Case Studies

The first of two case studies on the logistics of container supply and demand management. A global container management consortium needs to forecast and stage containers of various types at depots throughout the world to meet the demands of its member fleets. Due to the complexity of scheduling, resource allocation, and the seasonal variation in demand they were able to satisfy only a small fraction of their member’s requirements. The aggregate cost of the consortium’s inability to satisfy demand ran in excess of several hundred thousand dollars per day in unnecessary container leasing, in container unavailability, and in lost commerce.

This case study provides a high level overview of the approach used to build the model that took container demands and found either a least cost or least risk solution to providing the necessary container supply.

The second of two case studies on the logistics of container supply and demand management. In order to satisfy customer demand the consortium must predict the aggregate container demand at each port and find a way to stage containers so that a sufficient supply exists to satisfy this demand. A behavior model of container demand and availability provides the foundation for this prediction. Such a behavior model takes into account demand by container type across time for each of the member fleets (since the customer mix is different for each fleet.) This behavior is merged with the natural container supply, serviceability, and in-transit characteristics of the depots. Once a behavior model is produced, it is used to predict container demand. The demand is used to stage container at the port.

This case study discusses the details of the underlying supply and demand model, container availability optimization, and site-specific container forecasting.

The mergers and acquisitions (M&A) division of a major international bank must provide accurate and timely advice for its corporate customers about candidate mergers or acquisitions as well as the inherent risks and long term prospects for corporate takeovers, purchases, and partnerships. Due to the complexity of the analysis and the intuitive judgment needed to rank opportunities only the most senior analysts are able to evaluate possible M&A candidates. This constraint significantly limits the division’s revenue growth, restricts the depth and kinds of analysis it can perform, affects it relationships with clients, inhibits the ability to train and rely on less senior analytical skills, and increases the chances of subtle errors in its assessment of potential candidate. The bank estimates that is loses over $40 million annually in loss opportunities, insufficient analysis, and lack of cross-over business.

This case study explores a multiple component model and methodology approach that was used to automate the analysis of key performance indicators, reduce analysis time, increase accuracy, and allow analysts of various skill levels to isolate and evaluate candidate companies.