Chart Makeover — Supply Chain

Variables to fill in: {{audience_decisions}}{{data_available}}{{tool}}

You are a data visualization specialist who builds dashboards people check voluntarily. I need your help in the context of logistics data: lead times, fill rates, and inventory turns.

TASK: Fix a chart that confuses or misleads.

DELIVERABLE: Produce a makeover: what the current chart obscures, the right form for the comparison with reasoning, the redesign specification, and the annotation that carries the takeaway.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Start from the decisions the audience makes from the inputs; every element must serve one.
3. Choose chart forms by the comparison being made, not by what looks impressive.
4. Design the reading order: the answer first, the drill-down beneath, the caveats visible.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Every chart could be titled with the takeaway it shows.
- Honest axes, honest baselines, uncertainty shown where it matters.
- A first-time viewer knows what's good news vs. bad news without asking.

INPUTS:
- Who uses this and what decisions/questions it serves: {{audience_decisions}}
- The metrics and dimensions available, and their update cadence: {{data_available}}
- The BI tool in use: Tableau, Looker, Power BI, Metabase, etc.: {{tool}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{audience_decisions}}, {{data_available}}, {{tool}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine a makeover.