Big data practices are numerous: their use is found in neural networks, enterprise resource planning, cloud computing, and many statistical analyses. Among the criteria that demonstrate supply chain efficiency are supplier and customer integration, flexibility, and demand management (Gopal et al., 2022). Big data analytics (BDA) is a tool that allows for analyzing customer behavior (integration management), evaluating trends (demand management), and predicting outcomes (Seyedan & Mafakheri, 2020). BDA is used to transform purchasing processes and minimizes sourcing and resource management costs.
BDA is needed for accurate demand forecasting in an intelligent system and supply processes. Darvazeh et al. observed that BDA is unique for creating patterns based on valuable information about existing supply chain metrics (Darvazeh et al., 2020). Prescriptive analytics using BDA allows for the direction of alternative solutions and optimization based on mathematical and multi-criteria calculations. Templates allow companies to respond to current trends and realize opportunities faster than the competition.
BDA enables intelligent systems that integrate into the supply chain and solve problems such as in-transit inventory management, path shaping, supplier selection, and risk management. Gopal et al. found that BDA practices can reduce operational costs and repeat inspections (Gopal et al., 2022). According to Bahrami et al., BDA is a tool for identifying capabilities that companies critically need (Bahrami et al., 2022). The authors also note that BDA is primarily a strategic element used to create stability and sustainability for the company. Despite the apparent merits of BDA practices, not all companies have adopted this method (Darvazeh et al., 2020). Among the reasons are probably the lack of equipment to use the technology, concerns about data security (risk of leakage), and inability to access a wide range of BDA capabilities due to lack of training.
References
Bahrami, M., Shokouhyar, S., & Seifan, A. (2022). Big data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation.Modern Supply Chain Research and Applications, 4(1), 62-84. Web.
Darvazeh, S. S. , Vanani, I. R. , & Musolu, F. M. (2020). Big data analytics and its applications in supply chain management. In L. R. Martínez, R. A. O. Rios, & M. D. Prieto (Eds.), New Trends in the Use of Artificial Intelligence for the Industry 4.0. IntechOpen.
Gopal, P.R.C., Rana, N.P., Krishna, T. V., & Ramkumar, M. (2022). Impact of big data analytics on supply chain performance: An analysis of influencing factors. Annals of Operation Research. Web.
Seyedan, M.,& Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities.Journal of Big Data, 7(53). Web.