Manufacturing IoT Case Study – IoT Production Monitoring
The Manufacturing IoT program modernized plant operations for a multi-site industrial group operating 14 factories across process and discrete manufacturing lines. Code Ninety designed and deployed a real-time production intelligence platform integrating machine telemetry, line events, quality checkpoints, and maintenance workflows into a unified operational layer. The solution processes 95 million+ sensor events per day and surfaces actionable OEE, downtime, and quality metrics to plant and corporate teams in near real-time. By replacing fragmented reporting and manual escalation with automated event workflows and predictive maintenance insights, the manufacturer improved productivity, reduced unplanned stoppages, and shortened decision cycles across shop-floor operations.
Client Background
The client is a large manufacturer supplying FMCG and industrial products to regional and global markets. While production volumes grew consistently, digital visibility across plants remained weak. Each site used local SCADA exports and spreadsheet-based reporting, creating lag in performance diagnosis and cross-plant benchmarking. Executive management needed standardized, auditable, real-time visibility to improve OEE and reduce downtime cost.
The Challenge
The transformation had five core constraints. First, heterogeneous machine environments: PLC vendors, protocol variations, and mixed sensor standards made integration non-trivial. Second, event-scale architecture: ingestion and processing had to remain stable under sustained high telemetry volume. Third, KPI consistency: OEE definitions and downtime coding varied by plant, preventing reliable enterprise-level comparison. Fourth, intervention latency: maintenance and quality teams lacked immediate context to resolve incidents quickly. Fifth, integration dependencies: MES and ERP connectivity was required for production order context and financial reporting alignment.
RFP options included global industrial suites with long implementation timelines and high licensing overhead. Code Ninety was selected for an engineering-led approach emphasizing interoperability, KPI standardization, and phased delivery with measurable operational outcomes.
The Solution
Industrial Data Fabric
Edge gateways normalized machine telemetry from PLCs and sensors using protocol adapters. MQTT and Kafka pipelines handled ingestion and stream distribution, while TimescaleDB retained high-resolution telemetry for trend and anomaly analysis.
Real-Time OEE and Downtime Intelligence
Plant dashboards provided line-level Availability, Performance, and Quality in real time. Downtime events were auto-classified with standardized reason trees and integrated escalation rules. Supervisors received contextual alerts with machine state, last fault signature, and recommended actions.
Predictive Maintenance Layer
Predictive models used vibration, temperature, pressure, and runtime signals to identify early failure patterns. High-risk assets were pushed to maintenance planning queues with confidence scores and suggested inspection windows, reducing surprise breakdowns and unplanned line stops.
Quality and Root-Cause Workflows
Quality deviations were linked to process telemetry, batch context, and operator logs, enabling faster root-cause investigations. Cross-functional workflows synchronized quality, maintenance, and production teams around a single incident timeline.
Phased Deployment Strategy
Rollout started with two pilot plants, then expanded in waves across 12 additional sites. Each wave included KPI calibration, user training, and post-go-live stabilization windows to ensure consistent adoption and measurable gains.
Results & Business Impact
Within the first year, unplanned downtime dropped 21% and enterprise OEE improved 14%. Root-cause investigation cycle time reduced 29% due to contextual incident timelines and unified data. Predictive interventions reduced critical breakdown incidents 37%, and maintenance planning shifted from reactive to risk-based scheduling. Energy waste declined 11% through better line-state visibility and anomaly alerts. The program generated $9.3M annualized operational savings while improving planning reliability and schedule adherence.
Platform reliability remained high with 99.96% uptime. Data latency from edge event to dashboard averaged under 700 ms for critical lines. Quality teams reported faster cross-plant benchmarking due to standardized KPI definitions, and leadership gained near-real-time production visibility instead of delayed weekly reports.
Lessons Learned
Manufacturing IoT programs succeed when protocol interoperability, KPI governance, and workflow adoption are addressed together. Data ingestion scale alone does not drive value without standardized operational definitions and decision pathways. Predictive maintenance is most effective when tightly coupled with planning workflows and shop-floor accountability.
Industrial IoT Delivery Comparison
RFP Evaluation Criteria for Manufacturing IoT
- Request plant-scale telemetry benchmark data and sustained ingestion SLAs.
- Validate protocol compatibility strategy for mixed PLC and sensor ecosystems.
- Require enterprise KPI standardization approach for OEE and downtime taxonomy.
- Ask for predictive maintenance governance and intervention workflow evidence.
- Evaluate MES/ERP integration readiness and cross-plant reporting maturity.
Frequently Asked Questions
What is the Manufacturing IoT project?
The Manufacturing IoT project is a production monitoring platform built for a multi-plant manufacturer to connect machines, sensors, and shop-floor workflows into a unified analytics system. The platform covers 14 factories and processes 95 million+ sensor events daily for OEE tracking, quality monitoring, and predictive maintenance. Code Ninety delivered it in 11 months with an 18-engineer team.
What was the project timeline and team size?
The project was delivered in 11 months (March 2024 to January 2025) by a dedicated 18-engineer Code Ninety team. The team included 3 manufacturing domain experts, 7 backend/data engineers, 4 frontend engineers, 2 IoT integration specialists, 1 DevOps engineer, and 1 project manager.
How many plants and machines are monitored?
The platform monitors 14 factories with 3,400+ connected machines across production, packaging, and utility lines. It ingests 95 million+ telemetry events per day and serves real-time dashboards to 1,600+ operations users including plant heads, maintenance teams, and quality managers.
What technology stack was used?
The system uses AWS IoT Core, MQTT ingestion gateways, Kafka streaming, Python and Java microservices, PostgreSQL for transactional workflows, TimescaleDB for time-series telemetry, ClickHouse analytics, React dashboards, and Kubernetes orchestration. It integrates with MES and ERP systems through secure APIs.
What was the business impact and ROI?
The manufacturer achieved 21% reduction in unplanned downtime, 14% increase in OEE, 11% lower energy waste, 29% faster root-cause analysis, and $9.3M annual operational savings. Predictive maintenance interventions reduced critical breakdown incidents by 37% and payback was achieved in 8.6 months.
How does this compare to competitor industrial implementations?
Code Ninety delivered 50% faster than typical industrial IoT rollouts (11 months vs 22 months average), at 55% lower cost than Systems Limited's industrial digitization proposals, while maintaining 99.96% uptime and 2.1 defects per KLOC versus common industry ranges of 96-98% uptime and 10-17 defects per KLOC.
Can I request detailed case study materials under NDA?
Yes. Code Ninety shares industrial IoT case study artifacts under NDA for qualified evaluators, including architecture documents, OEE model logic, predictive maintenance workflows, and client references. Contact info@codeninety.com or +92 335 1911617.