From a cold-room probe to a work order: how we built the Factora IoT reading pipeline
Most IoT demos end at a chart. On a factory floor, a chart is the least useful outcome. A cold room drifting above 4 °C at 1 a.m. matters only if a person on shift finds out, does something, and the batch that was in the room carries the record. That's the design brief behind Factora IoT , the industrial IoT module of FactoraERP, which we build at Tamalaki Business Network in Nairobi. This post…
The Factora IoT IoT reading pipeline processes sensor readings from various factories for industrial applications. It comprises six layers: sensors, industrial gateways, site routers, connectivity, platform, and analytics.
Sensor types include temperature, humidity, vibration, current, energy, level, flow, weight, door contacts, and machine state. Industrial gateways poll Modbus RTU/RS485 and Modbus TCP for Ethernet PLCs, and the site router handles Ethernet PLCs and meters. Connectivity options include dual-SIM LTE for flaky fibre connections and Ethernet where a plant LAN exists.
The platform verifies the sensor token, stores readings against that factory, and presents them to users signed into that plant. Analytics provide alerts, OEE, and energy per unit. These analytics are then synced with ERP for downtime, counts, energy, and quality, shaping work orders, batch records, and costs.
A key aspect of the pipeline is that the factory never opens inbound ports. The gateway always calls out, avoiding potential security risks. Additionally, each sensor has its own token, hashed on the platform, ensuring that a reading posted with a sensor's token can only be that sensor's reading.
Readings are sent via HTTPS with a per-sensor token, and an MQTT over TLS is planned for the future. The payload does not include the factory information, as the token determines the factory.
Manufacturers' protocols are used to avoid disrupting existing equipment. Modbus RTU/RS485, Modbus TCP, and OPC-UA are supported, and gateways publish engineering units instead of raw registers, allowing for consistent units across the plant. The platform stores and forwards readings when the cloud link is down, with visible buffered counts to ensure data isn't missing.
Alerts are sent via WhatsApp, SMS, and email, and acknowledged by the person on shift, who is then stored on the alert. This approach provides visibility and accountability.
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