Management Science (MGSC)

This course illustrates applications of basic mathematical techniques in decision-making such as break-even analysis, data manipulation, aggregate planning and financial planning. Topics include linear functions and equations, linear programming and the simplex method, compound interest and annuities.

Students are introduced to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for data analysis: business and data understanding, data exploration and preparation, modelling, evaluation and deployment. Students will use Excel to analyze datasets and build models. Methods for value estimation, classification and probability analysis are introduced.

This course is designed to introduce some common decision aids for coping with uncertainty. Topics include: data collection, summarization and presentation, reporting and interpreting the accuracy of results, evaluating the effectiveness of a decision and determining relationships among factors for the purpose of prediction. Examples will be drawn from a variety of fields and disciplines. Since the analysis of data will involve the use of a computer, it is strongly recommended that a course such as CISY 1225 be completed beforehand.

Students are introduced to the role that operations play in enabling organizations to create value in alignment with their mission and vision. Students explore the interdependence between operations and other core business functions. Topics include operations strategy, product/service design, and management of project, process, quality and supply chain.

This course addresses operations issues that contribute to how firms compete with respect to cost, quality, time and flexibility. Topics include project management, product/service and process design, process flows, quality, and supply chain management (including forecasting, inventory management, JIT, and supplier relations). Examples will be drawn from both service and manufacturing sectors.

Students are introduced to supply chain management and key considerations for design and operation of a supply chain. Students examine the key drivers of supply chain performance and methodologies for supply chain analysis. The impact of digitization on supply chain will also be discussed.

Students will be offered a broad perspective of the sports world’s transition to digital transformation and the different benefits that data brings to this industry. Students will learn how to use data to make objective and measured decisions when managing athletes, fans, or entire organizations. Students will explore best practices in sports business analytics, including data visualization, applying predictive modeling techniques, and constructing decision modeling and optimization frameworks in sports.

Students explore how environmental and social sustainability concerns arise as constraints or opportunities in businesses and other organizations, and how these concerns can be best addressed through the strategic and operational decisions made by these organizations.