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Program SummaryThe primary goals of the concentration in manufacturing and operations management are two-fold.(1) To train students to apply scientific solutions to the problems currently being faced in industry, either as their role as a Consultant or as an Operations Manager. (2) To teach students how to think in a systematic manner so as to be able to effectively meet the challenges of tomorrow, and be able to learn (on their own when necessary in the future) specific topics that interests them. Unique Features of GSIAThe Graduate School of Industrial Administration (GSIA) is the business school at Carnegie Mellon University with a long tradition of outstanding education in all branches of management.GSIA is strongly committed to manufacturing and operations as evidenced by a strong MSIA (MBA equivalent) program in production and operations management (we were ranked first in the most recent U.S. News and World Report and have always been in the top two in other surveys) as well as an excellent PhD program (see student placement below). More generally, GSIA is committed to quantitative management research and has made innovative contributions leading to four Nobel Prizes in Economics, and the faculty in Operations Research have won Lanchester and the John Von Neumann Theory prizes (awarded by INFORMS). GSIA also has close ties with the engineering school in topics such as Green design, with the Robotics Institute in the area of Artificial Intelligence as well as with departments of Mathematics, Statistics and the School of Public Policy. The program in Manufacturing and Operations Management thus has an interdisciplinary outlook that is essential for management. Further, faculty regularly bring to the classroom relevant cutting-edge research findings that have yet to appear as journal articles. A broad range of (fairly advanced) elective courses dealing with current topics and new frameworks provides the students with a business outlook and a managerial capability that is uniquely possible at GSIA.
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GSIA Faculty and Research InterestsArthur Hsu Computational Methods; Logistics; SimulationSunder Kekre Electronic Data Interchange; New Product Development; Managing Variety Uday Rao Inventory; Scheduling; Production Planning Alan Scheller-Wolf Stochastic Models; Queuing Theory Sridhar Tayur Supply Chain Management; Just-In-Time; International Operations Egon Balas Integer Programming; Scheduling Gerard Cornuéjols Integer Programming; Location John Hooker Integer Programming; Logic; Artificial Intelligence R. Ravi Approximation algorithms Gerald Thompson Linear Programming; Optimal Control Michael Trick Computational Integer Programming; Scheduling Linda Argote Learning in Organizations Robert Miller Demand Flow Management; Econometrics Tridas Mukhopadyay Manufacturing - Information Systems Interface Kathryn Shaw Human Resource Practices Konduru Sivaramakrishnan Activity Based Costing; Agency Theory Fallaw Sowell Statistics in Manufacturing; Quality; Econometrics Kannan Srinivasan Marketing - Manufacturing Interface; New Product Development Ilker Baybars Deputy Dean
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45-765 Production and Operations Management (POM). This is the core production and operations class, with a prerequisite of Quantitative methods for Management Science (45-760). Topics include basic concepts in operations (examples: inventory, capacity planning, managing lead times, logistics), an introduction to emerging practices in supply chain management (example: delaying differentiation), inter-disciplinary aspects (examples: design for manufacturing, product-line management), re-engineering, quality and international operations.
Here are brief descriptions of some of the Operations Management electives.
45-866 Just-In-Time Manufacturing (JIT). The managerial aspects will include: What is JIT--do we need a JIT revolution--Push vs. Pull; How to reduce set-up times--SMED system; Integration of kanban and MRP type systems--implementation; Design for assembly, FMS; Quality and JIT; Accounting Implications; Supplier Relationships; EDI; New Methods to compete via Operations; Why implementations fail--what constitutes a successful implementation-- some examples in Japan and US. The course will also contain the following technical topics: 1. Supply Chain Management: Why do inventories exist--what are their ill-effects-- how do inventories fluctuate--how do inventories move--how to reduce inventories--inventory profiles. We will concentrate on centralized control of multi-echelon systems (assembly and distribution) including re-entrant flows. 2. Plant Management: Both deterministic and stochastic models will be considered. The effects of capacity and randomness of demand will be emphasized. We will study nested policies and cyclic schedules. 3. Controlling Serial lines using Kanbans: Bottleneck management--controlling lines with yield losses--cell formation--kanban allocation--batching -- will be studied using simulation and simple analytical models. 4. Time Based Competition: Vanilla Boxes, Component Commonality, Task-Redesign.
45-985 Logistics. This is a course on logistics management, which is a systems approach to the management of all of those activities involved in physically moving raw materials, in-process inventory, and finished-goods inventory from point of origin (supply) to point of use (customer). The teaching method will be combination of lecture, class discussions on assigned topics, and case analysis. Topics covered will include: distribution, facility location, inventory management, purchasing, role of information systems, global logistics and strategic planning. The objectives of the course are to: (1) understand the role of logistics in a business, (2) understand the individual components of logistics and their interrelationships within individual companies, and (3) develop an understanding of the analytic tools and techniques useful for solving logistics problems.
45-863 Management and Control of Manufacturing Systems (MCMS). This is an overview course on Manufacturing Strategy and Business Logistics. In this course, we examine how a firm can develop competitive advantage via excellence in manufacturing strategy, tactical planning, real-time control and business logistics. The five course modules include: Operations Strategy, Supply Chain Management, Production Planning and Inventory Control, Customer Demand Management & Product Distribution, and Integrative Systems Design. The course combines analytical modeling and insightful concepts with an investigation of several case-based scenarios.
45-937 Production Planning in a Global Environment (PPGE). This course provides you with a unique experiential learning environment where we simulate structured real-world situations via microcomputer games, case studies, and physical simulations. Thus we will evaluate the kind of decision support systems required for excellence in manufacturing. Lectures and readings will supplement our analysis of production systems. We also examine design and control issues in new product development and in establishing integrated (& international) manufacturing and logistics networks. Specific topics covered are listed in the Course Schedule. Topics include: logistical issues in supply chain system design, graphical techniques for material flow analysis, fundamental concepts in production planning and inventory control, factory modeling and simulation, cyclic scheduling, effects of variability, analyzing observational data, manufacturing information systems, group dynamics, economic analysis, international manufacturing networks.
45-949 Simulation Modeling for Production Systems. Simulation is a widely-used technique for building realistic models of complex systems. Designers of such systems use these models to experiment with alternative systems without having to build a real, physical system. The technique has applications in all functional areas of business although the examples presented in this class will be primarily be in the area of production systems. This is an introductory course on simulation modeling. The main objectives of the course are that you learn to do the following: (1) develop an understanding of when simulation modeling is an appropriate tool for analysis, (2) design a simulation experiment that models a real-world process, (3) analyze and interpret the output of a simulation experiment in a manner that is statistically valid, and (4) develop a basic proficiency in a simulation package called Arena/Siman so that you know how to implement a simulation model.
45-890 Statistics for Manufacturing. In this course both the theoretical foundations and applications of the statistical control for manufacturing processes are presented. The focus of the course is the determination of the optimal quality for a manufacturing process and the control (and monitoring) of the process to achieve that optimal quality. The optimal quality (variation) of a manufacturing process will be determined using the Taguchi Loss Function approach. This optimal quality is achieved by controlling the manufacturing process. This control can be achieved by the development and use of Control Charts. Quality can also be designed into a manufacturing process and the product being manufactured. Experimental Design to determine the optimal parameter settings for process and tolerance design will also be presented. The topics in the course will be motivated by case studies. The material will be equally split between the theoretical justification for these techniques and their correct implementation.
45-833 Stochastic Models in POM. We consider widely occuring real world problems that have a significant stochastic element: supply chain issues including supplier selection and contracts; capacity planning in semi-conductor industry; managing yield loss; quick response; hedging against exchange rate fluctuations; accurate lead time quotation; and integrating operations with marketing in a feature based product line. The technical tools used include Basic probability, Markov Chains, Queueing Theory, Birth and death processes, Analysis of simple queues, Queueing networks, Simulation based Optimization, Stochastic dynamic programs and Markov Decision Processes.
45-809 Strategic Cost Analysis. Cost accounting systems provide valuable information for management planning and control. An organization pursuing multiple products relies on accurate cost information to decide on which products to keep and which products to discontinue. Cost information is also valuable for assessing productivity improvements and for performance evaluation and control. An improperly designed cost accounting system can lead to costly errors in decision-making. Because of intense competition in product markets, firms are looking inward to become more cost-efficient. Tracing various resource costs accurately to products has become important in order to assess product profitability and make strategic product decisions. Manufacturing environments have also undergone change. Just-in-time and flexible manufacturing philosophies are gaining over traditional inventory-push and MRP systems. To support decision-making in these environments, cost accounting systems also need to be redesigned. Through a blend of lectures and case studies, this course will expose students to recent advances in cost accounting, and highlight important inputs into the design of cost systems. The cases will also cover a variety of decision settings and highlight the role of cost information in these settings.
45-832 Total Quality Management. (TQM) In a global world, improvements through re-engineering are becoming increasingly vital to a firm's success. Organizations that implement TQM change their culture from a reactive to a proactive focus, resulting in increased customer satisfaction, reduced costs and a greater competitive edge. TQM may be viewed as managing the entire conversion process to excel on all product/service dimensions that are important to customers. This includes studying the firm's entire value chain, competitive benchmarking, use of cross-functional teams, training for problem identification and continuous improvement. This course will focus on: (1) TQM Philosophy, (2) Implementation Issues, and (3) Major Tools (such as statistical process control, quality assurance, quality function deployment and design of experiments for improvement). A key factor in quality is ``variation''. We study how to measure this variation, how to reduce variation, how to identify its causes and its impact on quality and productivity. Topics such as the criteria for the Malcolm Baldrige Quality Award and the ISO 9000 series standards will be discussed. The goal of the course is to develop a strategic approach to quality improvement.
Electives dealing with inter-disciplinary aspects, such as interface with engineering, entrepreneurship or marketing, include the following: 45-846 Technology Development, Manufacturing and Marketing in the Corporation, 45-860 Commercialization of New Product Technology, and 45-926 Design, Manufacturing and Marketing of New Products.
Relevant Operations Research electives include the following: 45-960 Modeling for Management Science Applications, 45-936 Distribution and Location and 45-958 Sequencing and Scheduling.
Based on alumni feedback , the following two electives also come in handy in the real world of Operations: 45-993 Competetive Strategy and 45-904 Interpersonal Negotiation.
Option A. First Year: JIT, MCMS, Statistics for Manufacturing; Strategic Cost Analysis. Summer and Second Year : Logistics, Stochastic Models, PPGE, Simulation; TQM.
Option B. First Year: JIT, MCMS, Stochastic Models; Summer and Second Year: Logistics, Simulation, Statistics for Manufacturing, PPGE, Strategic Cost Analysis; TQM.