Description
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. These systems are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. The core AI concepts are:
- Learning: The ability to acquire and process information, and then use it to improve performance. (Machine Learning, Deep Learning)
- Reasoning: The ability to use logic and inference to draw conclusions and solve problems. (Knowledge Representation, Rule-Based Systems)
- Perception: The ability to interpret and understand sensory input, such as images, sounds, and text. (Computer Vision, Natural Language Processing)
- Problem-solving: The ability to find solutions to complex tasks and achieve goals.
In the area of supply chain, an AI system can streamline workflows, improve decision-making, and enhance productivity across supply chain operations. It has the following benefits:
- Increased Efficiency: Automates repetitive tasks, freeing employees to focus on higher-value activities.
- Improved Accuracy: Reduces errors in data entry, invoicing, and order processing.
- Enhanced Decision-Making: Provides real-time insights and predictive analytics for better planning.
- Cost Savings: Identifies cost-saving opportunities and optimizes resource allocation.
- Scalability: Adapts to growing business needs and complex supply chain networks.
Resources
Bashynska, I. and Prokopenko, O. (2024), Leveraging Artificial Intelligence for Circular Economy: Transforming Resource Management, Supply Chains, and Manufacturing Practices, Scientific Journal of Bielsko-Biala School of Finance and Law. Bielsko-Biała, PL, 28(2), pp. 85–91. doi: 10.19192/wsfip.sj2.2024.13.
FAQ
What are the challenges of using AI in the area of supply-chains?
(1) Complexity as supply chains are often more complex than linear ones, involving multiple stakeholders and reverse logistics flows; (2) Data Availability and Quality as there is reliance on data from various sources (customer partners, regulators, logistics), which is often scattered across different systems and (3) Integration Challenges as adding AI into existing legacy IT infrastructure such as CRM and ERP systems, can be complex and costly.