REDTag: A Predictive Maintenance Framework for Parcel Delivery Services
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 mana...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8959207/ |
| _version_ | 1819296337641865216 |
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| author | Stefano Proto Evelina Di Corso Daniele Apiletti Luca Cagliero Tania Cerquitelli Giovanni Malnati Davide Mazzucchi |
| author_facet | Stefano Proto Evelina Di Corso Daniele Apiletti Luca Cagliero Tania Cerquitelli Giovanni Malnati Davide Mazzucchi |
| author_sort | Stefano Proto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T04:56:31Z |
| format | Article |
| id | doaj.art-add7d6fb701947099611c8b783c89681 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:56:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-add7d6fb701947099611c8b783c896812022-12-21T17:14:23ZengIEEEIEEE Access2169-35362020-01-018149531496410.1109/ACCESS.2020.29665688959207REDTag: A Predictive Maintenance Framework for Parcel Delivery ServicesStefano Proto0https://orcid.org/0000-0002-8143-3611Evelina Di Corso1https://orcid.org/0000-0002-3988-3512Daniele Apiletti2https://orcid.org/0000-0003-0538-9775Luca Cagliero3https://orcid.org/0000-0002-7185-5247Tania Cerquitelli4https://orcid.org/0000-0002-9039-6226Giovanni Malnati5https://orcid.org/0000-0002-6798-2761Davide Mazzucchi6https://orcid.org/0000-0001-6361-6361Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyZirak srl, Mondovì, ItalyThe 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.https://ieeexplore.ieee.org/document/8959207/Big data analyticsIndustry 4.0intelligent transports and logisticsInternet of Thingsmachine learningpredictive maintenance |
| spellingShingle | Stefano Proto Evelina Di Corso Daniele Apiletti Luca Cagliero Tania Cerquitelli Giovanni Malnati Davide Mazzucchi REDTag: A Predictive Maintenance Framework for Parcel Delivery Services IEEE Access Big data analytics Industry 4.0 intelligent transports and logistics Internet of Things machine learning predictive maintenance |
| title | REDTag: A Predictive Maintenance Framework for Parcel Delivery Services |
| title_full | REDTag: A Predictive Maintenance Framework for Parcel Delivery Services |
| title_fullStr | REDTag: A Predictive Maintenance Framework for Parcel Delivery Services |
| title_full_unstemmed | REDTag: A Predictive Maintenance Framework for Parcel Delivery Services |
| title_short | REDTag: A Predictive Maintenance Framework for Parcel Delivery Services |
| title_sort | redtag a predictive maintenance framework for parcel delivery services |
| topic | Big data analytics Industry 4.0 intelligent transports and logistics Internet of Things machine learning predictive maintenance |
| url | https://ieeexplore.ieee.org/document/8959207/ |
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