Data-Driven Decision-Making Use Case: Applying Big Data Analytics to Forecast Important Decisions

Abstract

Due to decreased material and resource usage and other tooling needs, additive manufacturing (AM) has rapidly developed over the past 10 years. It has shown significant promise for energy-efficient and environmentally friendly production. As manufacturing technologies have advanced in the modern period, intelligent manufacturing has gained greater attention from academia and business to increase the sustainability and efficiency of their output. Few studies have examined the effects of big data analytics (BDA) in CSR activities on CSR performance, despite the growing number of businesses implementing BDA in CSR initiatives. As digital technology is incorporated into various processes, supply chain management is increasingly interested in Big Data Analytics (BDA). It efficiently makes the transfer of goods and information possible. Nevertheless, little research has been done on how much BDA can enhance supply chains' environmental sustainability, even though it offers several benefits. We provide a thorough understanding of "data science" in this paper, covering a range of sophisticated analytics techniques that may improve an application's intelligence and capabilities through astute decision-making in diverse contexts. In light of this, we conclude by outlining the difficulties and possible lines of inquiry within the parameters of our investigation. Our literature analysis indicates that an increasing number of data-driven decision-making methods have been developed specifically to benefit from the wealth of sensor-generated data in the context of Industry 4.0. This article aims to provide researchers, decision-makers, and application developers with a reference point on data science and advanced analytics, especially regarding data-driven solutions for real-world issues.