Process mapping is often treated as a documentation activity-draw the flow, add decision points, and share it with stakeholders. But when processes become complex, simple flowcharts can hide issues such as hidden deadlocks, rework loops, and resource contention. Petri nets offer a more rigorous way to model process behaviour. They allow you to simulate how work moves, test whether the process can actually complete, and validate the model against real event data. This is especially useful for analysts who want to go beyond static diagrams, whether they are studying through a business analyst course or building process improvement capabilities in their organisation.
What a Petri net models in a process flow
A Petri net is a formal model made of three core elements:
- Places: These represent states or conditions (for example, “Order Received,” “Documents Verified,” “Payment Pending”).
- Transitions: These represent actions that change the state (for example, “Verify Documents,” “Approve Order,” “Dispatch Item”).
- Tokens: Tokens represent items flowing through the process, such as a customer request, a claim, or an order.
The arrangement of places and transitions forms a network. Tokens move through this network according to strict rules, which makes Petri nets ideal for modelling concurrency, synchronisation, and waiting conditions-areas where standard process charts can become vague.
In practical terms, if you can model a process with tokens and rules, you can test whether the process can handle real-world conditions such as parallel tasks, merges, and conditional branching without breaking.
Firing rules: the logic that drives simulation
Petri nets work because transitions cannot happen whenever you “feel like it.” They happen only when the firing rule is satisfied.
A transition is enabled when all its input places contain the required number of tokens (based on arc weights). When an enabled transition fires:
- It consumes tokens from its input places.
- It produces tokens in its output places.
This may sound simple, but it creates powerful behaviour. For example:
- If two checks must be completed before approval, the approval transition can require tokens in both “KYC Complete” and “Risk Check Complete.” Approval will not fire until both tokens are present.
- If a task can run in parallel, a token can split across multiple branches using transitions that create multiple output tokens.
For analysts, firing rules turn a process model into a testable system. Instead of arguing in meetings about whether the flow is correct, you can simulate the movement of tokens and verify outcomes. This is a valuable mindset taught in many business analysis course curriculums because it forces clarity in how work truly progresses.
Token replay: validating the model with real event logs
Simulation is useful, but validation is where Petri nets become especially practical. Token replay is a method from process mining that checks whether a Petri net can reproduce real observed behaviour from event logs.
An event log typically contains:
- A case ID (e.g., order_id, ticket_id)
- An activity name (e.g., “Create Order,” “Approve,” “Ship”)
- A timestamp (and sometimes resource and cost details)
Token replay “replays” each case trace on the Petri net:
- When an activity in the log matches a transition in the model, the replay tries to fire that transition.
- If the required tokens exist in the right input places, the transition fires normally.
- If tokens are missing, the replay may create “missing tokens” (a sign the model does not fit the data).
- If tokens are left behind at the end of a case, it indicates improper completion or unmodelled paths.
This produces measurable quality signals. While different tools use slightly different definitions, token replay is often used to evaluate:
- Fitness: How well the model can reproduce real traces without forcing artificial tokens.
- Precision: Whether the model allows too many behaviours that never occur in reality (overly permissive models).
- Generalisation: Whether the model captures expected variation without being too rigid.
From a business standpoint, token replay answers a key question: “Does our documented process match what actually happens?” Many organisations discover that the real process includes extra loops, manual workarounds, or missing approvals. A business analyst course that includes process mining concepts typically emphasises this gap between “designed” and “executed” processes.
What Petri net analysis reveals that flowcharts miss
Petri nets are especially good at exposing structural issues:
1) Deadlocks and blocked cases
A deadlock occurs when tokens get stuck because required conditions can never be met. For example, a process might require both “Manager Approval” and “System Validation,” but under certain paths only one can ever occur. The token never reaches the end state.
2) Unbounded growth and bottlenecks
If tokens accumulate in a place, it often represents a queue or backlog. When you link this with timestamps, you can see where waiting time dominates cycle time.
3) Synchronisation errors
Merging parallel branches is a common source of errors. Petri nets make it explicit: does the merge require one token (OR-join) or multiple tokens (AND-join)? Many real-world process issues come from modelling this incorrectly.
4) Conformance gaps
Token replay highlights where the model deviates from reality-missing paths, unexpected rework loops, or steps that happen out of order.
Conclusion
Petri net analysis provides a practical way to move from process diagrams to process validation. Firing rules make process behaviour explicit and testable, while token replay grounds the model in real execution data. Together, they help analysts identify deadlocks, bottlenecks, synchronisation issues, and conformance gaps that are hard to detect with static flowcharts. For professionals building strong process skills through a business analysis course, this approach strengthens both technical rigour and business decision-making-because it replaces assumptions with evidence about how work actually flows.
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