Globalization has led to the development of new business opportunities, including strategic planning and sourcing. Strategic sourcing has become essential in increasing productivity while decreasing the necessary resources (Patowarya, 2019). Analysis of different datasets is important when deciding on the optimal level of strategic sourcing that will balance global sourcing with local sourcing (Tiwari et al., 2018). In this paper, the importance of big data analytics in achieving an optimal sourcing level will be discussed.
There are two primary sourcing methodologies which are local and global or international. Local sourcing is highly advocated for benefits like higher flexibility, extensive control, and community development (Tiwari et al., 2018). On the other hand, global sourcing has several advantages, like short lead times due to better infrastructure (Liu, 2020). There is a need to incorporate the two types to improve the supply chain. Big data analytics determines the best and most appropriate sourcing strategy.
Four main types of data analytics are relevant to procurement and supply chain management. Big data analytics, especially descriptive analytics, is useful in obtaining an optimal mix as it helps managers weigh the benefits and drawbacks while predicting business development (Tiwari et al., 2018). Diagnostic analytics is relevant in determining the challenges and benefits of using both methods and thus helps managers to understand the role of using the mix (Tiwari et al., 2018). Through these results, the manager will be able to weigh the benefits and limitations and apply an appropriate combination of sourcing methods. Predictive analytics helps forecast future trends in supply chain management, which will help determine the best sourcing strategy for a firm (Tiwari et al., 2018). Additionally, combining predictive and prescriptive analysis will help in decision-making linked to the improvement of sourcing (Tiwari et al., 2018). Thus, big data analytics has had an extraordinary impact on strategic sourcing due to the opportunities, lack of barriers, and options that managers are able to access currently.
Strategic sourcing is an essential component of organizations operating through supply chains. Moreover, with the development of big data analytics, businesses acquire opportunities to predict future processes, invest in appropriate strategies and understand what factors hinder productivity. While both local and global sourcing can be effective measures depending on organizational aims and current potential, the relevance of big data analytics is undeniable in determining an efficient framework and building a successful long-term strategy.
References
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Liu, Z. (2020). Achieving Supply Chain Resilience to improve performance under a global sourcing context.SSRN Electronic Journal. Web.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big Data Analytics and firm performance: Findings from a mixed-method approach.Journal of Business Research, 98, 261–276. Web.
Patowarya, J. J. (2022). Understanding the what, why, & how of strategic sourcing. Zycus Procurement Blog. 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.