Manufacturing & Industrial

Manufacturing & Industrial

Unifying Operational Technology (OT) and IT environments to bring visibility, predictive analytics and automated orchestration to the factory floor and supply chain.

Modern manufacturing relies on closing the gap between isolated factory-floor machinery and cloud-based analytics.
Production throughput
Overall Equipment Effectiveness (OEE)
Yield rate

Business Overview

Modern manufacturing relies on closing the gap between isolated factory-floor machinery and cloud-based analytics.

By converging OT and IT through secure, low-latency architectures, we enable real-time production visibility, predictive maintenance and seamless ERP integration without disrupting mission-critical operations.

The industrial sector faces unique constraints: decades-old PLCs, bespoke SCADA systems and zero-trust air-gapped environments.

Engineering for manufacturing requires a deep understanding of industrial protocols, edge computing and strict availability SLAs where network latency directly correlates with production halts.

Industry Challenges (Business, Operational & Tech)

Siloed Operations

Inability to aggregate data across multiple plant locations and disconnected MES/ERP systems.

Unplanned Downtime

Reactive maintenance resulting in catastrophic production halts and revenue loss.

Quality Inconsistencies

Lack of real-time visibility into assembly line anomalies and defect rates.

Data Latency

Cloud round-trips are too slow for real-time robotic assembly line decisions.

Air-Gapped Security

Safely bridging secure OT networks with cloud-native IT environments without compromising ISA/IEC 62443 compliance.

Legacy Hardware Reliance

Extracting telemetry from legacy PLCs and bespoke industrial controllers lacking modern API endpoints.

Protocol Translation

Converting proprietary equipment protocols into standardized streams for data lakes.

KryoNex System Solutions & Use Cases

Predictive Maintenance

Ingesting high-frequency sensor data at the edge to predict spindle wear before failure.

SCADA to Cloud Integration

Safely bridging OT data to enterprise data lakes for global yield analysis.

Automated Quality Assurance

Deploying computer vision models at the edge for real-time defect detection.

Core Technologies & Platforms

Apache Kafka

InfluxDB

AWS IoT Greengrass

Azure IoT Edge

Hyperledger Besu

Expected Business & System Outcomes

Production throughput
Overall Equipment Effectiveness (OEE)
Yield rate
Cost per unit produced
Maintenance cost ratio
Edge node availability
Telemetry ingestion latency
OT/IT network bridging uptime
Protocol translation overhead

Technical Implementation Guidelines

Consideration 1

Never disrupt the production line: All edge deployments must have failsafes to allow local operation if cloud connectivity drops.

Consideration 2

Protocol translation should occur as close to the machine as possible (Edge) to minimize bandwidth costs and latency.

System Specifications & Compliance

Enterprise Environment

Existing systems often include isolated networks with legacy PLCs running proprietary logic., Supervisory control is handled by heavily customized SCADA environments, manually feeding data into isolated MES and ERP systems.

Required Integrations

ERP, MES, SCADA, PLC, Industrial IoT, WMS

Regulations & Governance

ISA/IEC 62443 (Industrial Automation Security), ISO 9001 (Quality Management)

Standards & Protocols

OPC UA, MQTT, Modbus, PROFINET, EtherNet/IP

FAQ

How do you extract data from machines with no API?

We deploy edge gateways using Kepware or similar industrial connectivity platforms to translate legacy Modbus or serial protocols into MQTT for cloud ingestion.

What happens if the internet goes down?

Our edge architectures are designed to operate autonomously. Local compute nodes handle real-time decisions and buffer telemetry until the connection is restored.

Recommended Services

Recommended Solutions

Platform Capabilities

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