Artificial intelligence has emerged as a key technology driving business advancement in all industries. The computational process designed to complete tasks that would otherwise require human intelligence enables us to harness the power of big data. With the vast amounts of data generated by online consumer engagement, AI is essential for cultivating, categorizing, and analyzing it to draw meaningful insights.
More importantly, AI maximizes the potential of data collection and turns it into invaluable knowledge vital for businesses amid the post-pandemic reality, economic challenges and geopolitical instability. This new reality shows that the future is uncertain, and that businesses need new capabilities to develop greater resiliency to future challenges.
One way to mitigate future risks in the supply chain is to restructure it in an informed, data-driven fashion. This includes the use of AI to automate processes and enable better decision-making through predictive analytics. Multiple businesses are already embracing the power of AI to reimagine supply chains and enable their businesses to focus on data insights as they navigate the fluctuating market conditions.
In this article, we provide a collection of supply chain AI use cases that the industry can benefit from to optimize and automate operations, perform a range of forecasts and introduce other significant improvements - illustrated with concrete examples from the market.
Does your job involve managing the supply chain? Learn how you can optimize it with Artificial Intelligence and gain a competitive edge from these market examples.
Supply chain optimization
Undoubtedly, AI brought new opportunities for optimization and efficiency into supply chain management. The complex system involves multiple stakeholders and processes, including planning, sourcing, manufacturing, distribution, and logistics. By leveraging AI and other supporting technologies, businesses can streamline these operations and become more competitive in the marketplace.
1. Route optimization
AI can also help companies optimize their delivery routes by analyzing data on factors such as traffic patterns, weather conditions, and driver behavior. Using this information, businesses can not only identify the most efficient routes for their deliveries, but also make adjustments in real-time to minimize delays, should drivers experience any disruptions along the way. In addition to improved delivery times, route optimization can help to significantly reduce fuel costs.
UPS, a worldwide shipping and logistics corporation, utilizes a program named On-Road Integrated Optimization and Navigation (ORION). This system initially decreased driver miles by an average of eight per day. Recently, UPS enhanced the software with dynamic routing, which continuously assesses changing conditions and delivers optimized route guidance to drivers. This upgrade saves an additional two-to-four miles on average. UPS estimated that these daily mileage cuts can amount to $50 million in annual savings.
Here are some additional numbers that illustrate the benefits of ORION:
- saves 10 000 000 gallons of fuel per year;
- saves from $300 000 000 to $400 000 000 annually in fuel, vehicle running costs and salaries;
- saves more than 100 000 000 miles in routes a year;
- cuts 100 000 metric tons of CO2 emissions per year, an equivalent of taking 21 000 delivery cars off the road.
2. Warehouse optimization
Another area where AI is transforming the supply chain is in warehouse management. AI-powered robots and drones can perform tasks such as picking and packing orders, which can reduce the need for human labor and improve accuracy. For example, Amazon's 'Kiva' robots use AI to move inventory around their warehouses.
Additionally, AI can analyze data from sensors and other sources to optimize the layout of a warehouse, reducing the time and effort required to move products.
3. In-depth operational analytics
AI-powered supply chain analytics is a game-changer for the industry. With the ability to analyze large amounts of data in real-time, AI can identify inefficiencies and areas for improvement and allow managers to extract those insights that will help them make better decisions, reduce costs, and improve efficiency.
Some leading market solutions include IBM Watson Supply Chain Insights and SAP Supply Chain. These are suites of AI-powered tools and services designed for supply chain optimization. They leverage machine learning, natural language processing, and other advanced analytics technologies to provide insights and recommendations on various aspects of the supply chain, including demand planning and forecasting, inventory, logistics, risk management and others.
AI powered supply chain analytics is also embedded in Business Intelligence solutions dedicated for the sector. These will typically involve advanced data visualization techniques that support better decision-making.
4. Better inventory management
AI has already demonstrated significant potential in inventory management. It starts with greater visibility into the supply chain-related data and ends with AI powered analytics that supply chain managers can use to gain deeper insights into demand patterns, potential disruptions, and optimizing inventory levels through ongoing monitoring, predicting demand and identifying inefficiencies.
AI can be used to optimize the entire supply chain, from raw materials to finished goods. By analyzing data on supplier performance, transportation costs, and other relevant factors, AI can help businesses make more informed decisions about inventory levels and re-stocking schedules. This can help reduce overstocking, shipping costs and improve the overall efficiency of the supply chain.
For instance, Coca-Cola has been using the technology to optimize its inventory and prevent stock outs since 2017 and more recently applied it to its overall procurement efforts. Similarly, Walmart uses AI for the same purpose and to also avoid issues during the Black Friday sales.
5. Digital twins
Digital twins are virtual models of physical assets that can be used to simulate and analyze the performance of those assets in real-world conditions. In the context of the supply chain, digital twins can be used to create virtual models of products, equipment, and even entire supply chain networks. These models can then be used to simulate different scenarios, offering managers a great tool for identifying potential issues and optimizing supply chain operations.
Siemens has been at the forefront of using digital twins to optimize the supply chain and has developed a range of digital twin solutions that are specifically designed for the supply chain industry. This video explains in greater detail what digital twins can do for supply chain optimization.
The digital twins allow businesses to simulate different scenarios, identify potential issues, and optimize operations for improved efficiency and reduced costs. As companies like Siemens continue to innovate in this space, we can expect to see even more exciting applications of digital twins in the supply chain industry.
6. Dynamic pricing
AI can also be used to dynamically adjust prices based on demand and inventory levels. By analyzing real-time data on customer behavior and market trends, AI can help businesses determine the optimal price for a product. This will drive an increase in sales and reduction of time that products spend in inventory.
Uber is a good example of applying AI to adjust ride prices based on demand and traffic levels. Amazon and other retailers also rely on this strategy to correct their prices in accordance with customer demand, competitor prices and inventory levels. Most airlines and hotel chains also employ dynamic pricing all year round.
7. Labor optimization
AI algorithms can be used to optimize human resource usage by setting schedules that ensure the right workers are in the right place at the right time. It can also help businesses predict their staffing needs, which translates to reduced labor costs, minimized overtime and full utilization of the available workforce.
By using AI-powered labor optimization techniques, supply chain companies can increase efficiency, reduce costs, and improve the overall performance of their operations.
There are multiple AI-powered workforce planning solutions already available on the market, such as Workday Adaptive Planning. The solution combines AI and machine learning algorithms to help retailers optimize their workforce planning and scheduling. Companies like Amazon, UPS, or Ford have been benefiting from AI-powered workforce scheduling for years already.
Supply chain automation
In addition to optimizing the supply chain, application of AI offers powerful automation mechanisms. Supply chain management involves the movement of goods and services from suppliers to customers, and AI has truly revolutionized the way in which businesses manage this process. Let’s take a look at example use cases and the supporting technologies that are involved in supply chain automation.
8. Robotic Process Automation
AI-powered robotics is increasingly being used in the supply chain to automate repetitive tasks, such as picking and packing, sorting or maintenance and repair that had traditionally been performed by human workers. Improved accuracy is a significant advantage that these robots bring to supply chain management, as well as reduced costs and improved overall efficiency.
AI powered robots use advanced computer vision and AI algorithms to perform their tasks. They can quickly identify, sort and consolidate packages based on size, weight, or other criteria, making the process more efficient and accurate. Here’s how Amazon uses AI-powered robotic process automation to guarantee 24hr delivery times.
9. Autonomous vehicles
AI-powered, autonomous vehicles can be used to transport goods within warehouses, between different locations, or even shipping directly to the customer. These robots can autonomously navigate through the warehouse or other environments, optimizing the transportation process and reducing the need for human labor.
Daimler is one of the companies that work to develop self-driving trucks to automate delivery, minimize the risk of accidents and cope with freight volumes more efficiently. Uber recently started distributing Uber Eats orders in California via self-driving vehicles developed by Nuro, a startup that is on a mission to revolutionize last-mile delivery.
10. Warehouse automation
Warehouse operations can also be improved with AI and there are multiple examples that prove it. Large corporations like Amazon and UK-based Ocado are investing in AI and robotics to automate their warehousing processes.
Amazon has been investing in warehouse automation for a decade, but their robots still have limited intelligence. Ocado's highly automated warehouses are so effective that other grocery chains are buying their AI-driven warehousing technology. Their warehouses employ 3,000 cobots (robots designed to interact with human beings) controlled by an AI traffic controller and aim to automate the process completely. Here’s what it looks like:
Additionally, robotics companies are constantly working on developing new and improving the existing solutions, so we can definitely see the proliferation of supply chain automation examples in the future. Tesla has just released a new robot that is capable of “walking slowly and picking up stuff,” which can be extremely helpful in warehouse management and beyond.
11. Inventory management
Inventory management and its transformation with AI begins with data analytics that reveals demand patterns that can help with demand forecasting and inventory planning. You can read more about it in the next section on forecasting.
Secondly, AI-powered systems can automate inventory replenishment processes. By continuously monitoring real-time data, such as sales transactions, production schedules, and supplier information, AI algorithms can automatically trigger purchase orders or production requests when inventory levels reach predefined thresholds. Such supply chain automation eliminates manual intervention and ensures your inventory is replenished at the right time, reduces the chances of stockouts and minimizes the risk of supply chain disruptions.
Walmart has developed the Inventory Scan robot designed specifically for inventory management purposes. Take a look at what it’s capable of:
12. Back office automation
Billing errors can have a significant impact on a company’s bottom line. AI systems offer not only a quick and efficient way to invoice clients and process payments, but also reduce the risk of errors. There are multiple solutions available that automatically capture and review freight bill information, eg. this system developed by IBM. However, human intelligence is still required in some situations to solve billing issues.
13. Dispatch automation
Another way in which AI can enable supply chain automation focuses on transportation planning. Such planning involves coordinating the movement of goods from suppliers to customers, and AI-powered systems can optimize this process. AI-powered dispatch systems analyze data on transportation routes, delivery times, and carrier performance to automatically select the most efficient and cost-effective transportation methods. This not only reduces transportation costs but also ensures that goods are delivered to customers in a timely and reliable manner.
14. Smart contracts
AI can automate the execution of contracts and payments, reducing the need for intermediaries and increasing efficiency, enabling the so-called smart contracts. These contracts are self-executed and cannot be tampered with, which is guaranteed by the blockchain technology.
Blockchain provides unparalleled security and transparency of transactions by creating an immutable and decentralized ledger. Every transaction and event related to the supply chain can be recorded on the blockchain, ensuring a transparent and auditable trail of activities. This transparency enables all stakeholders, including manufacturers, suppliers, distributors, and customers, to access real-time information about the status and location of goods, ensuring trust and accountability throughout the process.
A good example in this category is ChainLink, a platform that enables supply chain process automation by connecting external data sources and APIs to enable smart contracts.
Supply chain forecasting
AI-powered predictive analytics is a critical technology that has enabled businesses to automate many of the processes involved in supply chain management. Predictive analytics uses machine learning algorithms to analyze data and make predictions about future events, such as demand forecasting, inventory optimization, and transportation planning. By analyzing historical data and identifying patterns and trends, predictive analytics can help businesses make better decisions about production, inventory management, and distribution.
15. Demand forecasting
Demand forecasting plays a crucial role in the supply chain in ensuring efficient operations, optimizing inventory levels, and meeting customer needs. AI has significantly transformed this area of the supply chain operations.
AI algorithms have the ability to analyze vast volumes of data, including historical sales data, customer behavior patterns, market trends, and external factors. Such advanced analytics employs machine learning techniques to detect hidden patterns, correlations, and seasonality in the data, enabling businesses to gain deeper insights into demand throughout the year.
AI can incorporate data from various systems, such as point-of-sale (POS) systems, enterprise resource planning (ERP) software, customer relationship management (CRM) platforms, and supply chain management (SCM) solutions. By harnessing this wealth of information, AI models can generate more accurate and holistic demand forecasts, accounting for factors like customer preferences, promotional campaigns, competitor activities, and economic indicators.
What is more, AI offers a holistic look at real-time data. It can continuously monitor sales transactions, social media trends, weather conditions, and other relevant factors, to provide up-to-date and dynamic demand forecasts. This can help you identify sudden shifts in demand and respond promptly to any fluctuations.
The integration of multiple data sources and application of advanced technologies enhances the accuracy and reliability of these forecasts and enables businesses to make more informed decisions regarding pricing strategies, production planning, and inventory management.
Ikea has developed a dedicated system that allows it to forecast demand in a smarter way. The company’s strategy involves looking at factors such as shopping preferences during festivals, in-store visit volumes, how customers shop after they have just received their salaries and even weather forecasts to project demand and adjust the availability of products both online and in brick-and-mortar stores.
The below video also explains how demand forecasting is implemented at Samsung Electronics to improve supply chain management:
16. Supply planning
Since AI-powered predictive analytics can be used to forecast demand, it can also be useful when planning the supplies and inventory. Predictive analytics helps determine optimal inventory levels, production schedules, reorder points and optimal stock levels. It is vital to selecting suppliers that align with the demand you’re expecting.
One aspect of predictive analytics that is particularly useful in procurement planning is scenario analysis. Based on the demand forecasts, you can use predictive analytics to run different scenarios and evaluate them against the impact of various factors on the supply chain. This could involve analyzing the effects of changing customer demand, supplier performance, transportation costs, or market conditions. These can also help you to identify risks and develop contingency plans.
This article explains how Coca-Cola leverages AI in its procurement efforts.
17. Predictive maintenance
For the supply chain to operate smoothly, the equipment and machinery involved in the process must be maintained accordingly. Traditionally, maintenance has been carried out based on fixed schedules or upon equipment failure, but AI has transformed this area of the supply chain with predictive maintenance.
AI algorithms are capable of processing and analyzing vast amounts of data generated by sensors, Internet of Things (IoT) devices, and other monitoring systems. This data includes equipment performance metrics, temperature, vibration, pressure, and other relevant parameters. AI and machine learning can analyze the data to detect anomalies, identify potential failure patterns, and generate insights about the condition of machinery or components.
These data-driven insights enable businesses to predict equipment failures before they occur, facilitating proactive maintenance interventions. Maintenance teams can prioritize their schedules, focusing on the equipment that requires immediate attention. AI can thus help businesses monitor the condition of their assets to avoid failures, reduce unplanned downtime, and extend the lifespan of their equipment.
General Electric is among the leaders in predictive maintenance. Take a look at how they optimize aviation needs through their Brilliant Factories:
Supply chain customer service
Customer service plays a pivotal role in the success of any business, and the supply chain isn’t an exception. AI has already demonstrated it has a potential to revolutionize customer services supply chain operations. By leveraging AI, companies can streamline operations, increase efficiency, and provide personalized experiences that meet and exceed customer expectations. When used smartly, it can give a significant boost that fosters customer satisfaction, loyalty, and ultimately contributes to the overall success of the supply chain.
18. Personalized recommendations
AI empowers businesses to deliver personalized customer experiences by leveraging data analytics and machine learning algorithms. By analyzing customer data, including purchase history, preferences, and behavior patterns, AI systems can generate valuable insights that can be subsequently used to tailor their offerings, recommendations, and promotions to individual customers, enhancing the overall customer service supply chain. AI-powered chatbots, which we’ve mentioned earlier in this text, further personalize customer interactions: they offer real-time support, answering queries, and guiding customers through the supply chain processes.
H&M is one of the leaders in using AI for personalized recommendations to its customers.
19. Personalized products
AI can also leverage predictive analytics to anticipate customer needs, preferences, and demands, allowing for product personalization for individual customers. This will typically involve analyzing external data to learn as much as possible about the customer to understand their preferences and behavior. Nike made an excellent use of this data by building a system that offers personalized shoe recommendations:
20. Streamlined inventory management
AI can be used to power intelligent inventory management systems that optimize stock levels, minimize shortages, and ensure you don’t have excess inventory lying around your warehouse. Algorithms can analyze historical data, real-time demand, and factors such as market trends to accurately forecast customer demand.
With those predictions, you can ensure your products are available as required, reducing the occurrence of out-of-stock situations, which can boost the overall customer satisfaction. Moreover, AI-powered inventory management systems can automate reordering processes, reducing manual intervention and human error while ensuring timely replenishment of stock.
Supply chain supplier relationship management (SRM)
The efficiency of your supply chain network relies on the diligence of the suppliers. The global nature and increasing complexity of the supply chain as well as geopolitical challenges pose multifaceted difficulties that make the task of managing the supply chain difficult. AI can ease this task for supply chain managers. By implementing AI-powered Supplier Relationship Management software, you can improve the supplier selection process, better monitor performance, strengthen relationships and mitigate risk.
21. Supplier selection
AI can be used to screen potential suppliers to streamline the selection process. AI algorithms can analyze vast amounts of data, including supplier profiles, past performance, certifications, financial records, and industry news to eliminate biased opinions. This enables organizations to make data-driven decisions when selecting suppliers, ensuring the identification of reliable and high-quality partners. With that, supply chain managers can reduce the risk of engaging with underperforming or unreliable suppliers.
22. Relationship management
Building strong relationships with suppliers is essential for effective collaboration and mutual success. AI can play a vital role in supplier relationship management by facilitating communication and collaboration. AI-powered chatbots and virtual assistants can automate routine communication tasks, such as order status inquiries and invoice processing, freeing up valuable time for both parties to focus on strategic discussions. Additionally, AI can analyze communication data to identify patterns and sentiment analysis, helping organizations gain insights into supplier satisfaction levels and proactively address any concerns. By strengthening communication and fostering collaboration, AI promotes stronger and more effective relationships between organizations and their suppliers.
There are multiple AI-powered Supplier Relationship Management software solutions available on the market, including one built by PwC:
Supply chain security
With the increasing interconnectedness and global nature of supply chains, ensuring security has become a paramount concern for businesses that operate them. Fortunately, AI offers innovative solutions that can significantly enhance security measures, mitigate risks, and protect the integrity of the supply chain.
AI-powered surveillance systems have significantly enhanced the monitoring capabilities within the supply chain. By employing computer vision techniques combined with video analytics, these systems can continuously monitor supply chain operations in real-time. With that, AI is able to detect anomalies, unauthorized access, and suspicious activities, helping businesses to strengthen their security mechanisms and optimize their processes.
23. Risk assessment
AI algorithms excel at processing and analyzing vast amounts of data, enabling organizations to identify potential risks and vulnerabilities in the supply chain. By leveraging historical patterns, weather conditions, social media data, and sensor information, AI can provide valuable insights for risk assessment. Predictive analytics powered by AI can accurately forecast potential disruptions, enabling companies to proactively address security threats and implement preventive measures. This not only minimizes the impact of risks but also improves the overall resilience of the supply chain.
24. Fraud detection and prevention
AI algorithms have proven to be highly effective in combating fraudulent activities within the supply chain. AI can analyze vast amounts of data related to transactions, invoices, and financial records, which allows it to detect fraudulent activities, such as counterfeit products, false documentation, and suspicious financial transactions. By leveraging AI, organizations can proactively prevent financial losses, safeguard their reputation, and protect consumers from counterfeit goods. Moreover, AI can help in maintaining the integrity of the supply chain by identifying potential vulnerabilities and implementing robust authentication and verification systems.
See how Visa uses AI to detect and prevent fraud in its payment network:
25. Blockchain-enabled security
The blockchain technology offers a range of benefits that can maximize security in the supply chain. It can be used as a robust framework for executing secure and trusted transactions, such as smart contracts, secure data sharing, counterfeit prevention, and simplified auditing. Leveraging blockchain and AI can certainly help you mitigate risks, protect against fraud, enhance consumer trust, and establish a resilient and secure supply chain.
The below video demonstrates a blockchain platform that uses AI to automate the execution of contracts and payments in the shipping industry, developed by Maersk and IBM:
Miscellaneous AI use cases
26. Quality control
AI has the potential to greatly enhance quality control in the supply chain by leveraging real-time monitoring, advanced data analysis and automation.
AI algorithms can analyze images, videos, and sensor data to automatically detect and classify defects or anomalies in products or components. By training AI models on large datasets of labeled defect images, the system can learn to identify even subtle variations and deviations from quality standards. This enables businesses to automate the inspection process, reducing the reliance on manual labor and improving the speed and accuracy of defect detection.
Furthermore, AI can even identify the root causes behind the detected issues. It analyzes data from various sources, including production records, customer complaints, and quality inspection results, to identify what contributes to quality deviations. This analysis enables businesses to address the underlying issues and implement corrective measures to prevent similar quality problems in the future.
Pepsi has been inspecting the quality of its products, including potato chips, for several years now:
27. Greater visibility
I’ve already explained how AI-powered analytics can offer real time-visibility into key business metrics and improve planning with various forecasts. In addition to those advantages, AI can be used to monitor supply chain processes and automatically identify exceptions or deviations from expected norms. This allows you to set rules and thresholds and AI will detect anomalies, such as unexpected delays, quality issues, or disruptions, and trigger alerts or notifications to relevant stakeholders. This enables timely intervention and resolution of issues, minimizing the impact on the supply chain and improving overall responsiveness.
Moreover, AI-powered platforms can enable enhanced collaboration and visibility for all supply chain stakeholders. They can serve as a central hub platform for sharing information, communicating updates, and collaborating on planning and execution. In that way, AI promotes transparency and visibility among suppliers, manufacturers, distributors, and customers.
AI has an immense potential to make the supply chain more sustainable by enabling smarter decision-making, optimizing operations and reducing carbon emissions. By leveraging AI technologies for demand forecasting, route optimization, energy management, supplier management, waste reduction, and predictive maintenance, businesses can reduce waste, minimize environmental impact, and promote sustainability throughout the supply chain. The integration of AI into the supply chain contributes to a more sustainable and resilient global economy, so businesses shouldn’t hesitate with adoption of this technology into their operations.
AI algorithms can optimize energy consumption by analyzing real-time data from sensors, smart meters, and other sources. It can also optimize lighting, temperature control, and equipment usage to minimize energy waste and promote energy efficiency in warehouses, manufacturing facilities, and transportation operations.
What is more, AI can analyze data on production processes, material usage, and waste to identify opportunities for waste reduction and recycling. It can also calculate and track the carbon footprint associated with various activities and processes. By evaluating the environmental impact of transportation, production, packaging, and other supply chain operations, AI helps businesses identify areas for improvement and implement sustainability measures. Add electric vehicles to this landscape and you can build a supply chain network that withstands the test of time and operates with the maximum respect for the environment.
See how Siemens is using AI to save energy at this salmon farm:
Can AI fix supply chain challenges?
Experts claim that AI won’t fix the issues faced by the supply chain, but it can definitely be improved when used wisely and in combination with other advanced technologies. Overall, AI is already revolutionizing the supply chain industry by providing new opportunities for optimization and efficiency.
As AI technologies continue to evolve, we can expect to see even more innovative solutions that will transform the way companies manage their supply chains. If you have an innovative idea for an AI-powered tool that could transform logistics or the supply chain, feel free to reach out to me at firstname.lastname@example.org and I'll connect you with our Artificial Intelligence team who will advise you on the best solution.