An Approach to Optimize Future Inbound Logistics Processes Using Machine Learning Algorithms

Albadrani, Abdullah; Zohdy, Mohamed; Olawoyin, Richard

doi:10.1109/eit48999.2020.9208238

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…(2016) utilised ML to predict future scenarios for future inbound-logistics processes, as a means of boosting the information available to logistics planners. In another, Albadrani et al . (2020) conducted a detailed comparison of machine-learning algorithms that they felt had the potential to enhance inbound-logistics planning.…”

Section: Literature Reviewmentioning

confidence: 96%

“…In one of the rare examples, Knoll et al (2016) utilised ML to predict future scenarios for future inbound-logistics processes, as a means of boosting the information available to logistics planners. In another, Albadrani et al (2020) conducted a detailed comparison of machine-learning algorithms that they felt had the potential to enhance inbound-logistics planning. Evangeliou et al's (2021) AI inbound-logistics framework for the automotive-materials sector was intended to redress the problems of erroneous decisions and labour-intensiveness.…”

Section: Inbound Logisticsmentioning

confidence: 99%

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Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics

Wu,

Tai

2024

JEIM

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PurposeIn the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.Design/methodology/approachFor diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.FindingsTesting of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.Research limitations/implicationsViewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.Practical implicationsAs well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.Originality/valueThis paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

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Section: Literature Reviewmentioning

confidence: 96%

Section: Inbound Logisticsmentioning

confidence: 99%