AI IN SUPPLY CHAIN MANAGEMENT: A DEEPER DIVE

The section provides an exploration of AI’s application in Supply Chain Management (SCM), focusing on logistics optimization, demand forecasting, and inventory management, supplemented with case studies.

Logistics Optimization:

AI-driven Methodologies: Dogru and Keskin (2020) [12] review AI applications in SCM, highlighting its role in enhancing operational efficiency, such as in IT operations management and last-mile logistics. Enholm et al. (2022)[26] provide a systematic review of AI in business operations, identifying key enablers and inhibitors, typologies of AI use, and their effects. Review, M. S. M. (2020) [23] discuss the integration of enterprise cognitive computing in business operations.
Case Studies: Amazon uses AI for dynamic route optimization  (Ramírez-Villamil, Montoya-Torres, Jaegler, and Cuevas-Torres 2023) [22], reducing delivery times and operational costs. DHL incorporates machine learning for precision in transit time prognostication (de Araujo and Etemad (2021)) [17]. FedEx utilizes AI-powered sorting robots and predictive maintenance algorithms to enhance hub operations (Chen et al., 2022) [8].

Demand Forecasting:

AI Models in Action: Agrawal, Gans, & Goldfarb (2022) [1] show AI’s efficiency in analyzing sales data, while He, K., et al. (2016) [16] demonstrate deep learning models uncovering sales patterns. Sentiment analysis is used to gauge public sentiment as a leading indicator of demand.
Case Studies: Coca-Cola integrates advanced predictive analytics for demand forecasting (Agrawal, Gans, & Goldfarb, 2022) [1]. Walmart uses AI to predict consumer demand and manage inventory (Ghadge, Dani, Chester, & Kalawsky, 2013) [13]. Adidas employs AI for real-time insights into demand trends (Osman, Alinkeel, and Bhavshar (2022)) [20].

Inventory Management:

How AI is Reshaping Strategies: Osman, Alinkeel, and Bhavshar (2022) [20] describe AI’s role in predictive reordering and real-time insights with IoT, enhancing efficiency and profitability. AI also optimizes returns by predicting return rates and offering product design insights.
Case Studies: Zara adjusts its production schedule in real-time using AI. Toyota optimizes its Just-In-Time inventory system with AI (Ghadge et al., 2013) [13]. Best Buy applies machine learning to assess local demand patterns for optimal stock levels (Chen et al., 2022) [8].
In summary, this section illustrates how AI is revolutionizing SCM by optimizing logistics, accurately forecasting demand, and efficiently managing inventory, with practical examples from industry leaders.