Intelligent Process Engineering (IPE) – More Details on Exploration and Analysis

In our first blog post (https://blogs.sap.com/2019/02/22/intelligent-process-engineering-a-new-approach-to-business-process-reengineering/) we have already reported about the new Design-Methodology of Intelligent Process Engineering. Today we will describe that in more detail.

The methodology is structured along five steps and begins with the high-level exploration of business processes, followed by the more detailed analysis. As a difference to popular approaches like process mining, IPE has the advantage that it is not dealing with the detailed analysis right from the start. IPE’s initial phase (“Explore”) forms hypotheses about the relevant (process) improvement areas of the com-pany, which could serve as a starting point for the analysis. This ensures an effective use of the available project resources.

Figure 1: First Phases of IPE

From Hypotheses to Analysis

“Before entering into a detailed analysis in the IPE, it is important to obtain a comprehensive picture of the potential improvement areas. Using tools like the SAP Transformation Navigator already allows to use existing data and information about the customer’s system landscape.”, said Dr. Rüdiger Eichin, Senior Director SAP Intelligent Enterprise Group. The subsequent targeted analysis will determine “more accurate clues as to where to look more closely” using data from the system like transactional usage, incidents and other type of data. It is important to provide the opportunity to use appropriate tools such as Qualtrics, allowing the extension of the transactional data set with experience data via questions to users or process experts. This also brings additional know-how and the process customer perspective into the analysis. “In the first steps there are two important characteristics of IPE: on the one hand, it is a data-driven approach and, on the other hand, working from an overarching view down to more details, which ensures that the engineering of the processes targets the most promising improvement areas.“, said Rüdiger Eichin in an interview. “It is also important to inform involved employees and to reveal to them which areas are subject of the analysis and design and which are not.” adds Stefan Schmidt, Senior Director SAP Intelligent Enterprise Group.

Expert Knowledge is Key in the Analysis Phase

In the analysis phase, when using a method such as IPE, various tools for data collection and evaluation are used. This can lead to a larger number of partial and intermediate results. These results must be evaluated and interpreted by experts. “The quantitative analysis based on real data allows for a variety of suggestions and interpretations, which requires a qualified assessment of experts,” said Stefan Schmidt.

Especially in the analysis, the application of data-oriented tools can be a challenge. On the one hand, there must be the technical prerequisite that the corresponding data is available in order to be able to make appropriate evaluations on the basis of enough information. These may include, for example, information from the system logs. On the other hand, the legal use of the information is an important topic and should be considered as an essential aspect to take care of. “Of course, it should be borne in mind that in all these steps of processing technical data, employee’s work councils may have to be informed. It is also important to pay attention to the necessary forms of anonymization, because it is not the goal of the methodology to do a performance measurement of employees, but to improve the processes.”, adds Stefan Schmidt. One challenge therefore remains to find a balance between a top-down approach that is hypothesis-based and data-driven and the communication towards employees: “This is an important success factor in applying the methodology,” says Rüdiger Eichin.

Qualitative Survey Methods and Standard Processes

In data analysis, it may not always be the case that the system-based data is sufficiently available to perform a process evaluation. “There is expert knowledge that has been included in the formation of hypotheses and must be verified in the analysis. In a multi-dimensional analysis, real data from transactional systems is extremely important, but they are not the only relevant data source.”, says Stefan Schmidt. To close this gap, there are qualitative methods of collecting data that can be used, for example, for expert discussions and surveys.

A data-driven approach and the use of existing data – in the presence or involvement of suitable experts – is a major advantage in the application of the methodology. In addition, orientation to standard processes helps to accelerate the analysis accordingly. For example, by using standard process flows of the Order-2-Cash process, you can quickly achieve a data-based view on the performance of real-world processes.

As a result of the analysis, the initial hypotheses can be verified (or proven to be wrong). A suitable option to do this assessment is using Design Thinking in a workshop as a methodology. This ensures the diversity of participating profiles and the acceptance of the outcome of the analysis phase. Such a workshop can also serve to decide on the next steps towards process design, which can be done either in a greenfield approach or by using existing processes via a brownfield approach. We will describe the necessary activities towards the implementation of process improvements in more detail in a subsequent blog article.

Next Steps to further Development

“Intelligent Process Engineering is a result of our cooperation with customers.”, says Stefan Schmidt: “The design method continues to evolve with partners and customers.” We’re talking to internal and external stakeholders, and all of them see this data-driven approach to be more advantageous than previous approaches.