Decision trees for supervised multi-criteria inventory classification

Francesco Lolli, Alessio Ishizaka, Rita Gamberini, Elia Balugani, Bianca Rimini

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A multi-criteria inventory classification (MCIC) approach based on supervised classifiers (i.e. decision trees and random forests) is proposed, whose training is performed on a sample of items that has been previously classified by exhaustively simulating a predefined inventory control system. The goal is to classify automatically the whole set of items, in line with the fourth industrial revolution challenges of increased integration of ICT into production management. A case study referring to intermittent demand patterns has been used for validating our proposal, and a comparison with a recent unsupervised MCIC approach has shown promising results.
Original languageEnglish
Pages (from-to)1871-1881
Number of pages11
JournalProcedia Manufacturing
Publication statusPublished - 18 Sept 2017
Event27th International Conference on Flexible Automation and Intelligent Manufacturing - University of Modena and Reggio Emilia, Modena, Italy
Duration: 27 Jun 201730 Jun 2017


  • multi-criteria inventory classification
  • decision trees
  • machine learning
  • inventory control
  • intermittent demand


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