LogiFlow: From Reactive to Predictive Maintenance — 35% Downtime Reduction
About the Client
LogiFlow Industries operates three manufacturing plants in the Mumbai Metropolitan Region, producing corrugated packaging and flexible film solutions for FMCG, pharmaceutical, and automotive customers. With over 1,200 production staff and a 24/7 operational schedule, any unplanned equipment stoppage carries severe consequences — both in direct production losses and in contractual penalties for missed delivery windows.
The company had been running its core machinery — flexographic printing presses, corrugator lines, and slitter-rewinders — on a reactive maintenance model inherited from decades of traditional manufacturing practice. When equipment failed, it stopped. Maintenance crews diagnosed the problem, sourced parts, and repaired it. The mean time to repair was 6.4 hours per incident. Across 28 major production assets and three sites, this was generating 180+ hours of unplanned downtime annually and consuming nearly 40% of the maintenance department's capacity in emergency response.
Project Details
- Client
- LogiFlow Industries
- Location
- Mumbai, India
- Company Size
- Large Enterprise
- Industry
- Manufacturing & Industrial
- Service
- Industrial Automation
- Technologies
- Azure IoT HubMQTTNode-REDOPC-UARaspberry PiPythonAzure MLPower BIReact
- Published
- June 19, 2024
What Was Holding LogiFlow Industries Back
LogiFlow's maintenance challenge was rooted in a fundamental visibility problem. The plant floor generated enormous amounts of operational data — vibration signatures from bearing assemblies, temperature readings from drive units, pressure fluctuations in hydraulic systems — but none of it was being collected, stored, or analyzed. Maintenance engineers relied on scheduled visual inspections and the intuition of experienced technicians who had worked the same machines for years. While valuable, this approach had a critical flaw: human inspection rounds happened twice daily, leaving 12-hour windows during which equipment could deteriorate without detection.
The control systems on LogiFlow's production assets were a patchwork of different vintages and protocols. Some machines ran Siemens S7 PLCs with modern Profinet interfaces; others had legacy Allen-Bradley controllers using proprietary serial protocols; the oldest corrugator line had no PLC at all and relied entirely on relay logic. There was no unified SCADA layer and no historian — each machine existed as an isolated data island. Any IIoT solution would need to bridge these protocols without requiring expensive PLC upgrades on assets that still had years of productive life.
The business case was clear but the technical path was not. LogiFlow had tried a point solution two years prior — deploying a single vendor's vibration monitoring kit on their most critical press — but the proprietary dashboard had limited customization, couldn't integrate with the other assets, and the vendor's support quality had declined. Plant engineering leadership was skeptical of point solutions and wanted a platform approach that could scale across all three sites and all asset types under a single operational view.
How Abstriq Solved It
Abstriq designed an open-standard IIoT architecture that treated data collection, edge processing, cloud ingestion, and analytics as distinct, independently scalable layers. At the sensor layer, we deployed over 500 vibration, temperature, pressure, and current draw sensors across 28 production assets — using industrial-grade piezoelectric accelerometers on rotating equipment and PT100 temperature sensors on drive and motor housings. Sensors were selected for IP67 rating and broad temperature tolerance given the plant environment.
At the edge, Raspberry Pi 4 industrial gateways running a hardened Raspbian image with a local MQTT broker acted as protocol translation and preprocessing nodes. Node-RED flows on each gateway handled the OPC-UA to MQTT protocol translation for PLC-connected machines, while a custom Python agent collected serial data from legacy controllers. Each gateway performed local statistical preprocessing — computing RMS, kurtosis, and crest factor from raw vibration time-series data every 10 seconds before publishing aggregated features to Azure IoT Hub, dramatically reducing cloud ingestion costs while preserving the signal characteristics needed for anomaly detection.
The ML pipeline used a two-stage approach: an Isolation Forest model for general anomaly detection, calibrated on 90 days of baseline sensor data, and an LSTM-based time-series model for equipment-specific degradation pattern recognition on the five highest-criticality assets. When the LSTM model detected a degradation trajectory consistent with bearing failure (a pattern identified in historical post-mortem data), it generated an alert with an estimated time-to-failure window of 4-8 hours — enough lead time for a planned maintenance intervention during the next scheduled break.
How We Built It
The Numbers That Matter
From 180 to 117 annual unplanned downtime hours
Optimized equipment run schedules based on sensor data
Average warning before equipment failure event
Shift from reactive to planned maintenance cycles
Technologies Used
Client Testimonial
“In 30 years in manufacturing, I've seen a lot of technology promises. This is the first project that delivered exactly what was promised — on time, on budget, and with real operational results. The dashboard gives our plant managers a level of visibility we've never had before, and the predictive alerts have already prevented three major breakdowns that would have cost us collectively over ₹80 lakhs in lost production.”
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