AI-driven downtime prediction that flags problems before they stop production

MachineMetrics ingests machine signals — spindle load, vibration, temperature — and runs them through machine learning models trained on historical failure patterns. When an anomaly is detected, the platform sends an alert to the maintenance team with a predicted time-to-failure. This allows teams to schedule repairs during planned downtime rather than reacting to sudden breakdowns.

Business outcome: Reduces unplanned downtime by up to 50% by shifting from reactive to predictive maintenance.

Live OEE dashboards that show exactly where production is losing time

The platform calculates OEE in real time — availability, performance, and quality — from machine data. Operators and managers see a live dashboard with colour-coded machine states (running, idle, down, off). Drill into any downtime event to see root cause: operator break, tool change, material shortage, or mechanical failure.

Business outcome: Gives every shift a clear, data-backed view of where efficiency is being lost, enabling targeted improvement actions.

Automated flagging of abnormal machine behaviour without manual thresholds

Rather than relying on static limits, MachineMetrics uses unsupervised learning to model normal machine behaviour. When a machine deviates — e.g., a cycle takes 10% longer than its historical average — the system flags it automatically. This catches issues that would be missed by traditional limit-based monitoring.

Business outcome: Catches subtle performance degradation early, preventing minor issues from escalating into major downtime events.

Data-driven scheduling that accounts for real machine availability and performance

MachineMetrics feeds actual cycle times, changeover durations, and downtime patterns into the scheduling engine. Production planners see a realistic view of what each machine can deliver, rather than relying on theoretical cycle times. The system can recommend schedule adjustments to balance load across machines.

Business outcome: Improves on-time delivery by aligning production schedules with actual machine capability, not idealised estimates.

Centralised dashboard that connects every machine on the floor into one view

The platform aggregates data from all connected machines — regardless of make, model, or age — into a single interface. Operators see machine status at a glance; managers get shift-level reports on utilisation, downtime, and throughput. Historical data is retained for trend analysis and continuous improvement.

Business outcome: Eliminates the need to walk the floor to check machine status, saving hours of supervisor time per shift.

API-first architecture that connects to ERP, MES, and CMMS systems

MachineMetrics exposes a REST API and pre-built connectors for common manufacturing systems. OEE data can flow into ERP for cost accounting, downtime events can trigger work orders in CMMS, and production counts can update MES in real time. This prevents data silos between the shop floor and business systems.

Business outcome: Creates a unified data pipeline from machine sensors to business intelligence, enabling better cross-functional decisions.

• Real-time predictive maintenanceThe AI models are trained on actual machine data, not generic benchmarks, making downtime predictions specific to your equipment and conditions.
• Deep OEE analyticsOEE is calculated from live sensor data, not manual entry, giving a true picture of availability, performance, and quality.
• Hardware-agnostic connectivityWorks with virtually any CNC machine, press, or robotic cell — no need to replace existing equipment to get data.
• API-first integrationThe platform is built to connect with existing ERP, MES, and CMMS systems, making it a data hub rather than an island.

• Requires hardware installationSensors and gateways must be physically installed on each machine, adding upfront cost and setup time.
• Not for small shopsWith pricing scaled per machine and a minimum viable deployment of 5-10 units, small job shops may struggle to see ROI.
• Technical expertise neededConfiguring the AI models and interpreting the output requires an internal champion with data literacy — it's not a plug-and-play system.
• Professional RealityMachineMetrics delivers its greatest value in plants with 20+ machines and a dedicated continuous improvement team — if you lack either, the platform may feel like overkill.

Mid-size CNC machining shop reducing downtime

A shop with 25 CNC machines uses MachineMetrics to monitor spindle loads and vibration. When the AI flags an anomaly on machine #12, the maintenance team schedules a bearing replacement during the next shift change — avoiding a 4-hour breakdown mid-run.

Large plant with multi-brand equipment

A plant with a mix of Fanuc, Haas, and Mazak machines uses MachineMetrics to normalise data across all brands. The plant manager sees a single OEE dashboard and identifies that a specific model consistently underperforms — driving a capital replacement decision.

Production planner improving on-time delivery

A planner uses actual cycle-time data from MachineMetrics to build realistic schedules. Instead of assuming a 90-second cycle, the system shows the real average is 102 seconds — the planner adjusts the schedule and on-time delivery improves by 12%.

Maintenance team shifting to predictive strategy

A maintenance manager uses MachineMetrics' predictive alerts to move from reactive repairs to scheduled interventions. Over six months, unplanned downtime drops by 40% and overtime labour costs fall by 25%.

Request a site assessment — MachineMetrics evaluates your equipment mix, network infrastructure, and data collection needs.

Install sensors and gateways on target machines — the team handles hardware setup and network configuration.

Configure machine profiles and data mapping — define which signals (spindle load, cycle count, vibration) to monitor for each machine type.

Set up dashboards and alerts — define OEE targets, downtime thresholds, and notification rules for your maintenance and operations teams.