The age-old saying “you don’t know what you’ve got until it’s gone” can resonate in many ways. It can make you stop and appreciate what you do have, and it can also make you take greater care. It’s a concept that’s well reflected in the journey of digitalising factories to make them “smart”.
There are two stand-out problems which can derive from not paying close enough attention to the equipment you already have. Firstly, there’s spending large amounts of capital on new equipment which may incur higher running costs and in turn affect the long-term budget. Secondly, there’s a misunderstanding of what is required to update existing equipment, which demonstrates a lack of knowledge and may result in a lack of confidence in existing kit.
To avoid these issues, there are two methods many manufacturers are turning to: assessing what already exists on site, and training and upskilling staff in the latest technologies.
Most equipment maintenance is either reactive (only taking place when a machine breaks), or preventive (performed at regular intervals to help avoid breakage). Both practices can prove time-consuming, costly and inefficient. But using digitalised predictive maintenance on existing legacy equipment, which combines the use of sensors and AI machine learning to detect when a machine could break, represents a far more streamlined and cost-effective way of doing things.
Predictive maintenance enables companies to foresee when equipment will need upkeep, which in many cases will consist of a simple retrofit of sensors and connected devices, connecting them to a cloud platform from which they can then be monitored remotely. This could result in huge savings in both the short and long term. Certain sensors can connect a smart device to any machine, which can open the doors of maintenance to both consumers and manufacturers. On an age where remote working capability is essential, this is more important than ever.
This is merely one example of where improvements in legacy equipment can result in improved efficiency and productivity. The idea of making a factory smart isn’t just about installing new kit, it’s also about making the most out of what you already have.
There are other ways in which existing equipment can be upgraded or enhanced to maximise its capabilities. Having a digitalisation expert or catapult examine your processes is one way to get this ball rolling.
Alternatively, improving your existing workforce’s knowledge of connected technologies can enable them to reassess your processes internally and see where upgrades can be made and managed. Having in-house staff with the most up-to-date skills and information is by far the most underestimated investment when it comes to digitalisation.
Connected and digital technology has been around for years and is here to stay. There should no longer be discussions of implementing “new” technology as most of them are already of age. Rather, the adoption and use of what is already out there, and making it work in harmony with what you already have is the key to a successful smart factory. The only restrictions are in the mind.
Some of the largest barriers to technology adoption in manufacturing are a reluctance to make large capital investments into the technology, or to embrace it in the first place. But if close attention is paid to the initial steps of upgrading existing equipment and upskilling staff into IOT experts, then other areas of improved efficiency through digitalisation can be naturally identified. Alongside identifying what the business problem is initially, the realisation that becoming a smart factory doesn’t necessarily mean large investments becomes apparent, and with a higher level of knowledge and skill in the areas of IOT behind your workforce, the will to adopt intensifies
Get in touch with your local digitalisation expert at www.gambica.org.uk to find out more about how you can be involved in helping to guide best practice within our industry, or if you are an expert yourself and want to discuss your views and experiences.
by Nikesh Mistry, Sector Head – Industrial Automation, Gambica