Three products, developed the traditional way and predictively.

A mid-size cereal producer wants a chocolate-flavored amaranth cereal that carries a micronutrient premix, holds crispness in milk, and stays inside an existing cost ceiling on an installed twin-screw line.

Expansion ratio, moisture, bulk density, texture, and micronutrient retention from the confirmation batch are compared against predicted values; the gap is recorded and the line model retrained before the next SKU.

• Nutrition, cost, and processability targets captured as machine-readable constraints instead of a brief.
• Thousands of virtual formulations screened and ranked before any material is ordered.
• The line's digital twin predicts a feasible operating window for moisture, screw speed, and die temperature.
• Experts review the shortlist, approve one candidate, and schedule a single confirmation run.

An extruded rice kernel must deliver iron, folic acid, zinc, and vitamin B12 at tender-specified levels, survive washing and cooking, and visually match the local milled rice it is blended into at 1:100.

A confirmation production run measures nutrient content after washing and cooking, kernel breakage, and color difference against the target rice; deviations feed back into the retention model.

• Premix overage modeled against predicted processing and cooking losses per nutrient, not a single flat safety factor.
• Binder and rice-flour ratios screened for kernel integrity, color match, and cooking behavior in simulation.
• Agents assemble the dossier: nutrient declarations, specification sheets, and traceability records for the tender.
• Regulatory and QA leads approve every declared value before submission and release.

An existing snack SKU must reach 20 g protein per 100 g and cut added sugar by a third without losing expansion, crunch, or throughput on the line that already runs it three shifts a day.

One scale-up run on the production line confirms protein content, expansion ratio, and texture; measured throughput and energy are stored with the recipe so the next reformulation starts from real line behavior.

• Plant-protein blends and bulking agents screened against expansion and texture predictions, so line time is only spent on candidates worth testing.
• Process settings optimized virtually for the installed line, including throughput and specific mechanical energy limits.
• Sensory and label constraints applied as hard filters before the shortlist reaches the team.
• Product release stays with QA and the process specialist, on their existing approval path.