What Celonis and SAP Signavio Process Intelligence Do Well

Both platforms are genuinely powerful at analysing process execution data once the right conditions are in place. Their core strengths include:

• Quantifying delays, rework, and process variants at a scale and objectivity that manual analysis cannot match
• Identifying conformance gaps, meaning deviations between how transactions actually moved through a system and how they were designed to move
• Supporting fact-based prioritisation by showing which issues occur most frequently and where the largest efficiency losses are concentrated
• Enabling continuous monitoring so that performance changes over time are visible and measurable

Celonis is particularly strong at handling very large event logs, complex object relations, and execution management, automating actions in response to identified process deviations. SAP Signavio Process Intelligence integrates deeply into the SAP ecosystem and supports fit-to-standard analysis as part of S/4HANA transformation programmes.

For processes that are well captured in transactional systems and where the data infrastructure is already in place, process mining provides a scale and credibility of insight that no workshop-based approach can match.

Where Process Mining Falls Short: The Four Gaps

The limitations of process mining are rarely technical. They are structural and contextual, and they become more significant as organisations try to move from diagnosis to action.

Gap 1: Process mining does not discover processes. It analyses them.

A common misconception is that process mining platforms like Celonis reconstruct how a process works from scratch. In practice, the client must first define the process scope and provide a structured data model before the mining engine can run. Celonis does not walk into an organisation and find the processes. The organisation must already know, at least broadly, which process it wants to analyse, which systems record it, and how the relevant data is structured.

This means that before a single process mining insight is generated, the organisation needs a baseline level of process understanding that it may not have. The very knowledge gap that process mining is often bought to close is a prerequisite for running it effectively.

This is precisely the upstream gap that Famla is designed to fill.

Gap 2: Accessing event logs is harder than it appears.

Process mining runs on event log data, which are structured records of when transactions occurred, in what sequence, and who performed them. For well-known enterprise systems like SAP, experienced process mining consultants have established connectors and data extraction frameworks. For anything outside that comfort zone, the picture changes significantly.

Many organisations run processes across niche, custom-built, or legacy systems where event logs are not structured in a standard way, where extracting the right data requires deep technical knowledge of the system architecture, and where no pre-built connector exists. Even experienced process mining practitioners face significant challenges when the underlying system is unfamiliar. Building a custom integration for a non-standard system can take weeks or months, delaying the entire programme before any insight is produced.

This creates an invisible selection bias in process mining programmes: organisations end up mining the processes that live in systems they can easily access, not necessarily the processes that matter most to the business.

Gap 3: System data is not the whole process.

Even when event log access is straightforward, system data only captures what the system recorded. It does not capture the phone call that resolved an exception, the manual workaround applied when the system behaved unexpectedly, the informal approval route used because the standard one was too slow, or the local variation in how a process runs in one region versus another.

In most organisations, a significant proportion of how work actually gets done lives outside system logs. Process mining produces an accurate picture of system-recorded transactions, not a complete picture of operational reality.

Gap 4: Insight does not automatically become action.

Process mining is very good at finding problems. It is less good at helping teams decide which problems to fix first, why those problems are occurring, or how to engage the people responsible for change.

A typical process mining engagement surfaces many issues simultaneously. The data shows what is happening and how often. It does not show which issues are systemic versus situational, which trade-offs are acceptable, or which improvements the organisation is actually ready to implement. And when findings are presented top-down, based on data that frontline teams had no role in generating, resistance is a predictable response.

Dashboards, however accurate, do not substitute for the shared operational understanding that drives lasting change.

What Famla Adds Around Process Mining

Famla addresses the gaps that process mining leaves open by adding the human and operational layer that event logs cannot capture, and by operating upstream of process mining where the data infrastructure does not yet exist.

Where Celonis and Signavio require a defined process scope and structured event log data to begin, Famla starts earlier. It captures structured process knowledge from the people doing the work, asynchronously, without requiring system access or data engineering. Existing documentation including SOPs, training materials, and process notes can be uploaded directly and processed as source material.

From that input, Famla:

• - Generates process diagrams automatically, without manual drawing or canvas work
• - Performs structured process analysis grounded in Lean Six Sigma and Operational Excellence principles
• - Surfaces improvement opportunities, bottlenecks, and variation that process mining would either miss entirely or require months of data engineering to reach

This serves two distinct roles. For organisations that have not yet deployed process mining, Famla provides the process understanding needed to define scope and prioritise which processes are worth the investment of a full mining engagement. For organisations that have already deployed Celonis or Signavio, Famla adds the operational context that turns mining findings into actionable improvement programmes.

How Famla and Process Mining Work Together

The two approaches answer fundamentally different but complementary questions.

In practice, this means teams spend less time debating data accuracy or ownership of findings, and more time on the decisions that actually matter: which bottlenecks are worth addressing now, which issues are systemic versus situational, and where improvement will deliver the greatest impact across the value stream.

Improving Change Adoption Beyond Dashboards

Change depends on people, specifically on the people who do the work understanding what needs to change and believing that the analysis reflects their reality. When process mining findings are presented top-down, resistance is a predictable response. The data may be accurate, but it does not feel like their problem or their solution.

Famla changes this dynamic by involving frontline teams in the discovery process rather than just the delivery of findings. When people contribute their operational knowledge, describing how they actually work, where friction occurs, and what makes their process difficult, they become part of the analysis rather than subjects of it.

This has measurable downstream effects. Shared understanding is built earlier. Improvement ideas from people closest to the work are of higher quality. Resistance decreases because the change is no longer something being done to the organisation. Implementation moves faster because the groundwork has already been laid.