Leading Iron Flow Battery Manufacturer

As a growing iron flow battery manufacturer scaling production to meet demand for long-duration industrial energy storage, the company faced a critical operational gap: no automated system to detect or communicate machine stoppages in real time. Unplanned downtime in energy storage manufacturing carries compounding costs — halted assembly lines, delayed shipments, and disrupted decarbonization commitments to customers. Supervisors had no visibility into equipment status without physically checking the floor, and maintenance teams learned of stoppages only after significant production time had already been lost. The absence of structured cause-code capture also made root-cause analysis slow and inconsistent.

The manufacturer implemented Tulip's composable MES platform with real-time machine monitoring as part of a broader digital transformation. Using Tulip's Edge IO hardware, production equipment was connected to the platform to stream live status signals without requiring custom sensor infrastructure. When a machine stoppage is detected, Tulip triggers an automation that instantly posts an alert — including a cause code and timestamp — to a dedicated Microsoft Teams channel shared with supervisors and maintenance staff. This edge-to-cloud integration required no bespoke engineering: the team built the alerting workflow directly within Tulip's no-code app environment, layering machine monitoring on top of existing quality and genealogy tracking apps already in production.

Machine downtime events are now detected and communicated to the relevant teams within 20 minutes — a meaningful threshold in high-throughput energy storage assembly where every halted hour has downstream impact. Before implementation, stoppages could go undetected for far longer with no structured response path. Key outcomes include:

• Sub-20-minute alert time from stoppage to team notification via Microsoft Teams
• Faster root-cause analysis enabled by automatic cause-code and timestamp capture at the moment of each event
• Improved supervisor visibility through real-time production dashboards alongside the alerting system
• Collaborative response model replacing ad-hoc floor communication with a shared, documented channel

• Real-time alerting is a prerequisite for predictive maintenance — even before investing in ML-based prediction, structured IoT alerts dramatically reduce response lag.
• Edge IO removes the sensor procurement barrier — Tulip's hardware connects directly to existing machines, making deployment accessible for SMEs without dedicated automation engineers.
• Integrating alerts into existing collaboration tools (Teams, Slack) drives adoption — maintenance teams respond faster when notifications arrive where they already work.
• Cause-code capture at alert time builds the dataset for future analytics — structured stoppage data collected now enables trend analysis and predictive modeling later.
• A phased MES approach works — starting with quality, then genealogy, then machine monitoring lets teams build confidence before expanding scope.