Smart Factory Integration & Retrofit Solutions

Yes, Kerone specializes in non-intrusive retrofits that add smart sensing, connectivity, and analytics capabilities to existing equipment without requiring complete replacement.

Kerone supports OPC-UA, MQTT, Modbus, PROFIBUS, DeviceNet, Ethernet/IP, and RS232/485 serial protocols, enabling connectivity to virtually any industrial equipment brand.

Kerone uses a phased implementation approach, installing sensors and connectivity systems during planned maintenance windows and staging software integration to avoid interrupting production.

OEE (Overall Equipment Effectiveness) measures availability, performance, and quality. Kerone's monitoring systems identify the root causes of OEE losses in real time, enabling targeted corrective actions.

Yes, Kerone's predictive maintenance systems use vibration analysis, thermal imaging, and machine learning algorithms to predict equipment failures before they occur, reducing unplanned downtime.

A digital twin is a virtual replica of a physical plant used for simulation, optimization, and training. Kerone develops digital twins using real process data and physics-based modelling tools.

Yes, Kerone's platforms include secure remote access via web browsers and mobile apps, allowing management and engineering teams to monitor and control production from anywhere.

Kerone's clients typically achieve ROI within 12 to 24 months through energy savings, reduced downtime, lower maintenance costs, and improved production yield and quality.

Connecting legacy industrial equipment to IIoT platforms introduces network attack surface that didn't exist when machines operated in isolation, so Kerone implements network segmentation that keeps the operational technology network separate from the corporate IT network, encrypted data transmission between sensors and gateways, and authentication requirements for any remote access to plant systems. Legacy equipment often uses older communication protocols like Modbus that lack native security features, which means protocol conversion gateways need to handle authentication and encryption on behalf of equipment that cannot natively support it. Regular security patching of edge computing nodes and gateway devices is necessary since these become new endpoints requiring the same security discipline as any other networked device. Manufacturers should treat smart factory retrofits as both an automation project and a cybersecurity project from the outset, not as automation alone.

A common mistake is retrofitting sensors and connectivity onto equipment without first defining what specific decisions the resulting data will support, leading to dashboards full of data that nobody acts on. Companies sometimes attempt to retrofit their entire facility simultaneously rather than starting with a pilot area to validate the approach and demonstrate ROI before scaling, which increases both risk and disruption. Underestimating the data infrastructure and IT support needed to maintain the new systems after installation is another frequent issue, since IIoT platforms require ongoing administration that legacy equipment never needed. Failing to involve operators and maintenance technicians in defining what information would actually help them in their daily work often results in systems that are technically sound but operationally underused because the workforce wasn't engaged in the design process.

A common misconception is that older equipment is too outdated to benefit from smart sensors, when in practice non-intrusive retrofits like vibration and temperature sensors can be added to almost any rotating or thermal equipment regardless of its age or original control system. Another misconception is that retrofitting requires replacing the existing PLC or control system, when most retrofit projects add a parallel sensing and data layer that observes equipment behavior without touching the original control logic at all. Some manufacturers assume retrofit data quality will be inferior to a purpose-built new machine's native sensors, when properly selected and installed aftermarket sensors often provide comparable or better data resolution than basic factory-installed instrumentation on older equipment. Clarifying these points typically shifts the conversation from whether retrofitting is feasible to which specific equipment offers the best return on retrofit investment.

Operators need training on interpreting dashboard alerts and understanding what specific sensor readings indicate about equipment condition, since raw data without context doesn't translate into useful action on its own. Maintenance technicians need training on responding to predictive maintenance alerts, including how to validate a predicted failure before committing resources to an unscheduled repair. IT and engineering staff need training on maintaining the underlying data infrastructure, including gateway devices, network configuration, and software updates that keep the system running reliably. Kerone typically includes hands-on training during commissioning along with documentation, but the most successful retrofit projects also designate an internal champion who continues reinforcing proper system use after the initial training period ends, since technology adoption tends to fade without ongoing internal reinforcement.

Yes, Kerone designs retrofit architectures with scalability in mind, typically starting with a pilot deployment on critical equipment and expanding sensor coverage, analytics capability, or additional machines once the initial deployment demonstrates value. Edge computing nodes and gateway infrastructure are usually sized with spare capacity for additional sensor channels, and cloud-based analytics platforms can typically onboard additional equipment without requiring a fundamental architecture change. This phased approach lets manufacturers validate ROI on a smaller scale before committing to a facility-wide rollout, and it spreads capital expenditure over a longer period rather than requiring a large upfront investment. The main planning consideration is ensuring the initial pilot's network and data architecture choices remain compatible with the eventual full-scale vision, rather than choosing a quick pilot solution that requires complete replacement when scaling up.

AI-powered visual inspection systems retrofitted onto existing packaging, finishing, or assembly lines typically catch defect types that manual visual inspection misses due to inspector fatigue or inconsistent attention, including subtle surface defects, dimensional variations, and assembly errors that occur intermittently rather than in obvious patterns. Defect detection rates commonly improve from inspection systems that operate consistently at every unit rather than sampling-based manual inspection, which only checks a percentage of production. Beyond catching defects, the data generated identifies which process step or shift correlates with higher defect rates, enabling root-cause correction rather than just downstream rejection. Implementation requires training the vision system on examples of both acceptable and defective product specific to that production line, since generic vision models rarely perform well without this calibration to the actual product and defect types present.

Process industries with substantial existing capital equipment that remains mechanically sound, such as chemical processing, pharmaceutical manufacturing, and food production with multi-decade equipment lifecycles, are particularly well suited for retrofitting since replacing functioning process equipment purely for connectivity rarely makes economic sense. Discrete manufacturing with shorter equipment replacement cycles may find it more economical to specify smart capability directly when equipment is naturally due for replacement rather than retrofitting equipment that will be replaced again soon anyway. Facilities expanding into new product lines or new geographic locations often default to greenfield builds with smart capability designed in from day one. The decision ultimately depends on the remaining useful life of existing equipment relative to the cost of retrofitting it versus waiting for natural replacement cycles to introduce smart capability.