Process Mapper

process-mapper

BPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders.

Purpose

Internal-operations work suffers from three recurring failure modes:

1. Implicit process — the steps exist only in tribal knowledge, so handoffs drop and onboarding takes weeks.
2. Invisible waiting — most of the elapsed time on any business process is queue / wait / approval time, not actual work; teams optimize the wrong stage.
3. Local optimization — Goldratt's Theory of Constraints is ignored; resources are added to non-constraint stages, gaining nothing.

This skill produces a documented process map, identifies where work waits, and points the constraint out by name with deterministic logic — not LLM intuition.

When to use

• Documenting a new business process (procurement intake, vendor onboarding, employee onboarding, incident handoff, expense reimbursement, customer onboarding, claims adjudication).
• An existing process is "too slow" but nobody can name the bottleneck.
• Cycle time is being measured but value-add ratio is not — so the team can't tell whether the process is healthy or waste-heavy.
• Cross-functional handoffs are dropping work and root cause is unclear.

Workflow

Five-step deterministic flow:

1. Intake. Capture the process as a JSON file with one entry per stage: name, owner, type (value-add | wait | rework), duration_minutes_p50, duration_minutes_p90. Use assets/process_template.md and its JSON skeleton.
2. Map stages. Run process_documenter.py to produce an ASCII swim-lane diagram + a normalized JSON artifact. The swim-lane separates lanes by owner so cross-functional handoffs become visible.
3. Measure cycle time. Run cycle_time_analyzer.py to compute total P50, total P90, value-add ratio (VA%), and a Little's-Law throughput estimate. Verdict: VA% > 25% = HEALTHY, 10–25% = TYPICAL, < 10% = WASTE-HEAVY.
4. Detect bottlenecks. Run bottleneck_detector.py with the appropriate --profile (saas / services / manufacturing / healthcare). Output is a ranked list with severity (CRITICAL / HIGH / MEDIUM), root-cause hypothesis, and one recommended action per finding.
5. Recommend. Pair the bottleneck list with the cycle-time verdict; recommend a single constraint-focused intervention per Goldratt's "subordinate everything to the constraint" rule. Don't recommend optimization of a non-constraint stage.

Scripts

scripts/process_documenter.py — Reads a process JSON, validates it, and emits a text-based BPMN-style swim-lane diagram in Markdown (lanes by owner, stages annotated with type + duration). Also outputs a normalized JSON artifact for downstream tools. Stdlib only. --sample prints a 6-stage procurement-intake example.

scripts/bottleneck_detector.py — Applies three deterministic detection rules: (a) stage P50 > 2× mean of value-add stages, (b) wait-state % > 40% of total cycle, (c) rework % > 15%. Thresholds adjust by --profile because SaaS, services, manufacturing, and healthcare have different "normal" wait ratios. Output is a ranked list with severity, hypothesis, action.

scripts/cycle_time_analyzer.py — Computes total P50 and P90 cycle time, value-add ratio (VA%), wait %, rework %, and a Little's-Law throughput estimate (WIP / cycle time). Per Lean canon: VA% > 25% = HEALTHY, 10–25% = TYPICAL (most non-manufacturing processes land here), < 10% = WASTE-HEAVY.

Quick example

# Renders a BPMN-style swim-lane diagram + normalized JSON for the built-in 6-stage procurement-intake example
cd business-operations/skills/process-mapper && python3 scripts/process_documenter.py --sample

References

• references/lean_six_sigma_canon.md — TIMWOOD wastes, value-stream mapping, Theory of Constraints, Kanban WIP, Little's Law. Cites Womack & Jones, Rother & Shook, Goldratt, Ohno, Liker, Pyzdek, Anderson.
• references/bpmn_essentials.md — Pools, lanes, gateways, events, message flows, common notation mistakes. Cites the OMG BPMN 2.0 spec, Silver, Allweyer, Freund/Rücker, OASIS, ISO/IEC 19510:2013.
• references/bottleneck_anti_patterns.md — Seven specific anti-patterns drawn from Goldratt, Kim et al., Spear, DORA, Deming, and process-mining research.

Assumptions

1. The user can provide stage-level cycle-time data (even rough P50 / P90 estimates). If they cannot, the first step is to instrument the process — not to map it.
2. "Process" here means a repeatable business workflow with discrete stages, not a one-off project.
3. The user has authority to act on bottlenecks (or can route findings to someone who does). Without that, the output is academic.
4. Stage type is honest: a "value-add" stage labeled as such by the user really does change the work product from the customer's perspective. Mis-labelling waiting as value-add is the most common data-quality failure.

Anti-patterns

• Mapping every process at once. Pick one. Goldratt: the constraint is a single point.
• Optimizing the non-constraint. If stage 4 is the bottleneck, speeding up stage 2 just builds inventory in front of stage 4. Subordinate everything to the constraint.
• Mistaking total cycle time for processing time. They are almost never the same; VA% reveals the gap.
• Adding people to a wait-bound process. Wait time is not solved by more headcount; it's solved by removing the handoff or batch.
• Treating rework as a separate problem. Rework loops belong in the process map. Hiding them understates true cycle time.

Distinct from

• business-growth skills — external sales motion, lead-funnel conversion, customer-success retention. Process-mapper is internal operations.
• engineering/slo-architect — system-reliability SLOs / error budgets / burn-rate alerts. Process-mapper is business-process cycle time, not system uptime.
• c-level-advisor (COO / CEO) — strategic prioritization of which processes to fix. Process-mapper is the tactical instrument used after that prioritization decision.
• project-management skills — Jira / Confluence ticket workflow tooling. Process-mapper is process design, not ticket tracking.

Forcing-question library (Matt Pocock grill discipline)

Before invoking the tools, the orchestrator (or /cs:grill-bizops) walks the user through these questions one at a time, with a recommended answer + canon citation. Never bundled.

1. "Do you have measured cycle times for the top-3 longest stages, or only estimates?" Recommended: insist on measured data. Canon: Goldratt 1984 (The Goal) — optimizing estimated bottlenecks reliably attacks the wrong constraint.

2. "Are you mapping the current process (as-is) or the intended process (to-be)?" Recommended: map as-is first. To-be after bottleneck is identified. Canon: Rother & Shook 1999 (Learning to See) — value-stream mapping starts with the current state, always.

3. "Where do handoffs occur between teams, and how long does each handoff wait?" Recommended: log every handoff with median wait time. Canon: Reinertsen 2009 (Principles of Product Development Flow) — wait time at handoffs is the largest invisible cost.

4. "What's your batch size at each stage?" Recommended: drive batch size toward 1 wherever possible. Canon: Anderson 2010 (Kanban) — batch size correlates 1:1 with cycle time variance.

5. "What's the rework rate per stage?" Recommended: surface it explicitly; rework loops belong in the map. Canon: Pyzdek (Six Sigma Handbook) — hidden rework drives 30-50% of total cycle time in service processes.

Walk depth-first. Don't open question 4 before 1-3 are answered. After all 5 are locked, invoke process_documenter.py → bottleneck_detector.py → cycle_time_analyzer.py in sequence.