Anitha P

Work place: Department of Information Science and Engineering JSS Academy of Technical Education, Bengaluru-560060, India



Research Interests: Applied computer science, Computer systems and computational processes, Theoretical Computer Science


P. Anitha is currently working as an Assistant Professor in the Department of Information Science and Engineering at JSSATE, Bengaluru. She is a research scholar in the field of Supply Chain Data Analytics at JSSATE Research Centre, Department of CSE, affiliated to Visveswaraya Technological university, Belagavi, India under the guidance of Dr. Malini M Patil, Associate Professor, Department of ISE, JSSATE, Bengaluru. She has completed her M. Tech in 2012 from Department of Computer science, BMSCE, Bengaluru under the Visveswaraya Technological University. She is a life member of Indian Society for Technical Education.

Author Articles
A Review on Data Analytics for Supply Chain Management: A Case study

By Anitha P Malini M. Patil

DOI:, Pub. Date: 8 Sep. 2018

The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a “next generation” architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.

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