PhD Defence: Meditya Wasesa
In his dissertation ‘Agent-based Inter-organizational Systems in Advanced Logistics Operations’ ERIM’s Meditya Wasesa offers new insights on the role of inter-organizational systems in mitigating coordination equivocality and uncertainties; the interplay among the ABIOS applications, required structural adjustments, and the potential of business performance improvement opportunities; and the development of two ABIOS prototypes: an auction based coordination mechanism and a predictive analytics application based on big data.
Meditya defended his dissertation in the Senate Hall at Erasmus University Rotterdam on Friday, 30 June 2017 at 11:30. His supervisors were Prof. Eric van Heck and Prof. Rob Zuidwijk. Other members of the Doctoral Committee are Prof. Peter Vervest (RSM), Prof. Rene de Koster (RSM), Prof. Cess Witteveen (TU Delft), Prof. Henk Sol (Groningen University), Prof. Kadarsah Suryadi (Bandung Institute of Technology), and dr. Andries Stam (Almende BV)
About Meditya Wasesa
Meditya Wasesa obtained his bachelor degree in mechanical engineering from Bandung Institute of Technology, Indonesia. He continued his study in the University of Duisburg-Essen, Germany, where he obtained his master degree in logistics engineering with the highest distinction (sehr gut). During his time in Germany, he worked for the after-sales logistics department of General Motors Europe GmbH. He founded and is managing the Indonesian Center for Logistics and Value Chain (ICLOV) in his hometown, Bandung, Indonesia. He has been working on numerous research and consultancy projects for several renowned private and stated owned companies. His professional interests focus on the field of logistics and information systems.
His PhD research at the Rotterdam School of Management has been funded by the Erasmus Research Institute of Management (ERIM) and Almende BV. His research focus is on the role and impact of agent-based inter-organizational systems, business intelligence systems, and predictive analytics techniques on business networks performance in the logistics sector. His research has been presented at numerous international workshops and conferences. One of research projects won the best poster paper award at the 8th Workshop on e-Business, Phoenix, USA. Two chapters of his dissertation are published in the Journal of Enterprise Information Management and Decision Support Systems respectively.
Thesis Abstract
“Agent-based Inter-organizational Systems (ABIOS) in Advanced Logistics Operations” explores the concepts, the design, and the role and impact of agent-based systems to improve coordination and performance of logistics operations. The dissertation consists of one conceptual study and three empirical studies. The empirical studies apply various research methods such as a multiple-case study research, coordination mechanism design, and predictive analytics using big data. The conceptual study presents a theoretical exploration and synthesis explaining the demand for inter-organizational systems (IOS) and the corresponding IOS functionalities. The first empirical study presents a multiple-case study exploring real-life ABIOS implementations in the warehousing and transportation business. The second empirical study provides an auction based coordination mechanism design for the container’s pick-up/delivery appointment reservation problem that involves the seaports and drayage operators. The third empirical study presents a seaport service rate prediction system that could help drayage operators to improve their predictions of the duration of the pick-up/delivery operations at a seaport by using the subordinate trucks’ trajectory data. Based on these studies, the dissertation offers new insights on the role of inter-organizational systems in mitigating coordination equivocality and uncertainties; the interplay among the ABIOS applications, required structural adjustments, and the potential of business performance improvement opportunities; and the development of two ABIOS prototypes: an auction based coordination mechanism and a predictive analytics application based on big data.
Photos: Chris Gorzeman / Capital Images
