Digital Analytics and Big Data in Weaving Optimisation
topic
Digital analytics applications in weaving use machine learning models trained on large datasets of loom sensor data, process parameters, and quality outcomes to identify the parameter combinations associated with the best OEE and quality performance, predict quality outcomes from current process parameters before defects occur, and generate optimised parameter recommendations for new fabric specifications from the patterns learned from historical production data across large loom fleets.
Role
Extends the analytical capability of weaving process optimisation beyond what human analysis of traditional data volumes can achieve by identifying complex multi-variable patterns in large datasets that reveal the interactions between process parameters and outcomes that simpler analytical approaches miss, with digital analytics being the emerging approach to process optimisation that will increasingly complement and eventually replace the parameter optimisation by individual expert experience that currently governs weaving process management in most operations.
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