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Experiment In-vitro and In-silico
in World´s Consumer Relevant Terms

Consumertec Laundry Lab Suite (CLLS)

A tool (dingy monitors + algorithms) used by detergency laboratories around the world to set modern balances between consumer benefits and formulation costs, so as to get the right value equation to consumers and companies in a worldwide basis.

Your experimentation goals:

To discern among detergency technologies or to compare laundry products performance, following a consumer-centred approach, using physical monitors, detergency lab facilities, spectrophotometers and algorithms to compute spectral data

To reduce cost and time with superior local relevancy replacing the conventional two step process: use of technical stained monitors at the beginning and use of consumer garments at the end, by another one step process that includes consumer realities since the beginning

How to achieve these goals?

a. Select and acquire CONSUMERTEC dingy monitors that include relevant visual context according to your experimental design 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.

b. Wash and dry dingy monitors and fabric ballast in line with your internal protocols, single cycle or multi-cycle. Collect spectral data from washing results, without and with UV cut-off filter, using current installed spectrophotometers

c. Apply CONSUMERTEC collections of 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 household laundry

What does CLLS change in your detergency lab? - How CLLS positively influences your business? - What means in consumer relevant terms?

CLLS is an in-vitro & in-silico platform to improve detergency labs' ability to discern among technologies using two criteria: relevancy and variability of world's consumer responses at real laundry scenarios. Consumer responses concerning cleanliness, whiteness, colour-care, and olfactive-cleanliness perceptions. What is CLLS' performance? - How competitive is CLLS? - How compatible is CLLS with Consumertec? Is sustainable?

Who require the use of this platform at internal detergency labs, and why?

    • Technology innovations teams (R&D teams), because CLLS influences the direction of technological innovations and product attributes, by providing a full map of consumer perceptual responses, upstream the creativity journey with less cost and faster. Also CLLS is used to validate physically results from virtual R&D
    • Consumer understanding teams (Consumer Market Knowledge or Consumer & Market Insight teams), because CLLS helps to correctly interpret consumer insights about benefit perceptions

Frequently asked questions (FAQs)









CLLS Components:




1.

In-vitro consumer relevant dingy monitors

Full set of unique test fabrics with visual or olfactive context to conduct physical experimentation about cleanliness, whiteness, colour-care, and olfactive-cleanliness; ready to be used for virtual experimentation

New catalogue. More...




2.

Collection of algorithms to process spectral radiance from fabrics and estimate consumer 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?




3.

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 Marketing and R&D personnel, to support brand and product innovation efforts

Curriculum. More...




Frequently Asked Questions (FAQs)


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

For more than 100 years the intuitive way to developed laundry products has been based on laboratory washing trials of artificial soiled test cloths and fast standardised washing procedures.

Currently conventional practices includes the use of test fabrics with full embedded soil material that occupies all test fabric surface, which is washed on small-scale laboratory equipment (Tergotometers or Launderometers). In the last decade test fabrics has claimed better discriminate power between laundry technologies which helped innovations teams to show some level of advantage between them. In a second step these technologies have to prove its performance in front of real consumers under different market realities. This two step technology innovation process usually ends with enormous dissatisfaction because the super winner in the first step means nothing in the second step, with tremendous waste or time and money. The problem has been clearly identified: systematic lack of consumer relevancy at the beginning of the technology innovation process.

CLLS once in use by internal detergency labs replaces the conventional process to one step. It provides innovation teams an ample set of consumer relevant dingy monitors with visual context and modern algorithms to predict consumer visual perception under myriad of visual scenarios keeping the natural variability that characterise the real market. Technologies are discern according to their something unexpected performance in the complex interaction between consumers and scenarios.


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

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!!

CLLS is in line with business model 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

CLLS provides the opportunity to include consumer realities at the beginning of the innovation journey, not at the end. The 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


What is CLLS' performance?
... 4 to 6 more discern criteria to select among technologies in the area of cleanliness or whiteness

Colorimetric procedures and its results applied at detergency laboratories, which are most of the times the output of the software embedded in the 'black box' named spectrophotometers or similar devices, are not only very limited but usually obsolete in comparison with modern understandings in the areas of fluorescence spectroscopy and visual perception. That is totally understandable because equipments are not industry-specific. Usually final results are only applicable for non-fluorescent fabrics, assuming one non-existent standard observer under one theoretical visual scenario which evaluate cleanliness and whiteness with just one visual modality. That is the perfect scenario for false negatives.

On the contrary CLLS provide results using modern and advance colorimetric procedures in a useful to way to understand i.e. maps of consumer perceptual responses assuming a pairwise comparisons. Specifically it takes into account fluorescent fabrics that change according to the UV relative content of the source of illumination, consumer ages and eyes' conditions of world's consumer, various cleanliness and whiteness modalities according to life learning and culture, which in combination offers between 4 to 6 more opportunities to discern among technologies on the basis of predicted consumer benefit perceptions. Technologies now have the opportunity to show advantages not considered before.


How competitive is CLLS?
... highly, it saves time and money to innovation journey

Lack-of-relevancy laundry technologies, at the end, will increment costs with no profits. Same consumer benefits perception can probably be achieved with less costly technologies mixed in creative balances.

CLLS replaces lab expenses on materials with no relevancy to consumer realities by other ones with high relevancy plus a collection of algorithms, with no extra cost, to predict different consumer useful responses at different visual scenarios.


How compatible is CLLS with Consumertec? Is it sustainable?

The unique integrated in-vitro and in-silico platform CLLS is the result of CONSUMERTEC, formerly DETERTEC, ample expertise in the worldwide laundry market. It is 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 the use of customised diverse machine equipments and in-house equipments to mimic hand washing process with short and long strokes
  • 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)

CLLS 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.


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?
... 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 CONSUMERTEC LAUNDRY LAB SUITE - CLLS.

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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