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Hyperautomation - this buzzword is currently on everybody's lips and represents an important component for the end-to-end digitization of process chains within the framework of company-wide digitization initiatives. Gartner names hyperautomation among the Top 10 strategic trends 2020. This article shows what´s behind it, which elements are included and provides an example of an integrated approach of hyperautomation.
Many companies focus on the digitalization and automation of processes only in sub-disciplines such as RPA (Robotic Process Automation) or iBPM (Intelligent Business Process Management) to solve concrete problems. Hyperautomation represents an approach that focuses on the effective combination of complementary tools to break down functional and process silos and to view business process automation from a holistic perspective. What does this mean? Quite simply, rather than looking at individual process automation tools, Hyper Automation looks at the benefits that can be achieved when different technologies are sensibly integrated or interlocked.
For example, RPA, as a non-invasive form of automation, can provide rapid support for the digitization of processes. However, the automation of an inefficient process does not provide a lasting positive effect. Moreover, workflows and processes are not always one-dimensional, routine, repetitive and certainly not stable. On the contrary: they can be long-lasting and often require intelligent decision-making and optimization. In such cases, the use of a single tool usually does not deliver satisfactory results.
In such cases, significant optimizations can only be achieved through the sensible combination of various tools from the process automation toolbox. But which tools can be used for this purpose? Besides the RPA mentioned as an example, these include BPM Suites and Workflow Engines, Decision Management Suites, Process Mining and Low-Code Application Platforms. These technologies are complemented by the intelligent use of Artificial Intelligence (AI) and Machine Learning (ML). In our exemplary interaction of technologies we will focus on three components: Process Mining, Business Process Management and Robotic Process Automation.
But first, we will take a look at a possible approach to the topic.
The development of a roadmap is a necessary first step for digitization managers. First of all, it is important to define the desired business result. It is also important to determine the processes to be optimized and then select and assemble the appropriate tools from the process automation toolbox.
In doing so, you should focus on three key components within your process automation goals:
Based on the three key factors mentioned above, identify selected use cases that you would like to optimize and analyze to what extent the interaction of various automation tools can contribute to this. In most cases, this is also the basis for calculating the ROI, which makes the added value of digitization initiatives transparent.
The three most important building blocks of process automation in our opinion will be briefly introduced below:
Process Mining is used to identify, monitor and improve processes that have already been digitized by intelligently reading and visualizing event logs that are written by various application systems (e.g. SAP). Process Mining offers extensive analysis functions of the actual processes in the company and provides the possibility to examine them with regard to inefficiencies, errors, optimization potentials and much more.
Process mining tools can be applied to the following scenarios, among others
You can get a deeper insight into Process Mining in the following blog article.
Intelligent BPM suites (iBPMS) have functions for orchestration of processes and automation of tasks within these processes. iBPMS include integration services, decision management functions, process orchestration including a uniform user interface, ad-hoc processes and analytics functions in one platform.
BPM tools are especially suitable for
- The mapping and automation of long-lasting and cross-organizational business processes that connect people, machines and things, and bridge functional boundaries to avoid system breaks, while providing a consistent and improved user experience
You can get a deeper insight into Business Process Management in the following blog article.
RPA is a non-invasive technology used to automate routine, repetitive and predictable operations by using software bots provided by RPA to mimic and automate user actions directly on the corresponding applications. The previous user action is performed by the bot.
You can use RPA in the following task fields, for example:
You can get a deeper insight into RPA in our technology page.
Process Mining, Business Process Management and Robotic Process Management can form a powerful tool in coordinated interaction, which can change the end-to-end digitization of process chains in a sustainable positive way. The three technologies complement each other excellently and together they can automate complex, long-running and cross-system processes to a high degree (if necessary also by enriching AI). This will be explained below:
As mentioned above, Process Mining is perfectly suited to analyze and evaluate already digitized processes (e.g. processes mapped in ERP, CRM or other systems) on the basis of log files written by these systems. These insights can show where the process breaks down, becomes ineffective, deviates from the optimal path or where additional processes are necessary to optimize the process flow (a typical use case in large companies is the Purchase-to-Pay process). Often some necessary optimizations are not possible in a legacy system, but can only be achieved through coordinated processes that must be mapped across the core systems. This is exactly where BPM comes in and can form a kind of process layer across the various systems to enable end-to-end digitization.
In addition, there are numerous processes in companies that could not be mapped in the legacy systems, or only at great expense, and therefore often still run in analog form. Here, too, BPM can support the digitization of processes. The resulting added value is obvious: the more processes are digitized, the more can be analyzed via process mining, since BPM also writes corresponding process log files. This creates a positive digitization spiral: process mining identifies the deficits, BPM can eliminate them and implicitly creates a broader database, which in turn is made transparent by mining.
Similarly, process mining can monitor running processes in legacy systems and, when certain events occur that may require more complex intervention, initiate a process in BPM that helps to route the resulting events and the measures defined for them through the company quickly, effectively and digitally.
At numerous points in these digitized processes, complex or repetitive actions may need to be executed in legacy systems or other applications. Here RPA can intervene in a supportive manner and execute these actions automatically. As mentioned above, these can be monotonous actions, or data input and output, which are necessary for the further course of the process mapped in BPM.
The bot started by BPM can execute the action and return the result back to the waiting process, which can continue to run with the information gained in this way, make decisions or actively request information from decision makers. In the same way, the execution of a bot can encounter events that trigger a more complex decision-making process in the company, which in turn is controlled by BPM.
Of course, there can also be direct interactions between the process mining tool and RPA, e.g. when events are triggered in legacy systems, which directly (and without BPM) activates a bot to execute corresponding actions (e.g. changes in third-party systems to keep data consistent).
The resulting variants resulting from the interconnection of the three systems are numerous. A further evolutionary step can be achieved by the targeted use of artificial intelligence or machine learning. By independently making decisions within the automated processes or executing bots, AI can further automate processes and make them more intelligent.
The step towards hyperautomation is a consistent further development in the field of process automation and process digitization. Of course, the use of the tools has to be planned and selected depending on various factors. These include, among others, the current degree of digitization, tools used and available, as well as the size (and thus financial capacity and number of specific process operations) of the company.
Often, current industry trends are also an important basis for the decision. Depending on these and other factors, it may make sense to start with one or two of the tools and then expand the hyperautomation approach as the process progresses.
Such considerations should be carried out as part of the company-wide digitization initiatives in order to enter the hyperautomation roadmap. It is not important to implement everything in the first step, but to take the first step while keeping the future target in mind. In this sense also applies here: "You don't have to be great to get started. But you have to start in order to become great. (Zig Ziglar)
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