AI solutions in supply chain are a game-changer for forward-thinking businesses, with broad applications that can transform how products are sourced, manufactured, and delivered.
Picture a world where warehouses regulate stock levels automatically in line with demand. Where shipments can be rerouted in real-time based on changes in weather or traffic. Where suppliers can be vetted, assessed for risk, and onboarded in a matter of seconds.
These use cases would have seemed like science fiction a decade ago. But today, AI is turning supply chain management into an interconnected, smart, automated process. No wonder the AI supply chain market is set to boom in the coming years, reaching over $157 billion by 2033, with a CAGR of 42%.
As the trusted AI development partner to businesses worldwide, we’ve seen firsthand the transformative impact AI solutions are having on virtually all aspects of supply chain management. In this article, we’ll explore some real-world examples of companies successfully using AI in their supply chains to deliver operational efficiency at scale.
13 Examples of companies successfully using AI in their supply chains
One of the reasons AI is such a transformative technology is its adaptability. It’s not the equivalent of a digital hammer, but rather a versatile toolkit that’s capable of learning, evolving, and improving. As such, it can be applied to an ever-increasing range of business tasks and processes — and the supply chain is no different.
The use of AI in supply chain management isn’t just a gimmick. According to a McKinsey report, AI-driven forecasting can reduce supply chain errors by up to 50%. And that’s just scratching the surface.
Below, we’ll look at 13 examples of companies successfully using AI in their supply chains. These use cases highlight the broad applications of AI for supply chain performance and optimization.
1. Amazon — customer demand forecasting
Demand forecasting is one of the most powerful examples of AI in supply chain. It enables businesses to leverage ML algorithms that analyze customer data, market trends, and other external factors that might impact sales of certain products. The algorithm then provides accurate forecasts for future demand.
Amazon, the world’s largest retailer, uses AI-driven demand forecasting to ensure that warehouse stock levels are optimized to meet future spikes or dips in product popularity. It achieves this across more than 400 million products with minimal human input. Amazon also uses AI to automatically reorder products that are low in stock or in high demand.
2. Walmart — optimized driver routing
Walmart has been a pioneer in retail AI adoption for years. In addition to offering AI-powered product recommendations, the retail giant has developed a proprietary AI/ML logistics solution called Route Optimization. The software optimizes driving routes in real time, maximizes packing space, and reduces miles driven to a minimum.
In addition to using this technology itself, Walmart has made it available to other businesses. Using Route Optimization, Walmart has been able to eliminate 30 million driver miles from its routes, saving 94 million pounds of CO2 in the process.
Route optimization
Source: Walmart
This use case isn’t just limited to delivery driving, however. At Intellias, we helped a multinational freight forwarding and logistics company build an intuitive platform that enables optimized routing, pricing, dates, and transport modes across the entire supply chain.
3. GXO — automated inventory counting
When coupled with computer vision, AI-powered software can transform inventory counts from a laborious, resource-heavy process to a rapid, automated one. Businesses are now able to deploy AI tools arme d with cameras and sensors to take snapshots of goods. AI algorithms then analyze the data to confirm whether the physical stock corresponds with the recorded stock.
Logistics provider GXO was one of the first companies to implement AI-powered inventory counting. Its system can scan up to 10,000 pallets per hour, generating real-time inventory counts and insights.
Source: Logistiek
4. JD Logistics — warehouse space optimization
In addition to counting inventory, AI solutions in supply chain enable businesses to optimize warehouse space. ML algorithms analyze the demand for different goods, as well as their dimensions and weights. Using this data, the system then recommends the optimal placement of goods to maximize space and speed up the pick-and-pack process.
For instance, JD Logistics has opened several “self-operating warehouses” that use AI-driven supply chain technology to determine the optimal location for goods. This application of AI in supply chain management has helped JD Logistics increase the number of available storage units from 10,000 to 35,000, boosting operational efficiency by 300%.
5. FIH Mobile — AI-powered quality inspections
A single damaged or defective product can cause manufacturers all manner of problems down the line. But with modern factories churning out huge numbers of new items, manual quality control checks are unreliable and time-consuming. In fact, poor quality control is estimated to cost businesses as much as 20% of annual sales revenue.
AI is proving a game-changer here. Combining computer vision technologies and AI models, businesses can now inspect products — whether automobiles, smartphones, or semiconductor chips — accurately, rapidly, and at scale. FIH Mobile, for example, has deployed Google’s Visual Inspection AI technology to automate its quality inspection process and improve operational efficiency.
6. Ocado — automated picking and packing
Many businesses are now relying on AI-powered robots to automate the picking and packing of consumer goods. In fact, Gartner predicts that more than 75% of large enterprises will use industrial robots in their warehouses by 2026.
Ocado, a British online-only grocery retailer, uses AI-powered robotic arms that can handle and pack a vast range of food items with accuracy, speed, and care. This has enabled Ocado to realize significant efficiency gains, with a 50-item order completed in just a few minutes. At the same time, warehouse staff can be redeployed to more high-value, strategic roles.
Source: Ocado Group
7. Lineage Logistics — cold-chain optimization
When it comes to storing and distributing perishable goods that must be kept at a certain temperature, effective supply chain management becomes an even more complex challenge. This is where so-called cold-chain optimization can be a game-changer.
Lineage Logistics, for example, uses an AI algorithm to ensure that food arrives at its destination at the right temperature. The algorithm forecasts when certain orders will arrive or leave a warehouse. This enables warehouse operatives to prepare by positioning pallets effectively. This use of AI in supply chain has enabled Lineage Logistics to boost operational efficiency by 20%.
Source: Lineage
8. FedEx — real-time vehicle tracking
Real-time vehicle tracking is another powerful use of AI in supply chain management. By fitting fleet vehicles with IoT devices and GPS tracking, companies can gain clear visibility into the location of trucks, as well as the temperature and condition of shipments. This data can then be fed into AI systems that provide real-time insights and alerts about traffic conditions and optimal routes.
One of the world’s largest delivery companies, FedEx has been quick to implement AI-powered vehicle tracking. Its FedEx Surround platform provides real-time visibility into its extensive transportation network. It also offers predictive delay alerts, prioritizes critical shipments, and actively intervenes to ensure that shipments get delivered as quickly as possible.
At Intellias, we help businesses improve supply chain visibility through smart, digital solutions. For instance, we worked with a Fortune 500 customer to develop a fleet truck tracking system that captures data through IoT devices and can determine the location of a stolen vehicle. As a result, our client was able to:
- Reduce costs and CO2 emissions by cutting fuel consumption
- Increase productivity through optimized routing
- Prevent fraud through geofencing and real-time mileage and fuel monitoring
9. Microsoft — predicting manufacturing bottlenecks
The smooth production of goods relies heavily on the availability of raw materials. When raw materials are harder to access, due to environmental or political factors, production can be delayed significantly or even grind to a halt. With predictive AI systems, companies can forecast shortages years in advance — and put plans in place to avoid costly bottlenecks.
Microsoft, for example, has integrated their Copilot AI system into its Dynamics 365 Supply Chain Management platform. This gives businesses the ability to implement AI-powered material resource planning (MRP) that responds to demand and other external factors in real time.
10. Maersk — AI-powered supplier negotiations
Shipping companies often work with a huge number of suppliers and vendors to deliver goods worldwide. Negotiating supplier agreements manually can be time-consuming, making the process difficult to scale. But with AI, all that is changing.
Maersk, one of the largest global shipping companies, is now using AI to automate supplier negotiations. The logistics giant uses an AI chat interface combining natural language processing (NLP), generative AI, and data analytics to enter human-like negotiations. As a result, Maersk can get deals signed faster and at scale.
11. Lenovo — AI-powered vendor risk assessments
Large enterprises might work with thousands of separate vendors across their supply chain. If one of those vendors turns out to be unreliable, this can have ripple effects across the entire supply chain. According to IBM, 87% of chief supply chain officers say it’s complicated to foresee and proactively manage risks.
With AI-powered predictive analytics, enterprises can analyze vendor data to understand and forecast future risk factors. Global electronics manufacturer Lenovo, for example, uses AI to predict delivery dates and delays across its 2,000-plus suppliers. This has allowed the company to optimize its manufacturing capacity and meet customer demand consistently.
12. Metro Shipping — customs clearance and compliance
Customs clearance can be a major supply chain challenge, causing unwanted delays and compliance issues. AI is transforming customs clearance by automating complex processes, ensuring regulatory adherence, and minimizing delays in global trade.
With the post-Brexit landscape causing regulatory headaches and longer turnaround times, Metro Shipping leveraged an ML-powered data analytics platform to automate the administrative and documentation side of customs clearance. This resulted in a 40% improvement in turnaround time while enhancing data accuracy by 99%.
13. Frito-Lay — Predictive maintenance
Equipment failures at production plants can cause major delays that cascade down the supply chain. Before the era of AI, understanding when machinery and equipment needed attention was more or less guesswork. But through a combination of IoT sensors and predictive analytics, manufacturers can now gather real-time data and use AI models to anticipate issues before they arise.
Frito-Lay, a US producer of snack food and part of the PepsiCo group, uses a range of sensors throughout its plants to identify mechanical failures before they happen. This has enabled a more proactive approach to plant maintenance. In the first year of using AI-powered predictive maintenance, Frito-Lay’s plants saw zero unexpected equipment breakdowns.
Source: Spotfire
This may sound like an edge case, but we’re working with more and more clients to integrate predictive maintenance into their warehouse processes. With our deep expertise in AI and IoT, we’ve developed PreFix, a smart system that spots anomalies and detects maintenance requirements before they turn into issues.
Why partner with an AI software development company?
These examples of artificial intelligence in supply chain management highlight the transformative impact AI is having on strategic operations. AI is bringing deep value to the supply chain, enhancing efficiency, reducing costs, and building resilience in a complex global market.
The question, then, is how do you maximize the opportunity? The AI applications in supply chain we’ve looked at in this guide all require deep technical skills, experience, and resources to implement effectively.
The journey to AI maturity isn’t just about implementing new tools. It involves working with large amounts of data, training AI algorithms, and ensuring seamless integration with your existing systems. Many businesses simply don’t have this level of expertise in-house. This is why it pays to work with an AI and technology expert.
At Intellias, we offer businesses like yours access to industry-leading technical expertise and resources. Our team has a deep knowledge of how the supply chain and AI align with business objectives, ensuring that your company avoids common pitfalls and maximizes its ROI. Here are a few ways we can help you harness the power of AI to achieve a smarter, more resilient supply chain:
- Access to expertise. By working with Intellias, you can tap into a ready-made team of AI developers and strategists. Whether you’re looking to fill gaps in your existing team or outsource your AI project entirely, we can help you scale your AI capabilities rapidly and cost-effectively.
- Custom solutions. Intellias is a full-stack agile development company that has been creating custom solutions for 20 years. We specialize in building intelligence-driven solutions that are tailored to your business needs, whether that’s inventory management, customer demand forecasting, transportation optimization, evaluation of supplier performance, or disruption prevention.
- Digital consulting. In addition to hands-on technical expertise, we can help you understand the bigger picture of technological transformation through digital consulting services. We’ll help you align tech adoption with organizational goals, giving you the tools and mindset you need to become a proactive, digital-first business.
- Customer-centric approach. At Intellias, we don’t offer generic, cookie-cutter solutions. We work with you to understand your business, its challenges, and its goals. We then offer tailored solutions that meet your specific needs.
- Ongoing support. Our collaboration isn’t over once your AI solutions are live. Working with Intellias, you’ll get access to ongoing assistance and regular updates to ensure that your systems are always working effectively and adapting to change.
Wrapping Up
Artificial intelligence in supply chain presents opportunities to revolutionize business operations, enhance the customer experience, and open up new horizons for growth. From predicting consumer needs to managing warehouses, AI-powered systems are reshaping the core of the supply chain industry, making sure goods are delivered on time, trucks are loaded smartly, and optimal routes are chosen.
The future of AI in supply chain holds the promise of further optimization and automation, allowing businesses to predict demand, streamline inventory management, and enhance overall operational efficiency. AI-powered solutions are anticipated to play a pivotal role in driving cost savings and ensuring supply chains are more resilient and responsive to ever-evolving market dynamics.
Yet, leveraging the full potential of AI algorithms requires expert assistance. Partnering with a seasoned AI software development company like Intellias offers companies deep technical expertise and agility. With Intellias, businesses aren’t just users of AI software solutions — they unlock a repository of knowledge and experience.
