Conference PaperPDF Available

Study on Use of AI and Big Data for Commercial System

978-1-5386-9346-9/19/$31.00 ©2019 IEEE
Study on Use of AI and Big Data for Commercial System
Sonali Vyas1, Sai Sathya Jain2, Isha Choudhary3, Aryaman Chaudhary4
1,2,3,4Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, India,,,
Abstract With the advent of Artificial intelligence and
machine learning, experiences are changing across the
globe. In the Commercial sector, companies are trying
to make customers interact with Machines and make them
experience the warmth of interacting with a human. This
case study scrutinizes the idea of putting life in a
vending machine through AI and Big data, taking an
example of a big soft drink giant Coca-Cola. According to
statistics, the number of Smartphone users is about to
rise to 2.9 billion in the year 2019. Hence, giving an
immense opportunity for the giant to reach ends. Coca-Cola
has already introduced technologies such as AI-powered
vending machine, recognition technique like OCR.
Keywords Artificial Intelligence, Big Data, optical character
recognition, vending machine, android pay
There are almost 500 soft drinks brand names which are sold
to consumers in almost 200 countries. Coca-Cola is the major
beverage corporation in the world. Each single day the world
consumes greater than 1.9 billion servings of their drinks
including brands like coca cola as well as Fanta, Sprite,
Powerade, minute maid etc. Such an operation clearly produce
a bulk of data immaterial from the origin which might be
production, sales blocks, retailing counters across the
globe and even customer feedbacks. Despite all, this
company managed to stay at the top with the brand value of
more than $73.1 billion (being the 3rdmost valuable brand
ranking).One of the reason the company has come this far
during a span of 130 years is the capability of the company to
hold novelty and innovative technology which includes Big
data technology.
The editorial article which is in print by Coca-Cola in August
2017 taking us all back to New Zealand, Masterton - a city
where the sheep’s residing in the city outnumbered people.
Amatil Australia and Amatil New Zealand have nearly
26,000 vending machines in their combined territories.
Updating one of these machines can require up to 45 mins of
on-site reprogramming. This came out as a big problem,
as the General Manager of Vending system services Andy
Kerr claims. The idea started with identifying the root cause
of the problem and challenging the odds. In 2015, Kerr’s team
was challenged to renovate client interaction with
transaction machines into experiences of people in a retail
store. They thought of a modern vending machine that
could offer specials, track sales, pre-empt preservation, refill
desires and admit mobile payments. A device that people
don’t need to touch to retrieve their drink purchase-An AI
Experience, Research and development department of the
company came with a newly-digital vending machine
Artificial intelligence deals with how much big data you give
to vending machines to see which “flavor shots” public
mostly prefer. It resulted, that inspiration of their launch was
“cherry sprite”. For such launch AI is not is needed to
make such type of decisions, since it need only collection
of the data using conventional questioning methods, which
are perhaps true. Though, coca cola in addition it used some of
natural language processing tools which are used to check the
internet and conclude what views people are holding about
their new flavor creation.
1. Scanning Social media
Coca Cola scans social media as in what manner its products
are presented or marketed in social media. In 2015 coca cola
was able to estimate that its products are mentioned
somewhere in the media holding an average of one visit every
two seconds. This sort of monitoring is the reason for their
new launching of “surge”. 90% of customers are now their
base customers because of their purchase decisions based on
social media content, so for that coca cola uses computer
vision to identify and algorithms to verify the response
of what customer says about their brand in general.
2. Reading product codes
Coca cola is utilizing AI and TensorFlow to accomplish
frictionless verification of-procurement. This attempt was
taken around a buyer unwaveringness program; where
consumer is required to enter a 14-digit code in their mobile
phones. Coca cola initially attempted to tackle the issue by
means of an old school technology OCR (Optical Character
Recognition), but unfortunately that initiated a problem
while trying to scan codes rapidly and perfectly. They at
this point swung towards profound learning with Google’s
TensorFlow platform. The result of this permitted the
consumers to take photos of the codes on the bottle caps which
were at that point converted into a code at a 99.97% accuracy,
which is taking a single second to process image, and the
accomplishment of this effort led to the technology which has
become a nucleus element for coca cola’s web-based
3. Machine learning for selfies
In 2015 Coca-Cola thought of computer vision using
Microsoft’s tool that speculated people’s age. If anyone is
uploading a picture holding a coke bottle on, it will unbolt “the special Coca-Cola
experience” which presume your age and tells the age of
the coke bottle that customer is holding. Isn’t it amazing on
how technology is. If you upload a picture holding a
coke bottle on, it will unbolt “the
special Coca- Cola experience” which surmises your age and
discloses to you the period of the coke bottle you’re holding.
4. Artificial Intelligence in vending machine
The Coca-Cola Company as of late appeared an AI-supported
vending machine for the Chinese Market Customers can utilize
the machine to both purchase refreshments and recycle and
reuse empty bottles and cans.By combining drinks sales and
recycling abilities, the machine is helping the organization
accomplish sustainable goals. Artificial Intelligence
technology, such as facial recognition and sound interaction
are incorporated in the vending machine, ensuring a fully
interactive ordeal for customers. Included on the vending
machines are two “eyes”, one for accessing purchased
beverages, and one for returning bottles to be recycled. In the
facial expression on facial recognition and sound interaction,
consumers who purchase a drink may likewise get mobile
reminders to pay “payback” the bottle once empty, and procure
credits by recycling. The machine and data analysis offers
additional operation and customer service support for Coca-
Cola’s different operations and distribution departments. With
supply network frameworks powered by Artificial Intelligence,
the machine will enable equipment to capture and balance
sales and recycling, as well as streamline forward and invert
Coca cola give leverage to the AI advancement organizations
like Salesforce which assembled them an application that
causes coca cola to direct inventories all the more productively
with this application as opposed to checking stock in coolers
individuals presently click an image of cooler and salesforce's
AI innovation known as Einstein will at that point include the
bottles the cooler and will likewise decide "Coca-Cola"
implies it will appear if some other is additionally kept in
found cooler.
6. Augmented Reality
Augmented reality (AR) is the concept where computer
graphics are overlaid on the user's perspective of this present
reality utilizing glasses, headsets is being trialed by various
bottling plant far and wide. This enables technician to
beneficiary data about devices they are adjusting and acquire
reinforcement from specialist at isolated areas who can
perceive what they are looking at and helps to analyze and take
care of specialized issues. It is additionally used to examine
issues with transaction machines and dispenser in isolated or
tough to reach sites, which includes cruise ships while they are
sailing in sea.
7. Big Data
Coca cola has robust information driven methodology
supporting choices appropriate over the commerce and it's a
well known fact that they have put broad assets into innovative
work in zones like artificial intelligence (AI) to benefit as
much as possible from the information it gathers.
This has paid off in various regions, which includes product
improvement. In 2017, it was uncovered that the dispatch of
novel flavor cherry sprite was propelled by information
gathered from those self-benefit drinks wellsprings that
allowed customers to blend their own beverages accordingly.
Since the equipment suggest a decision of flavor "shots" for
customers to add their beverages choices, coca cola could just
recognize the mainly famous flavor combination and transform
it into instant refreshment for a more extensive gathering of
8. Healthy choices
As offers of sweet, carbonated beverage items have turned
down in current years. Coca Cola has additionally guided into
data to encourage create and advertise a portion of its more
advantageous alternative, for example, squeezed orange juice,
which is circulated by the company under various brands
around the world(including minute maid and simply orange).
The corporation clubs climate data, satellite pictures, data on
product yields, price factors and acidity and sugariness
ranking, to guarantee that orange harvests are developed in an
ideal way, and keep up a predictable flavor.
9. Android pay dispatches in coca cola vending machines
The Coca Cola Corporation reported that by the ending of year
2015, 1, 00,000 of its vending machines would bolster apple
pay crosswise over North America. After that android
smartphone customers can get the comfort of purchasing a
coke and piling on and redeeming my coke rewards (MCR)
points-with their gadgets.
Android pay, which was unveiled by Google divulged in a
meeting in May 2015, which suggest ease, safety and decision
during payment for things with the help of android telephone
including soft drinks. Those equivalent 100,000 apple pay
empowered equipments which were being taken off were
additionally prepared for android pay before the finish of the
It all comes to fact of using records, artificial intelligence and
connected equipments to serve customers. There is a large
digital transformation due to advancement. It’s not just about
varying the digital experience but also about serving clients in
making decisions in a more efficient method. Coca-Cola is
one of the several brands across a array of industries
grappling as how to serve customers via digital platform
and focused on growth and better customer experience.
To conclude, the corporation with the prime and the
most precise data sets will dominate with the most effective AI
algorithms and using big data also. That’s where this company
can compete like nobody else.
[6] O'Leary, D. E. (2013). Artificial intelligence and big data. IEEE
Intelligent Systems, 28(2), 96-99.
[7] Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey.
"Business intelligence and analytics: from big data to big
impact." MIS quarterly (2012): 1165-1188
[9] Provost, Foster, and Tom Fawcett. "Data science and its
relationship to big data and data-driven decision making." Big
data 1.1 (2013): 51-59.
[10] Azuma, Ronald T. "A survey of augmented reality." Presence:
Teleoperators & Virtual Environments 6.4 (1997): 355-385.
[11] Marr, David. "Artificial intelligencea personal view."
Artificial Intelligence 9.1 (1977): 37-48.
[12] Tong, Christopher, and Duvvuru Sriram, eds. Artificial
Intelligence in Engineering Design: Volume III: Knowledge
Acquisition, Commercial Systems, And Integrated
Environments. Elsevier, 2012.
... Usage of innovative technologies like the Big Data technology helped the company reach this position in the market. Coca cola uses AI and machine learning to analyse large amounts of data, including production, sales blocks, retailing counters and customer feedbacks (Vyas, et al., 2019). ...
Full-text available
This paper gives an outline of the role of artificial intelligence in Sales and its impact on the Sales Processes. The paper discusses four case studies about companies like Coca-cola, eBay, Amazon (AWS), and Deutsche Telekom (eLiza). A conclusion is made that many small, medium and large-scale companies are now dependent on AI and cannot survive without it.
... Vyas et al. [54], studied Coca-Cola in the use of AI and big data. Coca-Cola has used AI algorithms to serve its customers. ...
The glass science and technology is a rapidly developing field which is focused on development of new glasses with excellent properties. Glasses are the non-crystalline materials with inherent stoichiometry i.e. non-disordered structure of atoms and molecules, thus inherently unpredictable. The ineffective trial-and-error methods are typical to glasses design. The classical computational methods such as ab initio and classical molecular dynamics simulation techniques are costly, time consuming and provide limited data of results. To overcome from such problems, the machine learning (ML) replaces the classical experimental and simulation techniques to produce results more precisely. In the recent years, a lot of studies are carried out on AI to develop new compositions of glasses based on the different types of input parameters. Researchers developed new glasses by improving the various properties of glass like edge strength, shear strength, tensile strength, delamination, etc. In this paper, an effort has been made to explore recent developments in glass manufacturing and technology by the implementation of ML techniques. In this paper, the development of glass of new composition, prediction of glass properties, and various inspection methods are discussed on the basis of application of ML techniques.
Full-text available
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
Full-text available
Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data science programs, and publications are touting data science as a hot -- even "sexy" -- career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this paper we argue that there are good reasons why it has been hard to pin down exactly what data science is. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of Data Science precisely right now is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii) we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this paper we present a perspective that addresses all these things. We close by offering as examples a partial list of fundamental principles underlying data science.
AI Innovation in Industry is a new department for IEEE Intelligent Systems, and this paper examines some of the basic concerns and uses of AI for big data (AI has been used in several different ways to facilitate capturing and structuring big data, and it has been used to analyze big data for key insights).
The goal of Artificial Intelligence is to identify and solve tractable information processing problems. In so doing, two types of theory arise. Here, they are labelled Types 1 and 2, and their characteristics are outlined. This discussion creates a more than usually rigorous perspective of the subject, from which past work and future prospects are briefly reviewed.
This paper surveys the current state-of-the-art in Augmented Reality. It describes work performed at many different sites and explains the issues and problems encountered when building Augmented Reality systems. It summarizes the tradeoffs and approaches taken so far to overcome these problems and speculates on future directions that deserve exploration. This paper does not present new research results. The contribution comes from consolidating existing information from many sources and publishing an extensive bibliography of papers in this field. While several other introductory papers have been written on this subject [Barfield95] [Bowskill95] [Caudell94] [Drascic93b] [Feiner94a] [Feiner94b] [Milgram94b] [Rolland94], this survey is more comprehensive and up-to-date. For anyone interested in starting research in this area, this survey should provide a good starting point. Section 1 describes what Augmented Reality is and the motivations for developing this technology. Four classes of potential applications that have been explored are described in Section 2. Then Section 3 discusses the issues involved in building an Augmented Reality system. Currently, two of the biggest problems are in registration and sensing, so those are the subjects of Sections 4 and 5. Finally, Section 6 describes some areas that require further work and research. 1.2 Definition