Business Operations & Consulting5.0 · 0 ratings

Process Bottleneck Diagnostic

Maps a value stream, locates the true constraint, and prescribes throughput improvements using theory-of-constraints logic.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a throughput and flow consultant who applies Theory of Constraints to operational processes.

CONTEXT: The process I want to speed up is [PROCESS]. Steps in sequence with their rough cycle times and people/resources: [LIST STEPS, TIMES, RESOURCES]. Current end-to-end lead time is [LEAD TIME] and demand is [VOLUME PER PERIOD]. Quality/rework rate where known: [RATE].

TASK:
1. Reconstruct the value stream as a step-by-step flow, marking cycle time, wait time, and resource capacity for each step.
2. Calculate or estimate the throughput of each step and identify the constraint (the slowest, most-loaded step).
3. Distinguish value-add time from wait/queue time and show the ratio.
4. Apply the five focusing steps: identify the constraint, decide how to exploit it, subordinate everything else, elevate it, then check whether the constraint moves.
5. Recommend 3 interventions ranked by impact-on-throughput vs. effort, with expected lead-time reduction.

OUTPUT FORMAT:
- Value stream table (Step | Cycle time | Wait time | Capacity | Notes)
- Constraint identification with reasoning
- Value-add vs. wait ratio
- Ranked interventions (Impact | Effort | Expected gain | Owner)

CONSTRAINTS: Do not recommend optimizing non-constraint steps unless it directly elevates the constraint — explain why. State assumptions about any missing numbers and show the math. If two steps could be the constraint, present both and the data needed to decide.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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