Product & Design

Generative design for packaging structure

Use generative design to propose packaging structures that meet protection requirements with less material, evaluating candidate geometries against strength, cube efficiency, and recyclability constraints.

AugmentationPattern-matchingCost reductionAccuracy

10–20%

reduction in packaging material per unit

Opportunity assessment

Business Impact
2

Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.

Feasibility
2

Challenging. Significant technical work required. Likely needs an AI engineer or specialist vendor. Not a quick win.

Data Readiness
2

Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.

Risk Exposure
4

Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.

Change Complexity
3

Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.

Tooling required

Specialist AI tool

Things to consider

  • Generative design is proven in industrial and automotive contexts and is genuinely harder in packaging, where cost per unit is pennies and tooling change, not geometry, is the binding constraint.

  • Material reduction only pays if the new structure runs on existing lines. Bring the packing operation and the converter into the brief from day one or you will design something you cannot make.

  • Damage rate is the counterweight. A 15% material saving that lifts in-transit damage by two points destroys the case — test to failure before rolling out.

  • Experiment starter: Take your three highest-volume secondary packaging formats and run a generative study against current strength and cube performance. Prototype the best candidate and drop-test it against the incumbent before committing to tooling.

AI Transformation Playbook

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