Big data analytics is a promising new field that is currently being utilized by numerous industries, including logistics. Big data can be utilized during numerous stages in the logistics chain of operation. It is most frequently implemented in transportation management systems that allow to track the movement of goods (Curoe, 2021). This discussion post will argue that the use of big data warehouses and predictive analytics is the optimal application in logistics management and can ensure customers’ demands are met at the right quality, price, place, and quantity.
Big data warehouse is valuable tool that can be implemented in logistics management. According to Silva et al. (2021), such warehouses can create voluminous data repositories that can inform various processes in logistics. Product availability, traffic congestion data, delivery times, and routes can be calculated with the use of big data (Yan et al., 2019). It should be noted that big data analytics can be predictive in nature, forecasting events based on the available information (Sodero et al., 2019). Therefore, it would be beneficial to employ predictive analytics based on big data to predict what products require replenishment, the necessary quantity for stock, efficient transportation routes, and the number of vehicles needed for operation. However, it should be noted that such employment requires substantial technological and human investment (Wu & Lin, 2018). Therefore, big data analytics application can benefit the process of logistics from planning to execution.
In summary, big data is being implemented in a variety of fields. In logistics, big data analytics, in particular, predictive algorithms, can meaningfully impact the supply and demand chain and guarantee high-quality customer experience. The use of big data can ensure the availability of the most popular products and quick delivery. In addition, although the utilization of new information technologies often translates into loss of jobs, in logistics big data requires immediate decision-making, thus, ensuring new workplaces.
References
Curoe, M. (2021). How big data analytics can improve your supply chain. Redwood Logistics. Web.
Silva, N., Barros, J., Santos, M. Y., Costa, C., Cortez, P., Carvalho, M. S., & Gonçalves, J. N. (2021). Advancing logistics 4.0 with the implementation of a big data warehouse: A demonstration case for the automotive industry.Electronics, 10(18), 1–18. Web.
Sodero, A., Jin, Y. H., & Barratt, M. (2019). The social process of big data and predictive analytics use for logistics and supply chain management. International Journal of Physical Distribution & Logistics Management, 49(7), 706–726. Web.
Wu, P., & Lin, K. (2018). Unstructured big data analytics for retrieving e-Commerce logistics knowledge.Telematics and Informatics, 35(1), 237–244. Web.
Yan, Z., Ismail, H., Chen, L., Zhao, X., & Wang, L. (2019). The application of big data analytics in optimizing logistics: A developmental perspective review.Journal of Data, Information and Management, 1(1-2), 33–43. Web.