A Machine Learning and Multi-Criteria Decision-Making Approach to Cycle Counting

<i>Background:</i> Inventory record inaccuracy (IRI) causes discrepancies between physical and digital inventories, leading to production delays and customer dissatisfaction. Cycle counting, in this context, is a common corrective action. Pareto-based ABC analysis is widely used to decide which item...

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Auteurs principaux:	Laura Vaccari, Elia Balugani, Francesco Lolli, Rita Gamberini
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2025-12-01
Collection:	Logistics
Sujets:	multi-criteria inventory classification (MCIC) cycle counting multi-criteria decision-making (MCDM) inventory control machine learning (ML) inventory inaccuracy
Accès en ligne:	https://www.mdpi.com/2305-6290/10/1/10
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