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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.
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
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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