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Laundry-VR ™

Laundry-results Virtual Reality Exploration

Improve laundry product's profit,
visualising washing results in an immersive
virtual reality rendered in world's consumer relevant terms

“The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.” Marcel Proust, 1923




Your concerns:

How to be sure that laundry product technical attributes (amazingly described by vendors or technical colleagues) are perceived by brand´s consumers as benefits? How to see washing results as consumers see them? In their domestic visual scenarios?

How to discern in consumer relevant terms among myriad of detergency technologies or between dozens of prototypes and products? All performing under several different and variable regional or local market realities?

Is there a good balance between company´s profit and consumer benefits?

Our approach:

Laundry-VR ™

Laundry-results Virtual Reality Exploration

A novel technological tool (hardware + software) to visualise washing results before and after some specific washing/drying processes in a full immersive virtual reality, rendered in relevance with both the actual visual scenarios found in the world’s laundry market and the ample observers’ variability due to age, culture, preference and experiences.

Washing results, expressed as large data sets, coming from laundry experiments both physical and/or in-silico, are processed with CLVVR algorithms (see below). The input data set contains spectroradiometric information about garments radiance before and after washes/dryings, the spectral characterisation of relevant source of illumination as well as the biology’s relevant information about the variability of observer’s visual system and perceptual discrimination thresholds. All in relevance to a group of specific local or regional laundry markets.

The outputs are immersive virtual environments that include garments and surroundings are created by a combination of physical-based rendering processes and virtual reality design tools, and finally presented in a stand-alone virtual reality head-mounted display (Oculus Go, May 2018).


Frequently Asked Questions (FAQs):

What is the current conventional scenario in laundry brands and products innovation? What is our approach?

Laundry-VR: what, how, who, when, where?

Does it represent the actual world? Is that validated?

What are the three most important benefits to the laundry industry? For your company?

Who needs to do what and by when for things to move forward?


What is the history of CONSUMERTEC in the area? Are Laundry-VR credible?

What does CLVVR change in your detergency lab?

How CLVVR positively influences your business?

What means virtual R&D in consumer relevant terms?

Are cleanliness, whiteness and colour-care perception relative?

Why numerical models to represent consumer visual perceptions?

Frequently Asked Questions (FAQs)

What is the current conventional scenario in laundry brands & products innovation? What is our approach?

Conventional innovation practices are based on 100% physical experimentation at lab conditions, using protocols far away from the ample diversity and variability found in world´s consumer laundry realities. Due to time and cost limitations, teams have owed to sacrifice consumer relevancy. Therefore, innovation environments at incumbent companies has become the most favourable condition to end up with costly products with poor performance in front of consumer eyes. The end results have been non-balanced value equations for consumers and companies.

So, innovation-driven organisations/teams at incumbent companies are under intense pressure, between rising consumer expectation, more product´s margins, and unpredictable moves by agile attackers in several local and regional markets. Innovators are being asked to reinvent themselves moving away from rigidly sequenced process, strict division of responsibilities and narrow focus on internal innovation; finally they are urged to create new value equations for companies and consumers, shortening the concept-to-product time frame.

Recently it has been stated that our industry key topics and priorities are largely based on data, to develop data strategy for product innovation, helping the industry understand consumer behaviour, market trends and product performance. For some, now it is about taking the laundry industry a place on the digital stage!

Our approach is a radical change using digital technologies very upstream innovation process to visualise large amount of detergency data. First, using digital technologies to process a minimum new physical experimentation or to generate new large simulated data based on previous experimentation, and second, using digital technologies to implement immersive virtual environments visually experienced in very affordable stand alone headset virtual reality displays. Business stake holders will have the opportunity to experience consumer benefits perception and product´s cost implication in advance.

Laundry-VR: what, how, who, when, where?

What is it?

It is a new digital tool (a combination of hardware and software) to visualise washing results in a full immersive virtual reality rendered in consumer relevant terms

It is focused to increase laundry product margins.

It combines a commercial affordable device purchased separately, like Oculus Go (a stand alone virtual reality head-mounted device), and novel and unique algorithms (CONSUMERTEC Laundry Visual Virtual Reality - CLVVR) that generates the big data coming from detergency experimentation, physical or virtual, in the areas of cleanliness, whiteness and colour-care

How does it work?

First, the collection of algorithms (CLVVR) generate data in line with actual surface changes and visual scenarios found in the world’s laundry market and the ample observers’ variability due to age, preference and experiences. It starts from physical experimentation data or generates simulated detergency data.

Second, the device is purchased and it downloads specific customised applications to deploy virtual realities which are physical-based rendered in consumer relevant terms

Who is intended to use it?

Brand and product innovation teams. Go-to-Market managers. Research scientists. Consumer & market research staff. Consumer insight and claim support personnel.

In general terms, managers looking for new brand's balances between consumer perception of benefits/non-benefits and brand's profits, based on an increment of laundry product margins.


At the very beginning of innovation process. New product initiatives. Current product audits. Product development cycles


At client´s locations assisted by CONSUMERTEC staff (at the beginning)

Does it represent the actual world? Is that validated?

First of all, Laundry-VR is not the result of conventional statistical modelling. It is a scientific analytic approach to visualise data.

The core of the technology are analytical models i.e. models based on current scientific knowledge about how things happen, in the fields of optical and fluorescence spectroscopy of surfaces, spectroradiometry of source of illuminations at real environments, the biology of consumer visual perception, and the current validated digital technologies to create physical-based renderings. In other words, Laundry-VR collects data or generate data, using scientific principles, and visualise them; it is not a statistical estimation with some level of correlation.

Other similar technologies to visualise scientific results has been developed and are in use in several fields, like life-sciences, biology sciences, molecular visualisation tool for proteins. It has been predicted that virtual reality and augmented reality could become standard lab tool over the next five year or so.

More on this topic in: Matthews, D., SCIENCE GOES VIRTUAL, Nature Vol 557 3 May 2018, 127-128

What are the three most important benefits to the laundry industry? For your company?

Increment product margins. The most important benefit of Laundry-VR is as a tool to reach new balances in laundry product’s profit, because it visualise the perception of benefits and non-benefits in consumer relevant terms according to technologies included in formulations. It balances benefits with margins. It is estimated that the VR technologies will have an impact of 20% increase on value proposition and 5% reduction in product formulation costs *.

Diminish R&D time and cost. It shortens the concept-to-product time frame, speeds innovation and fend-off disruption, because it a way to discover, in advance, consumer perception of cleanliness, whiteness and colour-care. It helps, upstream the innovation process and as close as you get, to viewing with different world’s consumer eyes, which is the fundamental step to get new insights during the innovation process. It is estimated that VR technologies will have an impact of 15% reduction in development cost *.

Less lab environmental impact. It disrupts the current amount of work at detergency labs. The once-a-dream 90% in-silico and 10% physical lab experimentation could become a reality with huge impact on the environmental issues like water consumption and other topics involved with conducting thousand of washes per year at lab facilities. It is estimated that VR technologies will have an impact of more 50% reduction in the lab environmental impact.

* Brossard, M., and Erntell H., "Accelerating product development: The tools you need now", McKinsey Quarterly, June 2018.

Who needs to do what and by when for things to move forward?

Brand, R&D or Consumer Understanding staff in charge of innovation projects that involve brands grow or laundry product margins has to define any need about new products or current products profits, that involve cleanliness, whiteness or colour-care consumer perceptions of benefits or non-benefits. Specifically, select the most challenge needs that requires to be attended in a non-conventional way.

Company´s staff has to contact CONSUMERTEC to explore alternatives to use Laundry-VR as a digital technology to visualise washing results in consumer relevant terms, as a tool to understand world´s consumer visual perception involved in brand grow, product´s attributes perception, and laundry´products cost.

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What is the history of CONSUMERTEC in the area? Are Laundry-VR and CLVVR credible?
... we have more than 30 years in the area with several unique innovations

The unique CLVVR algorithms, embedded in Laundry-VR, are the result of CONSUMERTEC, formerly DETERTEC, ample expertise in the worldwide laundry market. They are grounded in the following:

  • more than 30 years of conducting detergency test, using both conventional laboratory equipment and more importantly, consumer relevant washing process, including use of customised diverse machine equipments and in-house equipments to mimic hand washing process with short and long strokes, and use of indoor and outdoor (UV controlled) drying stages
  • more than 15 years in the field of advanced spectroscopy, including in-house design and fabrication of non-contact and contact spectrofluorimetric equipments based on fibre optics and CCD spectrometers
  • more than 12 years of preparation of dingy test fabrics with high relevancy to world's laundry practices including the realities of: China, India, Europe, USA, Latin America, Middle East and North Africa
  • more than 15 years using modelling techniques. Initially using the scripting capabilities of JMP/SAS software and currently with more universal languages like R and Python. Ample expertise on numerical models to represent consumer benefit perceptions like: garment cleanliness, garment whiteness, diaper yellowness, diaper bulkiness, among others
  • more than 7 years of conducting household experiential ethnography and consumer purchase behaviour experimentation, to interpret the link between consumer benefit perceptions and actions, and brand's purchases (brand's household penetration and purchase frequency)
  • fundamental understandings of the underlying sciences: radiative transfer models, fluorescence and optical spectroscopy and colour appearance models

CLVVR is a proprietary sustainable innovation hold by CONSUMERTEC because it is line with our core academic dedication: fluorescence spectroscopy and the neurobiology of perception and action. Part of this dedication has been shared with the scientific community through more than 30 presentations around the world in the last 20 years. See Presentations and Papers.

What does CLVVR change in your detergency lab?
... it focus innovation on consumer and market realities in a new way

The laundry industry as any other consumer industry has been working in the last decade looking for alternatives to experiment and innovate using virtual technologies most of the time and only validate physically under very specific relevant conditions.

CLVVR is our approach in this area and we are sure that it has the potential to change everything and forever the way we and the industry conduct detergency studies. Can you imagine a detergency study that include:

.. laundry monitors: virtually created using two or more (+2) fabric substrates, soiled or stained with two (2) different levels of more than ten (+10) different materials, with four or more (4+) dingy surrounding. All in relevance to more than ten (+10) specific laundry regions?

.. procedures: virtually washed in two or more (+2) machine or manual process, using several products/technologies at two or more (+2) concentrations, and dried indoors (no UV) and outdoors with two (2) levels of natural UV radiation. All in relevance to more than ten (+10) specific laundry regions?

.. washing results: virtually evaluated by three or more (+3) categorical observers, perceiving at three or more (+3) different visual scenarios, with a perception variability that include three or more (+3) visual sensitivity or preferences?

.. with a cost of less than 10% of the physical alternative, conducted in just a matter of minutes?

If additionally the final output is clearly understand from the perspective of finding the WHY of the final consumer perceptual response, then it is easy to understand why this approach change everything in the laundry industry.

CLVVR can predict laundry products and technologies with poor perceived consumer benefits under real conditions of use, taking into account the ample variability in the real regional laundry markets, prior to produce any prototype

CLVVR can expand and support the definition of products and technologies problems by a combination of empirical and modern analytical approaches. By this way go further than "black box experimentation" and new questions can be generated and solved

For the very first time ever in the laundry industry all participants, like suppliers and producers, have the alternative to interact in a virtual environment, with virtual prototypes and materials to change definitely innovation time and cost at both sides

CLVVR can substitute more than 80% of current laboratory physical detergency evaluation procedures, with substantial gains in consumer relevancy at a fraction of time and cost, speeding up creativity and innovation linked to the different regional laundry markets

CLVVR inputs are real or predicted data about changes of fabric's radiation and outputs are maps of consumer perceptual responses from visual evaluation of washing results, based on in-silico experimentation in a virtual laundry environment

CLVVR provides a tool kit which offers a unique opportunity to generate proprietary inputs based on previous physical experimentation. Its modular design is capable to match prior outputs from previous modelling and simulation tools or match inputs for posterior purchase behaviours models

CLVVR can be combined, in very smart ways, with physical experimentation to predict and validate consumer perceptual responses. CLLS (dingy monitors + algorithms) works together with CLVVR in this way.

How CLVVR positively influences laundry businesses?
... because consumers and scenarios occupies the centre stage in CLVVR

Laundry business is really about winning positive consumer experiences created by products and brands. Successful products in this highly competitive market are offering improved sensory experiences to demanding consumers whom perceive product benefits easily and consistently.

But nowadays competition is fierce, with an accelerated commoditisation of products and services, increasing price wars, and shrinking profit margins. The executives number one concern "sustained and steady top-line growth," can not possible be supported by existing low-productive innovation models. Today development and product introduction efforts in the consumer products industry are urged to accelerate the pace of innovation, with different cost structures, in a different time frame, being collaborative with external sources, and more importantly, are compelled to design products and technologies that offer superior perceived consumer value in line with benefits delivered by brands. In short, R&D has to develop affordable products/technologies with the ability to improve sensory experiences in consumer terms, fast and with less money, i.e. to develop better and cheaper products with a systematic focus on productivity. But, is that possible? how to execute this? ....That is the R&D challenge!!

CLVVR is in line with business models based on innovative developments focused on consumer and actual realities, and provides a competitive advantage because it reduces time and cost of the technology innovation process.

What means in consumer relevant terms?
... it means that detergency labs can include the variability of consumers and scenarios

CLVVR provides the opportunity to include consumer realities at the beginning of the innovation journey, not at the end. The virtual experimentation matrix includes:

Selection of white commercial substrate fabrics that contains fluorescent whitening agents from textile mills

Set of clean, soiled, stained and coloured dingy test fabrics according to more than ten (10) regional laundry markets. Stained areas and coloured areas include adjacent dingy fabrics

Ample selection of actual source of illumination at relevant laundry market scenarios, outdoor and indoor, natural and artificial, and mixtures, with different UV relative content and illuminance.

Consumer ages between 20 and 60 years, variable parameters for field size, lens density, macula optical density, photopigments optical density, and photopigments lambda maximum shift. Individual colorimetric observer models.

Selected different visual contexts and surrounding as a basis to perform modern numerical calculations of colour attributes

Set of colour differences sensitivities with a different degree of lightness, chroma and hue influences, in line with different sensitivities to perceive cleanliness and colour-care in the garment context

Group of whiteness preferences according to cultural and persistent white targets, in line with different lightness and colour opponency influences on whiteness perception

Are consumer perception context-relative?
... yes, cleanliness, whiteness and colour-care perception are relative terms

Visual and olfactive cleanliness, visual whiteness as well as colour-care visual perception depends on consumer realities. There is no absolute cleanliness, whiteness, freshness/cleanliness nor colour-care, all depends on consumers, environments and history.

Cleanliness perception depends on visual detection of remanent stained areas at places perceived as stained before laundry. That visual detection is conducted automatically doing a comparison between the prior stained area and its surrounding. Full remotion means that no remanent stained area is detected or the slight presence of remanent stained is nearly not perceived and accepted as clean. The level of consumer sensitivity to detect sometimes small differences depends,among others on previous laundry experiences, consumer age, garment life and significance, expected laundry performance, and importantly on the visual scenario present during visual evaluation of laundry results.

Whiteness perception, a complex relative perceptual attribute, is linked on the one hand to consumer ability to perceive surface's more basics attributes like lightness, red-green opponency and yellow-blue opponency, all under some specific visual scenario, and on the other, to consumer preference of what is considered as more white, which has roots on habits and culture. The evolution of whiteness models are linked to the evolution of the level of understanding of basic and relative perceptual attributes of colours like brightness, lightness, colourfulness, chroma, saturation and hue as well as phenomenon like chromatic adaptation and colour constancy, on the consumer side; and of light reflection and fluorescence, on the surface side. Currently the reference whites (like a recently purchased white garment) are surfaces that fluoresce usually with shading dyes, and these are relative to different world regions.

Colour-care perception has conventionally been treated as a colour change process after some washing and drying cycle. In that terms, it has been considered a relative minor problem in the laundry industry because consumers has no reference of the original colour garment. Nowadays the situation is different. Much of current coloured garments are in fact a combination of white areas with adjacent coloured areas so much of colour-care perception is a visual perception of some coloured area adjacent or surrounded by a white area, therefore is a case of colour contrast effects in which whiteness perception of the adjacent fabric affect the colour perception of the coloured area. This simple fact, easy to notice at lab, has tremendous implications for numerical calculations that needs the use of recent models to calculate colorimetric attributes taking into account adjacent surfaces.

Consumers cleanliness, whiteness, and colour-care perception are relative terms, so new numerical models have to include most of the variables of those perceptions, including: consumer age, sensitivities, visual scenarios, preferences and references.

All of these have a very important role for innovation, which is, to experiment virtually (in-silico) with large complexities, in order to find those technologies that really match the need to products capable to do the job expected by actual consumers around the world.

Why numerical models to represent consumer perceptions?
... because they can accurately represent variable perception realities

The ultimate way of consumer understanding is to represent it with a numeric function, capable to be optimised as our understanding improve. It is cyclic, better models to better understanding to better models. This is mandatory for our consumer industry due to the high level of competition and the complex consumer environment; we have to clearly understand how consumers perceive brand central benefits, like whiteness and cleaning, which in turn are key purchase intent drivers.

Marketing teams need to model consumer purchase behaviour in order to virtual simulate the influence of variables on business success or failure. Accurate perception models are the critical intermediate stage, and relevant in-vitro models act as a fulcrum in that effort.

R&D teams needs to experiment in virtual terms so as to be fast, with less costs, and to include a real matrix of relevant market elements. In the laundry market any other way is very expensive, time consuming and practically impossible to follow. Can you image a physical experimentation under five or more source of illuminations? with three or more different group of consumers with different visual conditions due to age? with four or more different group of consumers with different sensitivities and preferences concerning lightness, red-green opponency and yellow-blue opponency? with more than ten different stains and more than ten different white substrates? Probably not, but now all of this and more is possible with virtual R&D techniques. That is the purpose of CLVVR.

Why numerical modelling? The well known answer is: "I am never content until I have constructed a mathematical model of what I am studying. If I succeed in making one, I understand; otherwise I do not" William Thomson (Lord Kelvin) 1824 - 1907.

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