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7 ways AI is making supply chains more resilient

By Nadejda Alkhaldi, Innovation Analyst
Published on

There is a good chance that your company, like many others, built its supply chain with efficiency as the top priority over resilience. However, with recent devastating events such as the pandemic and the Russia-Ukraine war, the focus of the supply chain is shifting towards resilience. Now more than ever, companies need the ability to analyze events in real time, swiftly switch suppliers, and showcase flexibility to remain competitive.

AI and its subtypes can help you transform your supply chain management tactics and minimize dependence on a single supplier. Artificial intelligence solutions for supply chain continuously analyze data coming from the company itself, the suppliers, and a variety of other external sources to identify and even foresee any potential issues and recommend solutions in real time.

A recent survey by McKinsey shows that companies experience the highest cost benefits from artificial intelligence in the supply chain management domain. Given this enormous potential, let’s see what AI can do to improve supply chain resilience. Our artificial intelligence development company will also share four steps that will help you succeed in AI implementation.

7 key use cases of AI in supply chain to build resilience

Here are the most prominent supply chain AI use cases:

  1. Demand forecasting
  2. Delivery route optimization
  3. Autonomous delivery vehicles
  4. Predictive analytics for risk management
  5. Digital twins
  6. Supply chain visibility
  7. Supplier relationship management

Let’s take a closer look at each application.

Demand forecasting

Understanding the sources of demand has never been more challenging. With customer expectations changing quickly and becoming more diverse, businesses now rely on AI-powered supply chain tools to glean more demand-related insights, tune their production strategies, and restock accordingly.

McKinsey reports that using AI-driven forecasting tools reduces error by up to 50%, decreasing missing products and consequent lost sales by approximately 65%.

Artificial intelligence tools can predict demand fluctuations and upcoming trends. The technology can gauge customer sentiment by analyzing social media posts and product reviews. This enables companies to stock products that will be in high demand and refrain from hauling items that customers are not interested in anymore.

Delivery route optimization

Leveraging AI in supply chain management can help design better delivery routes and optimize fleet utilization. Considering such criteria as manufacturing sites, storage locations, potential wear and tear of machinery, and fuel usage, AI-based supply chain solutions come up with an optimal route that results in a speedier flow of items along the supply chain.

Autonomous delivery vehicles

Driverless cars and last-mile delivery robots can transform supply chains by decreasing dependence on human drivers. Autonomous trucks can cross vast distances without the need to rest, while AI-powered drones are particularly useful for locations that are hard to reach or are dangerous for human drivers. Some of these vehicles even offer loading and unloading functionality.

Predictive analytics for risk management

One way of leveraging AI for supply chain risk management is predicting supply chain disruptions. Feeding off historical operational data, AI could help identify and correct operational inefficiencies in real time, providing an in-depth look into the supply chain performance, opportunities, and risks. Doing so proactively allows supply chain executives to operate at lower costs without sacrificing efficiency.

Another example is optimizing supplier evaluation, flagging suppliers as low-, medium-, or high-risk. For that, an AI-based supply chain solution could weigh out such metrics as the impact on revenues a business is likely to face if a particular source is lost, the time it would take a specific supplier to recover from a disruption, the availability of alternative sources, and other data.

Digital twins

A digital twin is a virtual simulation of a company’s operations and its supply chain. A digital twin includes corporate assets, product flows, routes, inventory, and more. It helps make business decisions by simulating different scenarios, optimizing processes and inventory, stress testing for vulnerabilities and response strategies, and identifying the best network design to enhance overall supply chain sustainability.

Supply chain visibility

Research shows that only 2% of companies enjoy supplier visibility beyond the second tier. This is not enough if you want to closely monitor your suppliers and their practices. AI-powered tools can analyze product data in real time and track the location of your goods along the supply chain.

For example, AI can gather dispersed information on product orders, customs, freight bookings, and more, combine this data, and map out different supplier activities and product locations. You can also set up alerts, asking the tool to notify you about any suspicious supplier activity or shipment delays.

Supplier relationship management

You can use AI in supply chain to analyze historical and real-time data on your suppliers to anticipate any performance issues and spot unsustainable practices and deviations from the agreed upon schedule.

When it’s time to search for a new supplier, artificial intelligence can help you evaluate candidates by automating a scoring system across multiple criteria, such as delivery speed and compliance. After you find the right partner, natural language processing will assist you with contract drafting and review.

Just recently, Accenture conducted a survey among business leaders, and 87% of the C-suite executives working with supply chains expressed their intention to increase investment in generative AI. Check out our recent article to discover how Gen AI benefits the supply chain.

Real-life examples of AI in supply chain

So, which AI-powered solutions are already available on the market? And how are your competitors benefiting from this technology? Let’s take a look at some inspiring examples of artificial intelligence in supply chain management.

Companies offering AI in supply chain tools

Echo Global Logistics

Chicago-based Echo Global Logistics provides innovative solutions that help businesses manage transportation and logistics. The company developed an AI-driven tool for supply chain management that others can use to automate a variety of logistics tasks, such as supplier selection, rate negotiation, reporting, analytics, and more.


An artificial intelligence startup Altana built an AI-powered tool that can help businesses put their supply chain activities on a dynamic map. As products and raw materials move along the supply chain, they generate data points, such as custom declarations and product orders. Altana’s software aggregates this information and positions it on a map, enabling you to track your products’ movement.


Coupa is an American tech company operating in California. It developed an AI tool that allows enterprises to model supply chain activities, run different scenarios, and assess risks. Coupa is also using natural language processing to crawl social media, searching for information on suppliers. This can help you run a background check on candidates when you want to renew your supply chain.

Companies using AI in supply chain

Church Brothers Farms

Church Brothers Farms is a family-owned farming business committed to sustainability and producing fresh fruits and vegetables all year round. The company offers perishable products that can’t remain in inventory for long. Therefore, precise demand forecasting is an essential part of the farm’s success. Church Brothers Farms relies on AI-driven analytics to predict demand using real-time data. Their software can accommodate a diverse set of variables, including weather conditions, market trends, seasonality, historic data, and more.


The multinational transportation and delivery giant, FedEx, uses several robots in its operations. One example is Roxo, a robot that relies on AI to automate last-mile deliveries. The robot is designed to be used within a three-to-five-mile radius of storage facilities. Another AI-powered robot, DexR, can autonomously load trucks. It moves to the back of a trailer and connects to a conveyor system that supplies packages.


The global shipping giant, UPS, uses AI to manage the flow of packages. UPS’s staff gets a bird’s-eye view of the number of packages in the delivery network, the expected peaks in the volume of goods en route, as well as potential disruptions. The AI-based supply chain solution relies on historical and real-time information, including weather and traffic data, to devise the fastest and safest ways to deliver packages.

By infusing AI into its operations, UPS could save $200 million per year.

Embarking on an AI-driven supply chain transformation journey

Research shows that business leaders have already tried and succeeded in piloting AI. However, a whopping 79% reported struggling to scale the technology to cover broader initiatives.

Here are the steps that will not only help you test AI in supply chain on limited business cases but also scale the technology to serve company-wide initiatives.

Step 1: Formulate a business case and think over the strategic aspects of AI adoption

Only a third of companies ushering in AI-driven transformation perform a diagnostic audit before rolling out the technology. To make sure you are not missing out on the opportunities of AI, we recommend kicking off your digitalization project by identifying and prioritizing the possibilities for value creation across all supply chain segments—from procurement to manufacturing to shipping.

With an all-rounded assessment carried out, define the supply chain digitalization strategy and make sure it reflects the findings. It makes sense to start with digitalizing one segment of the supply chain that shows the highest value-creation potential to drive ROI faster.

And once the base solution is rolled out, you could evolve further, both horizontally, expanding the list of available features, and vertically, extending the capabilities of AI to other supply chain segments.

Step 2: Look for an optimal vendor to bring your solution to life

Due to the complexity and multifaceted nature of the supply chains, all of your expectations could hardly be met by a single vendor. So, don’t be afraid to examine what the supply chain technology market has to offer and integrate the optimum offerings into a solution that addresses your specific needs. Another piece of advice is to go for a vendor-agnostic integrator so you can prevent technology and solution lock-in.

Step 3: Oversee the development and integration of the solution

According to McKinsey, only 15% of businesses involved in supply chain management report feeling like their objectives are in line with those of their vendors. To prevent that and ensure a smooth roll-out, map the development process to the initial supply chain digitalization strategy and keep in mind the key value you intend to tap into. Prioritizing the value-creation opportunities and dividing the development process into increments according to the set priorities might help navigate end-to-end AI implementation.

Step 4: Ensure the solution’s smooth adoption and scale the implemented capabilities

Powering a supply chain with AI is a complex endeavor that goes beyond rolling out the technology. Digitalizing a supply chain also requires comprehensive change management and reskilling. So, before you jump on the AI bandwagon, we recommend laying out a change management plan to help you handle the skills gap and the cultural shift. Start by explaining the value of AI to the employees and educating them on how to embrace the new ways of working.

Invest in AI in supply chain with ITRex

AI in supply chain is a powerful tool that enables companies to forecast demand, predict delivery issues, and spot supplier malpractice. However, adopting the technology is more complex than a onetime integration of an AI algorithm. To succeed, businesses need to invest in change management and staff training, in addition to studying and implementing the technology itself.

Also, consider finding a reliable tech partner who will consult you on AI and help you build and customize AI-driven solutions. ITRex has vast experience in artificial intelligence. We will not only support you with building and integrating AI tools but also with aggregating and preparing the data that AI models need to function. We have dedicated data consultants who will assist you with data management. Our team has worked with both structured and unstructured data and will help you set up automated data collection, if needed.

Get in touch, and we will be there for you every step of the way.

7 key use cases of AI in supply chain to build resilienceDemand forecastingDelivery route optimizationAutonomous delivery vehiclesPredictive analytics for risk managementDigital twinsSupply chain visibilitySupplier relationship managementReal-life examples of AI in supply chainCompanies offering AI in supply chain toolsCompanies using AI in supply chainEmbarking on an AI-driven supply chain transformation journeyStep 1: Formulate a business case and think over the strategic aspects of AI adoptionStep 2: Look for an optimal vendor to bring your solution to lifeStep 3: Oversee the development and integration of the solutionStep 4: Ensure the solution’s smooth adoption and scale the implemented capabilitiesInvest in AI in supply chain with ITRex
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If you are looking to tap into the transformative power of AI and digitize your supply chain for better visibility, resilience, and responsiveness, drop us a line. Our experts will answer your questions and help navigate the transformation process with little to no risks.