Big Data analytics and anomaly prediction in the cold chain to supply chain resilience
Authors:
- Augustyn Lorenc,
- Michał Czuba,
- Jakub Szarata
Abstract
The purpose of the research was to develop a prediction method to prevent disruption related to temperature anomaly in the cold chain supply. The analysed data covers the period of the entire working cycle of the thermal container. In the research, automatic Big Data analysis and mathematical modelling were used to identify the disruption. Artificial Neural Network (ANN) was used to predict possible temperature-related disruption in transport. The provided research proves that it is possible to prevent over 82% of disruptions in the cold chain. The ANN enables analyses of the temperature curve and prediction of the disruption before it occurs. The research is limited to coolbox transportation of food under -20o C, but the method could also be used for Full Transport Load (FTL) in refrigerated transport. The research is based on real data, and the developed method helps to reduce the waste in the cold chain, improve transport quality and supply chain resilience. The presented method enables not only to avoid cold chain breaks but also to reduce product damage as well as improve the transport process. It could be used by cargo forwarders, Third-Party Logistics (3PL) companies to reduce costs and waste. The literature review confirms that there is no similar method to prevent disruption in the transport chain. The use of the Internet of Things (IoT) sensors for collecting data connected with Big Data analysis and ANN enables chain resilience provision.
- Record ID
- CUTc325adadabdc4a06a368858bea90bc3c
- Publication categories
- ;
- Author
- Other language title versions
- Analitika velikih podataka i predviđan̂e anomalija kod hladnog lanca u cilu postizan̂a elastičnosti lanca snabdevan̂a
- Journal series
- FME Transactions, ISSN 1451-2092, e-ISSN 2406-128X
- Issue year
- 2021
- Vol
- 49
- No
- 2
- Pages
- 315-326
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 325-326; Bibliografia (liczba pozycji) - 23; Oznaczenie streszczenia - Steszcz. ang., serb.-chorw.; Numeracja w czasopiśmie - Vol. 49, No. 2
- Keywords in English
- disruption in the cold chain, predict and prevent disruption, provide chain resilience, IoT for food transport monitoring, ANN prediction model, Big Data analytics, temperature anomaly
- DOI
- DOI:10.5937/fme2102315L Opening in a new tab
- URL
- https://www.mas.bg.ac.rs/istrazivanje/fme/start Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 70
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTc325adadabdc4a06a368858bea90bc3c/
- URN
urn:pkr-prod:CUTc325adadabdc4a06a368858bea90bc3c
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.