July 2019

| 12 | July 2019 www.smartmachinesandfactories.com | FEATURES – GAMBICA three-part digitalisation series | when sensed, will produce data. In the words of Chris Evans, marketing and operations group manager for Mitsubishi Electric, “if you’ve got a machine, it’s probably already gathering all you need to know”, and plenty more besides. But when considering what, of the infinite amount of available data to collect, the key is that your goal, to make a process cheaper or safer or less wasteful, is at the centre of everything you do. For a (non-industrial) example, the airline Virgin Atlantic reportedly collects about half a terabyte of data on aircraft performance every transatlantic flight. One terabyte for every return trip from London to JFK - I cannot speak for Virgin Atlantic, but I assume those transatlantic terabytes do not include a record of the temperature of the table tray at seat 32A. Though it would be easy to measure, the information would be of little use. Very few manufacturers wishing to implement smart manufacturing in their processes have the luxury of starting from scratch with a brand- new facility and production lines. Most would be dealing with some level of legacy equipment. ‘Legacy’ is a long continuum, some more recent equipment may very well have some level of connectivity but as Andy Graham, Wonderware product manager at Solutions PT points out, even for ‘non-networkable’ kit “you can quickly and easily get more out of a system with low costs sensors”. “Placing a £100 sensor on the side of a £200 pump, might seem like poor value for money” muses Andrew Hodgson, strategic sales lead for Siemens “but when you consider what that pump is pumping – what is the cost of the downtime should that pump fail? The equipment we supply, if goes in an electrical enclosure, you shut the door – it inherently works, and people forget about it.” Mark Butter, Omron UK’s general manager feels that this is “bordering on carelessness, not enough people think about the potential cost of downtime” should the equipment fail. Clearly, it can be simple to collect the data we want, to be able to visualise that data quickly and usefully is another step. Smart Manufacturing is often seen as the convergence of the IT and the industrial world and this goes for greater democratisation of the technologies too. Paul Streatfield feels that people increasingly “expect ease of use like the apps on their phones”. And thinks that being able to set up an IIOT gateway, collect sensor information and do something useful with it, without the need for a control engineer or network architecture, would smash a major barrier in uptake of smart technologies, particularly for SMEs. Given the increasing concerns about the lack of skills in the automation sector, a problem that is only expected to worsen, ease of use appears to be an important consideration. And then there is the matter of where the data goes. Smart manufacturing, this IT-OT convergence, has seen players who are not traditionally from the manufacturing sphere get involved. The big sell on is on big data and cloud computing. The abundance of cloud storage on our personal devices has normalised the idea of cloud computing in our mind too. Alternatively, ‘edge’ computing has been offered up as a safer, cheaper and quicker option. In truth the two are not mutually exclusive. Chris Evans explains “[the edge layer is] what was called MES [Manufacturing Execution System] where your SCADA would sit, now re-christened edge but now there is more technology there”. B&R’s managing director Alan Conn adds: “For us it can be 3 things, a bus coupler, a PLC and it can be an industrial PC depending on what you want to collect and how you want to collect and aggregate that data, but they all fall within an edge layer. You’re going to do a lot of the analytics there, then decide what you push up to the cloud.” For operations that require powerful processing, involve large data set or remote control and access, you might consider cloud computing. However, if you want a rapid sample rate or real-time analytics, have cyber