GENERATIVE BI · SELF-HOSTED · OPEN SOURCE
Manufacturing Analytics: Self-Hosted, Real-Time BI for the Plant Floor
Manufacturing analytics that runs at the edge or on-prem. AI text-to-SQL for OEE, downtime, and quality, connecting MES, SCADA, ERP, and IoT with no cloud upload of OT data and no per-seat pricing. Book a demo.
Business Intelligence Built for the Plant Floor, Not the Cloud
Manufacturing analytics is business intelligence for the factory, where the data lives in MES, SCADA, historians, and ERP, where decisions happen in seconds, and where shipping that data to a vendor cloud is often a line nobody on the OT team will cross. Analytify gives plant managers, operations leaders, and manufacturing IT and OT teams an AI-powered platform that runs at the edge or on-premises, so analysis of OEE, downtime, and quality happens right where the machines are, without production data ever leaving the plant. It is the rare combination of real-time, generative BI and full data residency that a factory can actually deploy on the line.
Every BI vendor will sell a factory a cloud dashboard. Far fewer will run inside the plant network, connect directly to MES and historian data, keep OT data off the public internet, and still deliver plain-English, AI-driven analysis on the floor. That gap, between the cloud tool a vendor offers and the on-prem system an OT and security team can approve, is exactly what a self-hosted manufacturing analytics platform closes.
See manufacturing analytics running at the edge, on your own line. Book a 30-minute demoWhat Is Manufacturing Analytics?
The distinction from general self-service analytics is the source and the speed. A factory generates high-frequency machine data, and a stale report from last shift is worth far less than a live signal from the current one. Manufacturing analytics is the discipline that treats real-time analytics, OT data sensitivity, and shop-floor usability as first-class requirements rather than afterthoughts.
Why Factory Data Needs a Different Kind of BI
Three forces make manufacturing analytics its own discipline. Downtime is brutally expensive: across manufacturing sectors the average cost of unplanned downtime is roughly $260,000 per hour, and in automotive it can exceed $2 million per hour because of just-in-time dependencies, according to widely cited 2025 industry research. The data is operational and sensitive: MES, SCADA, and historian data often cannot leave the plant network for security and continuity reasons. And the pace of investment is accelerating: Rockwell Automation reports that over 95% of manufacturers are now investing in AI as part of smart manufacturing programs.
$260K/hraverage cost of unplanned downtime across manufacturing sectors (2025 industry research). $1.5Testimated annual downtime cost to Fortune Global 500 firms, about 11% of revenue (Siemens True Cost of Downtime 2024). 95%+of manufacturers now investing in AI (Rockwell 2025 State of Smart Manufacturing).The takeaway for a plant manager or COO: the upside of analytics and AI on the floor is real, but only if it can run where the OT data is governed. An analytics platform that forces a cloud upload of machine data is a non-starter for the most sensitive, highest-value production workloads.
What Can Manufacturing Analytics Do? Core Use Cases
OEE and equipment effectiveness
Overall Equipment Effectiveness is the headline metric of the plant: availability multiplied by performance multiplied by quality. An OEE dashboard breaks the score into its three losses so a line can see whether the problem is stops, slow cycles, or scrap, and act on the right one first.
Production throughput and scheduling
Track output against plan by line, shift, and product, spot bottlenecks, and see throughput in real time instead of reconstructing it after the shift ends. This is where MES analytics turns the execution layer into a live view of the floor.
Downtime and root-cause analysis
Capture every stop with its reason code, rank downtime by cause and machine, and find the recurring failures that quietly erode availability. Catching micro-stoppages is often where the largest hidden losses sit.
Quality, scrap, and yield
Monitor first pass yield, scrap rate, and defect Pareto by line and product, and tie quality back to the upstream process settings that caused it, so quality stops being a lagging report and becomes a control input.
Predictive maintenance
Use sensor and vibration data with predictive analytics to anticipate failures before they stop the line, shifting maintenance from reactive firefighting to a planned, scheduled discipline that protects MTBF.
Supply, inventory, and energy
Essential Manufacturing Dashboards and KPIs
A strong smart factory BI layer tracks the metrics the floor, the maintenance team, and the plant leadership all watch. These are the core ones.
Build these manufacturing dashboards on data that never leaves your plant. Talk to our solution team| KPI | What it measures | Why it matters |
|---|---|---|
| OEE | Availability x performance x quality | Single headline measure of line effectiveness |
| Availability | Run time versus planned production time | Exposes downtime and changeover loss |
| Performance | Actual speed versus ideal cycle time | Surfaces slow cycles and small stops |
| Quality (FPY) | Good parts on first pass | First pass yield and rework cost |
| MTBF | Mean time between failures | Equipment reliability |
| MTTR | Mean time to repair | Maintenance responsiveness |
| Scrap rate | Rejected output as a share of total | Material waste and yield loss |
| Cycle time | Time to produce one unit | Throughput and capacity planning |
| On-time delivery | Orders shipped on schedule | Customer commitment and supply health |
Connecting MES, SCADA, ERP, and IoT
Manufacturing analytics is only as good as the systems it can reach. Analytify connects directly to the execution and enterprise layers a factory already runs, so MES analytics, historian readings, and ERP context land in one governed model instead of a dozen disconnected exports. It reads from SQL Server, Oracle, PostgreSQL, and SAP HANA where MES and ERP data live, and pulls IoT and sensor streams through a real-time data pipeline. A consistent semantic layer means a downtime reason or a yield definition means the same thing whether the question comes from the line or the boardroom.
Deployment and OT Security: Where Your Data Lives
This is the section every other manufacturing analytics page skips, and it is the one that decides the deal. Analytify is a self-hosted BI tool. It runs on plant-floor edge hardware, on-premises in your own data center, or in a private cloud account, so OT data from MES, SCADA, and historians stays inside the plant network and never transits a vendor cloud.
Because it is open source, the plant’s own IT, OT, and security teams can examine the code directly rather than trust a black box bolted onto the control network. For a function that touches operational technology, an auditable, self-hosted system is far easier to approve than one that assumes a permanent connection to a vendor’s cloud BI backend. Running at the edge also keeps analysis alive when the wider network is not, which matters on a floor where uptime is the whole point. The same self-hosted posture already underpins our work across financial services and managed services.
Ask Your Data in Plain English: AI Text-to-SQL for the Plant Floor
Self-hosting does not mean giving up modern AI. Analytify brings generative BI and AI-powered business intelligence inside the plant network, so a supervisor or process engineer can ask a question in plain English and get a governed SQL query back, all without the data leaving the floor.
Ask: “Show OEE by line for the last shift and break down availability loss by downtime reason.”→ Analytify writes the SQL against your MES and historian data, returns OEE per line, ranks the top downtime reasons, and keeps the query visible for review.
Pairing real-time AI with on-prem data residency is the combination no cloud-first incumbent leads with, and it is the most defensible thing a factory analytics stack can offer in 2026. It also means the people closest to the machines get answers without waiting on a central embedded analytics backlog.
How Does Analytify Compare to Tulip, Sight Machine, MachineMetrics, and Power BI?
The specialist platforms are capable, but most are cloud-first and priced per interface, per machine, or per seat. Tulip, for example, is cloud-based and bills per interface with a multi-interface minimum. For a plant weighing OT security and total cost, the deciding factors are hosting, auditability, and licensing.
For specific side-by-sides, see Analytify vs Power BI, Analytify vs Tableau, and Analytify vs Qlik Sense, or review pricing.
| Capability | Tulip / Sight Machine / MachineMetrics / Power BI | Analytify |
|---|---|---|
| Self-hosted, on-prem, or edge | Limited or cloud-first | Yes, by default |
| Open source and auditable code | No | Yes |
| OT data residency, no cloud upload | Often requires vendor cloud | Data stays in your plant network |
| Real-time floor analytics plus AI text-to-SQL | Real-time or AI, rarely both on-prem | Both, inside your environment |
| MES, SCADA, ERP, and IoT connectivity | Varies by product focus | Unified across all four |
| Licensing | Per interface, per machine, or per seat | Platform license, unlimited internal users |
Frequently Asked Questions
It is the use of business intelligence and machine data to measure and improve production. It connects MES, SCADA, ERP, and IoT to track OEE, throughput, downtime, quality, and maintenance, then turns that data into real-time dashboards the plant floor can act on.
OEE equals availability multiplied by performance multiplied by quality. Availability is run time over planned production time, performance is actual speed over ideal cycle time, and quality is good parts on first pass. An OEE dashboard splits the score into those three losses so teams fix the right one first.
Yes. Analytify is self-hosted and runs on plant-floor edge hardware, on-premises, or in a private cloud account, so OT data from MES, SCADA, and historians never leaves the plant network or transits a vendor cloud.
Across manufacturing sectors the average cost of unplanned downtime is roughly $260,000 per hour, and in automotive it can exceed $2 million per hour, according to widely cited 2025 industry research. Siemens estimates downtime costs Fortune Global 500 firms about $1.5 trillion a year.
It connects to MES and SCADA on the execution layer, historians for time-series machine data, ERP for orders and inventory, and IoT sensors for real-time signals, then unifies them in one governed model with a consistent semantic layer.
Yes, and the open code is an advantage. IT, OT, and security teams can inspect exactly what the software does before it touches the control network, while self-hosting at the edge keeps production data inside the plant.
OEE and its three components, MTBF and MTTR for reliability and repair, first pass yield and scrap rate for quality, cycle time for throughput, and on-time delivery for supply commitments.
Specialist platforms are typically priced per interface, per machine, or per seat, which scales up fast across a plant. Analytify uses a platform license with unlimited internal users on infrastructure you already run, usually far lower in total cost.
See Analytify running on your own data
Book a walkthrough and we will show Analytify against a stack like yours, self-hosted, with no per-seat pricing.
