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Preparing your next perspective
Uncover hidden bottlenecks, simulate “what-if” fixes and automate low-value hand-offs.
Event logs reveal variants, waiting time and repeated work that a procedure diagram may not show.
Event logs reveal variants, waiting time and repeated work that a procedure diagram may not show
Use operational evidence to understand how work actually moves, then focus automation on the handovers and decisions that need it.
Reconstruct cases, investigate friction and define automation only where the process justifies it
Connect case identifiers, activities and timestamps to reconstruct variants across systems and teams.
Investigate waiting time, repeated steps, exceptions and bottlenecks in their operational context.
Prioritise interventions with process owners, keeping human review and exception paths explicit.
Validate the log, reconstruct the journey and review findings with the people doing the work
Define process boundaries and assess event quality.
Reconstruct variants and compare them with intended procedures.
Trace delays and exceptions to plausible causes for operational review.
Design a focused intervention and monitor the agreed process measures.
Give process owners a shared view of variants, bottlenecks and implementable changes
An interpretable view of variants, handovers and recurring exceptions.
Prioritised changes with evidence, dependencies and accountable owners.
A defined workflow, exception policy and measurement plan for the next implementation.
Use consistent case definitions and comparable observation windows before and after an intervention
Check missing events, timestamp ordering and the meaning of case completion. Compare cycle-time distributions and rework rates across equivalent cases. Control for seasonality or case mix when reviewing a change; a new average alone does not establish its cause.
Connect workflow changes to operational ownership, exceptions and existing systems
Document cases that automation should not handle and identify the team that reviews them. Keep approval boundaries and escalation rules visible in the proposed workflow.
Attach each backlog item to its affected step, supporting evidence and accountable owner. Agree how the effect will be measured once the workflow changes.
Case identifiers and timestamps need a shared meaning across systems before analysis can be trusted
Begin with discovery, investigate a specific bottleneck or define a controlled automation pilot
Reconstruct the main journey and its variants to establish a common evidence base.
Examine a specific delay or repeated step, with operational interviews to validate plausible causes.
Specify a focused workflow change, human review paths and before-and-after measures.
Enable owners to refresh the analysis and distinguish structural change from data-quality noise
Run the analysis on a new observation window and investigate a changed variant with the process owner. Record when changes require a data check rather than a workflow intervention.
Use operational review to validate causes and determine which tasks should change
Event data exposes patterns, but causes often need validation with process owners and additional context.
No. Some steps should be simplified, removed or retained for human judgement before automation is considered.
Reconstruct cases, investigate friction and define automation only where the process justifies it.
Python
Neo4j
GrafanaCase identifiers and timestamps need a shared meaning across systems before analysis can be trusted
Choose one recurring process with a named owner. Map case IDs, activities and timestamps across systems.
Use consistent case definitions and comparable observation windows before and after an intervention.
Run the analysis on a new observation window and investigate a changed variant with the process owner. Record when changes require a data check rather than a workflow intervention.
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