Global sourcing (GS) is a procurement strategy where a company buys services and goods from around the world. Notably, many firms are interested in developing big data analytics (BDA) to help uncover patterns and correlations vital in making better-informed decisions (Chehbi-Gamoura et al., 2020). Therefore, BDA has the following roles if a firm deals with global sourcing. First, it aids the managers in using various data sources to develop their corporation and control through advanced tools (Razaghi & Shokouhyar, 2021).
The action helps in reaching a competitive advantage and finding better suppliers. Hence, firms mitigate the risk of coordination and integration in the supply chains by better evaluating their supply within the national borders. Second, BDA permits the users to capture, analyze and store vast amounts of data enabling firms to have a corporate database essential in handling their resources according to business priorities (Tiwari et al., 2018). The tools aid business strategic decision making, planning, control, and coordination which help mainly in sourcing, logistics, planning, and product design, hence promoting effective risk management.
Thirdly, BDA enables firms to effectively develop risk elasticity ability and utilize their firm knowledge helping to add positivity to their IT capabilities (Singh & Singh, 2019). The capabilities play a vital role in enabling the company to mitigate the negative impacts of chain disruption. For instance, due to technological advances, firms collect data from several sources. Hence BDA can recover lost data helping in disaster mitigation. Lastly, BDA enhances risk management through its potential to make more accurate estimates that better replicate the client’s desires (Seyedan & Mafakheri, 2020). It has various applications like trend analysis, demand prediction, and customer behavior analysis vital in supporting supply chain risk assessment.
References
Chehbi-Gamoura, S., Derrouiche, R., Damand, D., & Barth, M. (2020). Insights from big data analytics in supply chain management: an all-inclusive literature review using the SCOR model.Production Planning & Control, 31(5), 355-382. Web.
Razaghi, S., & Shokouhyar, S. (2021). Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance.The Bottom Line. 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(1), 1-22. Web.
Singh, N. P., & Singh, S. (2019). Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation.Benchmarking: An International Journal. Web.
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries.Computers & Industrial Engineering, 115, 319-330. Web.