TECH-3 — Asset health, foreign-body prevention & troubleshooting hub.

Predict failures and foreign-body risks from sensor data; search past issues and prepare operational reviews fast. Today: • Foreign-body work is often reactive. Teams want earlier signals from equipment monitoring and available condition data to identify potential foreign-body risks and recommend areas for preventive inspection or intervention. • Asset maintenance health needs predictive maintenance based on TBM, CBM, all available sensors and parameters, so inspection, maintenance or replacement timing can follow actual operating condition. • Troubleshooting needs a new way of go-see, check history & think (GSTD): faster access to historical knowledge across functions and shifts, with less repeated effort. • Operational reviews need to connect many sources for deep analysis; preparing reviews is still largely manual across forums.

• Support earlier detection and proactive prevention to reduce potential food-safety issues and related losses. Move toward proactive foreign-body risk identification and prevention, alongside investigation when needed. • Improve asset reliability and maintenance effectiveness by enabling condition-based maintenance decisions. This reduces unnecessary maintenance, minimizes unplanned downtime and supports more efficient use of maintenance resources and spare parts. Monitor the health of critical assets, detect abnormal conditions early and determine the optimum timing for inspection, maintenance or replacement based on actual operating condition. • Improve troubleshooting effectiveness through faster access to historical knowledge, reducing repeated effort and enabling more consistent, fact-based decision making across functions and shifts. This supports knowledge retention, capability building and production continuity. Teams consistently access and reuse trusted historical knowledge through one structured go-see, check history & think (GSTD) approach across functions and shifts. • Reduce manual effort in preparing operational reviews, improve decision quality through better visibility of cross-functional insights, and enable faster, more proactive management of factory performance and improvement actions. A single connected view of operational performance across all review forums. Automated pre-review analysis identifies performance gaps, trends, repeated issues and action effectiveness, allowing teams to focus on priorities, decisions and execution rather than data preparation.

• Technical and factory maintenance teams • Operations teams across functions and shifts • Teams preparing and running operational reviews • Quality and production continuity stakeholders

• All available sensors and parameters • Equipment monitoring and available condition data • Historical maintenance/incident/troubleshooting knowledge • Many operational-review data sources Note: Synthetic data could be potentially provided during the process, upon approval of the Nestlé team and in compliance with data law.

• To be determined Note: Nestlé's current environment includes Microsoft Azure and Microsoft Copilot. Solutions that can work with this environment are welcome, and final integration and deployment requirements will be confirmed with the Nestlé team.