Machine Learning to Optimise Supply Chain Performance in Coupled Manufacturing Systems: Case Study of Building Industry

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Authors

Publication Details

Journal
International Journal of Productivity and Quality Management
Published
2025-01-01
DOI
https://doi.org/10.1504/ijpqm.2025.10071236
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning to Optimise Supply Chain Performance in Coupled Manufacturing Systems: Case Study of Building Industry

Fatine Elharouni, Rachid Benmoussa, Zineb Nafi
International Journal of Productivity and Quality Management
Digital Transformation in Industry
article

Machine Learning to Optimise Supply Chain Performance in Coupled Manufacturing Systems: Case Study of Building Industry

Fatine Elharouni, Rachid Benmoussa, Zineb Nafi
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Productivity and Quality ManagementVol. 1(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 86%
Digital Transformation in Industry
DOISourceOpenAlex
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Machine Learning to Optimise Supply Chain Performance in Coupled Manufacturing Systems: Case Study of Building Industry — Fatine Elharouni, Rachid Benmoussa, et al. · International Journal of Productivity and Quality Management (2025) | TGRS Research Map | TGRS