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Supply chain

Supply chain analytics
Put your data to work.

See stockouts, fill rate, supplier lead times and cost to serve across your network. Ask questions of your own data, check the work behind every answer, and rerun the analyses your planners rely on.

Your weekly review deserves a better starting point.

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001 / What you can ask

Ask about stockouts, lead times and fill rate across your network

Example questions planners and supply chain leaders ask about their own data.

Yes, you can ask the awkward questions.

  • What is today’s stockout rate by SKU and location, and which nodes account for most of the gap?
  • Why are the top items short? Compare forecast error, lead time changes, late ASNs and quality holds.
  • Where are days of supply low, and which safety stock or lead time changes reduce the most risk?
  • Do approved substitute SKUs have enough units in the right distribution centers to cover the exposure?
  • Which suppliers and lanes are missing on time in full, and how does that affect fill rate and cost to serve?
  • How do service level, working capital and freight change at different risk tolerances?

002 / How it works

From planning data to analyses you can rerun
Keep the useful work.

Beye is the foundation your team builds on: connected data, real analysis, reusable tools and a history of the work.

Connect your sources

The data fabric brings in order, inventory, supplier and shipment data from the systems you already run, and keeps it current.

Ask and see the work

Ask about stockouts or fill rate in plain language. Each answer shows the query and steps behind it.

Build tools you rerun

Turn a stockout review or safety stock analysis into a tool with inputs, backed by real Python, and rerun it with new assumptions.

Keep a history

Revisions show who changed what. Saved runs keep their inputs, timing and results for reviews and audits.

003 / Frequently asked questions

Questions welcome.
Fine print, less so.

Ask by SKU and location, and Beye returns the answer with the query and data behind it, including coverage at nearby distribution centers. Save the analysis as a tool and rerun it each week.

Yes. Run the scenario in Python on your own data and compare expected in-stock and lost sales against your target. Beye does not change your source systems; your team decides what to act on.

Each answer shows the query and steps behind it, so planners can check the logic. Tools keep a revision history of who changed what, and saved runs keep their inputs and results.

004 / Related

Where this data meets the rest of the business

Manufacturing

OEE, downtime, scrap, yield and supplier performance across lines and plants.

Platform overview

How the data fabric, analysis, Python tools and history fit together.

005 / Your next step

Bring the messy part.
Let’s put it to work.

Yes, even that spreadsheet.

Bring a report, calculation or recurring check you run today. We’ll show you how Beye connects the data behind it and turns it into a tool your team can rerun.

Book a demo See it in action