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PM4PREF – Digital platform for logistics process analysis and tracking with smart packaging, using Process Mining techniques

2025-11-11Master thesis Embargoed access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.advisorMartinho, Ricardo Filipe Gonçalves
dc.contributor.advisorFerreira, Carlos José da Rocha
dc.contributor.authorBarbeiro, Diogo dos Anjos
dc.date.accessioned2025-12-10T14:26:52Z
dc.date.available2025-12-10T14:26:52Z
dc.date.issued2025-11-11
dc.description.abstractModern logistics operations generate vast amounts of process data but often lack tools that transform these records into actionable insights. This report addresses this gap by developing a decision-support platform for historical statistics, real-time monitoring, predictive analysis, and conformance checking of deliveries. The research is motivated by challenges faced at Prozis, a major European e-commerce company, where handling fragile and temperature-sensitive products requires timely detection of risks such as delays, route deviations, and inadequate environmental conditions. Following the Design Science Research Methodology (DSRM), the project Decision Support System – Intelligent Package (DSSIP) was designed and implemented. The system integrates process mining techniques with Internet of Things (IoT) sensor data and geolocation streams to provide transparency across ongoing deliveries. Its core modules include: historical statistics for exploratory analysis; a conformance checker using GPS based clustering to detect deviations from reference routes; predictive modeling of activity timestamps based on polynomial regression and contextual filtering; and a real-time dashboard for monitoring package conditions such as temperature, humidity, and shocks. Validation was carried out through experiments on historical event logs and simulated sensor datasets, combined with real world case studies from Prozis. Results show that contextualized predictions improve accuracy compared to global models, frequent transitions yield robust forecasts, and low-support activities expose limitations in process discovery. Usability testing (PSSUQ overall score: 1.93) confirmed that the system is effective and well-accepted by users. This research contributes to the state of the art by incorporating spatio-temporal data into process mining in a logistics setting and advancing context-aware predictive monitoring. The results emphasize the potential of process mining and IoT integration to enhance logistics resilience, reduce waste, and improve service quality in large-scale ecommerce operations.por
dc.identifier.tid204076170
dc.identifier.urihttp://hdl.handle.net/10400.8/14977
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectProcess mining
dc.subjectPredictive monitoring
dc.subjectSpatio-temporal analysis
dc.subjectUsability Testing
dc.subjectIoT Data
dc.subjectDecision support systems
dc.titlePM4PREF – Digital platform for logistics process analysis and tracking with smart packaging, using Process Mining techniques
dc.typemaster thesis
thesis.degree.nameMestrado em Engenharia Informática – Computação Móvel

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