Analytics

A Supply Chain Metrics Dashboard With AI

A supply chain metrics dashboard tracks lead time, on-time delivery rate, fill rate, inventory turnover, and supplier performance in one view. Without it, these numbers live in 4 separate spreadsheets and you spend 2 hours each week pulling them together for the Monday review. At aidowith.me, the Analytics Dashboard route builds this in 12 steps over about 1 hour 30 minutes. You'll start with a raw CSV export from your ERP or inventory tool, define the 6-8 metrics that matter most to your operation, and use AI to write the formulas, conditional formatting rules, and chart configurations. The route includes a supplier scorecard section that ranks vendors by on-time rate and defect percentage. A named range layer protects your formulas from breaking when the export format changes. By the end, you'll refresh the whole dashboard in under 5 minutes each week by swapping the data file.

12 steps ~1h 30min For analysts Free
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The Problem and the Fix

Without a skill

  • Lead time data is in one sheet, inventory turns in another, and on-time delivery in a third: reconciling them takes 90 minutes every Monday
  • Your supplier performance review relies on whoever remembers to pull the numbers, not a consistent source
  • You built a dashboard once but it broke when the CSV column headers changed in the next export

With aidowith.me

  • Consolidate lead time, fill rate, inventory turns, and supplier scores into one dashboard that refreshes in 5 minutes
  • Use AI to write the formulas and handle column mismatches so the dashboard doesn't break when the export format changes
  • Share a supplier scorecard that ranks vendors by 3 objective metrics without manual calculation

Who Builds This With AI

Ops & Analysts

Summaries, process docs, and structured output from messy inputs.

Managers & Leads

Reports, presentations, and team comms handled faster.

Marketers

Content, campaigns, and briefs done in hours instead of days.

How It Works

1

Define your 6-8 supply chain KPIs

List the metrics your weekly review uses: lead time, on-time delivery, fill rate, inventory turns, days of supply, and defect rate are the standard set. AI helps you pick which ones your data can support based on your CSV columns.

2

Build the data model and formulas with AI

Paste your column headers into the AI and ask it to write formulas for each KPI. It handles the SUMIF, AVERAGEIFS, and date-difference logic so you don't have to look up syntax.

3

Add charts and a supplier scorecard

Use AI-generated chart configurations to visualize trends and build a supplier scorecard table that auto-ranks vendors by their 30-day performance metrics.

Build Your Supply Chain Dashboard

Follow the 12-step Analytics Dashboard route at aidowith.me and ship a supply chain metrics dashboard in about 90 minutes.

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What You Walk Away With

Define your 6-8 supply chain KPIs

Build the data model and formulas with AI

Add charts and a supplier scorecard

Share a supplier scorecard that ranks vendors by 3 objective metrics without manual calculation

"My Monday prep used to take 2 hours. Now I drop in a new CSV and the dashboard updates in 4 minutes. The supplier scorecard alone saved 3 arguments with our procurement team."
- Supply chain manager, manufacturing firm
See full skill: Analytics Dashboard

Questions

The core set is lead time by supplier, on-time delivery rate, fill rate, inventory turnover, and days of supply. Add defect rate and return rate if your data supports it. The route at aidowith.me helps you pick metrics your CSV contains so you don't build formulas for data you don't have.

Google Sheets or Excel, plus any AI assistant like ChatGPT or Claude for formula generation. You don't need a BI tool or database. The route works entirely with spreadsheet exports from your ERP, WMS, or inventory system. If your export format changes, AI can rewrite the affected formulas in under 10 minutes so the dashboard doesn't stay broken.

The route teaches you to build a named range layer between your raw data and your formulas. When columns shift in the export, you update the named range definitions, not every formula. AI generates the range setup and a simple validation check that flags missing columns before they cause errors in your KPI calculations.