The industrial IoT discourse tends to begin with concepts such as sensors, connectivity, dashboards, and predictive maintenance.
However, there's another seemingly simple but often underestimated issue to address:
The ability to know the location of physical objects.
While software objects typically have a defined identity and state, the physical realm of the factory floor or industrial facility is inherently less structured.
A coil can pass through different stages such as production, temporary staging, and storage. Tools may be temporarily removed for maintenance. Replacement parts can be moved between different areas.
Work-in-progress items might remain in their locations longer than production plans anticipate.
This discrepancy can lead to a gradual divergence between the digital record and the real-world physical situation.
The Identity Challenge
An effective industrial visibility system must reconcile three crucial elements:
Identity: The specific identifier of the physical asset or material.
Location: its current physical position within the facility.
Operational State: Its status within the production or operational process.
While a database might identify that a coil exists, a location tracking system might reveal its position, and a production system might show its process state. However, the truly challenging engineering task lies in integrating these three viewpoints into a coherent and operational model.
Location is Just the Starting Point
An application might provide the information:
Coil-8472 Storage Zone B
This information is valuable, but it doesn't answer important follow-up questions:
Is the coil ready for shipment?
Is it awaiting an inspection?
Has it been stationary in Zone B for the last three days?
Was it expected to be elsewhere?
Was the relocation due to a rescheduling event?
This is where contextual data becomes significantly more critical than just the raw location of an object.
The Role of AI
Once sufficient data on movement and process events has been collected, Artificial Intelligence (AI) can be leveraged to identify recurring patterns. For example, a model could flag materials that consistently take longer than usual to transition between two particular stages.
This doesn't necessarily pinpoint the root cause. Instead, it highlights an area for further investigation. Ultimately, the next steps involve engineers and operations teams determining the specific reason for the deviation.
The bottleneck could be related to crane availability, congestion in the storage area, delays in inspections, or issues with production scheduling causing unnecessary movement.
The goal of an effective industrial AI system should be to facilitate this investigative process, not to create the illusion of automated solutions.
A Functional Architecture
A streamlined architecture would incorporate:
Physical Asset → Identification → IoT / Event Capture → Location & Movement History + Production Context → Analytics / AI → Human Decision
This architecture is beneficial because it positions AI as an integral part of a broader operational ecosystem. AI is not very useful without reliable input on physical realities, and conversely, a deluge of IoT data that doesn't serve a specific operational purpose can simply become another isolated data silo.
The Bottom Line
Industrial visibility is far more than a simple tracking problem. It is a complex undertaking that involves data modeling, system integration, operations management, and informed human decision-making. The most successful systems seamlessly link physical events with business context and employ analytics to shed light on operational intricacies. This core principle applies not only to traditional industrial environments like steel mills and warehouses, but also to a wide range of operations, including foundries, recycling centers, assembly plants, and logistics operations.
For More Info Visit: Aperture Venture Studio
Top comments (0)