Quantification
and objectification of compatibility is perhaps a defining feature in
navigating the Industry 4.0. In choice
of vendors, franchisees, licensees, franchisors, licensors, customers etc. a
key to long last partnership lies often in the compatibility of characteristics
between them. Customer and producer journeys in multi sided platform markets
evolve out of compatibility development among the various partners. Therefore the
platform objective would to sync these mutually similar compatible attributes
between the players on the platform.
Often a solution
to swifter payback period entails following a time tested model of franchising
or licensing. In identifying the right vendor, client or partner, a firm often
would turn a basic meeting ground of quantitative parameters like financial
credibility of the target partner, experience in the relevant field etc. with
suitable weightages. In addition, there is a judgmental call based on perhps on
gut or qualitative feel whether the target is right candidate or not. Depending
on the market structure geared towards the buyers or supplier side, the other
party too would seemingly examine quantitatively or judgmentally the presence
of similarity in attributes towards making a successful journey. The applications range from franchising, distributor
networks, logistics agency, licensing, export agency among others.
Nonetheless, it
is incontestable, that we live in a universe progressively centred by computable
thinking. The human enterprise is to objectify each and every attribute human
or otherwise. Irrespective of the merits or otherwise, this unidirectional
movement is conceivably acknowledged. Therefore in building the partner base
for any firm, quantitative approaches need to be structured on the vendor side.
Statistical tools are freely to be used. It is not uncommon to find a few
prophets of statistical infallibility argue for increasing sophisticating of
statistical tools to get better results. It is moot to determine a linkage
between statistical sophistication and increased reliability, validity and
credibility of statistical tests. It would not be unreasonable to conclude
yielding of diminish returns beyond certain level of input output analysis
something which
this
piece highlights.
In the instances
discussed, Dice coefficient is a popular statistical measure that is being
used. Serenson-Dice coefficient as it is known among other names is a tool for
statistical measure of similarity. A university has developed a new product for
which it has obtained a patent. To commercialize it can either choose to use in-house
or licensee it to a client. In the process of identifying the right target
client, Dice coefficient might yield greater utility in testing the
similarities between the licensor and the target licensee. Indubitably, the
challenge first is quantify the attributed for which the similarity has to be
tested. Further given the results, the interpretation of the same is human. It
is perhaps to use in conjunction with other parameters like citation analysis etc.
The applications
of statistical similarity tests like Dice coefficient, Jacquard index etc. go
beyond the ones highlighted above. Apart from use in vendor and customer
selection they have increasing applications in what is being described as
Industry 4.0. Many critics posit that
ancient texts are not composed by a single author but composed by multiple
authors at different points of time. A good way would be to test writing styles
and the vocabulary being used through the text. If there is similarity of
vocabulary through the text, it might be of single author else might yield to speculation
of being an agglomeration several parts being added at different points of
time. In image processing and identification, Dice coefficient seems to have
lot of applications. Going forward, vacation and tourism firms might begin
using data from customers, hotels, airlines, cruises, tourism spots etc. run a
similarity algorithms using Dice coefficient and its developed variants, identify
a right tourism spot for you. If someone wants to purchase a car or a bike,
there might be series of similarity tests run to find the right choice. A more
unnerving would be a marriage market wherein compatibility tests would be
performed using several attributes to decide whether the marriage will last or
not. This would be quite a journey from astrology to statistics or astrological
numerology to statistical numerology.
Yet these bring
about numerous challenges. Statistical tools are merely tools. They give
results based on the data. The first challenge is to build the quantitative
attributes. This entails a journey of quantifying the qualitative. Several
attributed which be intrinsically difficult to quantify might have to undergo a
conversion process. With advancements in imaging technologies etc. even human
images and at different points of time and different contexts can be assigned a
numerical meaning. This of course would be hoary and might not be something
palatable to human interest and desire. Technology need not be advanced for
these analysis. Even images from mobile phones or CCTVs could be of good use.
Errors in defining and quantifying attributes might pose new risks creating
perhaps new class of risks. More than errors, the biases that creep in in
designing algorithms through machine learning, deep learning, artificial intelligence
might lead to unintended consequences. Construction of variables and
quantifying the same have been a function of human endeavour. This is
increasingly transferred to machines. So is the interpretation. The results
while perhaps funny or hilarious in some circumstances, might lead to
horrifying consequences.
An instance
would suffice. Online ads are tailored to customers based on the past web
browsing behaviour. The data from the past browsing behaviour is fed into systems
and maybe let us say, Dice coefficient or similarity tools are used to judge
the preferences of particular web user and therefore customised ads are
tailored to his or her needs. Recently, a web user complained to railways that
there appeared a lot of sexual or porn ads whenever he logged in to IRCTC to
book his train tickets. He added, such ads were embarrassing. The reply of
course was classic. The platform is built on delivering ads to customers based
on their past browsing preferences and thus higher likelihood for clicking such
ads. In the event of failure to delete cookies, these ads would be inevitable. Implied
was the complainant perhaps used to browse lot of porn and therefore his failure
to delete the cookies meant such porn ads keep appearing on the sites he
browses given the higher likelihood of clicking the same. It was of course
funny and downright embarrassing for the complainant. In his over enthusiasm to
put IRCTC on the mat, his private behaviour was made public. It might just be
an embarrassment but going forward there can be instances wherein these can
lead to horrifying results.
While Dice
Coefficient and other measures of statistical similarities would gain increased
distinction in human pursuit to quantify and objectify every single trait
human, animate, inanimate or otherwise, assembly and elucidation of the same
might lead to numerous unintended externalities.
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