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The learning outcomes are to remember the overall context of global supply chain management from a stakeholder perspective, to understand the context of material handling movement in a mining industry, to apply the overall knowledge of linear programming in a supply chain context, to analyze the different constraints with flow of goods at different nodes in various location hubs and convert the same into the optimization problem and to evaluate carefully the different costs associated at different levels and then finding the optimal solution that minimizes the total cost.

This case proposes a mixed integer multi-echelon analytical model integrated with the scenario tree analysis. The integrated model is used to optimize the allocation of volumes at various stages of the supply chain of exporters of bulk materials like iron ore from Goa, India, to various countries in Asia. The scenario tree analysis is then used to evaluate decisions under certainty with demand as the stochastic parameter. The proposed integrated model has potential for collaboration in the supply chain and facilitating network design, inventory and transportation planning and policy analysis.

This course is suitable at the MBA level for the following courses: Operations Research (Focus/Session: Applications on Supply Chain Management), Supply Chain Management (Focus/Session: Global Supply Chain Management, Logistics Planning, Distribution Network), Logistics Management (Focus/Session: Transportation Planning) nd Operations Strategy (Focus/Session: Location Node Strategy).

Teaching Notes are available for educators only.

CSS 9: Operations and Logistics.

East Texas Oxygen (ETOX) delivered high-pressure cylinders of gases such as oxygen and nitrogen to twelve wholly-owned branches scattered throughout East Texas and Louisiana. Employees loaded and unloaded individual high-pressure cylinders off of and onto trailers manually and the firm had never had a related accident. Robert Jenkins had been challenged to decrease the cost of supplying the branches with cylinders and other supplies. He was considering recommending the palletization of delivery operations which required numerous changes within the organization. This case required students to determine the best routing for the delivery truck(s) and to determine whether or not the number of trucks and drivers could be reduced under palletization. Students were then required to do a capital budgeting analysis and make a recommendation of whether or not to palletize.

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

HP sells configure-to-order products. With millions of part combinations going into an order, the challenge is deciding which parts to keep in the portfolio to balance costs with revenues. The case explains how one would approach this problem before product introduction, but focuses on managing the existing portfolio.

Students will develop a systematic, data-driven approach to decide on the best product portfolio to sell for a configure-to-order business. Which SKUs are candidates for a “global core” product offering? For an extended offering? For elimination?