Transport and Logistics Case Study Data Set (Cargo 2000)
This page provides documentation and download options for the Cargo 2000 transport and logistics case study.
Figure 1 shows a model of the business processes covered in the case
study. It represents the business processes of a freight forwarding
company, in which up to three smaller shipments from suppliers are
consolidated and in turn shipped together to customers. The business
process is structured into incoming and outgoing transport legs, which
jointly aim at ensuring that freight is timely delivered to
Fig. 1. Transport and Logistics Process used in Case
Each of the transport legs involves the following physical transport
• RCS: Check in freight at departure airline. Shipment is checked in and a receipt is produced at departure airport.
• DEP: Confirm goods on board. Aircraft has departed with shipment on board.
• RCF: Accept freight at arrival airline. Shipment is checked in according to the documents and stored at arrival warehouse.
• DLV: Deliver freight. Receipt of shipment was signed at destination airport.
A transport leg may involve multiple segments (e.g., transfers to other flights or airlines). In those cases, activity RCF loops back to DEP (indicated by the “loop-back” arrow in Figure 1). In the case study, the number of segments per leg ranges from one to four.
The transport services are denoted by three-letter acronyms according to the Cargo 2000 industry standard. Cargo 2000 is an initiative of IATA, the International Air Transport Association (Cargo 2000 has been re-branded as Cargo iQ in 2016). It aims at delivering a new quality management system for the air cargo industry. Cargo 2000 allows for unprecedented transparency in the supply chain. Stakeholders involved in the transport process can share agreed Cargo 2000 messages, comprising transport planning, replanning and service completion events. Cargo 2000 is based on the following key principles: (1) Every shipment gets a plan (called a route map) describing predefined monitoring events. (2) Every service used during shipment is assigned a predefined milestone with a planned time of achievement. (3) Stakeholders receive alerts when a milestone has failed and notifications upon milestone completion, which include the effective time the milestone has been achieved.
The case study data comprises tracking and tracing events from a forwarding company’s Cargo 2000 system for a period of five months. From those Cargo 2000 messages, we reconstructed execution traces of 3,942 actual business process instances, comprising 7,932 transport legs and 56,082 service invocations. Each execution trace includes planned and effective durations (in minutes) for each of the services of the business process (introduced in Section II), as well as airport codes for the DEP (“departure”) and RCF (“arrival”) services. Due to the fact that handling of transport documents along the business process differs based on whether the documents are paper-based or electronic, we focus on the flow of physical goods, as our data set did not allow us to discern the different document types.
The reconstruction process involved data sanitation and anonymization. We filtered overlapping and incomplete Cargo 2000 messages, removed canceled transports (i.e., deleted route maps), sanitized for exceptions from the C2K system (such as events occurring before route map creation) and homogenized the way information was represented in different message types. Finally, due to confidentiality reasons, message fields which might exhibit business critical or customer-related data (such as airway bill numbers, flight numbers and airport codes) have been eliminated or masked.
When using the case study data set in your research, please cite one of the following paper to acknowledge its use by citing one of the following papers:
A. Metzger, P. Leitner, D. Ivanovic, E. Schmieders, R. Franklin, M. Carro, S. Dustdar, and K. Pohl, “ Comparing and combining predictive business process monitoring techniques,” IEEE Trans. on Systems Man Cybernetics: Systems, 2015.
A. Metzger, R. Franklin, and Y. Engel, “ Predictive monitoring of heterogeneous service-oriented business networks: The transport and logistics case,” in Service Research and Innovation Institute Global Conference (SRII 2012), ser. Conference Publishing Service (CPS), R. Badinelli, F. Bodendorf, S. Towers, S. Singhal, and M. Gupta, Eds. IEEE Computer Society, 2012.
Z. Feldmann, F. Fournier, R. Franklin, and A. Metzger, “Industry article: Proactive event processing in action: A case study on the proactive management of transport processes,” in Proceedings of the Seventh ACM International Conference on Distributed Event-Based Systems, DEBS 2013, Arlington, Texas, USA, S. Chakravarthy, S. Urban, P. Pietzuch, E. Rundensteiner, and S. Dietrich, Eds. ACM, 2013.
You can download the Cargo 2000 data set and the explanation of the data attributes in the following formats:
|Comma Separated Values
|Semicolon Separated Values
Please feel free to contact andreas.metzger _AT_ paluno.uni-due.de for further inquiries.
Research leading to these results has been supported by the European Union's Seventh Framework Programme FP7/2007-2013 under grant agreements 604123 (FIspace), 285598 (FInest) and 215483 (S-Cube).