Page 32 - IMDR JOURNAL 2020-21

IMDR’s Journal of Management Development and Research 2020-21

At the time of final convergence, the exact share is As has been explained in algorithm, the consumer
obtained. Next, the Transition matrix [T] is formed & transition matrices and results are initialized. The
based on sales records of suppliers A and B and matrix multiplication is performed using a nested
the transition model shown in Fig 2. It should be for loop. The output of first iteration is displayed
noted that the transition matrix [T] would be the as [4000.00, 2400.00]. On similar lines, number of
same through the analysis. Depending on number iterations are performed till the constant output is
of iterations there would be change in Consumer obtained.
Matrix. The iteration count I is provided to count V CONTRIBUTION TO ATMA-NIRBHAR
number of iterations. Initially it is set to 1. Then the BHARAT CONCEPT
matrices [C] and [T] are multiplied to obtain a new This paper contribution to the Atma-Nirbhar
consumer matrix [C’]. The number of iterations are Bharat concept announced by the Government of
continued till the values of consumer matrices are India. Out of the five pillars of the Abhiyan namely
yielding constant values. This is called convergence. economy, infrastructure, system. vibrant demography
Thus the exact share of consumers is obtained at and demand, this paper contributes to the demand
convergence. pillar of the scheme. By analyzing demand-supply
IV THE PYTHON PROGRAMMING BASED scenario using Python based Markov Chain, the
APPROACH demand-supply relation would be improved. The
Python is an interpreted, high level, general gap between demand and supply would be reduced
purpose, object oriented, platform independent, which would lead to improve the economy.
web enabled dynamically typed programming VI CONCLUSION
language developed by Guido Van Rossum at The Markov Chain is an effective tool for data
National Research Institute for Mathematics and analysis in various applications such as finance,
Computer Science in Netherlands in early nineties. stock markets, census measurements, marketing
As of today, it is one of the popular programming and supply chain management. However at the time
languages all over the world. It is widely used in new this technique was invented by Andrei A. Markov
technologies such as data science, big data, machine the problem of handling complexity due to huge
learning, Internet of Things, cloud computing and amount of data arising because of parameters
artificial intelligence. Google, You Tube, Instagram, such as more number of markets, large number of
Dropbox, Quora, Big Torrent, Delug, Cinema4D consumers, large number of suppliers and variety of
and Mozilla Firefox are some of famous and globally products was encountered. This problem took bigger
used applications based on Python. Python can be shape during the later period of development. This
effectively used to conduct Markov Chain analysis. problem can be tackled by adopting new generation
This is explained by taking a small sample of Python technologies such as Python Programming, Data
Source code pertaining to the above discussed case. Science, Big Data and Cloud Computing. In this
This sample code calculates first iteration. paper the Python programming based approach to
handle complexity for implementation of Markov
Chin technique has been presented. The proposed
method has been illustrated by an example of a
marketplace. The proposed paper supports Atma-
Nirbhar concept announced by the Government of
India under pillar ‘demand’.
REFERENCES
1. Dr.Shashikant Bakre, ‘How to manage your supply
chain?’, KDP Amazon Publishing 2015
2. Dr. Shashikant Bakre, Dr. Priya Gokhale, ‘ Python
Programming in easy steps’ , KDP Amazon
Publishing 2019
3. Ranjit Kumar , ‘Research Method logy- a step by
step guide for beginners’, SAGE publishing
4. John Paul Mueller, Luca Massaron, ‘Python for
Data Science’, Wiley India Ltd publishing
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