Physics-Based Packaging Intelligence
The Shift to eCommerce-Delivered Primary Packaging Changes Everything
Stop Guessing. Start Calculating. Reduce eComm Damage.
Your packaging was engineered for force profiles at the pallet and case level that are increasingly irrelevant. The relentless expansion of primary packaging structures moving through eCommerce requires a fundamental redesign to ensure your undamaged products are delivered to your customer's doorstep.
About 50% of consumers switch to a competitor after one bad experience, rising to 80% after multiple failures. Every damaged delivery doesn't just cost 17x in replacement; it costs customer lifetime value and brand damage.
Cognify competitors rely on statistical sampling and trial-and-error where our experts are grounded in a physics-based approach that powered Packaging 2.0 and now solves the complex realities of eCommerce Packaging 3.0. We combine high-speed data acquisition from actual supply chains with first-principles of computational physics and machine learning to predict, model, and prevent failure and minimize damage during the fulfillment process.
Why Primary Packaging Fails in Modern Fulfillment
The problem isn't your product or package design… the cheese got moved. Current packaging structures were engineered for force profiles at the pallet unit load and case level that are increasingly irrelevant in a world where primary packaging moves through both manual and automated eCommerce networks.
The Acceleration
Products that once moved safely nested together in corrugated cases and stretch-wrapped pallets now ship individually through 3PLs, sortation centers, and last-mile carriers. This acceleration to primary packaging delivery exposes your product to:
Potential drop heights of 60 inches and more
At sortation and delivery (it's the big drops that cause the damage)
Unpredictable orientations
Through automated handling systems
Thermal extremes
In uncontrolled last-mile vehicles
Compression loads
Never designed into primary packaging specs
The Physics Gap
Cognify deploys methodologies that relate product and package category-based minimum failure energy with stochastic models of measured dynamic loading events that occur in the fulfillment network. This holistic approach that is based on "real data" provides a quantitative design basis for products and product packaging that are deployed in eCommerce fulfillment. In addition, a validated eCommerce test method has been created to evaluate product/package success in eCommerce deployment.
The Pioneers of Physics-Based Packaging Engineering
Cognify is where computational physics meets real-world packaging complexity. We don't just design packaging; we capture actual force profiles from live supply chains through high-speed data acquisition, then model the exact forces, orientations, thermal profiles, and stochastic variables your products encounter from fulfillment center to customer porch.
Our Lineage
We pioneered and led the creation of physics-based methodologies that began in 1986 that enabled the acceleration of Packaging 2.0 development and efficacy, the era when case-and-pallet distribution demanded predictable, repeatable protection systems. Those same mathematical models, refined over decades of experience across multi-disciplinary team members, now power the solutions for eCommerce Packaging 3.0 for consumer products, pharma drug delivery, and federal supply chain protection applications.
Our Approach
We start from first principles, reducing complex systems to their fundamental physics, and build solutions using:
Ground Truth Data Collection
High-speed data acquisition systems deployed in your internal manufacturing and distribution systems and into your or 3rd party fulfillment networks to capture real force profiles, impacts, orientations, and thermal conditions
Computational Simulation and Analysis
Non-linear finite element methods for structural integrity and crush resistance, Computational Fluid Dynamics for fluid/thermal protection in cold chain applications, Multi-Body Dynamics for impact modeling, stochastic simulation for variable loading and orientation scenarios, and surrogate modeling approaches that mimic the behavior of expensive simulations while being computationally cheaper to evaluate.
Hybrid ML/AI Intelligence
Empirical physics as the baseline, surrogate models that approximate expensive computational simulations using machine learning techniques, enabling faster, more comprehensive exploration of design spaces where deterministic outcomes need estimation
Physical Validation
Validated Lab Testing protocols that mirror actual network conditions captured through data acquisition, not generic standards
Our Mission
Lead the transformation from legacy packaging assumptions to physics-based, computationally validated solutions that protect products moving in their primary packaging throughout modern fulfillment networks - for CPGs, pharma, eCommerce providers, 3PLs, and federal applications.
The Cognify Advantage
The physics-based approach that enabled reliable Packaging 2.0 started with us. While competitors were running drop tests and hoping for the best, we were building predictive computational finite element models of packaging structures to predict failure before the first prototype was constructed and solving performance problems before they made it into production.
We deploy high-speed data acquisition systems in actual fulfillment environments to capture real force profiles and orientations, not assumptions. Accelerometers, load cells, thermal sensors, and orientation tracking provide empirical baselines that feed directly into our computational models. This is the ground truth competitors lack.
Non-linear finite element methods for structural integrity and crush resistance, Computational Fluid Dynamics for fluid/thermal protection in cold chain applications, Multi-Body Dynamics for impact modeling, stochastic simulation for variable loading and orientation scenarios, and surrogate modeling approaches that mimic the behavior of expensive simulations while being computationally cheaper to evaluate.
We don't treat machine learning as a replacement for physics; we use it as an amplifier. Physics provides the empirical baselining through physics-informed surrogate models; all physics-informed machine learning models outperform their data-driven counterparts. ML/AI delivers predictive solutions where additional fidelity or estimation is needed to handle complex supply chains.
Competitors iterate based on failure data. We calculate based on physics validated against measured forces from your actual supply chain. The most elegant solutions seem simple in hindsight, however, once they've been understood, developed, and implemented through rigorous first-principles analysis, the solution path looks clear.
The same computational methods that protect warfighter equipment and food supply, optimize pharmaceutical cold chains, and solve extremely unique federal challenges apply directly to eCommerce packaging. The physics doesn't change; applications do!
Computational Physics for Modern Packaging
Understanding what needs to be done in packaging or protection to deliver the product to the customer undamaged is the fundamental challenge.
eCommerce Packaging 3.0 Solutions
The shift from case-load distribution to individual item fulfillment demands new engineering approaches.
What We Deliver:
- Supply Chain Force Profiling: Deploy high-speed data acquisition systems to capture actual forces, impacts, orientations, and thermal conditions throughout your fulfillment network to establish the "ground truth" loading scenarios that cause product and package damage and failure
- Minimum Failure Energy (MFE) Analysis: Provide a quantifiable basis for defining the allowable forces your package/primary package can sustain and defining the specific requirements for fulfillment protection
- Primary Packaging Optimization: Strengthen existing packaging to survive modern fulfillment forces based on measured, validated requirements
- SIOC (Ships In Own Container) Design: Engineer primary packaging that eliminates secondary protection while meeting network demands proven through empirical data
- Protection System Engineering: Design secondary packaging (mailers, boxes, cushioning) based on calculated energy-based requirements, validated against real supply chain conditions, not generic standards
The Economic Case for SIOC:
Amazon estimates SIOC can save up to $1.1 billion in packaging materials and transportation costs annually. For individual sellers, savings range from a few cents to several dollars per unit.
Applications:
Consumer products, fragile goods, variable-geometry products, multi-item shipments with interaction dynamics.
"Understanding what needs to be done in packaging protection to deliver the product to the customer undamaged is the fundamental challenge. This applies to CPGs, pharma drug delivery companies, eCommerce providers, 3PLs... anyone moving products in their primary packaging."
Pharmaceutical and Drug Delivery Packaging
Cold chain and controlled substances demand precision thermal and structural protection.
Our Expertise:
- High-speed thermal and environmental data acquisition throughout cold chain
- Thermal modeling for insulation, phase-change materials, and shipping duration
- Regulatory compliance engineering (FDA, DOT, international standards)
- Cost optimization while maintaining validated performance
The Economic Benefit:
Like eCommerce a decade ago, cold chain packaging is over-engineered because minimum thermal requirements are not well understood. We bring computational rigor validated by empirical data acquisition to replace the guesswork, reducing material costs while ensuring validated performance.
ML/AI for Packaging Intelligence
We deploy AI where physics alone cannot deliver sufficient fidelity.
Our Hybrid Approach:
- Ground truth provides the foundation from actual supply chains
- Physics models the behavior with FEA/CFD validated against measured forces
- Surrogate models bridge the gap for rapid design exploration
- ML/AI adds predictive power for stochastic variables
- Operational data integration with client data science teams
Real Results:
- Predictive damage modeling that forecasts failure modes before shipping
- Automated quality control using computer vision plus physics models
- Dynamic packaging recommendations based on product plus destination plus empirical network data
Sustainability Engineering
Sustainability is not a checkbox; it is an engineering constraint that must coexist with protection requirements.
Our Solutions:
- Material substitution analysis: 100% recyclable materials, biodegradable polymers, renewable substrates
- Right-sizing based on physics: Eliminate waste while maintaining calculated protection thresholds
- Closed-loop systems: Returnable packaging engineered for multi-cycle durability
The Market Reality:
Nearly three-quarters (73%) of consumers are open to choosing brands that offer more sustainable packaging. Minimal packaging takes the top spot, valued by 61% of shoppers - aligning perfectly with physics-based right-sizing approaches.
The sustainable packaging market is projected to expand from USD 313.72 billion in 2025 to USD 594.46 billion by 2035.
Federal and Defense Applications
The same computational physics competencies that solve commercial packaging challenges power our work in defense and federal sectors.
Warfighter Packaging & Protection
- •High-speed data acquisition in field deployment scenarios
- •Ruggedized containers for field deployment
- •Drop, shock, and vibration engineering for mission-critical equipment
- •Environmental testing and validation (MIL-STD compliance)
Advanced Computational Simulation
- •Non-linear finite element methods and CFD for structural analysis and thermal management
- •Multi-physics coupling for electro-thermal-mechanical problems
- •Surrogate models using physics-informed and data-based modeling approaches
Machine Design & Automation
- •Packaging converting equipment analysis
- •Motion control systems and precision robotics
- •Sensor development and integration for real-time data at material interfaces
The Cognify Process
Discovery and Data Collection
We start by understanding your product, your fulfillment network, and your failure modes. What is breaking? Where? Under what conditions?
Ground Truth Force Profiling
Deploy high-speed data acquisition systems (accelerometers, load cells, thermal sensors, orientation tracking) in your actual supply chain to capture real force profiles at every touchpoint.
Physics Modeling
FEA, CFD, and multi-body dynamics models predict behavior under real-world conditions validated against measured data. We calculate forces, stress distributions, thermal profiles, and failure thresholds.
Surrogate Model Development
Where full-fidelity simulations are too expensive or time-consuming, we construct surrogate models that mimic simulation behavior while being computationally cheaper.
Hybrid ML/AI Enhancement
Where stochastic complexity demands it, we layer machine learning on the physics foundation for predictive accuracy, trained on ground truth data from your network.
Physical Validation
Computational models are validated through targeted testing that mirrors actual network conditions captured through data acquisition, not generic standards.
Implementation Support
We work across your functional teams (packaging, operations, engineering, procurement) to ensure solutions work in the real world, on your lines, through your networks.
Who We Serve
Consumer Packaged Goods (CPG)
Navigating channel shifts to DTC and hybrid retail models while maintaining product protection and brand experience.
Economic driver: Damage rates directly impact margins
Pharmaceutical and Biotech
Ensuring cold chain integrity and regulatory compliance while scaling to meet direct-to-patient delivery demands.
Economic driver: Compliance failures risk patient safety
eCommerce and 3PL
Optimizing packaging across massive SKU catalogs while reducing dimensional weight charges and damage claims.
Economic driver: Every cubic inch of void fill costs money
Federal and Defense
Meeting MIL-STD requirements for ruggedized packaging that survives extreme handling across global logistics networks.
Economic driver: Mission readiness depends on arrival condition
Ready to Engineer Your Solution?
Let's discuss how physics-based packaging engineering can solve your eCommerce challenges.
Engineer Your Packaging for Modern Fulfillment
Products in primary packaging throughout the delivery journey demand physics-based solutions. Let us calculate your protection requirements.