Operations and Industrial Engineering

This course focuses on the data-driven decision-making that matches supply to demand in an organization and its supply chain, emphasizing the strategic impact of operations on competitiveness and sustainability. Emergent technologies are explored as opportunities for innovation. Descriptive, predictive, and prescriptive analytical techniques are introduced to structure and evaluate key operational decisions. Skills required to model a system’s operations, to address uncertainty and mitigate risk, to effectively evaluate resource needs, to integrate components into a coordinated system, and to efficiently develop and manage capacity and inventory are honed during the course.

Risk management deals with decision making under uncertainty. It is interdisciplinary, drawing upon management science and managerial decision-making, along with material from negotiation and cognitive psychology. Classic methods from decision analysis are first covered and then applied, from the perspective of business process improvement, to a broad set of applications in operations risk management and design including: quality assurance, supply chains, information security, fire protection engineering, environmental management, projects and new products. A course project is required (and chosen by the student according to his/her interest) to develop skills in integrating subjective and objective information in modeling and evaluating risk. (Students cannot get credit for both OIE 542 and OIE 541

This course studies the decisions, strategies and analytical methods in designing, analyzing, evaluating, and managing supply chains. Concepts, techniques, and frameworks for better supply chain performance are discussed, and how digital technologies enable companies to be more efficient and flexible in their internal and external operations are explored. The major content of the course is divided into three modules: supply chain integration, supply chain decisions, and supply chain management and control tools. Students will learn how to apply some of the techniques in Operations Research such as linear programming, dynamic programming, and decision tree to aid decision-making. A variety of instructional tools including lectures, case discussions, guest speakers, games, videos, and group projects and presentations are employed.

The environmental implications and responsibilities of organizations begin at an organization’s boundaries with management of their operations, but also extend to incorporate interorganizational relationships and networks, the supply chain. We will investigate the practice and theory of sustainable supply chains and operations management in organizations throughout the world. This course is intended to provide students with understanding the intra- and interorganizational implications of environmental sustainability practices and policies. The role of organizational supply chain management functions, activities, tools and methods and their relationship to the natural environment will be introduced and discussed. The goals are for students to grasp the scope of general supply chain/operations management and environmental sustainability as they relate to the firm; to be able to relate to the manners in which management may respond and collaborate internally and with suppliers, customers, and various other stakeholders influencing and influenced by operational and supply chain activities from practical and theoretical case studies; able to evaluate various factors and understand tradeoffs in management decisions as they pertain to environmental supply chain management.

This course focuses on operations strategy from a global perspective. Topics such as strategy of logistics and decisions to outsource are examined. As an example, the strategic issues concerned with firms that are doing R&D in the United States, circuit board assembly in Ireland and final assembly in Singapore. Cases, textbooks and recent articles relating to the topic are all used. Term paper based on actual cases is required.

This course provides an in-depth focus on prescriptive analytics, which involves the use of data, assumptions, and mathematical modeling of real-world decision problems to ascertain and recommend optimal courses of action. Starting from conceptualization of the problem, to using theory for translational modeling and techniques, to computational solving, and finally interpretation – likely in an iterative manner – students will gain knowledge of tools and practical skills in transforming real-world decision problems into actionable insights. Advanced topics in the prescriptive analytics domain will be covered, such as the use of integer variables to represent important logical constructs, using nonlinear functions to represent real-world decision aspects, the incorporation of stochasticity and uncertainty, and corresponding solution methods. Real-world problems will be selected from a variety of contexts that may include capacity management, data science, finance, healthcare, humanitarian operations, inventory management, production planning, routing, staffing, and supply chain. Students will complete an individual project that includes a report in the style of a technical report or research paper, as well as an oral presentation. Students may not receive credit for both OIE 4430 and OIE 559

This capstone course serves as a practical integration of the operations and supply chain theories, practices, tools and techniques that students learned in their MS program. The medium is a major team-based project, sponsored by an external organization. The course goals are: (1) to enrich students’ experiential learning and support the acquisition of the skills and capabilities to tackle real-world problems; and (2) to enhance students’ teamwork, interpersonal and consulting skills. Students will produce a written report documenting their solutions, and providing the financial, organizational, and technical rationale for their approach. They will also formally present their results to the project sponsors. Students are expected to have completed (or be currently completing) all the course requirements for their MS in Operations and Supply Chain Analytics prior to taking the capstone project.