Currently, data in all fields generates valuable information that can be utilized for commercial, operational, and research purposes, among other practices.
In companies, the activities we carry out are recorded in information systems and applications that usually focus on generating data for our business. However, the actions and tasks that are part of the daily routine of organizations generate data that is largely unused because we do not realize its potential. These are data of high interest regarding our processes.
These processes can be efficient or not, well-modeled or perhaps misaligned, prioritized for the business or maybe given a secondary place. Some of these attributes are not positive, and resolving the situation they present can be seen as the main challenge. However, the most complex part is identifying these attributes in order to improve the processes and make timely decisions.
In modern organizations, which are increasingly digital, we have applications such as ERP, CRM, BPM, RPA, service desks, among others, that support operations. Each of these applications generates process data that, if presented to employees and executives through dashboards, would allow for dynamic process visualization with indicators. It would be possible to detect operation problems, optimize processes, and achieve greater efficiency in companies.
The current market dynamics imply constant changes in product and service offerings, value chains, and business models. This obliges organizations to review all current processes in order to make the necessary changes that enable digital transformation.
In pursuit of achieving this, process mining emerges, which is a set of techniques for analyzing and monitoring business processes with the purpose of improving them. It combines data mining techniques, computational intelligence, and process analysis and modeling.
The key is that it uses real data or process records. It takes instances of processes generated by any task carried out by an employee as input. It takes data such as time, location, people, and devices involved, and its analysis allows for drawing conclusions on how to improve and make the business processes more efficient and secure.
The lack of knowledge about the real processes in organizations makes process mining an interesting solution for any organization. Process mining provides visibility and understanding of the actual performance of operations and business processes before initiating any process redesign and automation initiatives. Through analysis, it helps identify opportunities within the context of digitalization.
Process mining uses traditional process representation techniques such as BPMN (Business Process Model and Notation), CEP (Event-Driven Process Chain), UML (Unified Modeling Language), BPEL (WS-Business Process Execution Language), among others, just like in traditional business process management. This is achieved through process workshops and interviews, resulting in an idealized image of a process rather than a real-time view of the production process.
There are three techniques for process mining:
1. Discovery: This technique takes an event log and produces a process model without using any existing information, solely relying on Process Mining algorithms.
2. Conformance: In this technique, event logs (real processes) are compared with corresponding process models (ideal and predefined in BPMN), and the resulting matches or differences are identified to diagnose deviations or inefficiencies between the derived business process model and the ideal processes.
3. Enhancement: In this technique, process models are adapted and improved based on real process data.
Using these process mining techniques, it is possible to:
· Automatically discover process models, exceptions, and process instances (cases) along with basic frequencies and statistics.
· Discover and analyze customer interactions, as well as alignment with internal processes.
· Understand operations from different perspectives, not just a process perspective.
· Have visibility of key performance indicators using dashboards.
· Generate predictive analysis, prescriptive analysis, scenario testing, and simulation with contextual data.
· Improve existing or previous process models using additional data from recorded logs.
· Combine different interacting process models in a single process mining dashboard.
· Establish how processes contribute to business value (such as business operating models) – process contextualization.
· Foster effective cooperation between Business and IT.
· Enhance operational excellence by optimizing processes.
Process mining is not new. Gartner started reporting on it in 2008 as “Automated Business Process Discovery” (ABPD). The IEEE Task Force on Process Mining published a manifesto in late 2011 to promote process mining as a new tool for improving process redesign, control, and support.
Operational business processes. Many authors have contributed to the growth and dissemination of this practice; however, it is today when it has the greatest potential, thanks to current computing capabilities and technologies such as big data, machine learning, and automation. In the market, we find applications that allow for process mining, such as Celonis, Disco by Fluxicon, and My-invenio, among others. The latter, from IBM, has great potential due to its capabilities in process discovery, monitoring, and optimization.
Experts predict that by 2023, the process analysis market will reach USD $1.421 billion, representing an annual growth rate of 50.3%. Europe represents the largest market size due to the widespread acceptance of innovations and the large number of Process Mining software providers operating in the region.
North America shows the highest growth rate, representing the biggest opportunity in the process analysis market. Latin America is slightly behind both in terms of market share and growth rate, but undoubtedly, the need for digital transformation in our companies will boost the resurgence of the process mining market in our region. Its implementation in organizations presents a series of challenges: data quality, diverse and heterogeneous data sources, lagging IT technologies, low process maturity level in companies, among others. These complex challenges are faced by the majority of companies. However, if one wants to build a solid business with the ability to thrive despite the unpredictable dynamics of the market, it is imperative to work on process optimization, and process mining is definitely the best path to achieve that.
The operational data becomes more valuable every day, which is why we can affirm that business processes are a “gold mine” for the digital transformation of companies.
By: Omar Otalvaro, Director of Digital Transformation Projects at BPS.