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Experiment with Virtual Realities
in World's Consumer Relevant Terms

Consumertec Laundry Visual Virtual Reality (CLVVR)

Collection of algorithms used by technical teams to drive laundry innovation under full virtual realities (virtual: garments, soil and stains, products/technologies, hand- or machine-washing, indoor- or outdoor-drying, consumers, visual scenarios), in the area of cleanliness, whiteness and colour-care perceptions.

A model and simulation tool to answer complex questions in the field of Laundry R&D and to show performance before building any material, with a scientific reliable system that vastly reduces cost and time, keeping the focus on regional world's consumer purchase scenarios.

Is it part of your concerns?

How to discern among myriad of detergency technologies or between dozens of prototypes and products, all performing under several different and variable regional or local market realities; following a consumer-centred approach and looking to get the right value equation for consumers and companies?

This is our approach:

a. Virtualise technology or products (create virtual technologies or products) using a group of algorithms (CLVVR's Tool Kit) applicable to relevant previous physical experimentation or to prior outputs from molecular modelling and simulation capabilities

b. Run algorithms to select virtual dingy monitors that include relevant visual context as well as to select variables of virtual experimental designs in the areas of cleanliness, whiteness and colour-care. Define relevancy according to more than ten (10) worldwide laundry regions, including: USA, Europe, China, India, Latin America. Run algorithms to wash and dry dingy monitors, single cycle or multi-cycle, in a full virtual laundry environment, and to generate spectral data from washing results, without and with UV cut-off filter

c. Run algorithms to predict relevant and variable consumer perceptual responses, using a matrix that includes: source of illuminations, categorical observers, visual sensitivities and preferences, and consumer experience on laundry

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.

What is the history of CONSUMERTEC in the area? Is CLVVR credible?

What does CLVVR change in your detergency lab?

How CLVVR positively influences your business?

What means virtual R&D in consumer relevant terms?








CLVVR Components:




1.

Collection of algorithms to virtualise prior physical experimentation

Based on radiative transfer models, CLVVR's tool kit virtualise any change on fabric radiation collected in previous physical experimentation. Our approach is to construct numerical models that represent spectral optical thickness changes




2.

Collection of algorithms to predict spectral total, reflected and luminescent radiance of virtual washing results

Computation is based on the definition of virtual elements relevant to some specific laundry environment: fabrics, soils and stains, products/technologies, washing and drying stages




3.

Collection of algorithms to process spectral radiance from virtual washing results and estimate consumer perceptual visual responses at laundry visual scenarios

Conduct in-silico experimentation that generates numerous types of predicted consumer responses, at several consumer relevant laundry scenarios. No one human being perceive the same as other. In vision, lightness and colour perception are different for different people under exactly the same context. At different visual context all perceptions can change

Algorithms also characterise variable consumer realities by the frequency of occurrence of visual stimuli that generate perceptions, from visual environments collected in a worldwide basis

Are cleanliness, whiteness and colour-care perception relative?

Why numerical models to represent consumer visual perceptions?




4.

Training support. A unique program to learn or improve understandings about experimentation in consumer relevant terms

A battle tested, real-world learning, and customisable workshop focused to technical personnel, to support product and technology innovation efforts

Curriculum. More...




Frequently Asked Questions (FAQs)


What is the history of CONSUMERTEC in the area? Is CLVVR credible?
... we have more than 30 years in the area with several unique innovations

The unique CLVVR algorithms 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.


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.
Needs more details, explanations, examples?
Please contact us, we will be more than happy to explore ways to match platform features with your current procedures


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