详情页

Real-time temperature prediction in a cold supply chain based on Newton's law of cooling

Konovalenko, Iurii Ludwig, Andre Leopold, Henrik

2021

Many goods, including pharmaceuticals, require close temperature monitoring. This is important not only for complying with regulations but also for guaranteeing safety of use. A particular challenge in controlling a product's temperature arises during transportation. In cold supply chains (SCs), temperature is maintained by refrigerated containers. However, many situations, e.g. cooling system failure, lead to ambient temperature changes, and this needs to be detected as early as possible to prevent product damage. Existing approaches to temperature prediction are confined to long-term forecasts with relatively stable ambient temperatures and/or rely on multiple sensors in the known fixed positions. Since interventions in a SC are required immediately, there is a need for methods that provide real-time predictions regarding regular ambient temperature instability, i.e. when the ambient temperature changes unexpectedly in the short term. We propose a novel method that extends the applicability of Newton's law of cooling (NLC) to changeable ambient temperatures based on a set of temperature stability conditions and a sensor measurement error. In the method, an optimal number of measurements that characterize stable ambient temperatures and improve prediction reliability are selected. We compare the adapted NLC with artificial neural networks and autoregressive moving average models with respect to deviation prediction, prediction error, and execution time. Our evaluation based on real-world data shows that the adapted NLC outperforms existing baseline methods. In contrast to existing solutions, our method does not require any knowledge about the positioning of products within the container, further increasing its practical value.

DECISION SUPPORT SYSTEMS

卷号：141

ISSN：0167-9236

来源机构

Kuhne Logistics University

收录类型

SSCI

发表日期

2021

学科领域

循证管理学

国家

德国

语种

英语

DOI

10.1016/j.dss.2020.113451

其他关键词

NETWORK; TRACEABILITY; MINIMIZATION

EISSN

1873-5797

资助机构

European CommissionEuropean CommissionEuropean Commission Joint Research Centre; German Federal Ministry of Education and ResearchFederal Ministry of Education & Research (BMBF)

资助信息

Andre Ludwig is Associate Professor of Computer Science in Logistics at the Kuhne Logistics University (KLU) since April 2015. Before joining the KLU, he was Assistant Professor of the endowed chair of Logistics Information Systems at the Information Systems Institute of the University of Leipzig since 2012. Between 2008 and 2010 Andre was a senior researcher at SAP Research Australia and a research visitor at Swinburne University of Technology Melbourne. Andre has studied at the University of Leipzig, NTNU Trondheim, Curtin University of Technology Perth, and Swinburne University of Technology Melbourne and received a Doctorate and a Diploma degree in Management Information Systems from the University of Leipzig in 2008 and 2004, respectively. During his academic career Andre Ludwig worked in several research projects such as Adaptive Services Grid, LOGICAL (both funded by the European Commission), InterLogGrid and Logistics Service Bus (both funded by the German Federal Ministry of Education and Research). He currently leads the research initiative Logistics Service Engineering and Management and the Smart Services World research program SURTRADE, both funded as light house projects by the German Federal Ministry of Education and Research. He set up the Logistics Living Lab in 2013, an innovation platform for IT-based logistics in Leipzig and was involved in the development of the Future Logistics Living Lab operated by SAP, Fraunhofer IESE and NICTA in Sydney/Australia. Andr ' e Ludwig's current research topics include complex event processing and real-time monitoring of supply chain and logistics processes, and cross-enterprise application integration in logistics information platforms and engineering and management of adaptive service systems. His research is documented in more than 60 international peer-reviewed journal articles and conference proceedings, i.e. he published in ECIS, BPM, DESRIST, WI, BIS. With regard to impact, according to Google Scholar, Andre's work has been cited 450 times in total and 104 times in the year 2016 alone. His h-index is 11. He is a reviewer of numerous journals such as Electronic Markets, Journal of Systems and Software, Journal of Computer and System Sciences and Journal of Internet Services and Applications.

被引频次（WOS）

被引更新日期

2022-01

关键词

Cold supply chain Temperature prediction Newton's law Artificial neural network ARMA Event data

Real-time temperature prediction in a cold supply chain based on Newton's law of cooling

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