We expected to find a mature equipment category with standardized specifications. What we found instead was a market where “mannequin testing” means fundamentally different things to different manufacturers — and where the gap between what brands need and what is commercially available is wider than most product teams realize.
When a brand decides to move beyond flat-bench absorption testing and evaluate product performance under anatomically realistic conditions, the assumption is straightforward: identify the right equipment, specify the parameters, purchase, and start testing. The mannequin testing equipment market serves one of the world’s largest consumer product categories. Surely the options are well-established.
We recently conducted a systematic evaluation of mannequin-based testing systems across multiple equipment manufacturers. What we found challenged every assumption we started with.
What “Mannequin Testing” Actually Means — and Why Nobody Agrees
The first and most fundamental discovery was the absence of a shared definition. The term “mannequin testing” encompasses at least three distinct equipment categories that share a name but very little else.
Dynamic simulation systems replicate infant movement — mechanical leg articulation simulating walking gait, with real-time leakage detection at leg openings during motion. These systems answer a specific question: does the product maintain containment during active movement? The engineering is genuinely sophisticated — programmable step frequency, adjustable stride parameters, automated fluid delivery timed to movement cycles.
Static posture simulation systems take a different approach entirely. The mannequin is rotated through multiple positions — supine, prone, lateral — to test leakage behavior under different sleep and rest postures. The question these systems answer is different: does the product maintain containment across the range of positions a baby assumes during sleep? Some static systems include leakage monitoring at the waistband — a detection zone that dynamic walking systems typically lack, because waist leakage is primarily a postural rather than a locomotion phenomenon.
Hybrid and general-purpose laboratory systems represent a third category — equipment that includes mannequin testing as one capability within a broader laboratory testing portfolio. These systems may offer acceptable mannequin functionality, but it is typically not their core design focus, which can affect the depth of customization and the specificity of the simulation parameters available.
These three categories are not interchangeable. A brand that purchases a dynamic walking simulator expecting it to evaluate overnight sleep performance will be disappointed — and vice versa. Yet the market presents all three under the same “mannequin testing” label, leaving the buyer to discover the differences through trial and error rather than through clear category definitions.
Four Capability Dimensions Where the Market Falls Short
Across our evaluation, four capability dimensions emerged as the critical differentiators — and the critical gaps.
Dimension 1: Material Transparency
A typical R&D laboratory evaluating mannequin testing systems would likely prioritize the ability to observe fluid behavior inside the product during testing. Transparent or translucent mannequin materials allow engineers to see how liquid distributes through the absorbent system in real time — not just whether it leaked, but how it moved, where it concentrated, and where the containment system was under stress before failure occurred.
The reality: the vast majority of commercially available mannequin systems use opaque, skin-realistic materials. Transparent models exist in concept but require specialized material sourcing that most equipment manufacturers do not offer as a standard option. The few that acknowledge the capability typically subcontract the transparent mannequin form to a separate materials specialist — adding lead time, cost, and a coordination layer that complicates procurement.
This gap is not trivial. It reflects a fundamental mismatch between the equipment’s design heritage (manufacturing QC, where binary pass/fail is sufficient) and the emerging use case (R&D investigation, where observability is the entire point).
Dimension 2: Anatomical Customization
Product fit varies by size, and fit directly affects leakage behavior. A meaningful mannequin testing program needs forms that match the anthropometric profile of the target consumer population — which means custom sizing based on the brand’s own fit data, not the equipment manufacturer’s standard mold library.
The customization landscape varies dramatically. Some manufacturers offer full custom molding from client-provided dimensional data. Others work from a fixed library of standard sizes. The cost and lead time implications of custom molding are significant — dedicated tooling for each size adds both upfront investment and calendar time to the procurement process.
For brands testing across multiple product sizes (a typical diaper line spans newborn through Size 6, each with different anatomical proportions), the custom tooling investment multiplies rapidly. This is a procurement architecture decision, not just an equipment specification decision — and most brands do not realize it until they are already committed to a system that limits their size range.
Dimension 3: Data Export and Integration
Modern R&D workflows require test data to flow into digital analysis pipelines — statistical software, visualization tools, reporting templates. This requires structured data export: USB transfer, network integration, or at minimum a standardized file format that can be parsed programmatically.
Many mannequin testing systems were designed for manual data recording — an operator reads values from a touchscreen display and writes them into a logbook or spreadsheet. This was adequate for QC applications where the data volume is low and the analysis is simple. For R&D applications involving multi-variable test matrices across dozens of product configurations, manual data recording is a productivity bottleneck that undermines the value of the testing itself.
The data export gap is the most straightforward to evaluate and the most frustrating to discover after purchase. It should be the first question in any equipment evaluation — and it is typically the last.
Dimension 4: Dynamic vs. Static — and Why You Probably Need Both
The choice between dynamic and static simulation is not a preference — it is a coverage decision. Dynamic testing reveals locomotion-related failure modes (leg gap leakage during walking, product shifting during movement). Static testing reveals posture-related failure modes (waist leakage during side-sleeping, fluid pooling during prolonged supine positioning).
A product that passes dynamic testing can fail static testing, and vice versa. The failure modes are physically different — driven by different forces, at different anatomical contact points, under different compression profiles. A complete mannequin testing program requires both modalities. Yet purchasing both from the same supplier is not always possible, because some manufacturers specialize exclusively in one modality.
This forces a procurement decision that most brands do not anticipate: either accept a single-modality system and supplement with flat-bench testing for the coverage gap, or source two systems from different manufacturers and manage the integration complexity of non-standardized equipment.
What the Equipment Gap Reveals About the Industry
The mannequin testing equipment landscape is a microcosm of a broader pattern in hygiene product development infrastructure.
The testing methods that brands rely on to make product decisions — absorption speed, rewet, capacity — are overwhelmingly flat-bench methods. A fixed volume of liquid is poured onto a stationary product lying flat on a test surface. The measurements are taken under conditions that bear almost no resemblance to actual wear: no body weight compression, no movement, no postural variation, no time-dependent fluid redistribution.
Mannequin testing bridges this gap by introducing anatomical geometry, gravitational effects, and simulated movement into the evaluation. The data it produces is categorically different from flat-bench data — and, in our experience, far more predictive of real-world consumer complaints.
But the equipment infrastructure to support this kind of testing remains in its early stages. Standardization is minimal — different systems use different anatomical proportions, different fluid delivery mechanisms, different leakage detection methods. A test result from one system is not directly comparable to a result from another, because the test conditions are fundamentally different even when the nominal “test protocol” appears identical.
This is the same maturation gap that existed in flat-bench testing equipment two decades ago, before industry standards bodies harmonized test methods and equipment specifications. Mannequin testing has not yet reached that stage — which means that brands investing in this capability today are, in a sense, building their own standards from scratch.
The Equipment Selection Framework
If we were advising a brand starting a mannequin testing equipment evaluation today, the sequence would be:
Start with the test protocol, not the equipment catalog. Define the specific failure modes you need to detect, the specific product configurations you need to test, and the specific data outputs you need to capture. The equipment specification follows from the protocol design, not the other way around.
Evaluate the mannequin form and the test infrastructure as separate decisions. The best mechanical systems are not always paired with the most customizable mannequin forms. Separating these two procurement streams opens a wider solution space and often produces a better-optimized total system.
Budget for iteration from the outset. Every R&D team we are aware of eventually needs capability beyond the initial configuration — different sizes, different materials, additional detection zones. Starting with a system architecture that accommodates expansion avoids the cost of replacing the entire system when requirements evolve.
The equipment is not the end goal. The decision quality it enables is. And in a market where standardization has not yet arrived, the brands that invest in understanding the equipment landscape — rather than defaulting to the first supplier who responds — will build testing capabilities that produce better products.
Simon Gong | Founder & CEO, Corio Hygiene Innovation Team
