Did you know that, across industries, the highest cost benefit from AI is in supply chain management? According to a 2024 McKinsey Global Survey on AI, 46% of company leaders said that adopting generative AI reduced their costs and increased revenue by 10%–20%. An outcome like this isn’t just a glitch or temporary improvement; it is a result of fewer delays, greater visibility, and higher supply chain resilience that became possible due to artificial intelligence.
How can you achieve these savings? Where do supply chains and AI intersect? And what does AI even mean in the context of supply chain management? Read on to find the answers and learn how to use AI technologies like machine learning (ML) or generative AI from an experienced AI development services provider.
Understanding AI in supply chain management: Benefits for your business
Managing a supply chain can often feel like juggling too many balls at once, given inherent delays, inventory issues, and fluctuating demands, all creating stress. Fortunately, AI is here to help turn the chaos into streamlined processes with its three primary benefits for supply chain management and logistics.
Improves efficiency and productivity
AI can take operations to the next level and drive impressive results by making your business more efficient and productive. How? AI can automate processes to reduce the time and effort employees spend managing the supply chain. It can take over most manual tasks, such as data collecting, sending emails, and managing documents. Automating these tasks reduces human error and leaves employees with time for more strategic activities.
You can also use AI for supply chain optimization. For example, it can analyze large volumes of data to identify bottlenecks in your processes and make deliveries faster by planning the best routes.
Additionally, AI reduces human errors in stocking and delivering products, like putting the wrong label. This helps avoid redoing the same tasks and increases the efficiency of supply chain management.
Reduces operational costs
By automating repetitive tasks and improving accuracy, artificial intelligence in supply chains decreases operational costs. In fact, the successful implementation of AI for supply chain management helped businesses reduce logistic costs by 15% compared with competitors who didn’t use AI.
One way AI in supply chains can help with cost reduction is through tracking the maintenance of production equipment and delivery vehicles to alert managers when it’s time for a prophylactic check-up. Such preventive measures minimize downtime and costly repairs.
Another area of cost reduction relates to the human factor. Because AI reduces human errors, the cost of accident management also drops.
So, even though AI implementation is a costly investment, 70% of CEOs surveyed by Xometry said it produced a strong ROI for their supply chain operations.
Enhances decision-making
Artificial intelligence can help managers make informed decisions. By analyzing historical data and current internal and external variables that may affect your business, it can provide information about trends and insights that would otherwise go unnoticed. Let’s look at some examples.
AI can analyze impressive amounts of information from different data sources in real time, giving supply chain managers up-to-the-minute information such as stock levels, shipment statuses, and supplier performance.
By evaluating suppliers based on delivery times, quality, and cost, AI can recommend which suppliers to work with and which contracts to renew or terminate. This lets you make data-based decisions instead of randomly trying different suppliers who may underperform in critical areas.
On top of that, your business faces many potential risks from external factors such as geopolitical events, weather conditions, and market trends. AI can analyze these factors to identify imminent and long-term risks so you can make adjustments to prevent or minimize their negative consequences, such as supply chain disruptions.
AI systems can also analyze traffic patterns, considering weather conditions and fuel costs, to find the most efficient delivery routes. This speeds up deliveries and reduces transportation costs and environmental impact.
All this means you can stay ahead of the curve and make smarter decisions that benefit supply chain operations and your business in general.
As tempting as these benefits sound, it’s best not to implement AI in your business until you understand its nuances. So, let’s look into the AI technologies that deliver these benefits and how they work.
AI technologies used in supply chain management
ChatGPT may be the first thing that comes to mind when people hear artificial intelligence. They’re not wrong, but they aren't entirely correct, either. Artificial intelligence is a massive field in computer science, and ChatGPT, which is a type of generative AI, is only one of its technologies.
Companies that want to adopt artificial intelligence successfully should learn about the different AI technologies and how implementing specific types of AI in supply chain and logistics can help their business.
Machine learning
Machine learning is a subset of AI that processes and systemizes large amounts of data to provide businesses with insights and predict possible outcomes. It uses algorithms and data to imitate how people learn, gradually improving in accuracy. In supply chain management, machine learning can serve too many functions to list, so we’ll just mention a few:
- It offers predictive data analytics to help forecast demand, ensuring you maintain appropriate inventory and staffing levels.
- It identifies patterns and spots deviations to alert you of potential issues so you can prevent disruptions and increase supply chain resilience.
- It analyzes performance to identify bottlenecks in the supply chain so you can address them and improve your processes.
For example, the global shipping company UPS uses machine learning and AI to streamline package delivery. Their ML-based system predicts package volumes at different points in the supply chain and alerts managers about a possible delivery disruption, allowing them to allocate resources efficiently. In other words, UPS employees know exactly how many packages they need to reroute, distribute, and deliver tomorrow from different storage locations and have everything perfectly planned out to cut down on delays and keep their customers happy.
Robotic process automation (RPA)
Robotic process automation (RPA) is software that uses artificial intelligence systems to automate mundane and repetitive inventory management tasks. Basically, they mimic human-computer interaction for repetitive, rule-based tasks. Some of the ways RPA can help with supply chain management include the following:
- RPA speeds up order processing by automating data entry, integrating orders across channels, extracting information for invoices, sending confirmation emails, and generating reports.
- RPA can reduce administrative workloads by calculating, creating, sending, and tracking invoices much more efficiently than human workers.
- RPA bots systemize data with high accuracy, helping generate reports quickly with little human effort.
For example, UPS Europe uses an Automated Storage and Retrieval System (ASRS) called AutoStore in a Dutch fulfillment center to efficiently manage warehouse space and save employees from running around to store packages.
Because ASRS is connected to a vast physical storage system, it’s a little more complex than the simplest RPAs that perform only data-oriented tasks. But the principle is the same. Workers simply scan packages and put them on a transporter, and the ASRS does the rest automatically: it enters data, sorts packages, and delivers them to the correct storage bin. Systems like these can optimize the use of space to boost storage capacity by 400%.
Computer vision
Computer vision allows computers to interpret images and videos and make decisions based on what they’ve discovered. These systems improve quality control by analyzing images to ensure products meet specified standards. They can also monitor goods in transit, providing real-time data on their condition.
Additionally, computer-vision-guided robots navigate and operate in warehouses, optimizing storage and retrieval processes. For instance, in February 2020, Tyson Foods partnered with Amazon Web Services (AWS) to implement computer vision technology at its chicken plants for better warehouse management. They developed a chicken tray counting solution that provides real-time quantity and quality data in their production and packaging plants. The solution provides immediate feedback to employees on overproduction or underproduction, which is crucial — Tyson processes 45 million chickens per week, and chicken has a short shelf life.
Natural language processing (NLP)
Natural language processing (NLP) allows machines to understand language and communicate in a human-like manner. In supply chain management, NLP has several applications:
- AI chatbots can respond to and fulfill customer requests 24/7, improving response times and satisfaction.
- NLP can quickly read through and organize large volumes of documents, saving employees’ valuable time.
- NLP can analyze text from various sources of unstructured data to provide businesses with valuable insights.
AI tools like Google’s Video AI, which uses NLP, among other AI technologies, can process text, images, and videos from customer reviews and social media posts to create a real-time, comprehensive supply chain dashboard. This dashboard can send alerts for unusual customer demand changes due to competition, product issues, or other reasons.
Generative AI
Generative AI models are trained using unlabeled and unstructured data from sources worldwide, which gives them more extensive knowledge than technologies limited to labeled data. It not only learns to understand varied human language but can also generate language as well as humans.
Businesses can use Gen AI capabilities to create solutions specifically for their needs by fine-tuning AI models with their own data and creating effective prompts. For supply chain management, this means:
- Automatically generating content for the customer-facing side of supply chain management (e.g., catalogs)
- Summarizing large documents and only reading the key points to save time
- Generating instant, human-sounding responses to employee or customer queries
Altana, an AI startup, uses generative AI to combine public and private data to create dynamic maps of global supply chains. It has an assistant powered by a large language model (LLM) that answers employees' questions. Additionally, by capturing, analyzing, and sharing documents like invoices, bills, and purchase orders, Altana improves communication among supply chain partners, boosting efficiency and accuracy in logistics.
AI technology truly shines as part of narrowly focused supply chain solutions. Let’s look at how some apps work, starting with those directly impacting customer experience.
AI for customer experience in the supply chain
AI and supply chain management work great together, and AI can improve customer experience in supply chain management in quite a few ways.
GenAI-powered chatbots and virtual assistants
GenAI-powered chatbots and virtual assistants can understand language, including interpreting context and sentiment, and generate responses to customer questions. These chatbots provide 24/7 customer support, help with order tracking, answer common questions, and guide customers through the buying process.
For instance, tools like Zendesk and Drift use AI chatbots to provide fast replies and solve basic issues, making life easier for customers and businesses. However, GenAI-powered chatbots can also be used as virtual assistants for employees.
Predictive analytics for inventory management
As we already know, machine-learning-based predictive data analytics allow businesses to forecast customer demand and plan inventory and logistics accordingly. A manager only needs to confirm that predictions seem accurate and proceed with restocking tasks, such as contacting suppliers to order products.
If the ordering system is already fine-tuned, AI technologies can simplify the ordering process, too: Generative AI can write emails with requests to suppliers, and RPA can create reports and organize newly created documents.
IBM Supply Chain, for example, offers AI solutions that use predictive analytics to help businesses manage their inventory, ensuring they have the right products in the correct quantity at the right time.
Proactive customer service
AI can evaluate customer reactions to specific issues and predict problems before they happen. In the supply chain, this means informing customers about potential delivery delays, suggesting substitute products if their product choice is out of stock, or reminding them that a purchased product needs additional items, like batteries.
Salesforce’s Einstein AI tool can predict potential customer service issues, such as delivery delays, and suggest solutions even before a customer reaches out. Their new generative AI-based tool, Einstein Copilot, lets employees talk to a virtual assistant that can answer their questions using data from a company's CRM and general business knowledge.
Enhanced return and refund processes
AI can speed up and smooth out the return and refund processes by automatically handling return requests, issuing refunds, and returning packages in stock. A simple RPA solution can handle most of this work, and GenAI or a rule-based bot can step in when the return and refund process requires communication with customers.
Amazon, for example, uses AI to automate these processes. Moreover, the company is implementing an AI-powered model called Project P.I. (Private Investigator) that helps detect product defects and return them to sellers before they are shipped to customers. Computer vision plays a central role here as it analyzes products to determine signs of damage.
Supply chain equipment management
AI paired with IoT (Internet of Things) lets employees monitor equipment state and performance constantly or on demand. IoT sensors send data about equipment performance and warehouse conditions. AI then analyzes the data and gives managers insights to understand if conditions should be adjusted to prevent equipment failure. For example, low temperatures can reduce battery efficiency and lifespan in warehouses' barcode scanners and handheld devices. This isn’t something you can easily discover without sensors and AI in supply chains.
Software like the IBM Watson IoT platform can help manage elevators and conveyors used in warehouses. KONE company is proof, even though it uses the IBM Watson IoT Platform not for supply management purposes.
KONE applied IBM’s solution to connect and remotely monitor its elevators, escalators, doors, and turnstiles worldwide, predicting equipment issues before they cause downtime. The system constantly learns from vast amounts of sensor data, allowing real-time responses and remote management. This helps technicians prevent equipment failure and access the data required to quickly fix problems that occur.
AI for supply chain document management
AI-powered tools can automatically pull data from documents like invoices, purchase orders, and shipping lists. They can also extract important details from incoming documents, reducing the tedious job of entering data by hand.
Additionally, AI in supply chain management improves the efficiency of searching documents. Because it understands content, context, and sentiment, it can help to quickly locate specific information within hundreds of folders.
Apps like Rossum and DataRobot can quickly extract data from various documents, and a tool like ABBYY FlexiCapture ensures your documents are neatly sorted and easy to find. Zapier and ProcessMaker use AI to handle workflows, making sure everything moves smoothly and efficiently. All these apps can work together with advanced software that combines different AI technologies, like Salesforce Einstein Copilot.
One thing is clear: Combining AI technologies and applying them at every stage of supply chain management makes the entire chain more efficient and keeps customers and employees happy.
Conclusion
Supply chains and AI work perfectly together. Artificial intelligence automates work to reduce manual labor and brings an enormous level of efficiency to supply chain management. Combining multiple AI technologies and tools optimizes processes, improves demand forecasting, and provides predictive analytics that guides decision-making. With AI in supply chains, managers can proactively improve customer service, equipment maintenance, and more. All of this boosts revenue and profits without increasing employees’ workloads.
Using artificial intelligence in supply chain management isn't just a smart move — it's a game changer. So don’t wait — contact ElifTech for AI development services and start improving your supply chain management.