Case studies

This case study addresses a number of common transportation management issues. It provides an opportunity to analyse historical freight payment data in order to reduce transportation costs and improve customer service, allowing students to utilise real freight payment data and order shipment history data to make recommendations that have strategic implications for both cost and service performance.

This case illustrates the concept of consolidation and how freight forwarders use it to reduce transportation costs. The student must compare a ship-direct model against vehicular consolidation, then consider temporal consolidation, investigate the differences between a freight broker and a freight forwarder, and develop a rate analysis for use in negotiation.

This case focuses on supplier management through quantitative and qualitative analysis of data from a real company. In the process of solving the case, students determine and operationalize what constitutes a critical supplier, analyse the data provided, and identify the most critical suppliers.

This case study follows GHP Bearings staff as they create a supply chain map. Students will understand the supply chain network as a whole: including the relationships between suppliers and extending beyond immediate suppliers and customers rather than seeing the supply chain as a linear connection. Students will then analyse the entire supply chain network for strategic purposes and decide where should receive the greatest emphasis of management to give a competitive advantage.

Provincial Industries has decided that the time has come to expand internationally. In this case and the activities along with it, individuals will need to consider different attributes or factors in each category evaluated. These categories include: business climate, communications, financial services, health services and “Quality of Life”, infrastructure, and transportation. This evaluation will decide the new international office location.

This two-part case illustrates the use of economic order quantity to manage conflicting performance measures across different siloed functions in an organization. Part A requires students to assess the costs of various order quantities and quantify the concept of “robustness.“ Part B emphasizes managing the variables of annual demand, ordering cost, inventory carrying cost, and unit price to achieve strategic goals. The student must determine how to lower ordering costs to compensate for increases in the other variables as well as to help guide Just-In-Time implementation efforts.

The COO for Suape Container Terminal, the largest deep–water port in Brazil's Northeast must consider a proposal presented by the users' council that calls for the establishment of a reservation scheme that minimizes the risk of docking delays. Under this proposal, ocean carriers, on the one hand, agree to pay a reservation fee that significantly increases revenue for Tecon Suape. On the other hand, they expect Tecon Suape to compensate them financially when a berth is not available upon vessel arrival. Tecon Suape's management team must evaluate that suggestion, as the team prepares to enter contractual negotiations with the users.

9 million Thin Mints are made every day. In 2015, there was a spike in orders causing production shortages. In this case, students will evaluate how the CEO of Girl Scouts of San Jacinto Council, Mary Vitek, can mitigate the risk of supply disruption and utilize strategic sourcing in order to avoid any further shortages.

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.

The managing director of a steel plant faces the decision of how much of each raw material to order for the plant for the following month. Due to lower and upper bounds on the amounts of each raw material in a batch and varying amounts of electricity and time consumed for different raw materials, one can't simply use the cheapest raw material. A linear program and the solver optimization function of Excel will provide the optimal amounts that meet the constraints. Interestingly, the best mixture for a batch is not the best mixture for a monthly plan. Shadow prices indicate the value of relaxing constraints. The typical monthly model from a student will be nonlinear, although it can be written as a linear model. This case provides the basis for an introductory class on linear programming and linear versus nonlinear models.