting are discussed and then implemented utilizing spreadsheets and other commercial software. A review of the fundamentals of statistical analysis oriented toward business problems will also be conducted.

This course enables the engineering management student to acquire the knowledge and skills they will need to handle the variety and volume of information encountered in today’s workplace. The course uses Python, which is rapidly becoming the language of choice for information handling and data analysis. Students will work with both structured and semi-structured data.

This course emphasizes the development of modeling and simulation concepts and analysis skills necessary to design, program, implement, and use computers to solve complex systems/product analysis problems. The key emphasis is on problem formulation, model building, data analysis, solution techniques, and evaluation of alternative designs/ processes in complex systems/products. An overview of modeling techniques and methods used in decision analysis, including Monte Carlo and discrete event simulation is presented.

This course is a study of analytic techniques for rational decision-making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.

This course provides a hands-on introduction to the modern techniques for visualizing data and leverages such techniques with the corresponding problem solving skills necessary to complement data visualization into specific strategic decision making. The student will first learn to use the latest off the shelf software for data visualization. Specifically, the student will learn the following languages: R, D3, Google refine and Spot fire.

Getting usable information from the vast amount of data we are immersed into requires a combination of methodologies, tool, techniques, algorithms and ingenuity. Creating views, extracting trends, define patterns, identify clusters are all elements we need to manage large data. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. Final goal of the course is to provide the students with a “data toolbox” they can use in their activities. This “toolbox” contains methods and tools that students will use themselves during the course for real world applications. The course is hand-on, but no coding is required, using Open Source Data Science tools that are based on Graphical User Interfaces.

The supportability of a system can be defined as the ability of the system to be supported in a cost effective and timely manner, with a minimum of logistics support resources. The required resources might include test and support equipment, trained maintenance personnel, spare and repair parts, technical documentation and special facilities. For large complex systems, supportability considerations may be significant and often have a major impact upon life-cycle cost. It is therefore particularly important that these considerations be included early during the system design trade studies and design decision-making.

This course illustrates the theory and practice of designing and analyzing supply chains. It provides tool sets to identify key drivers of supply chain performance such as inventory, transportation, information and facilities. Recognizing the interactions between the supply and demand components, the course provides a methodology for implementing integrated supply chains, enabling a framework to leverage these dynamics for effective product/process design and enterprise operations.

Many social-technical systems in healthcare, energy and urban systems can be considered as multi-agent systems where different agents – people, organizations or autonomous technologies- with heterogeneous, and often opposing objectives interact and shape the complex collective behavior and evolving nature of such systems. Analysis, design and governance and design of such systems can be very challenging and require rigorous agent-based thinking rooted in analytical models. This course teaches fundamentals of multi-agent systems, starting from models of single agent decision making and planning under uncertainty, and moves to basic frameworks for multi-agent system analysis tools, with a focus on game theory and complex network analysis . The course will take a combination of analytical and conceptual methods, and will use agent based simulation techniques rooted in these methods. Students will apply the course material to a real-world project, close to their area of dissertation research.

This course covers the theory and application of modeling aggregate demand, fragmented demand and consumer behavior using statistical methods for analysis and forecasting for facilities, services and products. It also aims to provide students with both the conceptual basis and tools necessary to conduct market segmentation studies, defining and identifying criteria for effective segmentation, along with techniques for simultaneous profiling of segments and models for dynamic segmentation. All of this provides a window on the external environment, thereby contributing input and context to product, process and systems design decisions and their ongoing management.