Supply Chain Management (SCM) encompasses the management of goods, data, and financial flows associated with products or services. SCM faces numerous challenges. The complexity and uncertainty of modern supply chains make management and coordination difficult, while factors such as demand fluctuations, supply chain disruptions, and transportation delays add to the complexity of these challenges. To address these issues, an increasing number of businesses are turning to artificial intelligence (AI) technologies to optimize their supply chain management. According to a survey conducted by Statista on 600 enterprises, as of 2022, 94% of companies are utilizing AI to varying degrees in SCM  (Figure 1). The emergence of AI brings new possibilities to the realm of supply chain, enabling enterprises to achieve more efficient, flexible, and intelligent supply chain operations.
The application of AI in SCM
1. Supply Chain Automation
The emergence of artificial intelligence has greatly liberated productivity and reduced labor costs in repetitive and low technical tasks. From procurement and freight transportation to warehouse management and product quality inspection, artificial intelligence offers advantages such as lower costs, higher efficiency, and greater accuracy. Efficient logistics in the supply chain can now also be achieved through driving automation, with companies like Amazon , TuSimple , and Nuro heavily investing in autonomous driving trucks and other transportation automation technologies.
Artificial intelligence systems based on computer vision (CV) can assist in automating product quality inspections. These systems, being immune to fatigue, can help improve productivity and accuracy on production lines. For instance, BMW already employs computer vision to scan moving car models on assembly lines. Robots equipped with computer vision can automate repetitive tasks in inventory management, such as real-time inventory scanning, assisting in loading or unloading pallets, and moving goods within warehouses, thereby enhancing logistics efficiency (Figure 2). In transportation, artificial intelligence sensors can be used to monitor the condition of products. For example, AI embedded in Internet of Things (IoT) sensors can detect temperature and humidity changes to ensure perishable goods are kept at the correct temperature.
2. Predictive Analytics
Artificial intelligence can also enable advanced demand forecasting in the supply chain. Traditional forecasting models that rely solely on historical data, such as ARIMA, autoregressive integrated moving average, and exponential smoothing methods, have become outdated as the volume of data generated by businesses and external sources continues to increase. By leveraging intelligent algorithms and historical data, AI systems can accurately predict customer trends and make forecasts ahead of demand occurrence. This provides the supply chain with more preparation time, eliminating the need to catch up with market trends and enabling better customer demand fulfillment. According to a survey by McKinsey & Company, AI-driven forecasting can reduce errors in the supply chain network by 30% to 50%. Improved accuracy can also reduce sales losses caused by inventory shortages by 65% and lead to a 10% to 40% reduction in warehousing costs . Global furniture brand IKEA has also developed AI-based demand forecasting tools that utilize historical and new data to provide accurate demand predictions.
3. Enhance Supplier Management
Due to a lack of collaboration and integration with suppliers, many supply chains, such as those in the food and automotive industries, faced significant disruptions during the global pandemic in 2020. Artificial intelligence can enhance Supplier Relationship Management (SRM) by making it more consistent and efficient, thus improving supplier management. AI-supported SRM software can assist in selecting suppliers based on factors such as pricing, historical purchasing data, and sustainability. AI-enabled tools can also help track and analyze supplier performance data and rank them accordingly. AI-driven tools like Robotic Process Automation (RPA) can automate daily supplier communications, such as invoice sharing and payment reminders.
Improvement in SCM through AI
1. Higher Predictive Accuracy
Demand forecasting is one of the most widely used machine learning applications in supply chain planning. Analyzing historical data to estimate customer demand helps avoid inefficiencies caused by supply-demand imbalances in business operations. It also improves decision-making processes related to cash flow, risk assessment, capacity planning, and workforce planning.
Machine learning can utilize internal and external data sources such as demographics, weather, online reviews, and social media to enhance predictions based on real-time data. This optimization of the replenishment process from various aspects prevents overstocking or stockouts, improves customer satisfaction, enhances discount optimization, improves workforce planning, and increases overall efficiency. Platforms like Levadata, which provide AI-powered inventory management, can digitally demonstrate how much expenses AI systems save for a company’s operations (Figure 3). With these advancements, companies are minimizing costs associated with inventory cash and stockouts.
2. Cost Savings through Labor Liberation
The first and second industrial revolutions greatly enhanced productivity through the automation of manufacturing. Similarly, modern supply chain automation would not be possible without artificial intelligence. AI systems can operate error-free for longer periods than humans, which means employees no longer have to work long shifts performing repetitive tasks. This reduces the risk of errors that would require financial investment to rectify and improves efficiency and cost-effectiveness.
3. Improving Sustainability
Sustainability is an increasingly important concern for supply chain managers as a significant portion of an organization’s indirect emissions are generated through its supply chain. Artificial intelligence can help improve supply chain operations to make them more environmentally friendly and sustainable. AI-driven tools can optimize transportation routes by considering factors such as traffic, road closures, and weather conditions to reduce mileage traveled. By minimizing unnecessary travel, companies can reduce fuel consumption, emissions, and their carbon footprint.
Artificial intelligence has made significant progress in SCM (Supply Chain Management) and has proven to be beneficial in various areas. By extracting crucial data from customers, suppliers, and documents, AI helps streamline supply chain operations, including supply pipeline management, demand and inventory management, demand forecasting, warehouse optimization, efficiency improvement, and logistics. Its integration in SCM enables companies to reduce operational costs, minimize inventory backlog, enhance customer satisfaction, and promote environmental friendliness.
For instance, Gaviota, an automated awning and blinds manufacturer, deployed ToolsGroup’s SO99+ solution, resulting in a 43% reduction in inventory levels and a decrease in inventory cycles from 61 days to 35 days.
However, there are challenges associated with implementing AI in SCM. These challenges include technological barriers, high costs, and the pitfalls of demand forecasting. Small and medium-sized enterprises and startups often struggle to establish effective and accurate AI systems due to limited historical data. Holger Kleck, Head of IT Steering and Supporting at Audi AG, points out that while software engineers and algorithms are essential, leveraging domain knowledge of the business, processes, and industry is crucial to truly benefit from machine learning capabilities.
It is important to consider the specific needs and circumstances of the organization when selecting suitable AI tools to serve supply chain management effectively. By combining AI technology with industry expertise, companies can unlock the full potential of AI in SCM.
The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies.
(AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.)