AI in Supply Chain Management: Impact, Use Cases, Application, and Insights from Our Team

TL;DR 

• 60% of supply chain professionals plan to increase their investments in AI to enhance operational efficiency.
• Our team has developed an AI-driven supply chain solution that achieved a 30% reduction in stockouts, 20% lower inventory costs, and 25% faster order fulfillment. It facilitated a 15% year-over-year sales growth, driving improved efficiency and customer satisfaction.
• AI is applied in SCM across procurement, inventory management, logistics and transportation, warehouse management, sales, customer service, and risk management.
• Amazon’s AI-driven demand forecasting system accurately predicted daily demand for over 400 million products during Cyber Monday, reducing excess inventory by 20% and improving product availability by 15%. 
• Pfizer optimized its supply chain with AI, reducing cycle times by 67% and producing 20,000 extra doses per batch while achieving a 99.999% delivery success rate for vaccines.
• AI is set to transform global supply chains by enabling smarter decision-making, autonomous planning, and sustainability. It will optimize operations, reduce costs, and address challenges like CO2 emissions through real-time data analysis and AI-driven forecasting.

Real-Life Case from Our Team

Here is one of the recent examples of artificial intelligence in supply chain management. A global manufacturer approached us with pressing challenges that were severely impacting their operations. They faced frequent stockouts, excess inventory, inefficient restocking processes, and lacked real-time visibility across their warehouses. These issues were causing delays, increasing costs, and affecting customer satisfaction. 

Challenges

• Inaccurate Demand Forecasting. Traditional methods, heavily reliant on historical data, failed to account for rapidly changing market conditions, consumer preferences, and external factors like economic shifts or competitive actions. This resulted in frequent stockouts during peak seasons and excess inventory during off-peak periods, leading to lost sales and unnecessary holding costs.
• Inefficient Inventory Management. Disconnected systems and outdated processes made tracking inventory across multiple locations difficult. Delays in restocking and inaccurate stock levels tied up capital, while poor visibility created operational bottlenecks.
• Manual Processes. Manual inventory management tasks, such as stock counts, were prone to human error. These inefficiencies often led to discrepancies between actual and recorded inventory, complicating supply chain operations further.
• Lack of Real-Time Insights. With no real-time visibility into inventory across warehouses, the client couldn’t make timely decisions to optimize distribution or respond to changes in demand.

Our Solution

To turn things around, we introduced an AI-driven supply chain optimization platform designed to tackle their pain points directly: 

• Predictive Analytics. Machine learning algorithms  dug into their historical sales data, seasonal trends, and external factors like economic shifts. As a result, demand forecasts are accurate and proactive, preventing both stockouts and excess stock..
• Dynamic Inventory Management. We rolled out a cloud-based, real-time tracking system that allowed the client to monitor inventory levels across all their warehouses, adjusting automatically to meet demand. No more delays in restocking, no more guesswork.
• User-Friendly Dashboard. This centralized tool allowed stakeholders to see, at a glance, everything from inventory levels to demand forecasts to overall performance. The real-time insights empowered them to make informed decisions, quickly pivoting when needed to optimize the supply chain.

Key Features

• Machine Learning Models. Improved demand forecasting accuracy by up to 50%, significantly reducing stockouts and excess inventory.
• Real-Time Data Processing. Leveraged cloud technology for immediate updates on inventory and sales trends.
• Automated Reordering System. Enabled automatic stock replenishment, eliminating the need for manual intervention.
• Enhanced Supplier Collaboration Tools. Streamlined communication with suppliers, optimizing procurement processes.

Business Outcomes

Our solution delivered transformative results:

• Stockouts Reduced by 30%. 
• Inventory Holding Costs Lowered by 20%. 
• Order Fulfillment Speed Increased by 25%. 
• 15% Year-over-Year Sales Growth. 

Ready to take your supply chain operations to the next level? Contact us to learn how we can help optimize your supply chain and elevate your business performance.

What Are The Applications of AI in SCM Across Different Departments? 

How can AI be applied to supply chain activities across departments? Supply chain management isn’t just about logistics—it’s a complex network involving procurement specialists, vendors, warehouse managers, and more. Each department is crucial in ensuring smooth operations. AI is transforming how these interconnected functions collaborate and perform. 

“With so many people involved, even a small human error can have a huge cost. The supply chain is a complex, multi-layered process that requires a truly integrated approach—every detail, from planning to execution, must be spot-on. That’s where AI steps in. It’s the perfect tool to not only streamline operations but also minimize costly mistakes, helping you run a smoother, more efficient business,”

notes our COO, Nazar Kvartalnyi. 

Let’s explore how viable AI solutions enhance efficiency across this intricate web.

In Procurement

Procurement teams face the constant challenge of making decisions that balance cost, quality, and reliability. AI has become a great tool in this space by streamlining how businesses choose suppliers. Instead of manually sifting through endless data, AI quickly analyzes factors like pricing, delivery consistency, and product quality, guiding managers to the best options based on past performance. 

When it comes to contract review, AI acts as a safeguard, scanning documents for compliance and flagging potential issues before they become problems. Like an extra set of eyes, it ensures that the terms align with agreements, reducing the risk of costly oversights. 

Moreover, negotiation becomes more informed with AI’s ability to analyze trends in pricing and supplier terms. By understanding market shifts and identifying favorable conditions, AI helps procurement managers make smarter choices. 

In Inventory Management

AI has made inventory management more efficient by using data to predict demand. By analyzing past sales and market trends, it’s now possible to forecast which products will be in high demand at certain times. For instance, you can recognize the seasonal increase in toy sales each December and adjust inventory levels ahead of time to make sure there’s enough stock for the holiday rush.

AI also simplifies the reordering process. Instead of relying on manual checks, such systems continuously monitor inventory and automatically place orders when stock is low. 

With real-time tracking, you can also gain immediate visibility into your inventory—track stock levels at all times and react swiftly to demand shifts. This helps prevent stockouts and ensures the right products are always available when customers need them.

Read about IoT for smarter supply chain management and logistics in our blog

In Logistics and Transportation

AI improves your logistics operations by making delivery processes more efficient. It optimizes routes by analyzing traffic, weather, and other factors to determine the fastest and most cost-effective paths. This helps avoid delays, like rerouting a delivery truck around a traffic jam, ensuring timely arrivals and reduced operational costs.

Predictive maintenance is another way AI can help you. For example, your company can first collect and analyze data from vehicle sensors and then use it to forecast when maintenance is needed, preventing unexpected breakdowns. 

Find out more about logistics management software and app development process in our blog. 

In Warehouse Management

Quality control in warehouses is one more way AI can be used in complex supply chain management operations. For example, smart cameras can inspect products for defects. This way, you can ensure your items are in perfect condition during the packing stage, minimizing returns and maintaining high customer satisfaction.

Another area where AI adds value is space optimization. By studying order patterns and inventory levels, advanced systems now can determine the best way to organize products in the warehouse. Items that are ordered more frequently can be placed closer to packing stations, reducing the time it takes to gather them and improving the overall picking process.

 In Sales and Customer Service

In supply chain management, one of the key challenges is accurately forecasting demand, and that’s where AI can truly make a difference. By analyzing past customer buying behavior and global trends, these advanced systems help you predict which products will be in demand. For example, if you know winter clothing is popular in November, AI can advise you to stock up in advance, so you’re always prepared for the seasonal rush. 

Your sales team can also benefit from AI by understanding customer purchasing patterns. With this information, you can tailor your offers to meet the specific needs of different segments. For example, you can offer exclusive discounts to loyal customers based on their buying history, rather than just guessing what they might want. 

In Risk Management 

AI empowers your supply chain by helping you stay ahead of any potential risks. Through continuous risk analysis, it keeps tabs on supplier performance, market trends, and external factors that may disrupt your supply chain. Suppose a certain supplier is delaying the production process or there is going to be a price hike. In that case, AI flags it off early so you can rethink your strategy before things spiral out of control.

AI can also help your company with monitoring in real life. Moreover, this means gaining a complete view of your operations across the whole supply chain. AI offers continuous oversight to make sure any disruption is identified as soon as it happens. That’s a proactive approach, giving you the chance to take corrective action quickly and stop a small problem from growing into a larger one that might disturb your business.

“When you’re deeply involved in your daily operations, it’s easy to miss the bottlenecks or areas that need improvement. That’s where an outside perspective becomes essential. If you’re considering an AI solution to optimize your supply chain, it’s not just about finding a partner who can implement the technology—it’s about finding someone who takes the time to understand your business, its challenges, and how to truly make a difference. Following this approach, you’re setting your project up for success,”

highlights our Tech Team Lead. 

Ready to integrate AI into your supply chain operations? Contact us to discover how we can help your business thrive.

14 Use Cases for Solving Real Supply Chain Management Challenges with AI

How does AI affect supply chain performance? The past few years have been a wake-up call for supply chain managers. Disruptive events like the pandemic and the Suez Canal blockage have highlighted vulnerabilities in traditional systems. Companies that were prepared with robust forecasting and analytics weathered these obstacles far better than those relying on manual methods. 

“Remember the pandemic, when online orders surged, and many companies found themselves scrambling because they weren’t prepared with enough stock? Or the Suez Canal blockage by Ever Given, which disrupted shipping routes and forced businesses to rethink their plans? These events highlighted one thing: the power of forecasting and real-time analytics. With AI, they are now within reach,”

acknowledges Maksym Trostyanchuk, our Head of Delivery.

Let’s look at the top AI in supply chain use cases.

Demand Forecasting

AI-driven demand forecasting allows you to make accurate predictions based on historical and real-time data. For example, we worked with a vintage toy store that faced challenges predicting which item would trend during certain seasons. This led to frequent stockouts of popular SKUs and overstock of less-desired products. 

By leveraging AI, the store analyzed historical sales data and market trends to predict demand accurately. The system managed inventory levels automatically, ensuring bestsellers were available while minimizing excess inventory.

Contact us to explore AI-powered demand forecasting solutions for your business.

Inventory Optimization

AI helps maintain optimal inventory levels, automatically reordering products when stock levels reach a certain threshold. It minimizes the risks of both shortages and excess stock.

“For example, a company’s inventory system isn’t updated, and they run out of stock. The customer, unaware of the shortage, places an order for a product that’s not available. The manager has to call to explain the situation, leaving the customer frustrated. In the end, the company not only loses out on the sale but also risks losing the trust and loyalty of that customer. This is a problem that could easily snowball, but with the right tools in place, you can avoid this costly scenario,”

explains Maksym Trostyanchuk. 

Warehouse Automation

Robots equipped with AI automate warehouse tasks like picking, packing, and shelving, reducing errors and increasing speed. For example, Amazon uses robotic systems to improve warehouse efficiency. These robots scan shipments, move shelves to human workers, and pack items into boxes, seamlessly integrating with human associates to handle repetitive tasks. 

This collaboration allows human workers to focus on more complex responsibilities like quality control or handling customer-specific requests. The automation has increased productivity by 50% and streamlined the just-in-time inventory management approach, which minimizes holding costs, reduces labor expenses, and decreases the likelihood of overstocking or understocking. 

Additionally, by expanding its robotic fleet to over 750,000 units by 2023, Amazon has optimized operations and achieved significant savings in operational costs.

Route Optimization for Delivery

AI-powered systems optimize delivery routes for efficiency, cost savings, and environmental impact. For instance, UPS uses ORION (On-Road Integrated Optimization and Navigation) software to plan routes dynamically, saving 100 million miles and 10 million gallons of fuel annually. ORION’s advanced algorithms analyze over 250 million address data points daily, creating the most efficient paths for drivers while considering variables like package volume, delivery windows, traffic, and weather. 

The system’s ability to make real-time dynamic adjustments has cut delivery times by up to 8%, reducing delays and carbon emissions by over 100,000 metric tons each year. These optimizations translate to annual savings of approximately $200 million and a significant boost to operational efficiency.

Juan Perez, the former UPS Chief Information and Engineering Officer, said:

“ORION is purpose-built for local UPS delivery drivers, many of whom complete an average of 135 stops each day. “The new Dynamic Optimization component will improve the accuracy of UPS delivery time estimates and give our customers better visibility into their shipments.”

Supplier Management

AI systems analyze supplier performance, enabling informed decision-making. We worked with a global manufacturer to address supplier delays and quality issues. Using AI, we implemented a performance analysis system that assigned supplier scores based on metrics like delivery times and quality. It resulted in better contract negotiations and fewer disruptions.

Contact us to optimize your supplier network.

Risk Prediction

AI monitors risks like natural disasters and geopolitical events that could disrupt supply chains. This way, companies can use AI to receive timely alerts about possible supply disruptions.

“Let’s remember Hurricane Harvey in 2017. It devastated Texas, causing massive disruptions to supply chains. Companies, from automakers to electronics suppliers, were hit hard by flooding and port closures. Now, imagine if we had today’s AI technology back then. With the right system, it could have analyzed weather data and predicted the storm, giving businesses the foresight to prepare and minimize the damage,”

explains our COO, Nazar Kvartalnyi. 

Discover AI use cases in cybersecurity in our blog

Quality Improvement

AI-powered systems inspect products for defects with unmatched precision. It helps reduce the number of defective items and improve overall product quality. Nazar notes:

“In the production of electronics, automotive parts, or consumer goods, manual inspections can be time-consuming and prone to mistakes. A missed defect could mean a faulty product reaching your customers. To overcome this challenge, companies are now using AI-powered systems with cameras and computer vision to automatically inspect products, ensuring that only the highest-quality items make it to market. With AI, you can have peace of mind knowing that every product meets the standard, and your customers receive nothing but the best.”

Logistics Management

Efficient logistics systems are essential to reducing delays. We partnered with a logistics company struggling to manage its transportation networks, which resulted in frequent delays and inefficiencies. To overcome these challenges, we developed and deployed an AI-powered logistics management system capable of analyzing real-time transportation data to optimize routes and forecast potential disruptions. 

Leveraging machine learning algorithms, the system identified patterns in traffic, weather conditions, and other critical factors. It enabled automatic rerouting of shipments to minimize delays and ensure timely deliveries.

Reach out to discuss smarter logistics management. 

Customer Data Analysis

AI can analyze customer purchasing habits and preferences to create detailed segments, allowing businesses to tailor marketing strategies and offer personalized promotions. By understanding customer behavior at a granular level, you can improve campaign effectiveness and boost customer loyalty.

“AI’s ability to process vast amounts of customer data in real time means businesses can move from generic marketing to highly targeted strategies that resonate with individual consumers, maximizing engagement and conversions,”

notes our marketing manager.

Invoice Processing Automation

AI-powered systems can extract data from invoices in seconds, significantly reducing human errors and expediting payment processing. This automation saves time and enhances accuracy and compliance with financial regulations. As our Tech expert says:

“Invoice automation isn’t just about speed; it’s about precision. By eliminating manual input, businesses can reduce costly mistakes and ensure seamless cash flow management.“

Customer Relationship Management

We collaborated with an e-commerce client struggling to manage a high volume of customer inquiries. This has resulted in long wait times and declining customer satisfaction. To resolve this, we implemented an AI-driven chatbot system capable of handling inquiries, providing order updates, and assisting with returns. 

The chatbot, integrated with the client’s CRM and order management systems, leveraged natural language processing (NLP) to understand and respond accurately to customer queries. As a result, it reduced response times and improved overall customer satisfaction rates.

Looking to enhance your customer experience with AI-powered solutions? Contact us today to discuss how we can help.

Pricing Optimization

We worked with a retail client struggling to set optimal product prices, resulting in inconsistent sales and profit margins. To tackle this, we implemented an AI-driven pricing optimization system. The solution analyzed market trends, customer behavior, and historical sales data to identify the most effective price points.

Using machine learning algorithms, the system dynamically adjusted pricing based on competitor rates, seasonal demand, and purchasing patterns. This approach increased sales consistency and improved profit margins.

Streamlining Customs Clearance

AI can revolutionize customs clearance by automating compliance checks and reducing border delays. These systems ensure that all regulations are met, minimizing errors and expediting shipments.

“Navigating customs clearance can be a daunting task, with a web of rules to follow. For instance, the Union Customs Code requires precise classification of goods under the Harmonized System. A single mistake could lead to costly delays, fines, or even legal issues. And with the CBP demanding accurate documentation for all imports, the stakes are high. AI-powered systems can automate compliance checks and reduce the risk of human error.”

— Maksym Trostyanchuk, Inoxoft’s Head of Delivery

Reducing Carbon Footprint 

Companies can harness AI to optimize operations and lower carbon emissions, such as minimizing vehicle travel distances. Let’s look at the Maersk case.

Maersk’s Captain Peter AI assistant enhances the tracking and management of refrigerated containers, indirectly contributing to lower carbon footprints:

• Energy Efficiency. Real-time condition monitoring helps adjust container temperatures promptly, avoiding unnecessary energy consumption.
• Predictive Maintenance. Early identification of mechanical issues reduces emergency repairs and fuel use.
• Supply Chain Optimization. Data integration into logistics systems improves routing and scheduling, minimizing fuel consumption.
• Waste Reduction. By preventing cargo damage, the need for redundant shipments decreases, lowering emissions and resource use.
• Enhanced Visibility. Captain Peter offers real-time updates on container conditions, allowing Maersk to identify inefficiencies early and take corrective actions without additional energy expenditure.
• Cost and Emissions Savings. By reducing downtime and avoiding unnecessary shipments, Maersk cuts operational costs and contributes to a reduction in its overall carbon footprint, reinforcing its commitment to sustainable logistics practices.

“Damage to goods can cost a good deal of money, especially considering 30% to 50% of ocean cargo is uninsured. Captain Peter will help in identifying and addressing issues promptly, potentially saving the customer millions of dollars in lost cargo,”

reveals Anne-Sophie Zerlang Karlsen, Head of Global Reefer Management at Maersk. 

Other Notable Examples

Let’s also explore several other examples of companies using artificial intelligence in supply chain management for increased efficiency, cost savings, and better customer experiences.

How Amazon Used AI System to Forecast Demand for 400+ Million Products During Cyber Monday

Amazon’s AI-driven demand forecasting system analyzes vast data, including historical sales, social media trends, and economic factors, to predict customer demand. For instance, during the 2023 Cyber Monday, Amazon’s AI system accurately forecasted daily demand for over 400 million products. It enabled strategic inventory positioning across its global network. 

This resulted in a 20% reduction in excess inventory and a 15% improvement in product availability, leading to cost savings and enhanced customer satisfaction. Watch this video to see how Amazon’s AI system works in action.

How Pfizer Optimized Its Supply Chain with AI: A 67% Cycle Time Reduction and 20,000 Extra Doses Per Batch

Pfizer leveraged AI and machine learning to forecast demand accurately for critical medications, including during the COVID-19 pandemic. By partnering with Controlant, Pfizer introduced a real-time tracking system for vaccine shipments, ensuring compliance with temperature and location requirements. It resulted in a 99.999% delivery success rate. 

AI also optimized the manufacturing of the PAXLOVID medication, reducing cycle times by 67%, which allowed the company to produce 20,000 extra doses per batch. This predictive approach helped manage supply chain complexities. It ensured vaccines and treatments reached their destinations under the required conditions, preventing stockouts.

Read more about top machine learning companies in our blog

How Will AI Reinvent The Way Global Supply Chains Operate 

Artificial intelligence in the supply chain market is poised to foster seamless communication, enabling smarter decision-making and driving sustainability. The integration of AI-driven forecasting, real-time data analysis, and autonomous systems will optimize operations, reduce costs, and address global challenges. 

“Imagine a common shared language starting to emerge between airlines, various providers, and freight forwarders. You begin to have the ability to put the pieces together, creating a smart supply chain that works fluently, in unison. This is what I wanted to show you today—the fact that something completely hidden today, the very core of the global economy, is undergoing a profound transformation. The changes we see, whether ecological or geopolitical, can also be driven by a major shift I see from my perspective—the technological shift. All the advancements in AI, forecasting, and the tools we’re developing can be integrated for the greater good, benefiting society by reducing CO2 emissions while optimizing the supply chains we rely on every day,”

notes Thomas Larrieu, Supply Chain Management Expert and CEO of Upply.

So, how can AI be helpful for the future supply chain? 

Autonomous Supply Chain Planning

Currently, many industries rely on traditional, manual-heavy Sales and Operations Planning (S&OP), which involves significant human intervention and is prone to inefficiencies. However, autonomous supply chain planning transforms this approach with AI and machine learning, enabling fully automated and real-time processes. 

These systems integrate data from various sources—internal, external, and customer—allowing for seamless decision-making across the entire supply chain. For example, machine learning models can generate SKU-level forecasts up to 12% more accurately. Moreover, dynamic production scheduling can respond to real-time demand, reducing delays and improving efficiency.

AI-Enabled Last-Mile Delivery

The traditional last-mile delivery model is costly, relying on human labor and high fuel and maintenance expenses. AI-enabled autonomous vehicles and drones are set to revolutionize last-mile delivery by significantly reducing these costs. 

Last-mile delivery solutions powered by AI will eliminate the need for human drivers while cutting down on fuel and maintenance expenses, particularly when using electric or hybrid-powered vehicles. This transformation will reduce operational costs and contribute to greener logistics by lowering carbon emissions.

Sustainability Optimization

Freight transportation is responsible for approximately 8% of global greenhouse gas emissions, with warehouses and ports pushing this figure to 11%. AI-powered solutions can play a key role in reducing these emissions by up to 10% through optimized transportation routes and enhanced fuel efficiency. 

Moreover, AI can help companies transition to renewable energy sources, further driving sustainability efforts. By integrating AI into logistics, you can optimize their supply chains for both cost savings and environmental impact.

Find out the future trends in data science in our blog

Make Your Supply Chain Smarter with Us

We provide custom logistics software solutions tailored to optimize and streamline your supply chain operations. With over 10 years of experience and a team of 170+ experts, we deliver cutting-edge technology that solves your unique needs. Whether you’re managing fleets, optimizing routes, or integrating AI-driven solutions for better decision-making, our team can help you improve efficiency and reduce costs across the entire logistics ecosystem.

Working with top-tier clients such as Toshiba, Nivea, and Vestel, we leverage our expertise in transportation, warehousing, and manufacturing. Our team is committed to delivering seamless, scalable solutions that integrate with your existing systems and evolve with your business needs.

Talk to our experts and find out how our custom logistics solutions can drive your business forward.

Conclusion

AI in supply chain management transforms the sector by addressing inefficiencies, improving decision-making, and enabling better adaptability to disruptions. With 60% of supply chain professionals planning to increase AI investments for optimization, this advancement is being utilized across departments to streamline processes and reduce costs. 

We are an award-winning software development company specializing in creating innovative digital solutions for startups and small to medium-sized businesses. Our team combines expertise in logistics with advanced AI solutions, offering practical insights and strategies to help businesses improve their supply chain efficiency and resilience.

Contact us to redefine your supply chain management with AI-driven solutions.