OEE by line and shift
Availability, performance and quality broken down by plant, line, machine and shift, with Vis-a-Vis comparison against the same days last month or last year.
- Industries
- Industry, energy & environment
- Manufacturing
Manufacturing
From machine stop to margin impact —
From machine stop to margin impact —
one model for the whole plant.
TURBOARD brings production, quality, maintenance, cost and supply data into a single governed model, so plant managers, controllers and executives work from the same figures — per line, per shift and per product.
Video
Manufacturing analytics in action
A short tour of TURBOARD on manufacturing data: production performance, efficiency and cost analyses turned into dashboards that plant and management teams use every day. See how a question moves from the summary view down to the line, machine and shift behind it.
The challenge
Every system on the shop floor tells a different part of the story.
Machine counters and MES record output and stops, the ERP holds orders, materials and costs, quality teams log defects in their own tools, and maintenance keeps its work orders somewhere else again. Each produces a report, none of them agree, and the weekly production meeting starts with a debate about whose figure is right instead of why a line lost capacity on Tuesday.
TURBOARD connects these sources once and calculates OEE, scrap, unit cost and capacity from shared definitions. A downtime event can be followed from the plant summary down to the machine, the reason code and the shift, and its cost appears in the same model the finance team uses. Plants, lines and periods are compared on equal terms, so improvement work starts from facts everyone accepts.
500K+business users work with TURBOARD
Since 2004enterprise BI engineering, founded in Chicago
Cloud or on-premisefully on-premise deployment, AI included
PatentedBI technology, including a granted US patent
What you can do
The analyses manufacturers run every day
Downtime and loss analysis
Stops grouped by reason, duration and machine, with the lost output and its cost calculated, so the biggest losses are addressed first.
Quality and scrap
Defect rates, scrap and rework by product, batch and supplier, linked to customer complaints and returns where that data exists.
Unit cost and variance
Material, labour, energy and overhead per unit compared across periods and plants, with drill-down to the cost item that moved.
Capacity and order load
Planned versus actual output, utilisation by work centre and the open order backlog, showing where demand will exceed capacity.
Maintenance and supply
Planned and unplanned maintenance, MTBF and MTTR by asset, alongside supplier delivery performance and stock cover for critical materials.
Challenges & solutions
Where manufacturing reporting breaks down — and how TURBOARD helps
?Conflicting figures
MES, ERP and spreadsheets report different output and cost numbers for the same week.
✓One governed definition
OEE, scrap and unit cost are defined once in the model, and every dashboard and report uses the same calculation.
?Losses explained too late
Downtime causes are discussed days later, when the shift and the context are long gone.
✓Drill-down to the cause
From plant total to line, machine, reason code and shift in a few clicks, as the data refreshes.
?Plants that cannot be compared
Each site measures differently, so benchmarking turns into an argument about method.
✓Side by side on equal terms
Patented dashboard cloning puts plants, lines or periods side by side with identical definitions and layout.
?Reports that take a day to build
Controllers and engineers spend hours every week copying data into the same management pack.
✓Scheduled distribution
Production and cost packs go out automatically as PDF, Excel or PowerPoint, and each recipient sees only the plants they are authorised for.
JAS · Just Ask Simply
Ask why output dropped — and get the answer with its source
JAS, the AI assistant built into TURBOARD, answers from the same governed production model your reports use. Ask in plain language and she finds the existing report or writes the query, builds the chart and explains what changed. With personas, the same dashboard is interpreted for a shift supervisor and for the CFO without changing a single number.
Explore JAS capabilities →JJASQuestions you can ask
- Why did OEE on line 2 fall last week?
- Which machines caused the most unplanned downtime this month, and what did it cost?
- Compare scrap rates by shift for the last 90 days.
- Show unit cost by plant against the same month last year.