Think of a smart assistant that can instantly test thousands of packaging ideas on a computer—how strong they are, how much material they use, and how they look—so your engineers only build and test the few best options in the real world.
Traditional packaging development is slow and expensive, requiring many physical prototypes and tests to balance cost, sustainability, performance, and branding. AI can simulate and optimize designs digitally, cutting time-to-market, material usage, and experimentation costs while improving package performance.
Combination of proprietary historical test data, packaging performance data, and product-specific constraints (food safety, shelf life, logistics) that train better design and simulation models than generic tools, plus tight integration into Nestlé’s packaging and product development workflows.
Hybrid
Structured SQL
High (Custom Models/Infra)
Availability and quality of labeled historical test data for different packaging formats and materials; computational cost of running large-scale simulations and optimizations for many SKUs.
Early Majority
Use of AI directly in consumer-packaged-goods packaging development at scale, likely combining mechanical/material simulations with optimization models tuned to Nestlé’s product portfolio and sustainability constraints.