DCEIS 2015 Abstracts


Full Papers
Paper Nr: 2
Title:

Towards Strategic Information Systems Change Management

Authors:

Rūta Pirta

Abstract: Information systems (IS) are changing continuously accordingly the changes in enterprise business and operating models and other internal and external factors that influence the enterprise. To manage IS changes, engineering change management (ECM) process is performed. The one of the major problem in ECM process is that changes are not planned, evaluated, controlled and implemented (i.e. governed) appropriately, what frequently results with sub-optimal architectural decisions causing a number of problems to the enterprise. In this paper, an initial idea of approach to evaluate and control IS changes using Enterprise Architecture (EA) landscapes is proposed. The envisioned approach compares different EA landscapes to evaluate changes, their impact to related processes and data flows and generates architectural recommendations about implementation of the change in EA to meet the “ideal” or envisioned EA landscape. The main focus of this paper is the problem domain analysis, what include an overview of related research and empirical analysis of architectural change management in private sector companies and state institutions.

Paper Nr: 3
Title:

Detecting Shipping Fraud in Global Supply Chains using Probabilistic Trajectory Classification

Authors:

Ron Triepels and Hennie Daniels

Abstract: The globalization of trade puts significant pressure on effective customs compliance and supply chain intelligence by freight forwarders. Containerization and the asymmetric information provisioning of cargo negatively impact the ability to track goods in transit. Freight forwarders seek ways to improve their intelligence by applying data mining techniques to detect potential fraudulent declarations. This paper proposes a research project on the use of trajectory classification to analyze how goods are being transported between the consignor and consignee. The trajectory of cargo is expected to reflect patterns of fraud that are mainly ignored by modern fraud detection systems. Expected outcomes of the project are twofold. A framework will be built for freight forwarders to set up classifiers for the purpose of predicting fraudulent shipment trajectories. These classifiers are expected to improve the effectiveness by which customs compliance is enforced. In addition, supply chain data has the characteristics of big data and is therefore difficult to analyze. The framework is expected to contribute a new application of trajectory classification to the data mining literature and show how cargo trajectories can be efficiently classified.