REDTag: A Predictive Maintenance Framework for Parcel Delivery Services

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 realworld 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. INDEX TERMS Big data analytics, Industry 4.0, intelligent transports and logistics, Internet of Things, machine learning, predictive maintenance.

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

“…An integrated framework to track and monitor shipped packages, Fig. 29 was proposed in ( Proto et al., 2020 ). Framework relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of package at each delivery step.…”

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

“…Other IIoT applications include industry risk management (to reduce infectious disease outbreaks such as COVID-19) [49], smart grid (SG) [50], [51], smart goods tracking and transportation systems [52], [53], smart oilfields [49], and smart factories [45]; as listed in Fig. 2.…”

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…In Keartland and Van Zyl (2020), the condition of gearbox compartments is monitored through the analysis of their oil and the different concentration levels in it. Proto et al (2020) used a NFC approach to perform geopositioning of shipped packages in a parcel delivery service.…”

“…One approach is shown in Cheng et al (2020), Consilvio et al (2020), Zschech et al (2019), Bampoula et al (2021), Steurtewagen and Van den Poel (2021), where inspection and historical maintenance records and manual failure registers are used together with other kind of data depending on the domain and generally obtained through sensors. In the context of parcel delivery services, Proto et al (2020) follow a similar approach that combines manual registers of packages position with another data automatically collected. In the case of Usuga‐Cadavid et al (2021), maintenance logs provided by operators are used as the unique source for the PdM.…”