Vol. 8 No. 4 (2018): Vol 8, Iss 4, Year 2018
Articles

A comparative study to measure the impact of big data analytics on service supply chain process

Bhawna Singh
Research scholar, Amity University, Noida, U.P
Rushina i Singh
Assistant Professor, Amity University, Noida, U.P
Suman Shokeen
Assistant Professor, G.D Goenka University, Gurgaon, H.R
Published December 30, 2018
Keywords
  • Big data analytics, Service supply chain process, Enterprise Resource Planning (ERP), ANNOVA (Analysis of Variance), Individual efficiency, Lead Time, Decision Making, Data Transparency, Customer Satisfaction, Return on investment, Inventory Management, Order Fulfillment, Cost Reduction and Vendor Management
How to Cite
Singh, B., Singh, R. i, & Shokeen, S. (2018). A comparative study to measure the impact of big data analytics on service supply chain process. Journal of Management and Science, 8(4), 356-365. https://doi.org/10.26524/jms.2018.32

Abstract

Big data analytics is becoming a key to success for many organization as it extracts the productive value from a huge amount of raw data. This data helps in strategic decision making for continuous process improvements and advancement. This study also focusses on the impact of big data analytics on one of the most important process of an organization which is service supply chain process. ERP (Enterprise Resource Planning) is the tool which is used for big data analytics and based on that study was initiated for pre- and post-implementation of ERP for the years 2015 and 2017 respectively. Null hypothesis and H1 hypothesis are formed. Then, ten major factors have been identified which act as performance indicators for service supply chain process. Based on these factors data has been collected and analyzed. After obtaining the values one-way ANNOVA technique has been applied for testing of hypothesis. It has been observed that F calculated value comes out to be very high in comparison to the F table value which rejects the null hypothesis and proves that Big data analytics has an impact on service supply process.

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