How Big Data Analytics Impacts the Retail Management on the European and American Markets?

Many studies on big data analytics focused on specialized use cases in business environments. Studies have been performed on the use of big data analytics in order to learn about consumer associations and expertise, among others. Nevertheless, there is an absence of investigation within the retail industry contemplating the big data management, looking at the adverse effect on organizational performance and customer satisfaction. Merchants investigate analytics to obtain a unified picture of their operations and customers throughout online channels or stores and make strategic choices towards the management of retail. Thereof, this analysis was carried out by focusing heavily on the European and American retail sector to demonstrate the impact of big data analytics. A quantitative study technique was used to analyze 450 individuals in the European and American retail sector. The outcomes on the analysis mentioned that among the various big data analytics used inside the European and American retail sector, the individuals majorly emphasized social networking analytics. Future scientists can do research on the forthcoming retail fashion on the European and American markets, and the way the consequences of big data evaluation evolved within the previous couple of years and contend with the unpredicted long-term recessions within the European and American retail sector

THE IMPACT OF BIG DATA ANALYTICS ON SUPPLY CHAIN MANAGEMENT PRACTICES IN FAST MOVING CONSUMER GOODS INDUSTRY: EVIDENCE FROM DEVELOPING COUNTRIES

Leading trends over the last couple of years tend to be the increasing value of big data and analyzing the information through analytics. The information has tremendous value; fast moving consumer goods (FMCG) businesses must capitalize on the assortment of information by proper and in-depth evaluation with the usage of big data analytics (BDA). Objective: This article seeks to spotlight the changing dynamics of the SC managing atmosphere, to recognize the way the two leading trends will influence supply chain management (SCM) in future, to demonstrate the advantages which may be derived, and to generate suggestions to provide SC managers if BDA is adopted. Method: A survey was done amongst workers of multinational FMCG businesses across the world: the Americas, Asia etc. Systemic situation modeling is used in the quantitative evaluation to analyze the information gathered from surveys. The process of deriving value from the large quantities of information within the SCM is defined. Results: The adoption of BDA technologies can develop extensive value-added as well as a financial gain for companies and can quickly be a regular during the entire market. It is demonstrated, through examples, the way SCM location might be influenced by these brand-new developments and trends. Within the examples, BDA have been adopted, utilized, and applied effectively. Big data and analytics to draw out value coming from the information can create a big influence. Conclusion: It is clearly suggested chain administrators pay attention to these 2 trends, since better usage of BDA can ensure they hold abreast with innovations modifications, which could help improve company competitiveness.Keywords: Supply chain management; Big data analytics; FMCG; Developing countries https://doi.org/10.56249/ijbr.03.01.30&nbsp

IMPACT OF BIG DATA ANALYTICS ON DISTRIBUTED MANUFACTURING: DOES BIG DATA HELP?

Big data (BD) analytics has brought progressive improvement in the business environment. It provides businesses with optimized production, personalization and improvement in the way production is dispersed. Nevertheless, conflicts arise in the use of these methods in certain industries, like retail items, which usually basis on large-scale production and prolonged supply chain. The study develops a theoretical structure to investigate if big data coupled with different production solutions can provide for a dispersed production system. Through investigation of twenty-one buyer products business instances applying secondary and main data, the study investigated changing production processes, the inherent catalyst, the function of analytics, and its effect on distributed production. The study discovers several uses of distributed manufacturing principles to evaluate the current production processes worked for larger customer product solutions by using analytics and industry analysis. The evaluation’s suggested structure mentioned in this research has a deeper impact on planning, comprehension relationships, among factors of data analytics and distributed production

How Big Data Analytics is transforming the finance industry

The data revolution happening throughout the world has brought transformation in the financial services sector. This vast information has opened doors to understanding the needs of the customers, finding insights, and lowering risks. In addition, it helps the financial services industry to take actions to improve clients' satisfaction at a faster rate than it was previously possible. Financial firms can develop new insights using BD because they can now collect a large volume of data about their customers, their spending pattern, and provide services that are beneficial, convenient, and quick for the customers. They can expand the use of those insights not only for their consumers but also for their internal process optimization, benefiting everyone in the process. While the impact of BDA on financial service companies is ubiquitous, not so many studies have been published to understand which aspects of the financial services industry could greatly benefit from the rise of technology and BDA. Few published studies address the challenges faced by banks in this technology era if they do not have BD tools implemented. This research covers data from banks from January 2019 to January 2022 to address that gap of a several banks from America and Europe that faced declining customer satisfaction. It uncovers the best methods used by financial firms globally to implement BDA to improve the services. This paper will also look at how BDA has been successfully used in the banking industry, regarding the following elements: consumer behavior, channels use, consumer spending pattern and profile creation, product cross-selling based upon user-profiling, analysis of feedback and sentiment, management of secure transactions, and fraud etc. This study helps find out and makes contributions on how the financial services industry, such as banks, could leverage BDA and provide superior services. Further research could be conducted across other players in the finance industry to learn about how they are impacted by the BDA