The Pitfalls of Subjective Process Descriptions
Digitalization initiatives frequently falter because they are based on idealized process diagrams rather than actual operational data. This creates a false sense of control that collapses during implementation. Automating processes based on theoretical descriptions, often derived from job descriptions or manager interviews, ignores real bottlenecks and informal workarounds. If a manual process contains unnecessary steps, function duplication, or artificial delays, transferring it to code will only automate and multiply these inefficiencies, leading to automated chaos rather than improvement. Many companies find themselves dissatisfied with initial automation waves launched without objective analysis, highlighting the critical need for an evidence-based approach.
Uncovering Actual Process Execution Through Digital Footprints
The only reliable source of truth for how an enterprise truly functions is the digital footprint left by its information systems. Every user action in ERP, CRM, or document management systems is recorded in event logs. Process mining technology leverages these logs to discover how a process is actually executed, revealing deviations from documented procedures and identifying hidden workarounds, often referred to as “shadow processes.” For instance, process mining might uncover that employees consistently bypass official approval stages through personal chats, creating a hidden bottleneck that inflates actual execution times while official KPIs appear excellent. Analyzing these event logs allows for precise identification of timestamps, cyclical reworks, and specific delay points within a process.
Architectural Standards for Flexible Automation
For an automated process to be flexible and maintainable, its architecture must adhere to international standards. BPMN 2.0.2, an ISO/IEC standard, defines the graphical notation and semantics for business process modeling, enabling both documentation and management of processes through an orchestration engine. A common mistake is to hardcode decision logic directly into the process structure, creating overly complex diagrams. The DMN (Decision Model and Notation) standard addresses this by separating business decisions from the process flow. This separation allows decision rules, such as discount calculations or credit limit logic, to be updated independently by business analysts without requiring developers to alter the entire process structure, significantly reducing maintenance costs during frequent policy changes. Specialized low-code tools are often employed to build such solutions effectively.
Strategic Selection of Processes for Automation
Not every inefficient process should be automated first. To ensure a successful initial automation wave, processes must be evaluated against several criteria for readiness. Processes with low execution frequency or those requiring constant creative intervention are generally poor candidates for early automation. Key readiness metrics include the stability of decision rules (preferring static or DMN-describable logic), the level of standardization (favoring processes that adhere to BPMN 2.0.2 without significant shadow processes), and the structure of data sources (prioritizing processes with structured data in event logs). The potential for a measurable return on investment (ROI) is also critical, focusing on high-frequency operations with identifiable delays. Accurate ROI assessment should be based on comparing current and projected execution costs, utilizing log data for average step durations and error frequencies, while accounting for development and maintenance expenses. Automation without prior log analysis and an architecture based on BPMN and DMN standards risks unstructured data and potential maintenance costs that outweigh benefits.