Innovation has become a critical driver for organizational competitiveness particularly where there are high regulatory demands coupled with operational complexity. In food manufacturing, innovations go beyond competitiveness and profitability to include consumer safety, regulatory compliance and brand reputation.
Globally, the food manufacturing industry operates on increasingly strict regulatory frameworks such as Hazard Analysis and critical control points (HACCP), ISO 22000 and national food safety standards. Recent research demonstrates growth in the role of artificial intelligence (AI) and Machine learning (ML) in transformation of food safety systems. These technologies enable real time monitoring and advanced data analysis allowing organization to be proactive in detecting noncompliance, identify anomalies and predict potential risks with greater accuracy. Machine learning has been applied as well in monitoring and food safety risks prediction demonstrating strong potential for early detection and decision-making improvement. Research shows that ML models have capability to analyses historical and real data to identify patents and predict potential hazards with high level of accuracy. Wang et.al., 2022, argues that despite these capabilities, the adoption of such models in practical food safety systems are limited due to challenges related to data availability, integration and implementation.
- Quote paper
- Chebet Brenda Koech (Author), 2026, SmartSafe AI. AI-Enabled Smart Food Safety and Compliance System for Manufacturing Environments, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/1730803