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All Journal International Journal of Supply Chain Management
Khastgir, Antara
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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Demand Planning: Riding Disruptive Wave of AI and Accelerated Computing Khastgir, Antara; Kumar, Adesh
International Journal of Supply Chain Management Vol 13, No 2 (2024): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v13i2.6236

Abstract

Traditional demand planning methods often struggle to keep pace with the complexity, volatility, and vast datasets inherent in modern supply chains. Artificial intelligence (AI) offers a transformative solution, revolutionizing demand planning with its ability to analyze vast amount of data, identify complex patterns, and generate highly accurate forecasts. This paper explores the latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing (NLP). The focus is on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information. Furthermore, the potential of AI to optimize inventory management, enable scenario planning, and increase supply chain resilience in response to unexpected disruptions are examined. The paper also addresses practical challenges in implementing AI-powered demand planning solutions, and outlines areas for future research. Most importantly, the paper provides the robust methologies to integrate the emerging AI developments in demand planning process.
Enhancing Supply Chain Efficiency to Build Next-Gen Artificial Intelligence (AI)/Machine Learning Network Through Al-Driven Forecasting Krishnan, Manish; Khastgir, Antara
International Journal of Supply Chain Management Vol 13, No 3 (2024): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v13i3.6244

Abstract

The networking hardware industry is characterized by unique challenges when it comes to supply chain management. These include unpredictable demand patterns, complex logistics, besides disruptions caused by rapid technological advancements. This paper explores the integration of artificial intelligence (AI) into forecasting methodologies to enhance supply chain efficiency within the sector. Application of AI-driven forecasting models can help organizations improve demand predictions, refine inventory management, and streamline logistical operations. Drawing on recent research and industry practices, this article highlights the transformative impact of AI on supply chain efficiency and offers insights into best implementation practices. Furthermore, the research investigates the intersection of AI and networking hardware supply chain management, focusing on leveraging AI to analyze hardware failure patterns and interpret hardware-generated alarms and interrupts. By harnessing analytical capabilities of AI, modern organizations can extract actionable insights to reduce failure rates and enhance supply chain forecasting accuracy. This innovative approach enables more effective anticipation and preparation for hardware failures, optimizing spare part inventory management and minimizing the need for costly return merchandise authorizations (RMAs).
Semiconductor Supply Chain Efficiency: A PLM-Powered Optimization Strategy Khastgir, Antara; Krishnan, Manish
International Journal of Supply Chain Management Vol 13, No 3 (2024): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v13i3.6243

Abstract

Semiconductors are one of the most indispensable components that shore up modern society, as is evident from their extensive presence in majority of the products that we use every day and are dependent on. Being a key component of electronic devices, semiconductors enable improvements and progression in information and communication technology (ICT). It affects all round advancements affecting almost all the aspects of modern life - from computing, to communications, healthcare to transportation, military systems to clean energy, and several other applications. Semiconductors form an important part of our daily lives because of the role they play in the manufacture of electronic devices. This study scrutinizes existing literature to provide insight into the necessity of using PLM is the optimization of semiconductor supply chain and heightening of efficiency
Co-Authors Krishnan, Manish Kumar, Adesh