Schlauer Raum Blog

Why the cloud and AI are now fundamentally changing quality management

There’s no question about it. Cloud computing and artificial intelligence have dominated the IT development agenda for years. Nevertheless, it seemed as if operational quality management could continue as before for the time being. But the grace period is over. Both technology trends will fundamentally change the way quality-relevant knowledge is handled.


Top trend 1: Cloud migration

Our quality data in the cloud? Until recently, this question alone was considered absurd. After all, knowledge about the quality of products and the performance of production are some of the crown jewels of operational quality management. The only safe and debatable place for this was your own server room.

But these certainties are fading. And interestingly enough, it is the issue of security that is most likely to cause people to rethink their previous resentment towards the cloud. This is not least due to the shortage of skilled labour, which is becoming particularly acute in IT and especially in the security environment. In the battle for the best minds, often only the big tech providers have the resources to bring suitable employees on board. All of this means that it is primarily the highly specialised cloud service providers that have the most powerful and secure IT infrastructures.

Especially as previous data protection concerns no longer apply. Even leading US cloud hyperscalers now have data centres (DCs) on European soil. They have either set up their own locations, such as Google or Microsoft. Or, as Amazon likes to do, they rent space from colocation service providers who manage the buildings, take care of the networking and, in some cases, already have their own high-performance security operation centres (SOCs).

Incidentally, user companies can also go down the latter route themselves. In other words, anyone can move their own servers to a colocation data centre and continue to operate them there. If they wish, they can also continue to administer the computers themselves.

However, no matter how you organise the use in detail – whether as a public cloud solution with a hyperscaler, as a private cloud in a closed web infrastructure, or as a hybrid cloud in which private and public elements are suitably combined – you always benefit from a significantly higher level of protection and noticeably lower operating costs compared to your own data centre operation. There is also a higher degree of scalability, as any number of resources can be added and removed just as quickly. This is particularly interesting for QM, where additional analyses have to be carried out or new work teams have to be integrated into ongoing measures on an ad hoc basis regularly.

But what exactly does all this mean for our Germanedge solutions? Are they already cloud-ready, as IT experts like to call it? The answer is simply yes. Because all Germanedge products are based on our open web-enabled MES/MOM platform Edge.One. This includes the modules of our QM software – from advance quality planning to complaints management.

On request, we can accompany you on your journey to the cloud. Together with your IT managers and the cloud service provider of your choice, we develop a suitable infrastructure in which the Germanedge applications you use, the underlying database, and the interfaces to the other systems in your IT/OT world work together appropriately.

Top trend 2: Quality as a Service

Cloud solutions therefore have the advantage that customised configurations are possible at any time – both at application and integration level. In principle, the cloud therefore offers a similarly high degree of freedom as the on-premise world. But be careful: Solid project management remains essential in order to carry out customisations properly. Which of course takes time.

Apart from that, however, there is also a kind of shortcut to the cloud world. If you choose this option, you will have to make compromises in terms of configurability, but you will be able to work almost from a standing start. We call this approach Quality as a Service (QaaS). Essentially, these are lean browser apps that can be used to solve routine tasks easily and reliably. The applications utilise best practices that we have gained in a large number of customer projects.

The QaaS services utilise the Software as a Service (SaaS) principle as the delivery model. There are no infrastructure or licence costs for the customer. They only pay for the use of the service.

The GermanedgeNOW marketplace was launched in autumn 2023. There you will find SaaS solutions for the digital factory. The portfolio also includes a first app from the QM area. With document management, we have deliberately opted for an application that supports users from various parts of the value chain. We will continue to expand the market portfolio in the coming months. When it comes to QM topics, an app for creating 8D reports is at the top of the to-do list.

There are three different package solutions for each app: Starter, Professional and Enterprise. The packages differ in terms of functionality, performance and configurability. It is important to note that further customisations are reserved for cloud solutions in particular. From the customer’s point of view, it is therefore important to weigh up what is more beneficial in the current use case: the customised additional functions of a customer-specific cloud solution or the time and cost benefits of a standardised Quality-as-a-Service service. In principle, both options are open to you. Which one you choose will depend on the specific QM requirements that currently need to be solved.

Top trend 3: Artificial intelligence in QM

For a long time, AI was considered purely an expert topic. The triumphant advance of generative AI has fundamentally changed this view within a very short span of time. Especially in quality management, where chatbots such as ChatGPT, Bard or Amazon Q can already bring considerable added value. More on this in a moment.

But first, let’s take a look at a field of application in which the potential benefits of AI can be demonstrated particularly well. We are talking about predictive quality, the AI approach that makes it possible to predict quality defects before they actually occur in the application of products or processes.

Basically, this is nothing groundbreakingly new. After all, many companies already have an extremely detailed view of the life cycle of their products – combined with analysis systems whose task it is to identify emerging defects at an early stage. However, the added value of a suitable AI solution now lies in the fact that the existing data basis is enriched with suitable metadata and context data in order to gain additional insights into how the performance of the products or processes will develop. Suitable metadata can then be weather data, for example, which the AI uses to extrapolate the wear and tear of a given product solution based on location. Such analyses very quickly develop complexities that would overwhelm conventional computing models. Even in the big data environment.