A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains. (2021)

• Record Type: Journal Article
• Authors: Sharma, Vikrant; Kumar, Atul; Kumar, Mukesh
• Is Part Of: Materials today. Volume 47:Part 16(2021)
• Page Start: 5515
• Page End: 5519
• Publication Date: 2021
• Subjects: Big data analytics -- Barriers -- Manufacturing supply chains -- Best worst method (BWM); Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.03.374
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Due to its potential utility, Big Data (BD) recently attracted researchers and practitioners in decision-making. Big Data analytics (BDA) becomes more common among manufacturing companies because it lets them gain insight and make decisions based on BD. Given the importance of both BD and BDA, this study aims to identify and analyse essential BDA adoption barriers in supply chains. This study explores the current knowledge base using a BWM (Best Worst Method) to discuss these barriers. Data were obtained from five Indian manufacturing companies. Research findings show that data-related barriers are most significant. The findings will help managers understand the exact nature of the challenges and possible advantages of the BDA and implement BDA policies for the growth and output of supply chain operations.