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Get a grip on machinery data to drive down shopfloor costs

Tanya Weaver

With increased digitalisation, large volumes of machinery data inundate companies every day (Credit: Shutterstock)
With increased digitalisation, large volumes of machinery data inundate companies every day (Credit: Shutterstock)

Within factories, long gone are the days of manually recording information about machinery. With increased digitalisation, large volumes of data from these assets now inundate a business every day.

“Machinery is generating terabytes of data on a daily basis. When data volumes become so big, they can’t be stored on a laptop or flash drive. Often, huge distributed systems are used to collect and process that data,” said Parvez Alam Kazi, head of product at technology company Smartia.

But it’s not the amount of this so-called ‘big data’ that’s important, it’s what is being done with it that is. Through the use of analytics, companies can now gain insights from this data and it’s into this market that Smartia launched two years ago. The vision, according to Kazi, was to develop scaleable artificial intelligence (AI) solutions to help companies within the manufacturing and engineering sectors to transform big data collected from their production equipment, machines and sensors into actionable insights that could lead to production efficiencies and cost savings. 

The result is MAIO, an industrial intelligence platform, which collects industrial data from various assets and then, through artificial intelligence and machine learning, users can analyse this data and use it to build smart applications. For instance, applications for the remote monitoring of a process or an asset on the factory floor. 

Predicting maintenance needs

“Of course, there is also predictive maintenance, which is a buzzword in the industry, but with AI it becomes possible to predict when maintenance of machinery needs to be carried out. For example, the application built through MAIO will tell users that there is something wrong with the machine and it might fail in a month’s time. So it’s forecasting what might go wrong and then they have enough time to plan and schedule maintenance,” explained Kazi. 

“We have also worked with big companies like Rolls-Royce, for example, who we helped solve an inventory problem at one of their facilities where they have large volumes of inventory. Through the use of AI we helped them reduce that inventory by almost £1m.” 

But MAIO is not only aimed at manufacturing companies the size of Rolls-Royce. From the start, Smartia was very keen to develop a scaleable and tailored solution to enable small firms to adopt it without specific skills or a big budget.

“With MAIO, companies can start small with just one particular area of their factory or facility, and can have up to five users and one terabyte of storage. Once the value has been seen, they can connect their entire factory and then scale to multiple factories. 

“So it’s a very simple product not only in terms of technology but also in terms of the business model that makes it adaptable and very easy to implement in any facility. Of course there are complexities, but we’ve made it as plug and play as possible,” said Kazi. 

Smartia also recently collaborated with technology company Lenovo. By combining its MAIO software platform with Lenovo’s hardware technology, it can provide a complete, ready-to-use AI solution making it even easier for users to get onboard.


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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers. 

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