Cloud is seen as the fundamental first step for any scalable digitalisation strategy. In reality, a successful digital journey starts with a strong on-premise foundation.
Speak to any manufacturing manager and they’ll tell you that their organisation diligently collects a huge volume of data from a growing list of systems, devices and equipment.
Today, however, manufacturers are losing the value of 70% of collected manufacturing data¸ according to Gartner.
Why? For the simple reason that many businesses collect and store data without actually analysing it and using it to drive continuous improvements.
Take the world of pharmaceuticals. Manufacturers operating in the sector are required to record, collect and store a variety of different data points for regulatory compliance. The result is a veritable treasure trove of information detailing the history of their production processes. And yet, for many organisations this is seen as nothing more than a box-ticking exercise.
For Dr Bernard Cubizolles, Senior Global Marketing Manager at GE Digital, this represents an enormous missed opportunity.
“Management teams seeking to capture the true value of their data can become fixated on cloud and migrating all their systems and workflows and processes. In my conversations with them, I prefer to talk about what they want to do in the cloud, rather than simply why they should be there.
“A successful digital transformation starts on-premise and by laying a strong data foundation, so a business can drive a variety of benefits prior to migrating.”
I sat down with Bernard to discuss how manufacturing businesses can lay such foundations and unlock greater value from their production and operational data.
A golden opportunity
Bernard comes from a process manufacturing background and believes the sector’s concept of a ‘golden batch’ can be applied across almost all industries.
“Achieving a golden batch means looking across your production output to identify the batch or sequence with the highest quality and strongest cost-to-revenue ratio,” says Bernard.
“Analysing your production data should reveal the factors that created such a batch; like material mix, temperature, machine speed, operator shift, location and so forth.
These insights are then used to optimise your production, which will help improve efficiency, reduce costs, raise quality and support your continuous improvement activities.”
Accelerating digital transformation by up to 50% …
What Bernard and his team recommend all manufacturers do, regardless of sector or product portfolio, is look at your data, make sure you have a complete end-to-end view of what’s happening, and use it to optimise your activities.
“Analyse what happened, learn from what you’ve done and make sure that you’re fully leveraging all this insight-rich information that is so often collected, but rarely fully utilised,” he adds.
Crossing the streams
Achieving a truly holistic view of production involves processing a variety of separate streams of information such as manufacturing data, cost management data, human capital management data and demand planning data.
In the past, mixing these streams successfully was time-consuming, costly and challenging. But that’s no longer the case thanks in large part to two recent developments: the cost of sensors, and the cost of data analytics – both of which have fallen dramatically.
Every new product, device, machine or system is full to the brim with sensors, but more importantly, assets or equipment that have been your factory workhorses for decades can now be kitted out and connected to provide valuable, previously hidden, information.
“Connecting everything, new and old alike, is vital,” says Bernard. “The challenge with this industrial Internet of Things is filling in all the data gaps to create a complete view of what’s happening. You can’t run worthwhile analytics if only half your process is connected.”
Operating a fully connected enterprise, however, creates another challenge – keeping up with the sheer amount of data being generated.
“This is where a piece of Historian software and the cloud really play a key role as they can digest growing volumes of information; something that simply wasn’t possible even a few years ago because the infrastructure wasn’t available, and the cost was too high,” Bernard notes.
“Today, data from both automated systems and manually-operated processes can be quickly, efficiently and securely collected and distributed, enabling fast retrieval and powerful process performance analysis,” he continues.
Looking to the future
Most manufacturers exploring plant and equipment connectivity and data analytics have the goal of moving from condition-based maintenance practices to predictive maintenance strategies – i.e. limiting costly emergency repairs and downtime by detecting problems early.
The reason why is easy to understand. For any industrial business that’s managing complex equipment and processes every day, maintenance is crucial for not only running the operation efficiently and safely, but also for maximising the bottom-line.
“Predictive maintenance is one of the first capabilities we discuss with our customers,” says Bernard. “Over-maintaining equipment wastes valuable time, money and resource; and a piece of under-maintained equipment poses a safety risk. This balance between risk, cost and availability is what makes predictive maintenance so attractive.”
“The added advantage of conducting condition-based monitoring is that the analytics and insights help not only to optimise your assets in terms of maintenance, but also their efficiency and productivity,” Bernard concludes.
GE Digital’s customers are turning manufacturing data into value, learn how Procter & Gamble did it.
*All images courtesy of Depositphotos