A supply chain is a network that links all the business activities, making it the most vital element of any business. The pandemic in 2020 among other geopolitical disruptions has underscored how vulnerable supply chains can topple even the mightiest of organizations.
In 2024, the convergence of artificial intelligence (AI) and supply chain management has reached unprecedented levels of sophistication and efficiency. As industries worldwide continue to embrace digital transformation, AI emerges as the catalyst for reshaping the landscape of supply chain operations. From predictive analytics optimizing inventory management to autonomous vehicles revolutionizing logistics, the applications of AI are refining every aspect of the supply chain. More than 60% of CEOs believe AI can automate decision-making and improve supply chain efficiency.
Let’s unravel the top AI supply chain applications of 2024 and explore how businesses benefit from their implementation.
Supply Chain Automation
Modern-day supply chain automation relies heavily on AI. AI technologies within the supply chain, such as digital workers, warehouse robots, autonomous vehicles, and RPA, empower organizations to execute repetitive and error-prone tasks with unprecedented efficiency and accuracy.
Here are some essential supply chain tasks that can be automated with AI:
1. Back-Office Automation
Tasks like document processing can be automated with precision using intelligent automation or digital workers that combine conversational AI with RPA. Research revealed that RPA in the back office can reduce 70% of the cost of a full-time employee! Given the tangible ROI to organizations, a survey found that 53% of businesses are actively exploring RPA solutions.
2. Logistics Automation
By automating and optimizing every minute and mile of the delivery chain, AI technologies in a supply chain promote greater efficiency in logistics. Corporations are making substantial investments in transportation automation technologies like autonomous trucks and seeing massive benefits.
According to an American logistics company, AI and quick route optimization have saved the company $50 million!
3. Warehouse Automation
Advanced AI technologies like cobots contribute to enhanced efficiency, productivity, and safety by automating warehouse management processes. These collaborative robots are designed to work alongside humans in various tasks, enhancing efficiency and safety in the workplace.
The market for warehouse robots, estimated to be worth USD 4.7 billion in 2021, is expected to grow at a CAGR of 14% from 2021 to 2026. Currently, Amazon employs 200,000 robots in its warehouses, while 26 out of its 175 fulfilment centres are equipped with robots to aid people in choosing, sorting, transporting, and storing inventory.
4. Automated Quality Checks
The American Society for Quality estimates that the cost of compromised quality can skyrocket to 15-20% of annual sales revenue, translating into billions of dollars for corporations. AI-enabled computer vision (CV) systems can help automate quality checks and promote precision among products, mitigating the risks associated with human error and subjective judgment.
For instance, Samsung, relies on AI-powered Visual Quality Control to inspect products such as smartphones, televisions, and home appliances. AI-driven systems detect defects in screens, casings, and internal components, ensuring products meet high aesthetic and functional standards. The speed and accuracy of AI inspections have enabled Samsung to streamline its production processes and deliver high-quality products to consumers. Similarly, Tesla, uses computer vision systems to inspect and analyze the surface quality of car bodies, identifying defects such as paint imperfections, scratches, or misalignments.
Other giants like Pfizer, Nestlé, Siemens and Airbus also employ AI-based quality control systems in their various manufacturing and production processes.
5. Automated Inventory Management
Robots equipped with computer vision and AI/ML capabilities can automate repetitive tasks in inventory management, including real-time scanning of inventory.
Amazon, which has long been at the forefront of AI innovation in supply chain management, employs sophisticated AI algorithms for inventory management. Similarly, several companies across various industries are utilizing AI for inventory management to optimize stock levels, reduce costs, and improve efficiency. Walmart is another retail giant that leverages AI technology for inventory management in its stores and distribution centers. The company utilizes machine learning algorithms to analyze historical sales data, forecast demand, and optimize replenishment schedules to ensure that shelves are adequately stocked while minimizing excess inventory. Zara also uses machine learning algorithms to to analyze sales data in real-time, identify popular items, and adjust production and inventory levels accordingly to reduce stockouts and minimize excess inventory. Additionally, the consumer goods company, Procter & Gamble (P&G) employs AI for inventory management across its global supply chain.
Predictive Analytics/Forecasting
Nothing would be handier for a supply chain manager than being able to predict demand and market trends. While no prediction can be certain, machine learning algorithms come as close to accurate as possible.
AI-enabled demand forecasting applications can significantly increase forecast accuracy, optimize inventory, and reduce demand and supply fluctuations to control the bullwhip effect. Let’s look at some interesting use cases:
6. Inventory Optimization
By analyzing historic demand and supply data and trends, AI-enabled tools can help determine optimal inventory levels to avoid over-production and storage costs.
For instance, Zara employs AI to identify popular items, and adjust production and inventory levels accordingly to reduce stockouts and minimize excess inventory. Amazon also uses advance AI algorithms for demand forecasting, and optimize inventory levels.
7. Region-Specific Forecasts
AI-powered forecasting tools provide detailed insights on region-specific demands and help companies make better decisions. As each region has different trends, holidays, and requirements, AI forecasting tools help companies meet these varying needs according to region-specific parameters.
Coca-Cola employs predictive analytics and machine learning algorithms to forecast demand, optimize production schedules, and ensure that products are available when and where they are needed. Walmart utilizes machine learning algorithms to analyze historical sales data, forecast demand, and optimize replenishment schedules to ensure that shelves are adequately stocked in regions where there is demand. Even Zara leverages AI to keep up with rapidly changing fashion trends and customer preferences to cater to region-specific needs and demands.
Enhanced Supplier Relationship Management
Most of the issues encountered in a global supply chain are caused by weak supplier relationship management and a lack of collaboration and integration with suppliers. Here’s how AI can help improve supplier relationship management by making it more consistent and efficient:
8. Improved Supplier Selection and Communication
AI-powered SRM software can help in supplier selection by ranking the best suppliers based on pricing, purchase history, sustainability, supplier performance data and more.
These tools can also automate routine supplier communications like invoice sharing and payment reminders to prevent delays and missed deadlines.
In the Microsoft Supply Chain Center, Copilot boasts a cutting-edge news model that collects all supplier-related news. Leveraging the Azure OpenAI Service, supply chain managers can now send AI-generated, precisely targeted emails to suppliers for effective communication.
Improved Sustainability
With 80% of global corporate carbon emissions originating from the supply chain, sustainability is a growing concern of supply chain managers. Let’s look at how AI can make supply chain operations more sustainable:
9. Greener Transport Logistics
By analyzing factors like traffic, road closures, and weather conditions, AI-powered tools can optimize transportation routes to reduce the number of miles travelled, fuel consumed, and emissions released resulting in improved sustainability.
For example, UPS saved 10 million gallons of fuel in one year by implementing an AI-driven navigation system, while DHL reduced its delivery costs by 7% by using AI to optimize routes. Similarly, Walmart leverages AI route optimization softwares to reduce fuel costs and emissions.
Concluding Remarks
As we stand on the brink of a new era in distribution, one thing is clear: AI is not just a tool—it’s a catalyst for transformation. Embracing AI technologies in distribution is not just an option but a necessity for businesses aiming to thrive in a competitive, fast-paced market environment.