Last summer we launched our 5% Challenge in which we aim to donate 5% of our profits to charitable causes, challenging others to do the same. One organisation we support is 1% for the Planet and one of our chosen charities within this is the Bedfordshire Great Ouse Valley Environmental Trust (BedsGOVET). Our support is

OEE (Overall Equipment Effectiveness) is really the pinnacle when it comes to measuring manufacturing efficiency. Productivity remains an important metric, but in the pursuit of excellence, OEE considers more factors. So how do they differ? Productivity is a key term in the workplace and can be considered as a measure of the output of individuals

Machine Learning (ML) for packaging line maintenance is beginning to be widely adopted. This is impacting techniques such as predictive maintenance, preventative maintenance and condition monitoring. The advancements as a result can lead to increased efficiency, reduced downtime and a significant saving costs. Each technique is looked at individually below to see how Machine Learning

Machine Learning (ML) is beginning to be widely used in the manufacturing industry. It is helping to automate tasks, gain insights from the vast amounts of date produced and aid data-driven decisions. But what is it, what types are there and what can it actually do? Machine Learning is a branch of AI which focuses

Background Schneider Electric recently sent a reminder about the “End of PacDrive M Service and Repair Period” which is effective from 31st December 2024. PacDrive M products were sold until December 2018 and some spare parts remained available until last year. However, Schneider Electric no longer have any PacDrive M products available and repairs are

As we approach Quarter 4, many companies are busy planning and budgeting for the New Year. It is also often the time manufacturers announce product life cycles ends as well as issuing end-of-line updates. As an example, Schneider Electric recently reminded users the “End of Service and Repair Period” for PacDrive M is December 31st

In the fast-paced world of manufacturing, data is the backbone of innovation and efficiency. As the industry continues to embrace digital transformation, data-driven insights will become ever more essential for optimising production, improving product quality and maintaining competitive advantage. However, a significant amount of data generated by modern manufacturing systems remains untapped. This hidden resource, known

Time and budget constraints abound everywhere and manufacturing is no exception. Companies are also under more pressure to seek investments which yield maximum impact. Remote diagnostics and support can help improve response time in the event of breakdowns, getting production lines up and running again faster and reducing downtime. So how does it work, where

We’ve all seen the rise in AI Copilots and LLMs (Large Language Models) from ChatGPT and similar to Microsoft’s CoPilot.  Many of us will be using them if not daily, then probably pretty regularly. So what are they and where do they potentially fit in a manufacturing environment? An AI Copilot is a conversational interface

We’re aware of the cost implications of unplanned downtime. These are reported to be £5K per hour with companies experiencing up to 20 hours of unplanned downtime each week. Even though Planned Maintenance is the most important maintenance strategy being followed by companies, on average they are still potentially losing almost a full day’s production