This course introduces powerful analytics tools used to help make decisions under conditions of certainty and uncertainty. Applications in supply chain management, using case studies and computer software, are emphasized. Descriptive and predictive analytics tools include regression analysis and simulation, while prescriptive tools include decision analysis and optimization.
Supply chain managers need to understand the impact of their decisions on important financial ratios, risk, and ultimately, the firm’s value. This course introduces the tools necessary to interact with finance people at a practical level, and to make sound trade-offs between supply chain operations and finance. This includes understanding the organization’s financial performance from its balance sheet, income statement, and statement of cash flows. It also includes using major project valuation tools, such as net present value, and gaining familiarity with basic concepts of managerial accounting.
This course introduces students to inventory management frameworks and analytic techniques, focusing on independent demand items. Techniques include forecasting methods, lot sizing under steady demand, lot sizing under demand uncertainty for seasonal items and continuously stocked items, managing supply chain (multi-location) inventories, and causes and remedies for the bullwhip effect. Students will learn to apply these tools in manufacturing and service business settings.
This course introduces students to procurement and supply management frameworks and analytic techniques, and tools for managing dependent demand items. Techniques include material requirements and distribution requirements planning, supplier selection and tailored sourcing based on total cost of ownership, supply risk assessment and mitigation, supply contract design criteria such as service levels, using contracts to share supply chain risks and rewards, and supplier-buyer negotiation.
This course covers business processes and decision support techniques for freight transportation in supply chains. Emphasis is placed on optimizing the processes for acquiring and deploying transportation resources in the four major modes: road, rail, water, and air. The course addresses key questions of interest to transportation service providers (e.g., how to design a delivery network), and to their clients (e.g., how to manage the relationship with transportation service providers).
This course covers business processes and decision support techniques for designing and operating facilities within a supply chain network. While emphasizing the analysis of distribution centres, the course also includes the study of factories, ocean container ports, and retail facilities to understand how they can be deployed to optimize supply chain performance.
This project, done individually, requires students to provide their own SCM-related projects, usually at the firms where they work. Each student is also responsible for providing the contract (‘Memorandum of Understanding’ or MOU) and the non-disclosure agreement (NDA) to be signed by the course instructor and/or university, student, and client firm. These documents will define the project’s scope and deliverables, and ensure the student has access to necessary company data and personnel. Course requirements include: (i) weekly meetings with the instructor using Skype or similar software or in person, (ii) a final report to the instructor and the client firm, and (iii) a presentation to the client firm with the instructor attending. The client firm will provide input to the grading but faculty will assign the final grade.
The course will begin with instruction in the basics of project management and presentation skills. Practical challenges to implementing change within the client firm will be emphasized, to avoid unrealistic recommendations.
This project resembles SCMG607, but students work in two- to three-person teams assigned by faculty, instead of individually, and the project is more ambitious. With faculty input, teams will select from a provided set of projects. To enhance learning and creativity, faculty will form teams with diversity and complementarity of skills. Faculty will also ensure no student does both SCMG607 and SCMG608 projects at the same company. This is needed to avoid teams where one student has prior firsthand knowledge of the client firm, to which other team members must defer (thereby diminishing their own learning experience).
The university and the client firm will negotiate the MOU and the NDA that the course instructor and/or university, student team, and client firm will sign. Course requirements include (i) meetings with the instructor every two weeks using Skype or similar software or in person, (ii) a final report to the instructor and the client firm, and (iii) a presentation to the client firm with the instructor attending. The client firm will provide input to the grading, especially from the perspective of implementation, but faculty will assign the final grade.
The course will begin with instruction in the basics of consulting teamwork, including how to conduct a meeting, make a presentation as a team, and manage the team’s relationship with the client firm and among team members. Practical challenges to implementing change within the client firm will be emphasized, to avoid unrealistic recommendations.
Revenue management is an analytics-based approach for selling the right products and services to the right customer at the right price at the right time. It helps manage production and service delivery capacities, resources and product/service availability across different selling channels, in order to maximize performance and profitability. Besides building and using revenue management models, this course emphasizes how to explain a model and its analysis to managers without formal training in analytics.
Students in this course study the application of data driven analytics to core problems in supply chain management. This course builds on techniques introduced in SCMG601 (e.g., regression and simulation) and introduces new tools such as artificial intelligence and machine learning, to model and analyze problems of relevance in contemporary supply chains. The course emphasizes the use of large, realistic supply chain data sets to develop management insights.
Applications for Fall 2022 due by July 1, 2022.
Application portal opens in October 2021.
Applications for Fall 2022 due by July 1, 2022.
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