Process mining is becoming indispensable for process-driven enterprises, and it will soon become an essential tool for “enterprise mining,” to gain new insights into every aspect of your organization.
In part one, I discussed the success factors helping this technology to achieve the tipping point where it becomes essential. Here, I would like to highlight “enterprise mining,” an important further development which allows completely new insights into all dynamic structures of a company.
The great success of process mining is because analyzing real process execution leads directly to identifying weaknesses. This leads to the optimization of a company’s operational activities (see part one). Processes form a central object of analysis as they significantly determine both the strategic positioning and the efficiency of a company.
Extension to enterprise mining
Currently, the mining approach is being extended to other aspects of the enterprise. On the way to a digital twin of an organization, these perspectives grow together to a comprehensive enterprise mining – with new insights into the dynamic structures and their optimization possibilities.
Based on the analysis of process, the following additional aspects are of interest:
- Business ecosystem
Meaningful process analyses do not stop at the company boundary but are extended to the entire value chain. To optimize key performance indicators relevant to the customer (such as on-time delivery performance or service quality), it is necessary to track the entire business ecosystem, such as the entire production and distribution chains, including the activities of suppliers and partners.
- Task mining
The activities measured in the context of process mining (for example, creating an offer, checking an invoice, etc.) usually consist of individual work steps that can be analyzed in more detail. The term “task mining” has become established for the analysis of such interrelated work steps that take place at a desktop, the origin of which lies in the context of robotic process automation (RPA).
The results of task mining detail the (discovered) process steps process mining explores. Currently process mining and task mining are being integrated to look at and analyze in detail every activity that becomes visible in process mining analyses – regardless of whether a human or software robot executes it.
- Organizational mining
There is potential for optimization not only in structured processes, but also in the way and how employees, teams, departments, or entire organizations work together in rather unstructured scenarios. Customer inquiry processing between different contacts is a classic example of non-transparent collaboration and inefficient communication structures causing long lead times.
It is obvious to make the connection to process mining here as well, since often individual process steps (for example, creating a quote, defining a campaign, etc.) require complex interactions between employees and organizational units. These, however, do not follow a clear structure, but rather occur as individual ad hoc collaborations. Like task mining, the results of organizational mining can refine any process step.
- Customer journeys
The methodology of customer journeys has been established for the analysis and optimization of interactions with customers. The objective is to document the chronological sequence of touchpoints and to analyze customer satisfaction along this journey. Touchpoints can be, for example, logging into an online store or completing a shopping cart.
Customer journeys can be seen as an external counterpart to the business processes. Analogous to the process mining approach, customer journeys can also be automatically discovered, visualized, and analyzed if the corresponding transaction data (for example, log files for logging in, search queries, etc.) are available. Typical analyses concern response times or the emotional customer experience at individual touchpoints along a purchase or service process (“moments of truth”).
- Material and goods flows
Many optimizations in industrial companies concern the control flow of processes, and also the flow of goods and materials. This requires that data from both operational technology (OT) systems and information technology (IT) systems be combined. Even though there is still a significant gap between the two worlds in many companies today, this integration provides immense insight for operational excellence: Technological progress makes it possible to equip objects with sensors and processors so cost-effectively that all relevant information (such as position, temperature, pressure, etc.) can be transmitted. Industrial IoT (IIoT) applications generate a huge amount of raw data from sensors and devices. To deliver on the promise of Industry 4.0, it is necessary to filter out the data from the OT devices that is relevant for optimizing production or distribution processes and combine it with the corresponding IT data.
- Data and information flow
The analysis of data and information flows has long been associated with the analysis of business processes. In growing IT architectures, understanding the interfaces between applications is of enormous importance. The visualization of information flows is done with data flow diagrams describing the input and output data of process activities. The analysis of these relationships can also be used, among other things, for data protection issues in the context of regulatory compliance (GDPR).
Overall concept of enterprise mining
The integrated view of enterprise mining brings all the above aspects together in an overall concept.
The perspectives extending process mining enable a new view on the dynamic aspects and success factors of an organization. In the next few years, the degree of automation will also increase significantly in administrative areas so that data will be available even more easily to enable these analyses.
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This article first appeared on OPEX.