Persistent delays in delivery
Distinguishes the constraint limiting throughput from delays elsewhere in the process. Provides a ranked assessment of capacity, dependencies, batching and approval queues, supported by the underlying calculations.
— Operations & Process
Bottleneck analysis
Derives utilisation and queue pressure per process step arithmetically, ranks the real constraints, and interprets each as capacity, dependency, batching, approval or failure point under load.
- Operations & Process
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~25 minutes
For a full population, not a sample
No coding required
- Input
- Process activity and ownership data
- Output
- Ranked constraints with supporting calculations
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Bottleneck analysis for
Decisions on extra capacity
Shows where additional capacity is likely to improve overall throughput and where it may simply shift the queue. Frames recommendations around the constraint rather than local utilisation alone.
Planning for higher demand
Assesses which parts of the process are vulnerable to increased volume. Identifies likely queue build-up and failure modes to inform demand planning and operational safeguards.
Evidence for operational reviews
Provides a traceable diagnosis that connects process performance data to the constraint ranking. Gives stakeholders a shared basis for challenging assumptions and agreeing improvement priorities.
What it works from
- Process activity and ownership data
- Work volumes by process activity
- Touch times and waiting times
- Available capacity by process activity
What you get back
- Ranked constraints with supporting calculations
- Utilisation and queue pressure assessment
- Constraint classification covering capacity, dependencies, batching, approval queues and failure points
- Capacity recommendations grounded in the theory of constraints*
— HOW IT BEHAVES
How Bottleneck analysis produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the end-to-end process
Each row of your export is processed on the same basis, so no process step is skipped however long the table is.
Composed as work product
Findings on the end-to-end process are written up as a document that reads like professional output, with each claim tied back to a process step.
Each process step enriched in place
Derived columns are added row by row, keeping your source data and the judgement about each process step side by side.
Roll-up alongside process step-level detail
Detail rows are summarised into the grouped view of the end-to-end process without losing the underlying process steps.
Why this is expensive by hand
Slow processes often attract fixes at the most visible delay rather than the constraint that governs overall throughput. In practice it shows up as persistent delays in delivery: distinguishes the constraint limiting throughput from delays elsewhere in the process. Provides a ranked assessment of capacity, dependencies, batching and approval queues, supported by the underlying calculations. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 1–2 weeks of senior time whenever it comes round.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces ranked constraints with supporting calculations plus utilisation and queue pressure assessment. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. Net effect: 1–2 weeks down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Operations and continuous improvement leads
- Transformation and automation teams
- Service delivery and shared services managers
- Consultants running process diagnostics
Run it in Skillsize — or export it anywhere
Bottleneck analysis exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
Related Skill Templates
More Operations & Process templates →Operations & Process
Post Go-Live Adoption
Identifies where adoption of a new system, process or way of working stalls after go-live, with an evidence-backed report on barriers, workarounds and differences between functions.
Operations & Process
AI Readiness Assessment
Assesses AI readiness through anonymous staff evidence, identifying where AI can improve day-to-day work and the practical barriers that could prevent those gains.
Operations & Process
Operating Model Mapper
Maps how an organisation actually operates using anonymous accounts from its people, producing an evidence-backed diagnostic of responsibilities, decisions, handoffs and duplicated effort.
Operations & Process
Project status digest
Reads a set of weekly status notes for progress, blockers and RAG, charts the trend, and writes an executive digest focused on trajectory.
Operations & Process
IT Change Advisory Engine
Reviews a proposed change against your change policy and returns a governed advisory decision — approve, approve with conditions, defer to the change board or reject — weighing risk, blast radius and rollback, with a change manager sign-off required before anything reaches production.
Operations & Process
Refund & Compensation Engine
Assesses a refund or compensation request against the contract terms and refund policy that govern it, then returns a decision on approving it in full, in part, offering a goodwill gesture or declining, with every element of the remedy traced to the clause that allows it and anything beyond an agent's authority routed for review.