Overview of the automation jungle

As the digital transformation progresses, companies are forced to rethink their positioning in a digital and dynamic market with constantly changing requirements. A continuous improvement in the effectiveness and efficiency of the products and services offered is becoming indispensable. While the relationship between process and product can be seen as very close (e.g. product as the output of a process chain), service = process.

The transition to (partially) automated business processes enables companies to increase their competitiveness and meet their challenges.

HOWEVER - What options do companies have to automate all or part of their processes?

In addition to the challenges mentioned above, there is also a daunting collection of terms in this context:

Business Process Management (BPM) | Robotic Process Automation (RPA) | Machine Learning (ML), Hyperautomation | Process Mining | No/low/pro code | Intelligent Automation (IA) | Business Process Automation (BPA)

BUT - What are the differences? Are there correlations? Do all approaches, technologies and tools have to be used to reach a level of maturity that is considered successful?

THE ANSWER? YES AND NO and IT DEPENDS ??.

YES - there are differences, there are correlations. NO - not all technologies have to be used.

Approaches, technologies or tools only ever serve as a means to an end and are not an end in themselves. We all know the saying "The best tool is only as good as its user". Right? Exactly the same applies here.

This brings us to IT DEPENDS.

Various questions need to be answered in advance. Where do we stand (current status)? What level of process maturity do we and the external process participants (suppliers, customers, ...) have? What expertise do we / our human resources have? And so on… → Record current status!

This is followed by the question of the North Star (identify the purpose ??)! Where do we want to be in one, three and/or five years? ...? → Record target status!

THEN - in line with the answers, the appropriate approach, technologies and supporting tools are selected.

NOW, however, we also need some light in the cloud of terms. What is behind the terms? Are they just buzzwords? How do they relate to each other?

Let's bring BUSINESS PROCESS MANAGEMENT (BPM) to the foreground.

BPM is a holistic management approach that deals with the modeling, execution, monitoring and improvement of processes over their entire life cycle in alignment with corporate strategy goals. Technologies and tools help to improve processes in terms of time, cost, quality and flexibility.

Let's assume that our main goal is to achieve the highest possible level of BUSINESS PROCESS AUTOMATION (BPA). BPA means putting processes into a state in which activities in processes are technically automated using technologies and tools. This not only increases process flexibility, but also shortens throughput times and frees up human resources to focus on more value-adding activities. As a result, the above-mentioned dimensions are optimized:

Reduced time (shorter throughput times)

Reduced costs (recurring tasks are not handled by costly human resources)

Increased quality (potential human errors are minimized)

Increased flexibility (change of process, requires no familiarization phase)

HYPERAUTOMATION reflects the “optimal” interaction of all technologies used to fully automate processes. It aims to maximize automation and reduce human intervention to an absolute minimum. Hyperautomation therefore opens up a new dimension of competitiveness.

HOWEVER, we have often talked about "technologies in use". We should now put these into context.

Let's start with the modeling of processes. Depending on the degree of process maturity, company processes may already be documented (textually or graphically) and therefore "identified" - supposedly. PROCESS MINING helps to identify the processes currently "lived" (actual state) on the basis of large amounts of data in the form of digital "traces" (log files, databases, etc.) left behind by IT systems and sets them up digitally. This makes it possible to identify weak points, bottlenecks, unknown process variants, shadow IT, etc. and to foresee, mitigate or even eliminate them in the (automated) target state.

And what role do Robotic Process Automation (RPA) and Intelligent Automation (IA) play in this context?

WELL… Let's assume the highly probable case that we identify through the results of process mining that almost every process runs via a core system (such as an ERP, CRM system or even an in-house development). This suggests that, for example, the system cannot be eliminated without risk - at least not on an ad-hoc basis. Let's also assume that the system is outdated and has no technical interfaces. To avoid missing out on the automation potential, this scenario is a possible case for the use of robotic process automation (RPA). RPA defines software bots that operate applications (click buttons, read data from other systems, make data entries, etc.) just as a human would.

With Intelligent Automation (IA), a standard RPA bot is combined with artificial intelligence (AI) and machine learning (ML), in which the robot can independently recognize patterns, interpret data, make decisions, etc. using underlying self-learning systems. This means that the bot is no longer bound by predefined decision rules but can make its "own" decisions on a case-by-case basis.

And what role do the terms no-code, low-code and pro-code play in the overall context? Well, for some activities to be automated, one or the other approach may make more sense - it depends on the specific case. We would be happy to discuss this with you one-to-one.