BAF-1 — AI New Product Launch Accelerator.

Nestlé Vietnam · Posted 2 days ago

Consumer goodsPredictive Analytics / Forecasting

Budget: To be determined
Timeline: Based on solution provider proposal
Apply by: 22 Nov 2026, 23:59 ICT

Problem statement

Introducing a new SKU/product can take multiple trials and stabilisation cycles. Historical trial learning, process settings and quality results are not yet turned into predictive recommendations for the next launch. Before each trial, AI learns from similar products, past trials, process settings and quality results to recommend a stronger starting recipe/process window and flag likely risks.

Expected outcomes and success metrics

- Fewer trial iterations and losses. - Shorter time to stable manufacturing. - More right-first-time trials and faster time-to-market.

Target users and impacted teams

- AG/PTC, R&D - Manufacturing, QA, Engineering - Supply Chain

Data availability and readiness

- Trial reports - Recipes/formulations - Process parameters and quality results - Raw/pack material conditions, line capability - Technical Acceptance outcomes 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 / Forecasting

Also: Machine Learning. Based on Nestlé's description of AI that learns from past trials to recommend recipes/process windows.

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