The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries

Abstract

Purpose

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

Design/methodology/approach

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Findings

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

Research limitations/implications

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

Originality/value

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Citation

Narwane, V.S., Raut, R.D., Yadav, V.S., Cheikhrouhou, N., Narkhede, B.E. and Priyadarshinee, P. (2021), "The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries", Journal of Enterprise Information Management, Vol. 34 No. 5, pp. 1452-1480. https://doi.org/10.1108/JEIM-11-2020-0463