How are smart machines improving productivity?
IIoT and smart machines are offering OEMs and manufacturers a step-change in ways to improve flexibility and productivity. The emergence of Industry 4.0 is triggering significant transformation in manufacturing and production industries.
Making the case for the fourth industrial revolution (4IR) Schneider Electric’s Rainer Beudert, Marketing Director for the system and software Machine Solutions answered questions about how the Schneider Electric help customers achieve this
What are the current market trends?
Manufacturers are under pressure to meet demand for ever faster delivery of new products, coupled with shorter production lifecycles. To achieve this, organisations are adopting agile and flexible production plant systems and processes. Smart machines need to be more efficient and reliable to compete in the global market and cut costs. These trends have been going on over the last years, right?
These trends have been going on over the last years, right?
Yes, however there are many new technology driven approaches that are helping organisations to meet these demands. We are seeing innovation frameworks emerge in industries worldwide. For example: IIoT, Industry 4.0 and 4IR. The focus for these frameworks is to merge the operational technology (OT) with the information technology (IT) to provide new solutions for automating and networking industrial machines and systems.
What is the impact of these new approaches for Schneider and its customers?
This evolution creates various opportunities for us and for our customers: By using IIoT solutions, machines and processes can be continuously monitored and controlled – no matter where in the world they are located. In other words, machine data can now be captured, stored and analysed 24/7 thanks to IoT solutions and cloud computing. This data has the potential to deliver valuable insights and to generate business values for machine builders and users.
Is the collection of machine data the only aspect of IoT and cloud computing?
Data collection is just one part of a wider opportunity. For many years, automation business models have remained static. Today, there are many opportunities and alternatives to explore. A good example is equipment leasing models. Internet connectivity and remote management makes leasing of complex machines and pay per use viable.
You have mentioned hardware devices – what about software?
Many software players are moving away from license based models that rely on the fact that new functions will be attractive enough to motivate a customer to re buy a platform. There are many interesting alternative options like software as a service (SaaS), and offered in many variants from subscription models to hybrid concepts.
Can you give an example?
We have identified three main areas undergoing big shifts: The first one is virtualisation services. A prepared virtualisation service for our products can increase the value of our products for OEMs drastically by removing those impediments.
The second is Code Quality Management. It is difficult today for OEMs and their users to know if the code handed over is robust, maintainable and performing well. It is even more difficult to measure whether code has improved over time. In IT development, there are several standards that are transferring to the Operational Technology (OT) world.
The third area is data analytics, coupled with condition and event management services. The most obvious application for analytics in industrial systems is for predictive maintenance. But data analytics can also support preventive maintenance or productivity and workflow optimisation. Not to mention constant condition monitoring and event triggering. Analytics is the base for end to end automated decisions, one of the factors that makes a machine smart.
What do you define as a Smart Machines?
Smart machines are self-aware, react autonomously and provide information about production, configuration, condition, quality and Overall Equipment Efficiency (OEE) to other machines.
What is the role of Edge Control?
The basic idea of edge computing is that data processing power sits at the edge of a network instead of holding that processing power in a cloud or a central data warehouse. This way, data does not have to travel large distances, reducing latency and also improving quality of service (QoS).
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