For a continued BPM success, successful BPM projects must be achieved continuously” (Winkler, 2015) One of the most important realizations when working with business process management initiatives is that we are combining two crucial but distinct aspects: methodology AND technology and not “merely” a potpourri of different apps, programs or custom made developments. Business process solutions without a proper and mature framework of applied and proven practices, often leads to the reduction of the former aspirational and transformative BPM objective to become a limited, siloed standalone application that won’t stand the test of time and even less to be prepared to confront the challenges ahead of every business in the bleak post-COVID-19 outlook.
The good news is that the concept of BPM allows us to borrow a lot of the tried and proven practices, established by its “neighbors” (read: CRM, ERP, PIM, SCM, and many others). So, ensuring scalable solutions, underlying architectures and sound development methodologies are highly recommendable also for when it comes to the creation of solutions tasked with automating mission-critical business processes. Looking a bit beyond the bits and bytes hosting and driving BPM&RPA solutions, of course do we also have the entire repertoire of data science at our disposal.
Business processes that are instrumented by modern automation and orchestration platforms (like Bonitasoft, for example) are natural “sponges” that absorb all process and business-relevant information during the processing of a request or a case but that also, behind the scenes, creates a wealth of information that is derived from every activity the end-user engages in, throughout the entirety of the process flow.
That was also the essence of the conclusion of our international webinar series, jointly co-hosted by NSI Soluciones and Bonitasoft, during which we pointed out the different key components to achieve just that: the all-important step towards initiating the continuous improvement iterations for BPM driven processes:
First off, when we look at the data, in BPM, we can differentiate between business and IT-related data, being the first everything that encompasses all the business and process information sets describing the commercial and the operational innards of our processes, while the second data set provides us with insights of eventual architectural bottlenecks, patterns or shortcomings.
Zooming in on the business data we then can differentiate between process data that is accumulated constantly by our BPM, regardless of what kind of process we are running and the complementary data derived from forms that our business processes employ. In that sense, on the process data side of things, we will always cover the information of who did what, when, and how within the BPM whereas the complementary data will differ for each process as the forms for each process differ as well. For instance, we can have an HR process dedicated to vacation requests management, which gathers information off their requested vacation period, evaluation feedbacks, and decisions the process participants took along the workflow. Combining those two sets of information can help us shed light on eventual correlations between how much a given workload impacts on our approval times, resource distribution effectiveness and, ultimately, customer satisfaction.
Additionally, in BPM, we also have information that differentiates between the process results during runtime and reports which summarize our historical performances. As part of most of the current business process management disciplines, the runtime information in BPM is especially pivotal to dynamically and quickly adjust the process according to the live measurements of achieving results against established KPI’s. If a deviation is detected, the BPMS can then proceed to notify the process owners but also take corrective actions (such as reassigning the delayed task to an alternate resource) on time before we miss period-specific goals. The historical data, of course, will help us make sense and understand what exactly happened, what kind of patterns exist, and how we can improve our next process iterations to remedy a given situation. Nowadays, BPM specific historical data lends itself to be extended also to more advanced data analytics and machine learning, engaging in descriptive, prescriptive, and cognitive research. Bonitasoft specifically, offers the BICI module of its enterprise version, which natively allows the end-users to project certain outcomes given the current process results and to also extend these analyses to the field of data mining, known as process mining in BPM.
Naturally, the continued improvement fueled by data, analytics, and machine learning alone is seldomly effective and requires the human element to take root in any organization. For that specific purpose, we strongly recommend a corporation to adhere to an established body of proving practices, like the common body of knowledge (CBOK) of the Association of business process management professionals (the ABPMP), jumpstarting the cumulation of the learning curve and the sure positive results with BPM quickly. in detail, that also involves the practice of the creation of a center of excellence (CoE) for BPM as an intrinsic part of the corporate structure. That way continued data measurements, BPM extrapolations for improvements, and effective hierarchical communication can take place which in turn allows us to engage in the continued business process improvement cycle.