Currently, there is a separate class of products on the market that provide a solution to the problem of processing big data. For example, there is a system designed to provide other applications with the necessary data at the right time. It is used to normalize, study, and distribute data for transactions, which result in in stabilization of yield management (Choi et al., 2018). A distributed file system that allows storing information of almost unlimited volume for parallel processing of large amounts of information on a variety of nodes of standard equipment (Dubey et al., 2019a). It helps in stabilization structurally gathering information during transformation of raw materials into finished goods (Bag et al., 2020). This is also facilitated by the maintenance of an efficient and dynamic trading process on international platforms with big data, for communication with agricultural partners in a one-on-one format, as well as for the contribution and integration of market data (Dubey et al., 2019b). Thus, the model of distributed data processing on computer clusters includes the function of stabilization of yield management through parallel processing of big data and combining the results of yield calculations.
Planning is an essential component of production management; for availability of products to customers, it is necessary to calculate the production cycle time. This value includes many different factors, such as the timing of implementation, tracking, control and subsequent distribution to buyers of the yield (Singh and El-Kassar, 2019). Working with big data analytics helps not only to fix each of the production cycles for control. The use of existing algorithms also helps to predict the duration of the production cycle time in the next season, based on precipitation coefficients, degree of sunshine, fertilization and other factors.
References
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation & Recycling, 153(101), 1–10.
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Special Issue on Big Data in Supply Chain Management, 27(10), 1868–1883.
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019a). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 24(4), 518–536.
Dubey, R., Gunasekavan, A., Childe, S. J., Roubaud, D., Foropon, C., Bryde, D. J., Giannakis, M., & Hazen, B. (2019b). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. LJMU Research Online, 11(3), 918–956.
Singh, S. K., & El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213(189), 1264–1273.