Watch out for evolving standards and platforms and make them to your own standard before they will crush your current niche.
With this in mind, it is obvious that continuous data analytics and later artificial intelligent autonomous machines can enhance the production process and can make smart decisions in exceptional situations faster.
Even today, companies have a lot of data already but they don’t generate information from it – or they keep it in separate systems so that it’s impossible to derive decisions for the next control or management level out of it. Therefore, the IT (information technology) and OT (operation technology) need to grow together. Such an end-to-end backbone increases availability and productivity of the whole systems.
Source: Text & image : Lenze
Actually there is a big amount of different cloud platforms. It is hard to say how many platforms will survive. For machine builder the question which derives from that are they really willing to fight for your own platform or is it may be smarter to hitchhike on different cloud platform solutions providing the highest possible connectivity. In case of doubt a rule can probably be: The bigger the customers are, the bigger the company the customer will expect behind the cloud solution.
Several benefits by digital twins
The proceeding digitization changes the product development , too. On the engineering level: start a model driven engineering process and target the digital twin. Your customers, specially the big ones, will make this to a prerequisite very soon. One reason could be either because of the enhanced engineering process with shorter realization times from idea to released product and cost reductions. Another might be because of transparency reasons, risk reductions or efficient maintenance and change management use cases. The dream of a complete simulation is tightly connected with a complete data model and an unconstrained data flow; also a data model of every engineering tool of every engineering domain in the toolchain over the whole product lifecycle. This prerequisite is necessary for this engineering vision to become cost efficient. Today still too much manual modeling work is done multiple times.
Innovative companies use the data which is generated by their machines and create with the help of data analysis new information. Afterwards they market this as a service, for example in the upcoming maintenance. In the future the machines will learn with the help of their own data and on the basis of this execute intelligent decisions. Machine builders who are interested in machine learning, should start at zero from an organizational point of view: It is not enough, to simply stock up the software department with a few extra engineers. Because workflow and the needed skills are very different to each other. Additionally, the analytics team has to be connected throughout the whole company.
How can you be successful with Industry 4.0 and industrial IoT in the future? Read more in the whitepaper “The digital challenge” from Lenze.