Demonstration · Illustrative data · BI Consulting Services

JOST International × BI Consulting Services

Grand Haven, MI · Greeneville, TN · serving the US, Canada and Mexico

Prepared for Al
September 2026 · Plant & Order Performance

Two plants. One lead-time promise.

Open orders against the published 3-week fifth-wheel and 5–6-week landing-gear targets, on-time delivery, scrap & rework and OEM customer mix — on one page that recalculates in milliseconds because the model is an Import star schema, not DirectQuery.1

Recalculated in · Import model, no DirectQuery round-trips
Trend · lead time vs published target

Weeks of open orders on the books, by month

Backlog ÷ shipping rate, in weeks. Dashed line is the published target for the selected mix (3 wks fifth wheels · 5–6 wks landing gear).

Breakdown · backlog vs target by line & plant

Where the lead-time promise is at risk

Bar = current weeks of backlog. Tick = published target. Colour = status.

Signature · plant-to-OEM supply map

Shipments from two plants to OEM truck & trailer plants in three countries

Arc width = units shipped in the period. Hover a destination or a plant.

Grand Haven, MIGreeneville, TNOEM plant
Customer mix

Units by customer segment

Truck OEMs, trailer OEMs, dealers & aftermarket, vocational (HYVA).

Quality · scrap & rework

Scrap and rework as % of units produced

Stacked by month. Plant target: ≤ 2.5% combined.

ScrapReworkTarget 2.5%
What this board answers

Three questions, three numbers

Ask the data · mock

Plain-English questions, answered from the same model

Illustration of a private AI layer over the star schema. Pick a question — the answer recalculates with your filters.

Why this page loads in seconds when the source SQL is slow

Import star schema with incremental refresh — not DirectQuery

The pilot ran DirectQuery against the production SQL Server, so every click waited on the database. Here the heavy work happens once per refresh, off-hours; the report answers from an in-memory model.

  • 01Star schema. One fact table (order lines: ordered, shipped, on-time, scrap, rework) and five dimensions — Date, Plant, Product line, Customer, Destination — instead of wide ad-hoc views.
  • 02Incremental refresh. Only the last 3 days of order lines are re-read each night; 24 months of history stay as compressed partitions. Refresh runs in minutes, not hours.
  • 03Manual data in the same model. Scrap tallies and OEM forecasts from Excel land in a staging table with the same keys, so they join to the fact table like any other source.
  • 04Measured, not assumed. The timer in the header is this page recalculating data cells across every panel after your last click.
Also from BI Consulting Services

Private AI on your own server

The "ask the data" ideas on this board are designed to run on a GPU server inside your own network — an open-weight model over your own documents, cited answers, no per-seat licence, nothing leaving the building. The short deck below explains how it works. Scroll through, or download it as a PDF.

Private AI Servers — slide 1 of 14
Private AI Servers — slide 2 of 14
Private AI Servers — slide 3 of 14
Private AI Servers — slide 4 of 14
Private AI Servers — slide 5 of 14
Private AI Servers — slide 6 of 14
Private AI Servers — slide 7 of 14
Private AI Servers — slide 8 of 14
Private AI Servers — slide 9 of 14
Private AI Servers — slide 10 of 14
Private AI Servers — slide 11 of 14
Private AI Servers — slide 12 of 14
Private AI Servers — slide 13 of 14
Private AI Servers — slide 14 of 14

Questions? Book 30 minutes: calendly.com/powerbiconsultingservices/30min