Big data has a significant impact on the supply chain of organizations where the ultimate goal is to satisfy the needs of their clients. Organizations that effectively utilize big data in their operations have a competitive advantage in their respective industries. With big data, businesses have an opportunity to forecast market demands more accurately (Gunasekaran et al., 2017). They use big data analytic tools to analyze the needs of their customers, preference, and even purchase patterns, which help determine sales and appropriate market strategies. Businesses also have a chance to solve complex distribution networks in the market (Govindan et al., 2018). They use big data to model such scenarios and develop cheap, convenient, reliable distribution channels to reach clients; however, the analysis may establish the clients preferred distribution approach. In addition, big data enhances collaboration in businesses’ supply chain networks (Gunasekaran et al., 2017). Employees from different departments can collaborate by sharing their departments’ data that shows strengths and weaknesses to enable consolidated and effective decision-making.
On the other hand, big data can be a source of challenges in the supply chain system of an organization. Collection and analysis of inaccurate or obsolete data lead to low accuracy of the outcomes and, consequently, poor decision making (Sivarajah et al., 2017). In some cases, clients may provide unrealistic and misleading data. The process of collecting and analyzing data poses potential security and privacy threats to the organization (Talha et al., 2019). Unsecured data sources and storage are prone to external infiltration and malware attacks that might compromise the data’s integrity, thus leading to misinterpretation of the client’s demands. Moreover, the client’s privacy can be exposed, a breach of privacy that can result in legal action. The selection of inappropriate big data tools may result in poor decision-making (Cobb et al., 2018). Some employees lack prerequisite skills for data analysis and may use wrong big data tools or give misleading interpretations. This leads to a waste of time, money, and efforts, among other resources. In addition, sharing data from various departments for joint decision-making may lead to a conflict of interest due to different ideologies.
References
Cobb, A. N., Benjamin, A. J., Huang, E. S., & Kuo, P. C. (2018). Big data: More than big data sets. Surgery, 164(4), 640–642.
Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big Data Analytics and application for logistics and Supply Chain Management. Transportation Research Part E: Logistics and Transportation Review, 114, 343–349.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big Data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data Challenges and analytical methods. Journal of Business Research, 70, 263–286.
Talha, M., El Kalam, A. A. B. O. U., & Elmarzouqi, N. (2019). Big Data: Trade-off between data quality and data security. Procedia Computer Science, 151, 916–922.