Artificial intelligence and machine learning in supply chain management

As global supply chains become more complex, disruption in one link in the chain can significantly impact other areas. Having systems and processes in place to reduce the possibility and subsequent impact of supply chain issues is extremely important.

Advancing technologies, such as artificial intelligence and machine learning, make connectivity and supply chain visibility easier. More businesses are therefore using them to prevent and overcome supply chain challenges.

What is artificial intelligence?

Artificial intelligence (AI) simulates human intelligence processes so that computers or robots can carry out tasks usually done by humans.

AI was once only seen in science fiction. However, it is now common in everyday life. For example, navigation apps, facial recognition, smart assistants, and even robot vacuum cleaners at home use this smart tech.

AI can adapt in near-real-time to changing conditions and develop new knowledge by processing more data and revealing more patterns and trends than humans can.

AI technology is being used more and more by businesses to help them make informed strategic decisions faster than ever before.

For supply chain management, this could include generating demand forecasts to ensure stock availability or mapping transportation routes to prevent idle time and reduce fuel consumption.

What is machine learning?

Machine learning (ML) is a subset of artificial intelligence. It uses algorithms, software or systems to learn and adjust without specific programming. ML models teach themselves over time by analysing trends and spotting anomalies, and then offering predictive insights.

Using regularly gathered data, it notices patterns and suggests the next actions. This could include working out shorter warehouse picking routes, predicting upcoming faults in warehouse automation machinery to prevent breakdowns or tracking packages across their entire supply chain journey to optimise the route.

Machine learning can also highlight areas for improvement that a human might miss or take longer to identify. This helps deal with potential issues before they arise and reduces the chance of future problems occurring.

Benefits of artificial intelligence and machine learning in supply chain management

Intelligent, autonomous warehouse technology is already positively impacting warehouses worldwide. You can read about warehouse automation and technology in our article, where we discuss voice technology, radio frequency identification, automated guided vehicles, and robots.

Here are some benefits of combining machine learning with advanced analytics and real-time monitoring to get complete visibility across the supply chain.

Inventory management and AI

Inventory management teams have the difficult task of calculating healthy stock levels to meet demand without over- or under-stocking.

Using algorithms, AI can provide better quality data and analysis to give a complete overview of your warehouse and supply chain. With the ability to run different scenarios, AI and ML can provide information about the optimal stock levels to meet demand.

As the analysis is carried out daily, you will be able to see where your strategy needs to change to respond to rapidly changing market situations. For example, you can adjust the speed and volume of order processing to meet demand.

This level of understanding helps make intelligent and informed decisions and reduce inventory and operating costs.

Using AI to improve demand forecasting

Accurate demand forecasting is essential in supply chain management. More accurate forecasting helps set optimal inventory levels to reduce holding costs while improving stock availability.

Machine learning models don’t just use sales information to identify hidden patterns in historical demand data. They will analyse external factors, meaning they can detect emerging issues and threats in the supply chain before they disrupt the business.

Putting preventative measures in place will bring more effective results and reduce the negative impact on the business.

Machine learning makes better use of resources

Manual, paper-based processes are incredibly time-consuming and have a high risk of human error. Automating these processes will save admin time and reduce costs. It will also give your supply chain and inventory managers more time to focus on strategic activities.

You can train ML models and techniques to help identify possible areas of inefficiency and waste. You can then use recommendations to create a more flexible environment to deal with disruption effectively.

AI- and ML-enabled machines can reduce operator error and processing times, increasing efficiency and productivity. For example, by calculating the number of pallets that need moving and the subsequent equipment and labour required, you can ensure optimal efficiency in the warehouse.

Machine learning in warehouses

Machine learning can transform warehouse management. A more efficient warehouse can reduce overheads and highlight improvement opportunities.

For example, machine-learned computers can pack shipments and organise products. They can scan and report shipments into the warehouse, keeping an accurate track of your inventory. Combining technology with human operatives will speed up monotonous and time-consuming activities, providing more time for tasks only humans can do.

Machine learning can also automate checks for defects or damage in warehouse equipment or even stock. This reduces the chance of equipment breaking down or sending faulty goods to customers.

Integrating machines into warehouse processes can also improve warehouse safety. Using AI robots to drive dangerous machinery and store inventory in hard-to-reach places reduces the risk of accidents. If an accident does happen, the possibility of human injury significantly reduces.

Managing suppliers with AI and ML

Any problems in a supplier’s supply chain will impact your ability to meet demand.

Machine learning and artificial intelligence can offer valuable insights into supplier performance, such as product failures or missed deliveries.

The software can run different supplier scenarios based on lead times, prices or other parameters to help make real-time, strategic decisions. You can then choose the best supplier to meet your order needs.

Improved customer service

Machine learning techniques can improve supply chain visibility and help businesses follow orders from shipment to delivery. Real-time data allows for a better customer experience, as you can provide more accurate delivery information.

While customers still need human interaction, machine learning can support the customer service team. For example, support chats on websites allow people to find quick answers, filtering to a helpline if necessary.

Inventory optimisation software

Inventory optimisation software like EazyStock can connect to business systems and advanced warehouse management systems. Helping you forecast with confidence, stock the right items, and optimise your purchasing.

EazyStock automates everyday inventory calculations, including reorder points, order quantities and safety stock levels. It provides optimised order proposals that feed back to your stock ordering system, ready for purchasing.

Using EazyStock, you can classify inventory items, set dynamic stocking policies, and adjust stock levels based on moving factors, including forecasts, demand profiles, and supplier lead times.

EazyStock’s advanced statistical algorithms generate accurate demand forecasts considering trends, seasonality, and product lifecycle profiles.

Book a demo to learn more about how EazyStock can help you make informed purchasing decisions, leaving more time to focus on your strategic business goals.