Digital Transformation in the Textile Cutting Sector: An Industry 4.0 Approach to Operational Excellence

Márcia Barroso; Sónia Longras; Vânia Dias; Miguel Ângelo Pereira; António Rocha

doi:10.14513/tge-jres.00625

Authors

Márcia Barroso School of Technology, Polytechnic University of Cávado and Ave, 4750-810 Barcelos, Portugal Author
Sónia Longras 2Ai – Applied Artificial Intelligence Laboratory, School of Technology, Polytechnic University of Cávado and Ave, 4750-810 Barcelos, Portugal Author https://orcid.org/0009-0006-6235-5845
Vânia Dias 2Ai – Applied Artificial Intelligence Laboratory, School of Technology, Polytechnic University of Cávado and Ave, 4750-810 Barcelos, Portugal Author https://orcid.org/0009-0000-5503-9824
Miguel Ângelo Pereira School of Technology, Polytechnic University of Cávado and Ave – IPCA Author
António Rocha LASI – Associate Laboratory of Intelligent Systems, 4800-058 Guimarães, Portugal Author https://orcid.org/0000-0003-0863-6567

DOI:

https://doi.org/10.14513/tge-jres.00625

Keywords:

Digital Transformation, Industry 4.0, Textile Industry, Lean Manufacturing, System Integration, Technology Acceptance, Change Management

Abstract

Purpose – This study investigates how the integration of Industry 4.0 monitoring technologies with structured change management and employee involvement can improve FCI system adoption, operational data quality, and warehouse efficiency in the cutting department of a Portuguese textile SME.

Design/methodology/approach – Action research methodology was employed across four iterative phases – problem identification, action planning, implementation, and reflection – at ATêxteis S.A., a knitwear manufacturer. Data collection triangulated direct observation, FCI/ERP system logs, anonymous employee questionnaires, and supervisor interviews. Theoretical grounding draws on the Technology Acceptance Model (TAM) and Kotter’s eight-step change model.

Findings – FCI system registrations increased nearly three-fold (300 to 1,000 per month) between November 2022 and July 2023, with registrations per 1,000 pieces cut rising from 3.5 to 12.2. Remnant warehouse occupancy decreased from 28.35% to approximately 15% of total capacity. ERP integration enabled automatic detection of material consumption discrepancies, and quality non-conformance detection increased from an estimated 5–8 to 23 cases per period. Results confirm that change management and participatory training are the proximate drivers of digital tool adoption, even when system use is formally mandated.

Originality – The study provides rare empirical evidence on Industry 4.0 implementation in textile SME cutting operations, extending TAM and Kotter’s model to a manufacturing digitalization context. It introduces a production-normalized utilization metric (registrations per 1,000 pieces) and demonstrates the complementarity of Lean principles with digital monitoring for operational excellence in traditional manufacturing.

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