Artificial Intelligence (AI) has the potential to significantly transform supply chain management. However, both benefits and risks are associated with AI in the supply chain. In this article, we examine the role of AI in logistics and supply chains, specifically focusing on the advantages and challenges of AI in supply chain management, and we also provide some examples.
What is artificial intelligence, and how does it work?
AI is technology that assists machines in performing tasks that typically require human intelligence, especially those involving diverse variables and requiring creativity. An example is the use of AI in supply chain management for inventory control. In practice, this means that computers (AI) can learn from data and make decisions based on that knowledge. AI uses algorithms and statistical models to recognize patterns and make predictions.
Impact of AI in the supply chain
Supply chains are inherently complex structures with various variables and data sources. AI and supply chain management are a perfect match in potential. This combination allows parties within supply chains to analyze data and gain insights that human planners may overlook. This, in turn, leads to smarter planning, better inventory decisions, and several other benefits.
Benefits of Artificial Intelligence in Supply Chain Management
Improved Accuracy
One of the key benefits of AI in supply chain management is the enhanced accuracy of predictions. AI can analyze vast amounts of data and identify complex patterns, enabling parties to better anticipate demand fluctuations and optimize inventory levels.
Cost Savings
AI aids in reducing operational costs. By planning smarter routes and implementing better inventory management, companies save costs on transportation and inventory storage. It also eliminates unnecessary or time-consuming administrative work, allowing people to focus on essential tasks.
Better Product
Ultimately, supply chain services are a product like any other. If you can make it better, faster, or cheaper than competitors, parties will choose you. A faster and more efficient supply chain leads to a competitive advantage. In the case where your competitors also use AI, it prevents a competitive disadvantage.
Risks and Challenges of AI in the Supply Chain
Inaccurate Data and Predictions
In theory, AI is the most advanced method for analyzing complex data structures and making decisions based on them. However, in practice, humans need to relinquish control. AI is not infallible, and when it makes incorrect decisions – AI is also dependent on the quality of the input – it can be challenging to determine the cause.
Insufficient Understanding of AI
During major technological revolutions, such as the rise of the internet, there’s a risk that everyone wants to jump on board immediately without delving into the intricacies. AI, in particular, requires thoughtful consideration and choices. The risk exists that parties provide data or make decisions without fully understanding the consequences.
Ethical Considerations
Numerous risks and ethical considerations surround AI, including its application in logistics and supply chains. AI makes decisions, and the consequences affect real people. Sensitive information may end up in unintended places, and the efficiency improvement brought by AI can also lead to job displacement.
Examples of Artificial Intelligence in Supply Chain Management
In practice, AI is already applied in various ways in supply chain management. A simple example is the use of AI to identify container numbers from camera images and then process them in the Terminal Operating System (TOS). Another example is the use of AI for predictive maintenance of transportation equipment. By analyzing data, AI identifies risks before they manifest as problems. AI is also commonly used for inventory management. AI analyzes real-time data and provides recommendations for restocking inventory, preventing excess inventory and shortages. There are more examples where AI in supply chains achieves a level of efficiency that is unattainable with only humans and traditional software.
