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Enhancing Supply Chain Insights with Generative AI- Driven Data Analytics and Visualization

EasyChair Preprint no. 12925

17 pagesDate: April 6, 2024


Data analytics and visualization play a pivotal role in unraveling the complexities of

modern supply chains, enabling organizations to make informed decisions and drive

operational excellence. This abstract delves into the integration of generative AI

techniques with advanced data analytics and visualization tools to gain deeper insights

into supply chain data. Additionally, it explores the benefits of interactive dashboards,

data-driven decision-making, and real-time monitoring facilitated by this integration.

Generative AI techniques, characterized by their ability to synthesize data and simulate

scenarios, offer a powerful tool for analyzing complex supply chain data. By generating

synthetic datasets and simulating diverse scenarios, generative AI enables organizations

to uncover hidden patterns, identify trends, and predict future outcomes with greater

accuracy. Furthermore, generative AI-driven analytics facilitate proactive decision-

making and risk management, empowering organizations to stay ahead in dynamic

market environments.

When combined with advanced data analytics and visualization tools, generative AI

enhances the accessibility and usability of supply chain insights. Interactive dashboards

powered by generative AI-driven analytics provide stakeholders with intuitive interfaces

to explore and visualize complex data sets. Through dynamic visualization techniques

such as heat maps, network diagrams, and trend analysis, stakeholders can identify

opportunities, detect anomalies, and derive actionable insights in real-time.

Moreover, generative AI-driven data analytics and visualization enable data-driven

decision-making across all levels of the supply chain.

Keyphrases: Data Analytics, data-driven, decision making, Generative AI, insights, interactive dashboards, supply chain, visualization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Dylan Stilinski and Lucas Doris and Louis Frank},
  title = {Enhancing Supply Chain Insights with Generative AI- Driven Data Analytics and Visualization},
  howpublished = {EasyChair Preprint no. 12925},

  year = {EasyChair, 2024}}
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