IoT Predictive Maintenance: 75% Less Downtime, 18% Cost Savings

@Faclon Team

November 14, 2025

2min read

About the Company

A leading multinational corporation founded in 1992, specializing in automotive solutions and manufacturing components for the automotive and transportation industries. With a commanding $7.3 billion market cap as of 2025, the company operates 74 manufacturing plants worldwide across 20 manufacturing units, employing over 6,000+ professionals across all business verticals. The organization focuses on innovation and cutting-edge manufacturing technologies to maintain its strong global market presence and competitive edge.

Problem Statement

Critical energy forecasting challenges threatening operational efficiency

Time-consuming manual analysis Handheld sensor data collection across hundreds of rotating machines followed by expert assessment caused significant delays in repairs
Safety hazards Manual data collection across manufacturing plants posed substantial safety risks for auditors despite protective equipment
Productivity losses Diagnostic delays resulted in unplanned downtimes from critical rotating machinery failures, causing significant productivity losses
Lack of real-time visibility Urgent need for automated data collection and remote diagnostics to ensure timely repairs and prevent equipment failures

How Faclon Solved the Problem

AI-based Energy Demand Forecasting for sophisticated prediction accuracy

Acquire

Deployed 40+ MEMS-based triaxial vibration sensors with wireless data collection via Wi-Fi, eliminating IT interference

Analyze

Implemented real-time vibration and temperature data capture with automated fault frequency adjustments and ISO benchmark comparisons

Alert

Configured instant SMS/email/WhatsApp notifications triggered by deviations from ISO standards for immediate response

AI Intelligence

Integrated machine learning algorithms analyzing FFT spectrums every 30 minutes with diagnostic inference every 24 hours

Platform Integration

Connected 18 digitized machines through I/O Sense Cloud platform with comprehensive dashboard monitoring

The Outcome

Exceptional results across energy management operations:

75% reduction in unplanned downtime

Dramatic improvement in operational continuit

12 critical defects detected

Proactive identification preventing major equipment failures

480 man-days saved

Significant labor efficiency gains through automation

18% reduction in maintenance spending

Substantial cost optimization achievement

Proven ROI realization

Typical return on investment achieved within 1-1.5 years

Case study success

Blower Motor alignment issues detected and corrected, preventing costly gearbox damage

Behind the Scenes

Advanced technical implementation and strategic deployment considerations

Sensor deployment strategy

Strategic placement of sensors on motors (drive-end and non-drive end), pumps, blowers, and compressors based on criticality assessment

Customization approach

Asset-specific configuration of fault frequencies and ISO benchmarks tailored to different machine types and operational conditions

Network architecture design

Wi-Fi junction boxes with 4G connectivity ensuring seamless data transmission up to 80 meters range with IP67 protection

Change management process

Transition from manual handheld sensor operations to automated continuous monitoring requiring staff training and process adaptation

Data processing methodology

FFT spectrum analysis, noise analysis, and trend analysis algorithms working in tandem for comprehensive asset health assessment

Scalability framework

Modular deployment across 14 different machine types (from Hot Water Rinse to PBO HAC Blowers) ensuring systematic coverage of critical assets

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