By Paul R Salmon, FCILT FSCM
Introduction: Why Packaging Optimisation Matters More Than Ever
In supply chains, packaging has often been treated as a fixed afterthought—something designed once and left untouched for years. But as the twin pressures of cost containment and sustainability intensify, packaging is now becoming a strategic lever in operational performance.
The stakes are high:
Globally, packaging accounts for over 40% of plastic usage and a significant portion of landfill waste. Inefficient packaging design can waste up to 30% of container space, directly inflating transport costs and emissions. Poorly optimised packaging contributes to higher product damage rates, leading to costly returns, warranty claims, and reputational harm.
For both commercial and defence supply chains, the challenge is no longer simply to protect goods in transit—it is to do so efficiently, sustainably, and adaptively in an environment where volumes, destinations, and material availability can change overnight.
This is where AI-powered packaging optimisation is emerging as a game-changer.
1. What Is AI-Powered Packaging Optimisation?
AI-powered packaging optimisation uses artificial intelligence and machine learning algorithms to design, test, and select packaging solutions that balance cost, sustainability, protection, and volumetric efficiency.
Rather than relying on manual calculations, historical designs, or traditional CAD adjustments, AI takes a data-first approach, ingesting thousands of variables—product dimensions, fragility, climate risks, shipping mode, sustainability targets—and generating packaging configurations that meet the specific needs of a supply chain.
In short, it replaces trial-and-error guesswork with precision-engineered, simulation-tested results.
2. Why Now? The Convergence of Pressures Driving Adoption
Packaging optimisation has always had potential—but in 2025, its importance is amplified by five converging forces:
a. Cost Inflation in Freight and Materials
With container freight rates fluctuating wildly and raw material prices rising, every cubic metre saved in packaging translates directly into bottom-line savings.
b. ESG and Regulatory Pressures
Extended Producer Responsibility (EPR) schemes, plastic taxes, and Scope 3 emissions reporting requirements mean companies are now accountable for the carbon footprint of their packaging decisions.
c. E-commerce Growth
The shift to direct-to-consumer models has increased the variety of packaging SKUs needed and the speed at which packaging must adapt to new products.
d. Supply Chain Volatility
Defence and commercial supply chains alike face fluctuating demand and unpredictable deployment routes. AI can quickly recalculate optimal packaging for new shipment modes or climatic conditions.
e. Technological Maturity
The rise of affordable 3D scanning, digital twins, and AI simulation software makes packaging optimisation far more accessible than it was even five years ago.
3. How AI Optimises Packaging
The core strength of AI in packaging is its ability to model millions of permutations—something human designers cannot do quickly or cost-effectively.
Here’s how the process typically works:
Step 1 – Product Profiling
AI ingests detailed data about the product:
Dimensions and shape complexity Weight distribution Fragility scores (drop sensitivity, vibration tolerance) Environmental sensitivity (temperature, humidity)
For defence logistics, this could mean factoring in ballistic protection for munitions crates or shock resistance for avionics components.
Step 2 – Material Selection and Trade-Off Modelling
AI evaluates multiple material options—corrugated cardboard, moulded pulp, plastics, foams—and ranks them against:
Protection requirements Sustainability ratings Availability in the target region Cost volatility and supplier lead times
This is especially valuable in contested supply environments where material availability can shift suddenly.
Step 3 – Structural Design via Generative Algorithms
Using generative design, the AI creates multiple packaging configurations optimised for:
Minimal material use Maximum stacking strength Compliance with transport regulations (e.g., ISTA standards, NATO codification rules)
The software might produce non-intuitive designs—angled folds, modular inserts, or honeycomb structures—that outperform conventional layouts.
Step 4 – Digital Simulation and Stress Testing
Digital twins of the packaging are stress-tested in virtual environments:
Drop and vibration resistance Compression loads for warehouse stacking Moisture and temperature exposure Shockwave modelling for military transport
In commercial retail, this avoids unnecessary over-packaging. In defence, it ensures mission-critical kit survives transit without field failure.
Step 5 – Volumetric Optimisation for Logistics
AI calculates how the new packaging affects:
Pallet and container utilisation rates Load planning for aircraft, ships, and lorries Slotting efficiency in automated warehouses
Even a 5% improvement in cube utilisation can yield significant transport cost reductions across large-scale operations.
Step 6 – Cost, Carbon, and Compliance Reporting
Finally, AI generates a decision dashboard showing:
Cost per unit packaged Emissions impact per shipment Regulatory compliance status Comparative performance of alternative designs
This enables procurement, operations, and sustainability teams to make joined-up decisions.
4. Commercial Supply Chain Applications
In commercial logistics, AI-powered packaging optimisation is already delivering measurable wins:
FMCG: A European beverage manufacturer redesigned bottle cases with AI to allow 10% more cases per pallet, reducing annual transport costs by €1.2 million. E-commerce: A US retailer integrated AI into fulfilment centres, cutting average parcel volume by 28%, saving millions in dimensional weight (DIM) shipping charges. Pharma: An AI-calculated insulated packaging solution reduced dry ice requirements by 40%, lowering costs and improving sustainability without compromising cold chain integrity.
5. Defence Supply Chain Applications
Defence supply chains present unique challenges—unpredictable routes, hostile climates, and urgent deployment timelines. AI offers clear advantages here:
Deployable Kits: Optimising crates for rapid loading into aircraft or naval containers, increasing the amount of kit transported per sortie. Sensitive Electronics: AI can calculate the exact thickness of shock-absorbing materials needed for radar or avionics components, reducing both weight and cost. Sustainability in Operations: By reducing packaging waste in-theatre, AI supports military ESG commitments and reduces the resupply burden. Contingency Planning: AI can redesign packaging for alternative materials in case of disrupted supply lines, ensuring resilience during conflicts or sanctions.
6. Benefits Beyond the Box
AI-powered packaging optimisation is not just a packaging initiative—it’s a supply chain performance enabler.
Cost Savings: Reduced materials, improved load factors, and fewer product damages. Sustainability Gains: Lower emissions and waste contribute to ESG targets. Speed to Market/Deployment: AI cuts design-test cycles from months to days. Customisation at Scale: Tailoring packaging for individual SKUs, regions, or missions. Improved Stakeholder Alignment: Linking procurement, sustainability, and logistics teams via shared data dashboards.
7. Barriers to Adoption and How to Overcome Them
Despite the clear benefits, adoption in many organisations is slow due to:
Data Gaps: Without accurate product and logistics data, AI outputs are limited. Change Resistance: Packaging is often seen as a “solved problem” and low priority. Upfront Costs: Investment in AI platforms and integration can be a hurdle.
Overcoming these barriers requires:
Data Discipline: Establish volumetric, weight, and material data collection as standard practice. Pilot Projects: Start small to prove ROI before scaling. Cross-Functional Buy-In: Engage sustainability, procurement, and operations early. Supplier Collaboration: Work with packaging vendors willing to integrate AI into design cycles.
8. The Road Ahead: Autonomous Packaging Optimisation
The future will see real-time AI packaging optimisation fully embedded in fulfilment and defence logistics workflows:
Automated dimensioning systems will scan items and instantly generate optimal packaging specifications. Robotics will cut and assemble bespoke packaging on demand. AI logistics twins will continuously re-optimise packaging for changing routes, costs, and sustainability targets.
In defence, this could mean deployment-specific packaging produced in forward operating bases using local materials and 3D printing—reducing the logistics tail and increasing agility.
Conclusion: From Cost Centre to Strategic Lever
Packaging is no longer just a protective layer—it’s a strategic supply chain tool. AI-powered packaging optimisation turns it into a driver of cost savings, sustainability gains, and operational resilience.
For the commercial sector, this means staying competitive in cost-sensitive, regulation-heavy markets.
For the defence sector, it means delivering more capability to the front line, faster, with a smaller environmental and logistical footprint.
As AI technology matures, the organisations that embrace packaging optimisation will not only reduce waste—they’ll also unlock new performance advantages across their entire supply chain.
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