Where is artificial intelligence used in supply chains?

Large organisations often form a central part of complex supply chains. They must work with many stakeholders, including suppliers, manufacturers, distributors, and retailers.

Together, these stakeholders must negotiate contracts, transportation, warehousing, communication, and distribution. Integrating artificial intelligence into these business functions makes coordinating them more efficient. Reducing operational costs and improving customer satisfaction.

Also, we can examine the particular tasks in these functions that gain advantages from using artificial intelligence in supply chain management. These activities range from demand forecasting and inventory management to communication and risk management.

1. How can AI predict future demand and forecasting?

Being able to predict and respond to changes in customer demand is critical for the survival of most commercial entities. Demand may change by volume, timing, location, and demographic. Traditional methods for predicting trends rely on numerical operations based on historical data.

However, traditional methods may fail to respond to external factors that are not already present in the data. An example is an extreme weather forecast. They also become much less effective when considering many different products and the relationships between them. For example, if people started buying more natural building materials, they might buy less construction plastic instead of more of everything.

2. Route optimisation and AI

Planning the how and when of goods transportation between sites or to customers is no easy task. There is usually too much to take into account for a person to do this by hand.

Logistics software must consider vehicle types, fuel usage, and transportation time. As well as, number of stops, vehicle and delivery site availability, and any regulatory restrictions. All at the lowest possible cost.

This is where artificial intelligence, based on historical data and forecasts, can help. It can help predict weather and traffic conditions and their impact, as well as make alterations to planned routes. An AI algorithm can even change shipment routes based on real-time customer demand using end-to-end supply chain data. Such an application can get the most out of fleet assets by maximising their operational utility.

3. AI and inventory management

The goal of inventory management is to maintain sufficient stocks of required products at the lowest possible cost. Spreadsheets were once the mainstay for managing this problem. But using them is a very manual process, and it can be difficult to make significant structural changes. Such as those triggered by supply chain disruptions or demand fluctuations.

In these cases, the response needs to be immediate, and spreadsheets can struggle to keep up.

This is where artificial intelligence can give an organisation an edge. Neural networks and reinforcement learning can solve complex inventory problems efficiently. If an item runs out of stock or takes up too much space for too long, the algorithm receives negative reinforcement. If the stock arrives in good time and has a minimal cash flow impact, the algorithm receives positive reinforcement.

Through each iteration of this training process, the inventory management application gets better at navigating real-world variables.

4. Communication

Effective communications are the backbone of modern supply chains. Customers, suppliers, manufacturers, and logistics businesses are constantly sharing information with each other. These exchanges can be more consistent and efficient when automated using AI. Natural language processing, in particular, has the potential to help support customers and manage suppliers.

5. How AI and risk management can work together

Supply chain managers must assess risks across the entire network. Including planning, supply, processing, demand, and environmental risks. With each risk measured by its likely severity and frequency. Allowing managers to assign a priority to risks for mitigation.

Manual risk assessments can be slow, and the results can be subjective and vary according to who makes the assessment. Different artificial intelligence technologies can assist managers with the automatic evaluation of these risks. Each supports more reliable and consistent decision-making.

Expert systems can ask pertinent questions and provide a rule-based assessment of the risk's severity and frequency. These systems are effective in small organisations. They can become more difficult to design and maintain in larger supply chains.

Machine learning based systems are more effective in more complex networks. Providing that enough data is available to these algorithms, they can uncover hidden risk vectors. They can quickly isolate the most important vulnerabilities that require attention.

Multi-agent systems are the most complex and expensive systems to commission. However, the system can analyse risks that relate to each other or conflict with each other. They can also quickly look at the full effect across the supply chain as the severity or frequency of risks changes.

Conclusion

Artificial intelligence is a growing area of technology that is receiving significant attention from tech evangelists and large organisations. There are many current, and emerging, artificial intelligence applications in supply chain management. This software is available to improve the performance of everything from individual drive motors to entire end-to-end supply chains. Studies on the effectiveness of software that reacts like a person would, have shown marked improvements versus conventional approaches.

Overall, supply chains that embrace and successfully implement artificial intelligence will reduce costs, optimise inventory levels, and increase customer satisfaction. They will strengthen these critical networks and help prepare them to face new and unpredictable disruptions.