Pennine Weavers is a specialist commission weaving plant with a long pedigree of producing the world’s finest cloth.
Originally established as a Commission Weaver by its current Chairman, John Hodges in 1969, the weavers operates on an impressive scale, boasting the ability to produce lengths of fabric up to 3000m. However, impressive quantity has been combined with even more impressive quality – while its home town of Keighley was once home to over 56 textile mills, Pennine Weavers has been able to succeed in an increasingly competitive market by fostering a reputation for high-end products and continuous innovation.
Crucially, this commitment to excellence has allowed Pennine Weavers to operate within a framework that accounts for a continuous streamlining of manufacturing processes. Working alongside Future Fashion Factory on an AI Autoplan Project, Pennine Weavers have further entrenched their position at the forefront of technological development.
Collaborating with Future Fashion Factory, Professor Stephen Westland, and their software partner, Juno Software, Pennine Weavers unpicked the challenges behind the implementation of Artificial Intelligence as part of a weavers’ operations and processes.
“Pennine Weavers has always prided itself at being at the forefront of systems development and implementation in the textile industry and we believe this project will not only have benefits for Pennine Weavers but potentially the whole industry. Working alongside the University of Leeds, we believe we can develop an AI-based planning system which will maximise effectiveness and efficiency of the resources employed internally, and deliver considerable benefits to both our customers and suppliers.”Gary Eastwood, Managing Director of Pennine Weavers
Production planning for bespoke luxury fabric has typically been a laborious and time-consuming task, requiring both in-depth technical expertise and precision. However, by using machine learning and AI techniques to implement an automated workload scheduler the project was able to develop an intelligent digital system to maximise the efficacy and efficiency of premium fabric manufacturing.
Challenges around the ways to streamline production processes have been critical to the industry, as issues caused by human error – even from experienced staff – can have a devastating impact. As such, Professor Stephen Westland stated, “it was really rewarding to work on this topic with an industrial member of the FFF network, developing an automated workload scheduler will have a critically positive impact on the company”.
Through the AI Autoplan project, Pennine Weavers have been able to explore options that utilise machine learning and artificial learning within the production environment, an application with huge potential to maximise effectiveness both internally and to suppliers and consumers alike.
The outcomes of the project could have a significant effect on the efficiency and cost effectiveness of the business, reducing lead times, costs, risk and therefore ensuring that they stay at the forefront of weaving both nationally and globally. However, utilising machine learning in this way could improve planning across industry, driving growth and reducing waste on a profound scale.
The project only scratches the surface when it comes to application viable AI applications across the textile industry – no doubt the technology behind the automatic workload scheduler can be used to facilitate a host of other automated processes. As such, Professor Stephen Westland stated, “UoL researchers were very pleased to be able to contribute to identification of suitable AI technologies; it was a pleasure to be able to work alongside technology partner Juno in this project.”