Abstract
Despite decades of technological progress in textile weaving, many manufacturing plants in developing countries still experience frequent machine stoppages, equipment failures, and excessive energy consumption. In Peru, these challenges are intensified by aging machinery and limited adoption of digital technologies. This study proposes a practical hybrid approach that integrates Lean Manufacturing principles - specifically Jidoka and Total Productive Maintenance (TPM) - with accessible Industry 4.0 tools. The approach combines IoT sensors for realtime loom monitoring, basic machine learning algorithms for predictive maintenance, and smart meters to manage electrical energy consumption. The proposal was implemented in a Peruvian textile plant operating 48 looms over a three-month period. The results show a 25% increase in machine availability, a 20% reduction in maintenance costs, a 15% decrease in energy consumption, and an 18% reduction in defects, leading to an overall improvement of 22 percentage points in Overall Equipment Effectiveness (OEE). These findings demonstrate that the integration of Lean methods with cost-effective digital technologies provides a feasible and scalable pathway for small and medium-sized manufacturing companies to improve operational efficiency and move toward more sustainable production systems.
| Original language | English |
|---|---|
| Pages (from-to) | 304-308 |
| Number of pages | 5 |
| Journal | Proceedings of the International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT |
| Issue number | 2026 |
| DOIs | |
| State | Published - 2026 |
| Event | 17th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2026 - Cape Town, South Africa Duration: 13 May 2026 → 15 May 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Digitalization
- Industry 4.0
- Lean Manufacturing
- Predictive Maintenance
- Textile Manufacturing
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