Operations and Supply Chain Analytics Graduate Certificate

Operations and Supply Chain Analytics Certificate

Program Details

Degree

Certificate

Department

School of Business Graduate Program

Available

On Campus & Online

Contact

Office of Graduate Admissions1-888.511.1306[email protected]
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The Operations and Supply Chain Analytics Certificate explores how predictive and prescriptive analytics can help organizations make sense of large-scale logistics problems. The curriculum will prepare you to analyze and interpret data and design actionable strategies to manage production and service operations through advanced business intelligence and analytical tools.

Courses - Operations and Supply Chain Analytics

BIA 674 Supply Chain Analytics

Supply chain analytics is one of the fastest growing business intelligence application areas. Important element in Supply Chain Management is to have timely access to trends and metrics across key performance indicators, while recent advances in information and communication technologies have contributed to the rapid increase of data-driven decision making. The topics covered will be divided into strategic and supply chain design and operations, including -among others- supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing. The primary goal of the course is to familiarize the students with tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real life problems, and to teach students a wide range of methods and tools -in the areas of predictive, descriptive and prescriptive analytics- to efficiently manage demand and supply networks.

Choose 3 out of the following courses:

BIA 650 Optimization and Process Analytics

This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. This course emphasizes the application of mathematical optimization models over the underlying mathematics of their algorithms. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: healthcare, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercises will involve the use of Microsoft Excel’s "Solver" add-on package for mathematical optimization.

MGT 657 Operations Management

Covers the general area of management of operations, both manufacturing and non-manufacturing. The focus of the course is on productivity and total quality management. Topics include quality control and quality management, systems of inventory control, work and materials scheduling, and process management.

BIA 658 Social Network Analytics and Visualization

Given a data matrix of cases-by-variables, a common analytical strategy involves ignoring the cases to focus on relations among the variables. In this course, we examine situations in which the main interest is in dependent relations among cases. Examples of “cases” include individuals, groups, organizations, etc.; examples of “relations” linking the cases include communication, advice, trust, alliance, collaboration etc. Application areas include social media analytics, information and technology diffusion, organization dynamics. We will learn techniques to describe, visualize and analyze social networks.

MIS 637 Data Analytics and Machine Learning

This course will focus on Data Mining & Knowledge Discovery Algorithms and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases will help managers to make near-real time intelligent business and operation decisions. The course will begin with an introduction to Data Mining and Knowledge Discovery in Databases. Methodological and practical aspects of knowledge discovery algorithms including: Data Preprocessing, k-Nearest Neighborhood algorithm, Machine Learning and Decision Trees, Artificial Neural Networks, Clustering, and Algorithm Evaluation Techniques will be covered. Practical examples and case studies will be present throughout the course.

If desired, the above courses can be applied toward a full graduate degree from the School of Business, such as the master's in Business Intelligence & Analytics or the MBA.