Impact of big data analytics in supply chain management operations to make better decisions and improve

Abstract

Aim: This thesis enlight the prolifically in supply chain management in the use of Big Data(BD) analytics. To generate a plan of modification, they have to determine what happened as well as why it happened. A resource person can recognize that at which point action is needed, based on some business rule which is already defined. And the same way they will find more accurate forecast results. Rather than this, It helps in generating the best course of action irrespective of supply chain operation activities to improve the overall performance of the supply chain. newlineMaterials and Methods: In this thesis, tried to include the fields which define the importance of supply chain management into the promising field of data analytics with a discussion of examples reported in the literature, discussion of a research problem, discussion of outlines an architectural framework and methodology and discussion of benefits. In preparation of the model, usage of time series forecasting techniques helps in determining forecast supply chain operation activities like sales, demand, marketing, finance, etc. Time series forecasting helps businesses make informed business decisions because it can be based on historical data patterns. It can be used to forecast future conditions and events. newlineResults and Discussion: It is shown in the thesis various data extraction has done using the time series forecasting model and highlighted with help of an illustration. Also made comparison between other known time series forecasting model and proposed forecasting model. In the end, results indicate that how proposed forecasting model is impactful to take sales forecasting related decision using the technique. newlineConclusion: The importance of analytics and the proposed model lies in cost-saving, time reduction, understanding sales conditions, boost consumer acquisition and retention, enhanced sale insights, and new product development. In this case, can see that the sales analysis and prediction model are giving nearly more accurate sales prediction.