--- title: "PostgreSQL Historian for Smart Manufacturing" description: "Replace the legacy historian with PostgreSQL. Capture PLC, SCADA, and machine data at full fidelity, query in SQL, and run AI on your plant floor." url: "https://www.tigerdata.com/smart-manufacturing" --- # More uptime higher yield faster cycles Postgres-native infrastructure for PLC, SCADA, and historian data. Capture every tag at full fidelity. Query in standard SQL. Power the AI on your roadmap. // IGNITION + WINCC OA READY // AIR-GAP READY - **84.2%** oee — Line A • 24h - **-47%** downtime — vs. baseline - **1.3%** scrap — Line A yield - **-21%** energy — KwH/unit vs baseline ## Every plant is exploring AI. Almost none are ready to deploy it. The bottleneck to AI adoption is data infrastructure. You have to fix fragmented historians, vendor-locked formats, and cloud-only platforms that can't survive the plant floor. - **98%** EXPLORING AI* — of manufacturing in 2026 - **20%** ready to deploy* — have the data structure to act - **78%** — of plants stuck between curiosity and deployment. That gap is where Tiger Data lives. Redwood Software • 2026 Manufacturing Outlook Survey of 300 manufacturing leaders ## The bottleneck is the historian. Traditional historians trap plant data in proprietary formats, cap retention at months instead of years, and force ETL before anything downstream can query it. The AI on your roadmap needs the opposite: **full-fidelity history, open formats, and SQL-native access** from line to enterprise. ### TRADITIONAL HISTORIAN - Proprietary formats - Months of retention - ETL before query - Vendor lock-in ### TIGER DATA - Postgres SQL open - Years of full-fidelity history - Query raw, in SQL - Line → plant → enterprise > Tiger Data replaces the modern historian with PostgreSQL. ## The SCADA platforms picked their historian replacement. It's us. The platforms actually running plant floors run on TimescaleDB ### SIEMENS ETM • SIMATIC WINCC OPEN ARCHITECTURE Selected TimescaleDB as the default time-series database for WinCC OA. _April 23, 2026_ - **shipping** — WinCC OA 3.21, later this year - **commercial path** — Tiger Cloud upgrade - **proof point** — CERN co-developed the NextGen Archiver on TimescaleDB ### inductive automation • ignition Named Tiger Data a Strategic Partner for Ignition. _September 23, 2026_ > CERN runs TimescaleDB as the time-series backend for the NextGen Archiver on WinCC OA — the highest-stakes industrial telemetry workload on the planet. ## Postgres-native. Plant floor ready. - **Full fidelity capture** — Sub-second tags retained for years. 10× compression. - **Air-gap ready** — Plant ops never depend on the cloud. OT stays in control. - **Standard SQL** — PostgreSQL across telemetry, events, MES, quality. - **Line → plant → enterprise** — Optional cloud sync for fleet OEE and benchmarking. - **AI and agent-ready** — Vector + hybrid search on contextualized history. - **Chosen by Ignition and WinCC OA** — Non-invasive. No rip-and-replace. ## From PLC tag to plant-floor action. - **LINE** — PLC Sensors (Tag_101=45.5) - **CONTROL** — Ignition • WinCC OA (+ context) - **PLANT** — TimescaleDB Enterprise (SQL • raw • years) - **ENTERPRISE** — Tiger Cloud (fleet OEE) - **ACTION** — Mes • BI • agents (decisions) ## What plants ship on Tiger Data Three deployments. Three different industries. One common pattern: keep what works, query everything else. ### Shipped to production in 60 days. 70% lower TCO. _(MACHINE MONITORING • SMB MFG)_ **challenge.** Small and mid-size shops were locked out of machine monitoring by high costs, 10-device minimums, and complex deployment. InfluxDB couldn't handle high ingest across thousands of devices. **Solution.** Built Sense Manufacturing on Tiger Cloud + AWS. Devices stream power and vibration every few seconds. Full-resolution telemetry in Tiger; relational data in Postgres — one SQL stack. > We built around Tiger Data. It was a very key piece — definitely not an afterthought in terms of the architectural design. > — farbod moghadden, CTO & Co-founder, Sense Manufacturing - **60d** First commit → production - **~70%** Lower TCO vs. competitor - **$50K→$3K** avoided CNC rebuild [Read the full case study](https://www.tigerdata.com/blog/how-startup-takton-shipped-just-two-months-tiger-data) ### Hundreds of machines, real-time, across plants. _(recycling • plant construction)_ **challenge.** As recycling plants grew, hundreds of machines and conveyors generated sensor data that legacy systems couldn't scale to monitor or analyze in real time. **Solution.** Tiger ingests time-series data from machines across plants. Dashboards expose machine performance, enable predictive maintenance, and surface inefficiencies live. > Tiger gives us the visibility to maintain our fleet proactively — across every plant, every machine, every shift. > — Plant Engineering, Eggersmann Gruppe - **93%** compression on raw sensor data - **100s** of machines, real-time - **Live** predictive maintenance [Read the full case study](https://www.tigerdata.com/case-studies/eggersmann-gruppe) ### Predictive analytics on the open-source factory stack. _(SMART FACTORY • OPEN SOURCE)_ **challenge.** Their previous database struggled with slow queries, inefficient retention, and high maintenance overhead on IoT sensor data — blocking predictive analytics for their manufacturing customers. **Solution.** Adopted TimescaleDB with continuous aggregates and automated retention policies. Chose it over InfluxDB specifically for predictive maintenance workloads. > TimescaleDB let us scale predictive maintenance to more manufacturing clients without growing the ops team. > — Engineering •, United Manufacturing Hub - **Faster** queries → real-time analytics - **↓ ops** via auto retention + caggs - **Scaled** predictive maintenance [Read the full case study](https://www.tigerdata.com/case-studies/united-manufacturing) ## Bring your plant into the AI era. Start with one line. Sit Tiger next to what you run today. ### SI, OEM, or platform vendor? Joint reference architectures, Co-selling. Embedded options.