A persistent problem in manufacturing systems is the misalignment between enterprise records and physical events.
An ERP system may indicate material issue but an MES may show a production progress, or RFID/RTLS infrastructure may register a movement event - what business logic can be derived from these signals?
One viable approach is to design the logistics visibility system as an event processing problem.
- Define a small set of events that you want to make actionable:
AssetDetected
ContainerArrived
MaterialIssued
ReplenishmentRequested
WIPZoneEntered
WIPZoneExited
These are just example names - your actual classes will depend on the problem domain, but there should always be an event ID/time, asset/container ID, location/zone, and source device where applicable.
It is crucial to avoid "noise" from the physical world. A single RFID reader or BLE gateway may provide raw observations that require deduplication, validation, or contextualization before producing an actual business event.
- Normalize across devices
Modern smart factories often combine RFID readers, BLE gateways, UWB anchors, barcode scanners, vehicle telemetry, and legacy production systems.
Each of these data sources produces messages in different formats and with different levels of fidelity and confidence.
Normalization layer would parse these events into a common set of internal event objects, retaining as much source metadata as needed for debugging and auditing.
- Deduplicate and decouple
Wireless devices are prone to produce duplicate messages or get disconnected unexpectedly. The same physical object may appear multiple times in proximity to the same device, or appear in several different locations at the same time due to wireless interference.
The pipeline would need to handle event deduplication, temporal decoupling, and idempotency.
For example, your code would not want to raise a replenishment request for a container that has been several times detected near the same RFID reader.
- Enrich events with contextual information
Location data is frequently insufficient for downstream consumption.
Use the domain knowledge to enrich the events with additional data: what was the destination zone, what was the issued material, did it match the expected quantity, or what is the current status of the associated WIP order.
This is where the raw telemetry data turns into actual information.
- Decouple detection from decisions
Another pattern that helps production-grade systems deal with the noisy data is to split the detection (what happened) and the decision logic (what should we do).
For example, when a container is seen near a production cell, the first part of the pipeline would determine if it has entered a certain zone - a lower-level detection event.
Then, a higher-level rule engine could analyze this event and a set of other criteria (did the container contain the right materials, at the correct quantity, etc.) to make a business decision: whether to update the item status, confirm a WIP order, or raise a quality control alert.
- Monitor the operational performance
Beyond simply processing the events, it can be valuable to measure both the technical and the operational business metrics.
The technical layer should track the health of the pipeline: what percentage of events needed deduplication, contained invalid data, or came from unregistered devices.
The business layer should track how well the visibility layer supports the factory operations: how quickly materials were found, what percentage of expected movements were actually observed, or how frequently inventory discrepancies had to be resolved.
Both technical and business metrics help to evaluate if the pipeline is working correctly, and if it provides sufficient value to the operations team.
The broader industrial logistics visibility problem
PlantLog AI is a deep-tech company that focuses on industrial AIoT applications, including in-plant logistics, asset tracking, inventory management, and material flow visibility. The PlantLog AI website has more information about the company's approach to the logistics visibility problem in manufacturing.
The key considerations from this post - producing validated events and enriching them with business context - are applicable to a wide range of industrial applications, and can be used in conjunction with many different technologies, including RFID, Bluetooth, Ultra-Wide Band, computer vision and others.
Top comments (0)