Case Study

IoT Monitoring Provider Reduces False Alarms 40%+ and Scales Anomaly Detection with AWS Machine Learning

At a glance

A leading provider of food safety monitoring and compliance solutions faced excessive false alarms from thousands of temperature sensors, creating alert fatigue while missing real equipment problems. Their existing system required constant manual maintenance and couldn’t scale.

NMD deployed an AWS machine learning solution that reduced false alarms by over 40%, identified real equipment issues before failure, and created a scalable architecture handling 10,000+ sensors.

Industry

Use Case

Solution implemented

The value equation

Company Snapshot

The company provides cloud-based monitoring and alerting services, integrating wireless sensors with real-time analytics to help hospitality, food service, and retail customers proactively manage assets, maintain food safety compliance, and minimize spoilage or downtime.

Location

US

Customer Situation

The company’s monitoring platform for temperature-sensitive assets like walk-in coolers and freezers struggled with their current architectural system. It generated excessive false alarms from normal temperature fluctuations, required extensive manual maintenance, and couldn’t scale to meet growing demand projected at 10,000+ devices.

In food service and hospitality, undetected equipment failures cause food spoilage, safety violations, and costly emergency repairs. Traditional threshold-based monitoring creates alert fatigue through false positives while missing gradual degradation patterns preceding equipment failure.

NMD Solution

NMD reviewed the company‘s architecture and replaced their rigid k-means approach with advanced machine learning better suited for time series anomaly detection at scale.

Solution Deployed

Within 2.5 weeks, the solution delivered a 40%+ reduction in false alarms and significantly improved identification of true equipment anomalies by distinguishing normal fluctuations from genuine degradation patterns.

The SageMaker architecture eliminated operational complexity, lowered costs through managed services, and created a scalable foundation for 10,000+ sensors with consistent results across diverse device types.

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