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.