Business Intelligence for Manufacturing: From Plant to Enterprise
Plant BI for shift-level decisions, enterprise BI for executive rollups, and the data model that connects them. Standard KPI definitions, governance, and the semantic layer that stops every plant from inventing its own version of OEE.
Why Multi-Plant Manufacturers Have Three Versions of OEE
In a multi-plant manufacturer, every plant has its own way of calculating OEE. Plant A excludes planned downtime from availability. Plant B includes it. Plant C includes setup time but not changeover. Plant D defines "good parts" as anything that ships, including rework. When the COO asks for an enterprise OEE rollup, the number is meaningless because the plants are measuring different things — and worse, each plant manager defends their definition because it makes their numbers look better.
The fix isn't a new dashboard. It's a governed semantic layer with KPI definitions that every plant agrees to, calculated centrally from the same source data, with enough decomposition that each plant can still see its own performance honestly. That requires alignment across plants (politically hard), a shared data model (technically hard), and the semantic layer technology to enforce the definitions (Power BI, Tableau, ThoughtSpot). All three matter.
How Manufacturers Apply It
Standardized KPI Library
Single definitions for OEE, FPY, scrap rate, on-time delivery, and the 15-20 KPIs that matter to manufacturing leadership. Calculated centrally, distributed everywhere, governed through change control. The end of "my OEE is 78%" / "no, our OEE is 71%" arguments at the monthly ops review.
Plant Operations Dashboards
Shift-level dashboards showing real-time production status, OEE by line, downtime reasons, quality holds, and shift handover summaries. Built for the plant's actual workflow — not generic templates. Used on the floor, not just in the office.
Enterprise Manufacturing Rollups
Multi-plant rollups for the COO and CFO — OEE, output, scrap cost, on-time delivery, and labor productivity rolled by region, division, and product family. Drillable from enterprise summary to specific line and shift in three clicks. Same numbers as the plant dashboards because they use the same semantic layer.
What You Receive
Manufacturing BI rolled out across plants and the enterprise: shared KPI library with governed definitions, semantic layer in Power BI / Tableau, plant operations dashboards designed for shop-floor workflow, executive rollups with drill-through, the change control process that keeps the KPI definitions stable, and the training that lets plant analysts build their own reports without breaking governance.
Related Xylity Capabilities
Business Intelligence Consulting
The full Business Intelligence Consulting practice across industries.
Manufacturing Industry Hub
All manufacturing technology services from Xylity.
All 22 Industries
Industry-specific consulting across the verticals we serve.
Business Intelligence for Manufacturing — FAQ
Honestly, you don't — not without executive sponsorship. We've done this enough times to know that the technical work is easy and the political work is hard. We bring a battle-tested KPI library based on SEMI E10, ISO 22400, and industry consensus, then facilitate the workshops that get the plants to align on the definitions and the COO to enforce them.
All three can work. Power BI wins on cost and Microsoft ecosystem integration; Tableau wins on visualization sophistication; ThoughtSpot wins on natural language querying for casual users. The right choice depends on your existing stack, budget, and whether your audience is analysts or operators. We'll recommend based on facts, not vendor preferences.
Yes. Pre-qualified BI developers and analytics engineers with manufacturing KPI fluency, semantic layer experience, and Power BI / Tableau / ThoughtSpot certifications. 4-stage consulting-led matching, 92% first-match acceptance.
One OEE Definition.
Every Plant. Finally.
Governed KPI library, plant dashboards, and enterprise rollups built on the same semantic layer — so the numbers reconcile.