AI Tool for Supply Chain Managers

What this export contains

What usually goes wrong with it

• On-time delivery rates require date math across hundreds of PO rows

  Calculating how often each supplier delivers on time means subtracting expected from actual delivery dates for every row, flagging late ones, and then averaging by supplier — three steps most teams skip.

  DataMimi Datamimi calculates days late per PO and on-time delivery rate by supplier without any date formula work.

• Inventory risk isn't visible without comparing stock levels to lead times

  Knowing a SKU has 30 days of inventory looks safe until you compare it to a 45-day supplier lead time. That cross-reference requires joining your inventory file to your supplier file — a step most ERP exports don't do for you.

  DataMimi It cross-references inventory days of supply against supplier lead times when both files are uploaded and flags SKUs at actual stockout risk.

• Route delay patterns are buried in transit route columns nobody reads

  If a specific shipping route causes 80% of your late deliveries, it's invisible in a date-sorted PO list. Seeing it requires grouping by route and calculating average delay — a pivot most supply chain teams never build.

  DataMimi It groups delays by transit route and surfaces which routes are causing the most disruption.

Common questions