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TECH-2 — New product launch, capacity & autonomous RTD line.

Nestlé Vietnam · Posted 2 hours ago

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

Problem statement

Learn from past trials to shorten NPI (New Product Introduction), find capacity bottlenecks and self-adjust the RTD (Ready-to-Drink) line. Today: • New SKU (Stock Keeping Unit)/product introduction can require multiple trials and stabilization cycles. • Disconnected production, loss and planning data make bottlenecks difficult to identify. Capacity opportunities need evaluation while respecting quality, safety and asset limits. • RTD (Ready-to-Drink) operation still depends on significant operator intervention to detect, correct and recover from deviations across processing, UHT, filling and packing.

Expected outcomes and success metrics

• Faster time-to-market. Reduce trial losses. Support innovation growth. Learn from historical trials, process settings, quality results and similar products to support faster industrialization and more stable manufacturing readiness for innovation launches. • Increase efficiency and process stability, reduce micro-stops and losses, accelerate recovery and minimize operator intervention, enabling progression toward lights-out manufacturing. Self-adjusting end-to-end RTD (Ready-to-Drink) operation where process, filling and packing equipment automatically detect, correct and recover from deviations within predefined safety, quality and operating boundaries. • Improve throughput and asset utilization. Support growth and service with existing capacity. Enable faster investment and production decisions. Optimize capex, changeover, speed and availability trade-offs. Reduce capacity-study time and engineering effort. • Identify bottlenecks and validate capacity scenarios across NESCAFE and MAGGI. Support production and inventory decisions. Measure capacity gain, throughput, service level and capex avoidance. Baselines and targets to be confirmed.

Target users and impacted teams

• Technical and manufacturing teams • Production, planning and capacity teams • RTD (Ready-to-Drink) operations (processing, filling and packing) • Engineering teams supporting capacity studies

Data availability and readiness

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 AIOther

Based on Nestlé objectives for learning from historical trials, capacity bottleneck and scenario analysis, and self-adjusting RTD (Ready-to-Drink) process parameters within safety and quality boundaries.