Treffer: Impact of Global Supply Chain Disruptions on Indian Manufacturing and Retail: A Big Data Perspective.

The modern global supply chain network, particularly in an Indian setting, has been subjected to unprecedented shocks occasioned by port congestions, pandemic-related shocks, and geopolitical tensions. This paper on, "Impact of Global Supply Chain Disruptions on Indian Manufacturing and Retail: A Big Data Perspective," takes an integrated big data analytics methodology to represent, visualize, and interpret the impact of these disruptions on India's manufacturing and retail industries. Taking advantage of large datasets from the World Bank's Logistics Performance Index, Ministry of Commerce and Industry reports, Kaggle's Logistics Ease Across States data, and several leads numeric datasets, the research creates solid regression models, predictive projections, and supply chain network risk visualizations. The research methodology integrates linear regression to relate port congestion to delivery delay (R² = 0.984), time-series LSTM forecasting to forecast inventory stockouts (93.4% accuracy), and network analysis to illustrate supplier dependency risk mapping across key global geographies like Taiwan, China, and Bangladesh. From regression scatter plots, bar charts of model performance (MAE and RMSE), and network graphs illustrating key supplier vulnerabilities, the research presents disruption effects in threedimensional perspective. Predictive models like ARIMA and LSTM show how early signs of inventory shortage can be sensed and addressed. Network analysis uncovers high concentration risks in semiconductor, API, and textile procurement, highlighting the pressing need for diversification and resilience planning. The literature review draws heavily from works by Chopra & Meindl (2019), Ivanov (2020), Christopher (2016), and global industry reports from Deloitte, McKinsey, and Accenture, offering theoretical and practical perspectives on supply chain agility, resilience, and digitalization. Data processing tools employed include Python libraries like pandas, matplotlib and more ensuring methodological accuracy and reproducibility. Key insights indicate that Indian producers are severely exposed to upstream risks through excessive dependence on few suppliers and weak logistics networks. Predictive analytics and integration of big data become crucial catalysts in ensuring real-time decision-making and business continuity. Suggestions focus on creating regional redundancies, investment in digital supply chain technologies, and stimulating local capabilities to minimize import reliance. Finally, this dissertation adds to the emerging debate on supply chain evolution in a risk-prone world and offers policy-relevant recommendations for policymakers, supply chain executives, and business stakeholders who want to future-proof India's manufacturing and retail industries against external disruptions. [ABSTRACT FROM AUTHOR]

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