---
title: "OEE Analytics on Live Data: From Nightly Exports to Real-Time Dashboards"
url: https://daily.dev/posts/oee-analytics-on-live-data-from-nightly-exports-to-real-time-dashboards-gdrmu8xbp
source_url: https://cratedb.com/blog/oee-analytics-real-time-dashboards
type: article
source: "CrateDB"
published: 2026-05-29T08:02:41.596Z
updated: 2026-05-29T08:03:14.100Z
tags: ["data-science", "backend", "manufacturing", "real-time-analytics", "cratedb"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

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# OEE Analytics on Live Data: From Nightly Exports to Real-Time Dashboards

**[CrateDB](https://daily.dev/sources/cratedb)** · 7 min read · 0 upvotes · 0 comments

## Summary

Batch-export OEE pipelines introduce 15–60 minute data lags that prevent shift supervisors from responding to production issues in time. By replacing the export step with CrateDB as a real-time analytics database, sensor data becomes queryable within milliseconds of ingestion via standard SQL and PostgreSQL wire protocol. A concrete OEE SQL query is provided that calculates Availability × Performance × Quality per asset per hour on a rolling 8-hour window. ALPLA, a global packaging manufacturer with 181 facilities and 900 sensor types per factory, cut query times from 3–5 minutes to milliseconds (250x improvement) after migrating from SQL Server to CrateDB, without rewriting existing SQL or changing reporting tools.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://cratedb.com/blog/oee-analytics-real-time-dashboards>

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---

Tags: [#data-science](https://daily.dev/tags/data-science), [#backend](https://daily.dev/tags/backend), [#manufacturing](https://daily.dev/tags/manufacturing), [#real-time-analytics](https://daily.dev/tags/real-time-analytics), [#cratedb](https://daily.dev/tags/cratedb)

[View this post on daily.dev](https://daily.dev/posts/oee-analytics-on-live-data-from-nightly-exports-to-real-time-dashboards-gdrmu8xbp)