Principle #5: Concept
Researchers have designed supply chain Decision Support System (DSS) environments for the air cargo industry for decades. These environments are typically based on different philosophical models. Also, they differ in how they forecast demand and how they drive logistics, handling and storage decisions. Their goal is to generate plans and schedules that consider some of the elements of the supply chain. No matter which approach is taken, these systems and their embedded rules dictate many daily supply chain activities. Therefore, they have a substantial impact on operating behavior, and consequently on overall supply chain performance, operational effectiveness and security. How much they enhance air cargo supply chain performance depends upon both the accuracy of their input data and the modeling approaches employed. We believe that these decision support systems need to address uncertainty in an explicit manner – most do not.
Principle # 5: Application to the Air Cargo Supply Chain
A recent study applied advanced Decision Support Systems techniques for planning and scheduling to the problem of scheduling truck arrivals at the Hong Kong International Airport - HKIA (8). Assumptions included collaborative sharing of current flight schedules with air carriers, a focus on air cargo handling operations for outbound flights only, and adequate docking capacity at the airport. There are a number of outbound flights with confirmed air waybills; the terminal operator schedules arrivals of the delivery trucks so that some of the shipments can be transferred directly to the departing flights without requiring extra handling and storage at the terminal, an approach that is analogous to that permitted in the CCSP for screened cargo.
The model also considers that multiple shipments (for different air carriers) may be delivered to the airport on a single truck, and that cargos come in different sizes and weights, which adds complexity to the cost minimization computation, but accurately reflects the way things work in the “real world”.
The benefits of using the advanced scheduling algorithm relative to the random, “First Come, First Served” (FCFS) in the current system at HKIA were substantial, with an average cost savings of 39.2%. The savings are due to the ability of the advanced scheduling approach to coordinate the arrivals of trucks so that a larger percentage of shipments can be transferred directly to the line-up areas, saving their handling and storage costs.
In addition to cost savings, this advanced scheduling approach has the added advantage of avoiding congestion at the air cargo terminal and guarantees that all shipments will arrive on time, eliminating late shipments (an average of 3.9% of all shipments in the FCFS approach) and reducing truck wait times, which averaged 99 minutes from arrival to unloading, substantially. As previously noted, the less time cargo is in transit and waiting means fewer opportunities for tampering, theft and sabotage.
Planned follow-on research will incorporate stochastic programming techniques to explicitly represent uncertainty in the scheduling process.