Martyn Williams, managing director of industrial automation software expert Copa-Data UK, discusses how machine builders can use predictive analytics to minimise the maintenance and downtime costs of their products
The cost of production downtime varies significantly from one industry sector to another, but without a doubt, when it occurs, downtime is a troublesome and expensive inconvenience for all manufacturers.
More often than not, halts in production could be avoided, so imagine just how much manufacturers could save if machine data was available to anticipate breakdowns.
The good news for industry is that the rise of the Internet of Things (IoT) is allowing machine builders to design and manufacture intelligent machines with predictive analytics capabilities.
Preparing for the smart era
Common causes of production stoppages on the factory floor include ageing equipment, human error or incorrect machine usage. To minimise downtime caused by unplanned maintenance, manufacturers have always sought to predict issues with preventative maintenance initiatives.
The advent of the Industrial Internet of Things (IIoT) enables companies to look for ways to exploit increasingly available production data and change the way they operate.
Spearheaded by internet-enabled technology, the manufacturing sector is bearing witness to the next industrial revolution. Connected machinery is causing a shift in the way the industry operates, making production lines more efficient, agile and more self-sufficient.
To pave the way for the smart factory, machine builders need to equip their solution with the right tools for data collection, analytics and connectivity.
To simplify this transition, machine builders can future-proof their products to leverage the growing network of smart devices in industrial facilities and the increasing amount of data from the factory floor.
Using data to deliver
Smart data from IoT-enabled equipment can be employed to forecast the degradation of industrial machinery. Predictive analytics enable trend analysis, reviewing the operational data of equipment to uncover if and when a machine is likely to break down.
In addition, pattern recognition can decode the relations between certain processes and product failures, enabling fast identification of the cause of equipment breakdown – priceless insight that industrial machine builders can offer their customers.
To collect, archive and analyse complex industrial machinery data, machine builders need HMI/SCADA software capable of high performance. When combined with a cloud computing platform capable of storing big data, such as Microsoft Azure, good HMI/SCADA software provides clear data visualisation for operations, supervisors and managers – giving plant managers and engineers the peace of mind that everything is running smoothly.
In fact, predictive analysis for industrial machinery could constitute an entirely new revenue stream for forward-thinking, entrepreneurial machine builders.
Using predictive analytics, machine builders can provide an entirely new service in the form of an ongoing, predictive maintenance plan supported and updated by the industrial machinery itself. Derived from sensor data and predictive analytics, preventative maintenance plans offer manufacturers a sure fire way to avoid unexpected production downtime.
For manufacturers, taking advantage of this technology does not necessarily require a complete overhaul of the factory’s machinery. To avoid this costly expenditure, manufacturers could retrofit their current machinery and install independent, IIoT enabled SCADA software, like zenon, on top.
This enables manufacturers to feel the benefits of predictive analytics, without a complete system refit. What’s more, independent SCADA software will enable their existing equipment to communicate with newly installed machines, regardless of the make or model.
Providing new opportunities
Machine builders could also take predictive analytics one step further. Industrial connectivity and remote access enables machine builders to explore machine-as-a-service (MaaS) business models. Whether it’s for quality improvement, sales forecasts or preventative maintenance, predictive analytics gives machine builders an edge over their competitors and the opportunity to create entirely new revenue streams.
Similar to software-as-a-service (SaaS), product-as-a-service (PaaS) or platform-as-a-service (PlaaS), the concept of MaaS refers to physical products, accompanying services and monitoring software.
Instead of a one-time-transaction, the customer subscribes to the product and pays a recurring fee for maintenance or additional services. Machine builders could easily adopt the model and employ predictive analytics for the preventative maintenance of their machines.
Due to the competitive nature of manufacturing, machine builders have to consistently innovate, improve and above all, future-proof their products to stay on top. Reducing downtime remains a core priority for all industry sectors, so the opportunity to anticipate and avoid it is priceless.
Sharing production data with those who know, use and maintain a machine can create opportunities for manufacturers, as well as machine builders, identifying problems, reducing downtime an ultimately, preparing industry for the next revolution.