An Industry Platform for Data-driven Logistics in Small and Medium-sized Enterprises

Research output: ThesisEngD ThesisAcademic

477 Downloads (Pure)

Abstract

Whereas real-time data nowadays is widely available and advanced data-driven approaches are emerging, organizations in the Dutch logistics industry, in particular Small and Medium-sized Enterprises (SMEs), lack the expertise to effectively identify which data-driven logistics applications are suitable and which tools to use to subsequently adopt these approaches in their daily operations. Based on the design science research methodology, this PDEng thesis presents the results and findings related to the research, design, development, demonstration, and implementation of an industry platform for data-driven logistics based on the Open Trip Model (OTM), to create re-usable data-driven applications for SMEs in the Dutch logistics industry. The core contribution of this PDEng thesis is the architecture for an industry platform for data-driven logistics that is tailored to the need of SMEs. The industry platform architecture consists of the following components and interfaces: 1) a graphical user interface for SMEs to access platform services, 2) APIs for data exchange based on the OTM, 3) an OTM compliant database for unified storage of logistics data, 4) an agent repository for re-usable data-driven applications and development tool for testing various algorithms, and 5) a scalable infrastructure for provisioning computing resources to deploy, run, and monitor agents. The industry platform architecture is complemented with a design canvas, workshop materials, implementation guidelines, and adoption framework to transfer the industry platform functionality to SMEs as part of a learning community. The industry platform concept is verified by means of an expert panel consultation, deployed for testing, and its use is demonstrated and validated using case-based research at a Dutch logistics services provider. The results and findings are disseminated to the scientific community via peer-reviewed publications and presentations during international conferences and workshops. Additionally, the results and findings are communicated to the logistics community via professional reports, presentations and workshops at industry events, and articles in the media. The instantiated industry platform and case study support awareness building of potential data-driven logistics applications in the logistics industry and lowering the barriers for SMEs to start adopting data-driven approaches in their daily practice. The industry platform provides a foundation for further empirical research and a rich testbed for experimental development of data-driven logistics approaches in logistics. Future research will focus on comparing modern, traditional and hybrid data-driven approaches, experimenting with federated learning among SMEs, and incorporating data sharing concepts as part of the federated data sharing infrastructure that is currently being developed for the Dutch logistics industry.
Original languageEnglish
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Iacob, Maria Eugenia, Supervisor
  • van Hillegersberg, Jos, Co-Supervisor
  • Wouterse, Marcel Johanna Theodoor, Co-Supervisor, External person
Award date3 Jun 2022
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-5365-0
DOIs
Publication statusPublished - 3 Jun 2022

Keywords

  • industry platform
  • data-driven logistics
  • learning community
  • Small and medium sized enterprises
  • SMEs
  • open trip model
  • OTM

Fingerprint

Dive into the research topics of 'An Industry Platform for Data-driven Logistics in Small and Medium-sized Enterprises'. Together they form a unique fingerprint.

Cite this