Towards logistics 4.0: an edge-cloud software framework for big data analytics in logistics processes

Abstract

Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely insights from data-in-motion through streaming analytics. Logistics 4.0 is a relatively new field of research which demands the development of scalable and efficient software solutions and their deployment to successful real-life case studies. In this paper, we propose a software framework for streaming analytics in an edge-cloud computational environment aiming at covering the whole data analytics lifecycle in logistics processes and thus, advancing the evolution and realisation of the Logistics 4.0 concept. The proposed framework takes advantage of edge computing technologies, streaming analytics and proactive decision making in order to monitor, analyse and support decision making in the frame of Logistics 4.0. It is applied and evaluated in a maintenance service logistics use case from the aerospace industry.

KEYWORDS:

• Streaming analytics
• logistics
• machine learning
• edge computing
• cloud computing
• Internet of Things

Acknowledgements

This work was partly funded by the European Union’s Horizon 2020 project: UPTIME “Unified Predictive Maintenance System” (https://www.uptime-h2020.eu/) (Grant agreement No. 768634). The contents of this paper reflect only the authors’ view and the Commission is not responsible for any use that may be made of the information it contains. The authors wish to acknowledge the Commission and all the UPTIME project partners for the fruitful collaboration.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Horizon 2020 Framework Programme [project UPTIME “Unified Predictive Maintenance System”, grant number 768634].

Notes on contributors

Moritz von Stietencron

Moritz von Stietencron is a research scientist in the IKAP department Intelligent ICT for Co-operative Production in BIBA – Bremer Institut für Produktion und Logistik.

Karl Hribernik

Karl Hribernik is the Head of the IKAP department for intelligent ICT environments for cooperative production in BIBA – Bremer Institut für Produktion und Logistik (https://www.biba.uni-bremen.de/).

Katerina Lepenioti

Katerina Lepenioti is a researcher and a Ph.D. candidate at the Information Management Unit of the Institute of Communication and Computer Systems in Greece.

Alexandros Bousdekis

Alexandros Bousdekis is a senior research scientist at the Information Management Unit of the Institute of Communication and Computer Systems in Greece.

Marco Lewandowski

Marco Lewandowski is a research assistant of information and communication technology applications in production in BIBA – Bremer Institut für Produktion und Logistik and the founder of SWMS consulting.

Dimitris Apostolou

Dimitris Apostolou is associate professor in the Department of Informatics at the University of Piraeus and a Senior Researcher at the Institute of Communication and Computer Systems in Greece.

Gregoris Mentzas

Gregoris Mentzas is full Professor of Management Information Systems, School of Electrical and Computer Engineering, National Technical University of Athens and Director of the Information Management Unit (IMU), a multidisciplinary research unit (http://imu.ntua.gr).