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.

Decision Trees for Supervised Multi-criteria Inventory Classification / Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Balugani, Elia; Rimini, Bianca. - In: PROCEDIA MANUFACTURING. - ISSN 2351-9789. - 11:(2017), pp. 1871-1881. ((Intervento presentato al convegno 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) tenutosi a Modena (Italy) nel 27-30 June 2017 [10.1016/j.promfg.2017.07.326].

Decision Trees for Supervised Multi-criteria Inventory Classification

Lolli Francesco
;
Ishizaka Alessio;Gamberini Rita;Balugani Elia;Rimini Bianca
2017

Abstract

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.
27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM)
Modena (Italy)
27-30 June 2017
11
1871
1881
Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Balugani, Elia; Rimini, Bianca
Decision Trees for Supervised Multi-criteria Inventory Classification / Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Balugani, Elia; Rimini, Bianca. - In: PROCEDIA MANUFACTURING. - ISSN 2351-9789. - 11:(2017), pp. 1871-1881. ((Intervento presentato al convegno 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) tenutosi a Modena (Italy) nel 27-30 June 2017 [10.1016/j.promfg.2017.07.326].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1152792
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