Abstract

The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a real-world dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data.

Highlights

  • The emergence of Industry 4.0 factories has fostered the diffusion of Internet of Things (IoT) technologies and big data analytics tools in the industrial sector [1]

  • This paper presents a predictive maintenance framework tailored to the context of intelligent good transports and logistics

  • WORKS In this paper, we investigate the application of machine learning models to address the data-driven prediction of courier package breaks in smart good transportation systems

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Summary

Introduction

The emergence of Industry 4.0 factories has fostered the diffusion of Internet of Things (IoT) technologies and big data analytics tools in the industrial sector [1]. The so called Logistics 4.0 has deeply increased the needs for transparency in the supply chain and integrity control in good selling and delivery (i.e., sell the right product at the right cost and deliver it at the right time and place). Following technological applications: (i) Resource planning, (ii) Warehouse Management Systems, (iii) Transportation Management Systems, (iv) Intelligent Transportation Systems, and (v) Information Security. Since the scope of this work is to push advanced Information Technology solutions into good transports, it falls into category (iii). The globalization and the spread of online shops are the key factors that caused the significant growth in the number of commodities delivered by specialized couriers. To design intelligent logistics systems that are able to address today’s challenges, couriers need to adopt advanced

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