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
- Anomaly & Fraud Detection
- Asset & Infrastructure Management
Solution implemented
- Pipeline administration architecture that separates data processing, model training, registration, and inference deployment concerns for independent management
- Preprocessing for temperature reading data to break down into trend, seasonal, and residual components
- Anomaly detection feature highlights and differentiates between short-term temperature spikes (which indicate immediate issues) and longer-term trends (which suggest gradual asset degradation or control loss)
- Central repository for model registration to facilitate dynamically spinning up additional inference endpoints and accommodate multiple sensor inputs
The value equation
- More consistent results across a diverse range of devices and sensors
- Reduced false alarms by over 40% and improved the identification of true anomalies
- Lower total cost of ownership as manually maintained infrastructure is replaced with AWS-managed services
- Eliminated operational complexity to support increased investment in refinement and innovation
- Offering expansion opportunities, including in-depth analysis, predictive maintenance, and comprehensive cost analysis solutions
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
- Time Series Preprocessing: Data cleaning with decomposition into trend, seasonal, and residual components, removing extreme outliers while preserving legitimate anomaly signals
- Isolation Forest Algorithm: Detects both short-term spikes (doors left open) and long-term trends (failing compressors)
- Scalable AWS SageMaker Architecture: Central model repository enabling dynamic scaling and multiple sensor inputs
- Flexible Training: Single-device models for accuracy or multi-device models for reduced complexity
- Managed Services: Replaced manual EC2 infrastructure with fully managed SageMaker services
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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