Manufacturing Analytics Platform
Enhancing production efficiency with real-time monitoring, predictive maintenance, and quality analytics across a multi-site manufacturing operation.
Unplanned Downtime
Reduction in equipment failures
Production Output
Increase in overall equipment effectiveness
Quality Defects
Reduction in defect rate
Maintenance Costs
Reduction in total maintenance spend
The Challenge
A multi-site manufacturer with 4 production facilities and over 280 pieces of connected equipment was losing significant revenue to unplanned downtime, inconsistent product quality, and reactive maintenance practices. Their existing SCADA and MES systems generated data but lacked the analytics layer to turn it into actionable intelligence.
Key Challenges Identified
- Unplanned equipment downtime averaging 14% across all production lines
- Reactive maintenance culture resulting in costly emergency repairs
- Quality defects identified only at end-of-line inspection, creating waste
- No cross-site visibility into production performance or benchmarking
- Siloed OT and IT systems preventing holistic operational analysis
- Manual shift handover reports causing information loss and delays
- Inability to correlate environmental conditions with quality outcomes
These issues were costing the company an estimated R42 million annually in lost production, scrap, emergency maintenance, and energy inefficiency. They needed an analytics platform that could bridge the OT/IT gap and deliver predictive, prescriptive intelligence to operators and management alike.
Our Solution
Moby Designs engineered an industrial analytics platform that ingests data from shop-floor equipment, environmental sensors, and enterprise systems to deliver real-time insight and predictive intelligence:
IoT Data Ingestion Layer
A high-throughput data pipeline connecting to PLCs, SCADA systems, environmental sensors, and quality inspection equipment via OPC-UA, MQTT, and REST protocols, normalising data from 280+ assets into a unified time-series store.
Real-time Production Dashboards
Shop-floor and management dashboards displaying live OEE (Overall Equipment Effectiveness), cycle times, reject rates, and energy consumption per line, shift, and site with configurable alerting thresholds.
Predictive Maintenance Engine
Machine learning models trained on vibration, temperature, pressure, and current draw data to predict equipment failures 48-72 hours in advance, with automatic maintenance work order generation.
Quality Analytics Module
Statistical process control analytics that correlate raw material properties, machine parameters, and environmental conditions with quality outcomes, enabling root-cause identification and in-line quality prediction.
Energy Optimisation
Real-time energy monitoring per machine and production line, identifying consumption anomalies and recommending scheduling adjustments to reduce peak demand charges and overall energy spend.
Digital Shift Handover
A structured digital shift handover system capturing production summaries, outstanding issues, safety notes, and maintenance flags, ensuring zero information loss between shifts.
Technology Stack
- Data Ingestion: Apache Kafka for real-time event streaming
- Time-series: InfluxDB for high-frequency sensor data
- Analytics: Python with scikit-learn and XGBoost for predictive models
- Backend: Node.js microservices for API and business logic
- Frontend: React with custom industrial visualisation components
- Infrastructure: On-premises edge computing with Azure cloud analytics
Implementation Approach
- Phase 1: IoT connectivity and data pipeline (3 months)
- Phase 2: Real-time dashboards and OEE tracking (2 months)
- Phase 3: Predictive maintenance models and alerting (4 months)
- Phase 4: Quality analytics and SPC integration (3 months)
- Phase 5: Energy optimisation and reporting (2 months)
- Phase 6: Multi-site rollout and continuous model tuning
The Results
After full deployment across all 4 manufacturing sites, the platform delivered significant operational and financial improvements within 14 months:
- Unplanned downtime reduced from 14% to 3.8%, a 73% improvement in equipment availability
- Overall Equipment Effectiveness (OEE) improved by 22%, from 64% to 78% across all lines
- Quality defect rate reduced by 61% through in-line prediction and early intervention
- Maintenance costs decreased by 38% as predictive scheduling replaced reactive repairs
- Energy costs reduced by 19% through consumption optimisation and peak demand management
- Scrap and rework costs decreased by R8.4 million annually through quality analytics
- Mean time between failures (MTBF) increased by 156% for critical equipment
- Shift handover efficiency improved measurably, with zero critical information gaps recorded
“The analytics platform from Moby Designs gave us eyes on our operations that we never had before. Predicting failures before they happen has changed our entire maintenance philosophy and saved us millions.”
VP of Manufacturing Operations
Multi-site Manufacturer
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Our industrial analytics platform connects to your existing equipment and systems, delivering the predictive intelligence that drives measurable improvements in uptime, quality, and cost efficiency.
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