Business process management (BPM) has been a success since we started to digitalize work automation in the 1970s. In recent years, however, automating business processes has become more of a struggle.
What’s causing this change?
The answer is in the changing nature of the work we are trying to automate. BPM initiatives automated task-oriented and repetitive work. By optimizing processes end-to-end, businesses became more efficient and improved the quality of service. Companies liked what they saw and extended work automation initiatives to knowledge work, which is goal oriented and based on expertise.
Although knowledge work is not particularly suited for BPM (because it’s unpredictable and changes over time), companies initially reaped the benefits of distributing expert knowledge across the organization. Eventually, though, the complexity of implementation resulted in overly-complex systems that were hard to use and maintain.
It became increasingly clear that a new approach is needed. So, if BPM isn’t the answer, how can companies manage business processes that change over time?
Understanding Adaptive Case Management
Adaptive case management (ACM) focuses on emerging processes, what we call unpredictable business processes that reveal new characteristics and required actions as they evolve. Emerging processes are found anywhere outcomes can change from one minute to the next. For example, they are found in the financial industry when onboarding ultra high net worth individuals, negotiating tailor-made contracts, managing complex claims, developing products, and compliance checks—just to name a few.
Each case is considered distinct and may evolve uniquely until it reaches a specific goal. In contrast to conventional BPM software that addresses task-based work, ACM offers support for knowledge workers, whose tasks require flexibility to change.
Managing Emerging Processes
At the core of Appway Platform’s ACM capabilities is the case. A case consists of two parts, data and rules, which constantly interact throughout the case’s entire lifecycle. Whenever data changes, the rules are re-evaluated to spot any rule or constraint that may have been broken.
To make case rules manageable, they are organized into four logical layers:
Correctness. The correctness layer monitors formal correctness of
a context’s individual data entities.
Collaboration. The collaboration layer orchestrates case
collaborators. Assuming a case is
managed by a team and not an
individual, the case engine provides
the means to manage these
Consistency. The consistency layer represents the applicable
business logic for a given case. This
includes all the rules and
constraints that must be fulfilled to
be business compliant.
Control. The topmost layer is the control layer, which is intended to
provide a certain level of control to
the course of action in a case.
A key element of adaptive case management is that it empowers knowledge workers to act according to their own expertise and experience without leaving the safety and convenience of a managed environment. None of the rules implemented in the four layers of case logic are prohibitive per se; they simply spot and highlight issues with case consistency without compelling the case collaborators to take immediate action.
And this is just the beginning.
Given recent advancements in machine learning and artificial intelligence, we anticipate tremendous improvements in supporting knowledge workers in their daily business lives. Adaptive case management, in any case, will act as the solid base for this development, providing a flexible and versatile environment without sacrificing control and manageability.