Effective supply chain management is more critical in a globalized economy. Artificial intelligence may be the key to making supply chains more resilient and flexible.
What is AI in Supply Chain Management?
Our economy is increasingly globalized, with manufacturers, retailers, and logistics companies connected in a world-spanning network that can easily be knocked out of alignment by a single mishap. Take the Suez Canal blockage of 2021; a single ship running aground in the wrong place blocked enough shipping traffic to cost $10 billion over just six days.
Incidents like this highlight the necessity of flexibility and resilience in supply chain management. Many shipping and logistics companies are looking towards artificial intelligence (AI) to enhance their capabilities and develop new strategies for protecting their business, as well as the usual motivations of more efficient operations and cost savings.
At its most basic, AI is a general term for applications and programs that can simulate human intelligence and our capacity for decision-making. This field includes many subdivisions, such as machine learning, which has a computer system “learn” by reviewing enormous amounts of data related to a particular topic and deriving patterns from said data.
This enables AI to outperform traditional computer software, which relies on being pre-programmed with instructions to perform various tasks. AI’s capability for discovering patterns and relationships makes it a perfect fit for supply chain management, which relies on countless data points and physical entities interacting simultaneously.
In fact, artificial intelligence has so much potential that even the White House has encouraged businesses to invest in its use as part of its initiative to strengthen America’s supply chain resilience.
How does AI Work in Supply Chain Management?
An AI system in supply chain management relies on two primary components: the AI software that performs the calculations and data analysis and the sensors and components that gather said data.
- As software, AI programs can run on any system with enough processing power to support them. This includes both cloud-based servers and dedicated AI computers that boast the hardware needed to support them.
- Various systems are used to gather information for AI to analyze, such as IoT sensors that track packages and employees in a warehouse and RFID tags to scan containers as they enter and exit shipping hubs. Other forms of technology intersect with AI, such as digital twins that can be used to replicate supply chain systems virtually.
Benefits of AI in Supply Chain Management
By implementing AI in their supply chains, companies can reap benefits like:
- Lower Operating Costs: AI can learn how to manage complex tasks such as tracking inventory, which helps identify points of inefficiency and prevent bottlenecks from occurring. This leads to fewer delays, lower operating costs, and a more efficient operation overall.
- Less Waste and Fewer Errors: An AI doesn’t get tired or distracted like human employees, making it perfect for performing repetitive tasks or detecting defective products.
- Better Inventory Management: By using AI to predict market trends and product demand, companies can gauge consumer interest in a product and recommend stocking inventory based on these predictions. This leads to better demand forecasting and helps prevent over or understocking.
- Improved Warehouse Efficiency: By analyzing input/output patterns and foot and vehicle traffic within a warehouse, AI can make recommendations for optimal routes for workers and machinery. This helps make warehouses more efficient as workers and products can navigate the space more easily.
- Simulated Testing: By pairing AI processing with digital twins, companies can simulate different scenarios and test new approaches without disrupting real-world operations. For example, a shipping company could simulate what would happen if a port closed down and how that would affect the rest of the supply chain, which helps that company plan for that kind of contingency.
Challenges for AI in Supply Chain Management
Like any other new technology, AI has several challenges it must overcome in the supply chain sector, such as:
- Downtime For Training / Implementation: Any new technology or tool comes with downtime to learn how to use it effectively. Companies need to roll out deployment carefully to avoid negatively affecting their current operations.
- Initial Cost: As with any upgrade, there’s a price tag. Businesses need to weigh the initial costs of adopting AI in their supply chain management against the long-term savings it can bring. They should also partner with original equipment manufacturers that can develop AI computers to their exact needs and help save their budget.
- Adequate Data: AI tools are only as powerful as the data they receive, so feeding them enough quality data is critical for having informed decision-making. This means that before you implement AI, you must ensure you have a network of data-gathering sensors in place and enough storage to contain the terabytes of information that need processing.
Embrace AI-Powered Supply Chains with Cybernet
AI is poised to revolutionize supply chain management, leading to more efficient operations and better planning for both the warehouse and the broader network. If you’d like to learn more about how AI can improve your supply chain, explore our industrial AI box PCs or contact our team for a custom solution.