Pragmatism 4.0: Industry 4.0 in Tangible Terms
Everyone is talking about Industry 4.0 and its opportunities: more data, more control, more productivity. And yet, many companies are reluctant to make the step into industrial digitalisation. The flood of content around the topic of Industry 4.0 is enormous. The decision-maker remains unsure when it comes to concrete implementation. In addition, the number of providers offering Industrial Internet of Things (IIoT), whether platform developers, software manufacturers or cloud providers, is steadily rising. These factors make it increasingly difficult for entrepreneurs to decide what the right course of action is.
It is very important to my team and me to solve the concerns of our customers quite pragmatically. The customer’s perspective and developing directly applicable solutions is very important to us. Manufacturing and mechanical engineering companies face clear challenges today: how can they increase availability of their production? How can they connect a wide variety of devices to a single platform with little effort? How can problems in production be made more recognisable and how can they be communicated? How can existing data be used to sustainably improve production forecasts and processes? We want to answer those questions.
Approach 1: Provide Direct Applicability and Concrete Benefits
We only work on solutions that offer an immediate value to production workers. They should be able to monitor their KPIs, react immediately in the event of problems, and be able to restore failed equipment quickly. We are working on a standard IIoT application designed to increase the transparency and availability of producing cells. SaaS products have major advantages over custom solutions: they build up on industry standards and use predefined settings and visualizations. They also take feedback from users into account and make it available to all users with every update.
Approach 2: Ensure Connectivity
To connect a production line to a platform is still a big challenge. This is due to the fact that interfaces are not uniform and that in robots, machines or even vehicles, a wide variety of devices must be networked together, many of which are from different manufacturers. From a technological point of view, smooth connectivity is the basic prerequisite for applied industry 4.0 in a business. We want the on-boarding to be done entirely without code and with just one click. The device type should be detected automatically within a few minutes and send data. And only the data that is actually used in terms of data management and evaluation should be displayed.
Approach 3: Support Communication in Manufacturing
Communication is a central aspect of production. No manufacturing company can afford to lose relevant information or have it not be available. We are currently testing how the operator can use voice commands via Alexa to obtain, for instance, information about what needs to be done to make the machine run again. Or he should be able to ask Alexa to look for a specific solution in the manual or online. If updates are available, Alexa should notify the operator. In addition, we are contemplating how responsible production staff can be involved immediately in case of problems. A messenger service is designed to ensure, via push notification, that incidents during production are correctly addressed and escalated. Failure to do so will involve other people until the problem is completely resolved.
Approach 4: Make the Most of Existing Data
Machine data is one of the most valuable resources of a company. But it gets very little attention. Companies know their production environment like the back of their hand, but most do not realise how much potential lies in their data or which data should never be ignored. Which machine data indicates possible sources of error? When exactly can an order be executed? When must blanks be available for use and when should finished parts be stored in the warehouse? Our data experts conduct research to answer these questions and their analysis can provide insights that can be used for predictive maintenance.