Operate more efficiently in your textile manufacturing set-up

Do you know?

AI-embedded approach to maintaining machinery/equipment can help you:

• Reduce Unplanned Downtime by 50% - 65%.
• Reduce Maintenance Costs by 10-40%
• Extend Equipment Lifespan
• Enhance Safety levels.

What is Predictive Maintenance?

Predictive maintenance is a high-impact area for manufacturers in driving operational efficiencies. It refers to approaches that anticipate equipment failures before they occur, ensuring that the production lines run smoothly with minimal downtime.  It leverages technologies like IoT sensors, analytics, and AI. Many industrial manufacturing giants like GE, Siemens, Ford, Bosch, and Enzo have been using this as part of their maintenance routines for years now.

The adoption of this approach in the textile manufacturing is rapidly increasing particularly in circular knitting, fabric production and even in readymade garment production facilities.  A recent research by Alexandria Engineering Journal establishes that fault lines in knitting machines can be detected and classified with 92% accuracy.

How does AI/ML help here?

1. Monitor Machinery Condition: Install IoT sensors on machinery to monitor equipment operating condition and surrounding environments in near-time. These sensors collect data continuously, which is then analysed to detect patterns that indicate wear and tear or potential failure.
2. Optimise Maintenance Schedules: Instead of relying on scheduled maintenance, predictive maintenance uses data to schedule repairs only when necessary. This approach prevents unnecessary maintenance, which can be costly and time-consuming, while also avoiding unexpected breakdowns.
3. Accessories & Spare Part Availability: By predicting when parts will wear out, you can optimize your inventory levels, ensuring that you have the right parts on hand when needed without overstocking.
4. Energy Efficiency: Monitor the performance of your machinery to ensure it operates at optimal efficiency. Predictive maintenance can identify when a machine starts consuming more energy than usual, allowing for timely interventions that reduce energy costs.
5. Equipment Efficiency: Measure and optimise the operational efficiency of the machines. Optimise machinery usage and production schedules by continuously observing the production output, energy usage, and defect rates.

Some examples

• Ahlstrom-Munksjö, a global leader in fiber-based materials has integrated predictive maintenance in its manufacturing plants. By employing IoT sensors and machine learning algorithms, Ahlstrom-Munksjö has been able to monitor the condition of its textile machinery in real-time, predicting equipment failures before they occur. This has led to a reduction in unplanned downtime and optimized maintenance schedules, allowing the company to maintain high production efficiency and quality standards.
• Arvind Mills uses predictive maintenance to improve energy efficiencies across its power plants.
• Unilever: Unilever implemented predictive maintenance across its global manufacturing plants, resulting in a 50% reduction in maintenance costs and a 20% increase in equipment lifespan. Their use of AI-driven analytics helped predict machine failures weeks before they occurred.