Objective: Literature indicates big data is a competitive edge, which boasts a firm’s overall performance. With the rise of big data (BD), e-commerce firms are using the tools to engage more with customers, offer better products, and innovate more to gain a competitive advantage. Nevertheless, past empirical studies have shown conflicting results. Design: Building on the capital-based perspective and the firm’s inertia concept, we created a model to explore how BD and BD analytics capability impact innovation results in e-commerce businesses. We carried out a two-year empirical investigation project to secure empirical data on 1703 data-driven innovation tasks from USA and Asia. Findings: We showed that there is a tradeoff between BD and BD analytics capability, in which the optimum balance of BD depends on the amount of BD analytics ability. BD analytics ability exerts a good moderating impact, that is, the better this capability is, the higher the effect of BD on gross margin and sales growth. For U.S. innovation tasks, BD has an inverted U-shaped relationship with sales innovation. For Asian innovation tasks, when major data capital is minimal, promoting big data analytics capability improves sales innovation and disgusting margin up to a specific point. Policy Implications: Establishing BD analytics capability over that time could prevent innovation efficiency. Our findings offer guidance to e-commerce firms on producing strategic choices about source allocations for BD and BD analytics ability. Originality: A limited research has been carried out to show the impact of using BD analytics tools to drive innovation. This is one of the first articles that dive into using BD to foster innovation in the e-commerce business.

The research is focused on the role of big data analytics in logistics of transportation in multinational companies across the world. It shows new opportunities for current supply chain practices, while adding operational excellence and value. A survey was carried out among staff members of multinational companies from the Americas, and Europe. For the statistical investigation of survey data, an equation modeling was used. Results reveal that demand management, seller rating, and vendor satisfaction are the most important factors. The study also found that analytics affect efficiency, operational excellence, customer service, and cost savings in the supply chain industry. The goal is to reduce the gap between demand management and supply chain management by improving customer satisfaction, visibility, and visibility. The big data can produce substantial value added and monetary gains for companies and will quickly become common throughout the industry.

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

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.

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.