Skip to content

BAF-2 — AI Foreign Body Prevention (Focus on Seal & Gasket).

Nestlé Vietnam · Posted 2 days ago

Consumer goodsPredictive Analytics / ForecastingGenerative AI
Budget
To be determined
Timeline
Based on solution provider proposal
Apply by
22 Nov 2026, 23:59 ICT

Problem statement

One digital record per seal & gasket: move from fixed-interval replacement to risk-based management – see status, get early alerts and inspect/replace recommendations, and investigate in minutes instead of searching files. Today: - Issues are evaluated only after they occur; SMEs search file by file to rebuild seal & gasket history and find the root cause. - Seals & gaskets are replaced on a fixed schedule, not on actual condition or risk.

Expected outcomes and success metrics

- Fewer line stops and APP disruptions. - Lower quality/sterility risk. - Much faster investigations. - Shift from reactive investigation to proactive risk identification and prevention.

Target users and impacted teams

- Production - Technical - Quality - Production planning

Data availability and readiness

- Seal & gasket master data, location, maintenance history, runtime - Temperature, pressure, CIP chemicals - Inspection photos, production plan, SME rules Note: Synthetic data could be potentially provided during the process, upon approval of the Nestlé team and in compliance with data law.

Integration, deployment and infrastructure

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.

Relevant AI technologies

Predictive Analytics / ForecastingGenerative AI

Also: Machine Learning. Based on Nestlé's description of risk scoring, prediction and a chatbot on seal & gasket data.

Sign in to keep reading

You can read the first part of this use case. Sign in or create a free account to read the rest.