Performance Optimization of Industrial Supply Chain Using Artificial Intelligence

Area of Science:

• Supply Chain Management
• Artificial Intelligence
• Operations Research

Background:

• Organized retailing is evolving due to economic, social, and technological factors.
• Supply chain performance is influenced by strategic, operational, and environmental aspects.
• Artificial Intelligence (AI) offers optimization capabilities for supply chains.

Purpose of the Study:

• To determine AI's specific contributions to supply chain management.
• To identify current and potential AI strategies for enhancing SCM research and practice.
• To address the scientific gap concerning AI in Supply Chain Management (SCM).

Main Methods:

• Comprehensive analysis of previous research on AI in SCM.
• Inclusion and exclusion criteria applied to articles in logistics, marketing, SCM, and manufacturing.
• Methodical analysis and synthesis of findings.

Main Results:

• AI approaches commonly used in SCM were identified.
• Potential AI techniques for SCM were explored.
• SCM subfields benefiting from AI and those with high potential were discussed.
• The Improved Feed Forward Network with Particle Swarm Optimization technique was used for performance categorization.
• Key performance indicators include inventory management, customer satisfaction, profitability, and competitive advantage.

Conclusions:

• AI significantly contributes to optimizing supply chain processes and performance.
• Further scientific investigation is needed to fill existing research gaps.
• AI implementation leads to improved competitive advantage and organizational performance metrics.