Cold chain break detection and analysis: Can machine learning help?

Loisel, Julie; Duret, Steven; Cornuéjols, Antoine; Cagnon, Dominique; Tardet, Margot; Derens-Bertheau, Evelyne; Laguerre, Onrawee

doi:10.1016/j.tifs.2021.03.052

Cited by 52 publications

(31 citation statements)

References 65 publications

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“…There is also potential in combining or even replacing the physicsdriven models in this study with data-driven models. For instance, we could use sensor data along the supply chain of fresh produce to forecast the effects of the microclimate on the fruit quality evolution using artificial neural networks (Badia-Melis et al, 2016;Liu et al, 2019;Loisel et al, 2021). Data-driven models can accommodate multiple factors such as preharvest conditions, packaging materials, and real-time hygrothermal conditions, into a multi-modal empirical forecasting network that provides more accurate predictions.…”

Section: Discussionmentioning

confidence: 99%

Digital twins for selecting the optimal ventilated strawberry packaging based on the unique hygrothermal conditions of a shipment from farm to retailer

Shrivastava

Schudel

Shoji

et al. 2023

Postharvest Biology and Technology

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Section: Discussionmentioning

confidence: 99%

Digital twins for selecting the optimal ventilated strawberry packaging based on the unique hygrothermal conditions of a shipment from farm to retailer

Shrivastava

Schudel

Shoji

et al. 2023

Postharvest Biology and Technology

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“…In combination with intelligent decision support, the balance between system performance and EC can be optimized. With big data technology, not only can user participation in FCCL be improved through data centralization and exchange, but cold chain break problems can also be compensated for through information mining (Loisel et al., 2021; Reguera et al., 2019), with data support being provided to establish a digital twin system of FCCL. Blockchain can be integrated into existing cold chain infrastructure to promote more secure, more transparent information transfer that is both responsive and cost‐effective.…”

Section: Future Trends For Sustainable Fcclmentioning

confidence: 99%

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

Chen

Qian

Yang

et al. 2022

Comp Rev Food Sci Food Safe

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Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon.Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.

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“…Tese technologies include contactless smart packaging [24], smart label and ledger using RFID, IoT, and BC [25], and fnally automated warehouse logistics that require the integration of RFID, big data, and cyber-physical system [26,27]. Finally, in the food transportation and logistics stage, cold chain optimization and on-time delivery are very important issues that need to be addressed efciently and efectively [28,29]. Tus, the application of reinforced learning and prescriptive intelligence in route optimization is required using AI and ML [30].…”

Section: Related Workmentioning

confidence: 99%

Modelling and Forecasting Fresh Agro-Food Commodity Consumption Per Capita in Malaysia Using Machine Learning

Alfred

Leikson

Boniface

et al. 2022

Mobile Information Systems

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This study focuses on identifying and analyzing spending trend profiles and developing the per capita consumption models to forecast the fresh agro-food per capita consumption in Malaysia. Previous published works have looked at statistical and machine learning methods to forecast the demand of agro-food such as ARIMA and SVM methods. However, ordinary least squares (OLS) and neural network (NN) methods have shown better results in modelling time series data. For that reason, the primary objective of this study is to model and forecast the consumption per capita (PCC) of several selected fresh agro-food commodities in Malaysia using the OLS and NN methods. The secondary objectives of the paper include investigating the performance of OLS against NNs with three different topologies, discussing the correlation between Malaysia GDP per capita and the agro-food commodity PCC, and finally assessing whether the PCC data are increasing over time or decreasing over time and whether the trend in either direction is statistically significant by using the Mann–Kendall statistical test. Based on the results of the agro-food consumption per capita (PCC) forecasting, several critical agro-food commodities are also identified in this work. The material of the study consists of the per capita consumption of thirty-three (33) agro-food items that can be categorized into rice, livestock, vegetables, fisheries, and fruits, total gross domestic product (GDP) per capita, and the total population of Malaysia between 2010 and 2017. Based on the results obtained, the neural network ( N N T 1 ) model was found to produce the lowest total MSE of 17.95, for all 33 fresh agro-food investigated in this study. Several agro-food commodities have been identified as having significant positive (e.g., rice, spinach, cabbage, celery cabbage, eggplant, cucumber, poultry, lamb, squid, tuna, star fruit, jackfruit, durian, sweet corn, and coconut) or negative (e.g., pork, mackerel, papaya, guava, mangosteen, pineapple, banana, rambutan, and watermelon) trends using the Mann–Kendall trend test. This study also demonstrated that the production of critical agro-food commodities (e.g., rice, chili, cabbage, celery cabbage, poultry (chicken/duck), beef, lamb, crab, mango, and coconut) should be improved to ensure self-sufficiency ratios (SSRs) of more than 100% to accommodate the increased projected consumption in Malaysia by the year 2025. This paper concludes that neural network methods produce better prediction, and future works include forecasting agro-food demand based on other independent variables such as weather conditions, disease outbreak, and stock market trends. There is a need to explore further the capability of ensemble models or hybrid models based on deep learning methods using multi-source data, as these have been shown to improve the performance of the base model. With these ensemble models combined with multi-source data, a more comprehensive analysis of the PCC can be obtained.

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Cold chain break detection and analysis: Can machine learning help?

Cited by 52 publications

References 65 publications

Digital twins for selecting the optimal ventilated strawberry packaging based on the unique hygrothermal conditions of a shipment from farm to retailer

Digital twins for selecting the optimal ventilated strawberry packaging based on the unique hygrothermal conditions of a shipment from farm to retailer

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

Modelling and Forecasting Fresh Agro-Food Commodity Consumption Per Capita in Malaysia Using Machine Learning

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