Addressing Data Engineering Challenges in the Cold-Chain Sector to Reduce its Environmental Impact Through Analytics
Abstract
Data analytics offer a great opportunity for organizations within the cold-chain industry to become more sustainable and reduce their environmental footprint. Yet, despite recent progress in data sciences, they still face challenges to ensure data quality and streamline data processes through adapted full-scale ecosystems, limiting their ability to perform sustainable analytics. Most data techniques and tools stem from the data-science community and are not always suitably adapted to domain specificities. For instance, nearly all data-quality frameworks are designed to individually consider entities, i.e. representations of real-world elements. However, achieving environmental impact reduction in industrial settings, particularly within the cold-chain sector, often requires modelling and studying entire systems comprised of multiple interdependent entities. This publication challenges the application of conventional data science and engineering concepts in data ecosystems within the cold-chain sector and proposes an alternative approach to sustainably address prevalent challenges and thus optimize analytical processes.
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BibTeX
@misc{marcy2026addressing,
title = {{Addressing Data Engineering Challenges in the Cold-Chain Sector to Reduce its Environmental Impact Through Analytics}},
author = {Mathilde Marcy and Camille Fertel and Thomas Suquet and Gérald Cavalier},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.34762},
eprint = {2609.34762},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.34762},
}
RIS
TY - UNPB
TI - Addressing Data Engineering Challenges in the Cold-Chain Sector to Reduce its Environmental Impact Through Analytics
AU - Mathilde Marcy
AU - Camille Fertel
AU - Thomas Suquet
AU - G{é}rald Cavalier
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.34762
UR - https://arxiv.org/abs/2609.34762
AB - Data analytics offer a great opportunity for organizations within the cold-chain industry to become more sustainable and reduce their environmental footprint. Yet, despite recent progress in data sciences, they still face challenges to ensure data quality and streamline data processes through adapted full-scale ecosystems, limiting their ability to perform sustainable analytics. Most data techniques and tools stem from the data-science community and are not always suitably adapted to domain specificities. For instance, nearly all data-quality frameworks are designed to individually consider entities, i.e. representations of real-world elements. However, achieving environmental impact reduction in industrial settings, particularly within the cold-chain sector, often requires modelling and studying entire systems comprised of multiple interdependent entities. This publication challenges the application of conventional data science and engineering concepts in data ecosystems within the cold-chain sector and proposes an alternative approach to sustainably address prevalent challenges and thus optimize analytical processes.
ER -