By Eduard Cojocea, CTO, Esenca Sizing
The professional clothing industry has a quiet problem. The people who wear workwear have changed. The systems we use to fit them, in many cases, have not.
Walk onto any modern industrial site, hospital floor, logistics hub, or service facility across Europe, and the workforce looks fundamentally different from the one our generic sizing systems were originally designed for. It is more diverse in body shape, gender, age, ethnicity, and physical ability than at any point in industrial history. Yet the sizing infrastructure underneath most professional clothing programmes still leans on assumptions from a much narrower demographic: a small number of standard sizes, a sample bag of pre-worn garments, and a customer service representative trying to make a judgement call in a noisy room with a queue of people waiting behind them.
This is not a niche complaint. Poor fit in workwear is not a cosmetic issue. It is a safety issue, a productivity issue, a retention issue, and increasingly, a sustainability issue. A high-visibility jacket that does not close properly, a glove that slips, a boot that pinches, a coverall that restricts movement; these are not inconveniences. They are the difference between a worker who can do their job safely and one who cannot.
If the professional clothing industry is serious about workforce diversity, it has to be serious about inclusive fitting. And inclusive fitting, done at scale, requires a different kind of infrastructure than the one most providers still rely on.
Why traditional fitting struggles with a diverse workforce
The traditional on-site fitting model was built for consistency, not variability. A field representative arrives at a client site with a van full of sample garments. Employees queue up and try on what is available, and a size is assigned often based on what fits closest, sometimes based on what the representative thinks will fit closest if the right sample is not in the van that day.
The model has several structural weaknesses that become more pronounced as the workforce diversifies.
Samples wear out. Garments that have been tried on and washed hundreds of times shrink, stretch, and lose their original shape, which means the reference point itself is unreliable. Absent employees get sized by proxy a colleague of similar height and build stands in, or the representative estimates. Standard-size grids do their best work in the middle of the distribution and their worst work at the edges, which is exactly where many under-represented body types sit. And the entire process depends on subjective human judgement, which introduces variation across regions, representatives, and days of the week.
It is worth being honest about why standard size grids look the way they do. They were never designed purely to fit people; they were designed to keep production efficient, fewer sizes mean longer runs, simpler inventory, and lower unit costs. In consumer fashion, that trade-off, a degree of fit accuracy exchanged for manufacturing economy, is often acceptable. In workwear and PPE, it is not. When a poor fit translates into a safety failure rather than a return, efficiency at the cost of fit stops being a sensible compromise and becomes a liability.
None of this is a failure of intent on the part of the people doing the fitting. Field representatives do extraordinary work under difficult conditions. The issue is that the system asks them to deliver consistent, inclusive outcomes using tools designed for a less varied population.
What inclusive fitting actually requires
Inclusive fitting is not a slogan. It has specific operational requirements.
It requires every worker to be measured individually, not assigned a size by approximation. It requires the measurement process to be objective and repeatable so that the same person measured twice produces the same result regardless of who is doing the measuring. It requires the process to be accessible, meaning it has to work for employees who are absent on the day of a site visit, employees who work remotely, employees who would prefer to be measured privately for reasons of comfort or dignity, and employees whose schedules make queuing up for a fitting impractical. And it requires the data to flow cleanly into ordering and fulfilment systems so that an accurate measurement actually results in an accurate garment.
In other words, inclusive fitting requires digital infrastructure. It requires the ability to capture precise body data from every individual in a workforce, regardless of where they are or when they are available, and to turn that data into a size recommendation that is grounded in the actual garment specifications rather than a generic chart.
This is the gap that machine learning-based body measurement is now closing.
Mewa: inclusive fitting at industrial scale
Mewa Textil-Service has been supplying workwear and cleaning textiles to European businesses since 1908. Their service model is built on a simple promise: every employee receives garments that fit correctly, every time. Honouring that promise across hundreds of thousands of workers, in dozens of industries, with traditional fitting methods, was becoming structurally difficult.
The Mewa team worked with Esenca Sizing to move from physical, sample-based fittings to a digital measurement infrastructure. Field customer service representatives were trained to use Esenca Sizing’s smartphone-based body measurement application and dashboard, allowing each client to be set up as a distinct digital entity with its own required garment set. Custom reporting software was integrated into Mewa’s internal ordering systems so that size recommendations flow directly into operational fulfilment.
The operational shift is significant. Where a representative could previously measure at most forty employees in a single day, the digital tool now supports up to three hundred measurements per device per day. On larger sites, multiple devices run in parallel, allowing hundreds or thousands of employees to be measured during a single visit. Employees absent on the day no longer need proxy sizing or a repeat visit; they can complete the measurement on their own when available. The platform also handles the workers that a standard-size grid was never going to fit. Where someone’s measurements fall outside the available size range, Esenca Sizing captures the detail needed for a made-to-measure garment instead of forcing them into the nearest standard size.
The accuracy outcome is the part that matters most for the inclusivity argument. Mewa evaluates the success of its fitting process using return data, on the principle that workers only return garments they cannot actually wear. After the rollout, the return rate dropped to 3%, a 97% fit success rate at the first attempt.
“The integration of Esenca Sizing is a key milestone in our digital transformation strategy. It strengthens our digital service offering, enables more efficient and scalable fitting processes, and delivers clear value for both customers and service teams.”
— Michael Kümpfel, Chief Customer Officer, Mewa
What the Mewa rollout demonstrates is that inclusive fitting and operational efficiency are not in tension. A system designed to measure every individual accurately, regardless of body type or availability, also happens to be the system that scales best, reduces returns most effectively, and produces the cleanest data for downstream operations. Inclusivity is not a cost centre. It is the engine of a better operating model.
Beyond body measurement: hands, feet, and the next frontier
Body measurement is the most visible part of the inclusive fitting conversation, but it is not the whole conversation. Hands and feet are where personal protective equipment most directly meets the body and where ill-fitting products carry the most immediate safety consequences. A glove that is too loose compromises grip and dexterity. A boot that is too tight causes long-term injury. Yet hand and foot-sizing in most PPE programmes still relies on far cruder methods than body sizing — a small set of size categories, a self-reported guess, or a tape measure used inconsistently across sites.
The same principles that make body measurement more inclusive at scale apply to hands and feet: objective digital capture, individual data for every worker, and integration with garment and equipment specifications. This is an area where the industry has more ground to cover and one where the operational case for change is, if anything, stronger than for body measurement.
Where this leaves professional clothing
The professional clothing sector sits at a useful intersection. Unlike fashion retail, it has direct, measurable feedback on whether its products fit, as every return, every complaint, and every repeat site visit provide data. Unlike consumer wearables, it has clear safety and compliance reasons to take fit seriously. And unlike most other parts of the textile industry, it has the customer relationships and operational footprint to implement infrastructure changes at speed.
Inclusive fitting is no longer a forward-looking aspiration. The technology to capture every worker’s measurements accurately, objectively, and at scale exists today and is in production across major European service providers. The question for the industry is not whether inclusive fitting is possible. It is how quickly the rest of the sector will choose to make it standard.
The workforce has already changed. The opportunity now is to bring the systems we serve them with into the same century.
