Getting from employing basic digital solutions to leveraging the full transparency of a connected network of machines – in the strategic overview of production planning – is a long road. Still, the significant boost over the hesitant competition one can get from starting early will erode over time as the overall maturity of companies increases.
Developed by a consortium of research institutions, the Industry 4.0 Maturity Index is meant to be a helpful breakdown of the different developmental stages of digitalization in manufacture. The digital maturity levels of varying companies can be broken down into six stages.
Hopefully, this index clears up some of the confusion as to what the future might hold for you.
Programming machinery by using digital technologies is the first step on the digitalization hierarchy. Machinery is not connected to each other, and the production data they collect is usually an individual report after a cycle is finished. It is probably better to describe this stage with CNC or 3D printing, where machinery is programmed, but there is no momentary feedback, and the information is insular.
Machines are in some way connected to each other, maybe as it was already possible from the early days of the century by the internet, but the connection is slow and ineffective. This leads to a decided gap between the actual reality of the shop floor and the incoming data. While the individual components are connected, they aren’t integrated into a system yet. This and the previous are both the preliminary stages of Industry 4.0, where most of the manufacturing community still is.
This is the stage where you have an up-to-date model of what is happening in real-time, all the time. You have data pouring in from your line, and access to a digital model of the shop floor with real-time information. You might lack the necessary organization and integration of data at this stage, but you get recordings of actual conditions and processes.
The fourth stage represents an attitude shift from gathering technical data to leveraging, interpreting, and understanding it in a relevant context by applied engineering knowledge. You can now begin to harvest the benefits of your process digitalization efforts via production optimization.
5., Predictive capacity
This stage is further along the logical line of interpreting relevant data, where you can use such to get insights into the possibilities of future work stoppages or machine failures. AI software assistance is used to help anticipate future developments and make necessary adjustments to the overall production strategy.
This is the highest – albeit mostly futuristic – state of maturity where IT systems will make independent decisions. Fully realized Industry 4.0 systems will make process alignments automatically and without delay. The extent to which these decisions are allowed to be made will be calculated based on a cost-benefit analysis considering the use of human or machine decision-making and the complexity of the actual decision. The system’s adaptability will be on a level where it can react to changing conditions, handle routine and emergency tasks, and self-organize based on historical and real-time data analysis.
The pandemic situation has, without a doubt, proven how global issues can mess up already complicated systems that sit at the intersection of mechanical engineering and economics, like manufacturing. Lately, the more and more prevalent supply chain issues and material shortages prove the very same point. Namely, you best cover your bases and get ahead of the competition in what you can if you want to be the last one standing. Now is probably the best time to help yourself to that competitive edge and shortcut your way to a later stage, lest the next global issue finds you unprepared.