Workflow diagnosis: finding rework, delays and handoff gaps

What workflow diagnosis must establish

Workflow diagnosis follows a unit of work from its trigger to an accepted outcome. Its practical purpose is to explain delay, repeated effort and failed handoffs before selecting technology. Start by asking why a request waits, why information is entered again, and who can resolve an exception. An AI summarizer will not solve missing decision rights. The method below is a proposed implementation approach. Sampling choices, service targets and release thresholds require local agreement; they are not universal requirements prescribed by the referenced tools. A useful diagnosis ends with evidence someone else can inspect and an improvement someone can test.

Define the case and its finish line

Ask the process owner to describe one start, one finish and one accepted deliverable. For quoting, the boundary might run from receipt of a complete inquiry to delivery of an approved quotation. Specify how cancellations, missing information, split requests and reopened cases are counted. A customer is not necessarily a process case: one customer can have several concurrent inquiries. Agree a mapping before joining records. The data custodian should identify available history, retention limits, permitted fields and inaccessible material. Work on authorized, minimized copies so that diagnosis does not become an excuse to expand access to unrelated commercial or personal information.

Create an event history that can be replayed

Microsoft describes event data in terms of cases, activities and timestamps, while distinguishing event history from current-state tables. [S1] Build a local event dictionary before calculating durations. Separate draft creation, submission and acceptance rather than labeling all three as completion. Preserve original timestamps, time zones, source references and missing-value reasons. If systems lack a shared identifier, document the join rule and the unmatched share; do not silently let a model merge records with similar names. Keep extraction and cleaning versions. An export timestamp is not an acceptable substitute for the unknown time at which an activity actually occurred.

Combine system evidence with real work

Select ordinary, delayed, returned and cancelled cases. Ask people to walk through actual working artifacts: what arrived, which reference they consulted, what judgment they made, and how they knew the next team accepted the handoff. Fluxicon's process perspective connects events into paths that can expose bottlenecks and deviations. [S2] Telephone calls, checking and informal coordination may still be absent from the logs. Label observations as system evidence, participant recollection or analyst inference. Reconcile disagreements against the original artifact instead of choosing the neatest diagram. Missing records should be treated as an evidence gap, not automatic proof that somebody failed to work.

Distinguish effort from elapsed time

Separate active handling, queue time, interdepartmental waiting and repeated work wherever the data permits. Report a typical completion time and the slower tail together with the observation window. With a single timestamp, a gap between events is measurable; active labor throughout that gap is not established. Include open cases in a separate aging view so unfinished work does not disappear from the analysis. Code return reasons such as absent specifications, missing pricing authority, wrong versions and unclear recipients. These categories lead to different interventions. A capacity problem, a data-quality problem and a decision bottleneck should not all be handed to IT as one automation request.

Check data quality before reporting improvement

Before calculating change, inspect duplicate events, reversed timestamps, time zones, inconsistent activity labels and unmatched cases. A batch-written history can make many actions appear simultaneous even when the underlying work happened at different times. State whether durations use calendar time or business hours, especially across weekends. Retain counts before and after cleaning together with exclusion reasons. Ask the process owner whether excluded records concentrate in difficult exceptions. If the usable sample contains mostly easy work, narrow the scope of the conclusion instead of presenting an attractive average as representative of every case.

Turn symptoms into improvement hypotheses

For each issue, record the observed consequence, evidence, suspected mechanism, alternative approaches and a test. A useful sentence states the context, missing input or failed handoff, measurable consequence and proposed change. Fixed specification fields may need form validation. Repeated data transfer may need an existing integration. AI becomes a candidate when interpretation of unstructured material is central to the task. Define its input, output, human check and prohibited actions. Include a simpler non-AI option in the comparison. Rank proposals by outcome, reversibility, evidence quality and maintenance work rather than by how advanced their demonstrations appear.

Use linked event and issue registers

Keep event facts separate from analytical hypotheses. Link the two through stable case identifiers and retain the authorized source as read-only evidence. Cleaned tables, charts and recommendations are derived artifacts that should be reproducible from a stated source and rule version. An event row can contain case identifier, activity, start, finish, time zone, acting role, source record and missing-data reason. An issue row can contain the affected node, symptom, affected cases, evidence reference, hypothesis, option and decision owner. A trial row adds the baseline, comparison period, quality floor, human intervention route, stop condition and review date. Different roles confirm scope, field meanings and actual working paths.

Illustrative scenario: quotations stuck in clarification

Consider a hypothetical manufacturer whose sales team experiences slow quotations. Diagnose receipt-to-approved-delivery rather than timing only the writing task. Interviews might reveal repeated specification clarification, while the event history shows complete inquiries moving quickly. Test a mandatory specification checklist first; then consider an AI-generated missing-information draft that a salesperson checks before contacting the requester. This is an illustrative method, not a reported customer result. Retain comparable cases without the AI assistance. Account for differences in complexity and for the checklist itself. Incorrect missing-item suggestions, additional checking and customer follow-up belong in the cost and rework measurements, even when document drafting becomes faster.

Accept the evidence, not just the diagram

Trace selected paths back to events and original records. Confirm sequence, completion definitions, duplicates, unmatched records and treatment of unfinished cases. Ask operators what work remains invisible. The deliverable should include scope, event dictionary, cleaning rules, main variants, exception categories, baseline and a prioritized improvement list. Agree the comparison method before running a trial so teams cannot select only successful examples afterward. If reliable histories do not exist, deliver a collection plan and a clearly qualified observational baseline. That remains useful work, but it is different from claiming a precise process-mining result. A second analyst should be able to reproduce the major conclusions without relying on the original analyst's memory.

Questions about missing logs, AI and employee scoring

Can diagnosis begin without event logs? Yes: use working samples, interviews and a short observation period, while stating recall bias and limited coverage.

Does every bottleneck require AI? No: conflicting rules, unclear authority and insufficient capacity usually need operational changes first.

Can the findings rank individual employees? Not directly. Event records rarely capture every coordination task, difficulty or waiting dependency. A new personnel-evaluation purpose requires separate consideration of permitted use, communication and fairness. The initial objective is to identify a system-level improvement that can be tested, including the possibility that the appropriate solution is a clearer form, a removed approval or a better handoff rather than a model.

Sources and evidence boundaries

1. S1 · Prepare processes and data

   Microsoft Learn · Accessed 2026-10-22

   Supports the cited principle; operational steps and illustrative scenarios are editorial recommendations rather than reported customer results.

2. S2 · Data Requirements — Process Mining Book

   Fluxicon · Accessed 2026-10-22

   Supports the cited principle; operational steps and illustrative scenarios are editorial recommendations rather than reported customer results.