OEMs who build machines for industrial end-users are finding it harder to remain competitive. Do they keep with a ‘smaller, faster and cheaper’ model, or step back to consider how the industry is changing from stand-alone to a connected model? According to Schneider Electric, whatever they decide, several factors are challenging OEM competitiveness.
Firstly, the time for delivering new machinery has emerged as a big marketplace differentiator. Many traditional engineering design tools have reached the limit of their efficiency. This makes it harder to shorten the time it takes to build, deliver and install new machines. This is one reason Schneider Electric is supporting the adoption of IEC 61499.
Furthermore, these problems are also compounded by the available talent pool. Many experienced machine builders are retiring, and there is a risk that their knowledge and skills will go with them. The recruitment of talented young workers is increasing as many of the incoming workforces seek opportunities where new digitised technologies are already in place.
A further issue is one of machine productivity. Traditional technologies are placing a constraint on any further productivity improvements. Attempting to increase current machine productivity requires high investment for minimal return.
Machine downtime is both costly and time consuming for end-users and OEMs. End-users view the availability and reliability of the machine as a high priority and a key value driver. The ability to address the reliability of machines more proactively is now emerging as a competitive advantage.
Most OEMs have succeeded in optimising their mechanical designs through more powerful PLCs and drives. Yet, much more is possible by streamlining design, build, and support processes. New enablers like EcoStruxure Machine Expert now make OEM digitisation both practical and affordable.
For OEMs seeking new paths to build a competitive advantage, it is a good time to leverage the benefits of digitisation technologies. Several new trends are developing based on the use of artificial intelligence (AI).
AI path to competitive advantage
AI covers a range of sciences from advanced analytics and machine learning to natural language processing and neural networks. It is the programming that enables machines to emulate human behaviour, such as verbal instructions. It is already in use in everyday life for fraud prevention, evaluating credit decisions and healthcare screening. AI also identifies over 90 per cent of the estimated 160 billion spam emails sent globally every day. One subset of AI most often applied in industrial automation is machine learning (ML).
ML uses analytics to allow machines to learn by leveraging data to identify improvements, problems and to increase process efficiency. Analytics capabilities also allow OEMs to adopt related artificial intelligence and predictive models to improve the performance of their machines.
For predictive maintenance applications, learning algorithms use reference values from a machine with optimal behaviour. When machine data start to deviate from reference values, it may indicate signs of potential failure enabling early intervention. Using analytics software can notify support personnel, creating an opportunity to react and reduce unplanned downtime.
OEMs can also utilise this tool to create a remote services offer, to monitor the efficiency and durability of their client’s machines. Moreover, collecting data from more machines gives the OEM better data allowing them to spot trends in machine performance.