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18 Examples of How Businesses Apply AI in the Supply Chain

A comprehensive overview of the most popular applications of artificial intelligence in supply chain management. An essential read for firms that want to stay ahead of the competition and harvest AI benefits

Updated: March 04, 2024 10 mins read Published: October 30, 2023

Artificial intelligence (AI) is a game-changer for supply chains, becoming a need rather than a luxury. A 2023 Meticulous Research study reports the market for AI in supply chain is expected to reach $41 billion by 2030, growing 39% yearly from 2023. Envision a world where supply chains are self-aware, can forecast tomorrow’s customer demand, and can analyze their own inefficiencies and re-route shipments in real time based on rapid weather changes.

Picture warehouses corresponding autonomously with distributors and regulating stock before shop managers are even aware there’s a need to replenish it. Imagine self-driving cars and drones delivering products and being able to see where your order is in an app. This isn’t distant science fiction: it’s a very close reality that AI is making real. As we dive into this article, we unravel how artificial intelligence in supply chain management enables informed business decisions, operational speed, and market adaptability that wasn’t there before.

Applications of AI for supply chain

Supply chain forecast and statistics

AI in supply chain management doesn’t just upgrade the sector’s effectiveness; it is a paradigm shift that turns coping with operational challenges into proactive strategies. McKinsey research has found that AI-powered systems reduce supply chain errors by 20% to 50%, which helps to reduce lost product orders by up to 65%. AI is able to pick up patterns in a big pool of data, identifying new information from seemingly unrelated data points. Gartner reports that 25% of decisions in the supply chain industry will be made using AI-driven systems as of 2025. Insights from these systems’ data help to automate mundane processes, integrate previously unintegrated systems, and improve supply chain efficiency. The most often-used applications of AI in supply chain are:

Predicting consumer demand

AI tools enable demand prediction in supply chains with a holistic, multi-dimensional approach. In particular, AI services use computational power and big data to precisely predict what customers want and need every season of the year. Here’s how AI transforms demand forecasting.

Analyzing historical data

AI leverages historical data to forecast future shopper demand and make sure the company has adequate inventory levels. For instance, Nike uses AI to predict demand for new running shoes even before they are released. Back in 2018, Nike precisely predicted demand for the Air Jordan 11, which were the most popular running shoes of the year.

Including external data

AI can process external factors such as social media posts to increase the accuracy of shopper demand predictions. Big firms like PepsiCo have leveraged AI to analyze what people are discussing and searching for. Based on AI insights, PepsiCo released to the market Off The Eaten Path seaweed snacks in less than one year.

Real-time monitoring

After release, companies can utilize real-time monitoring along with AI to enhance their offering. As per Deloitte report, 43% of respondents believe AI is enhancing their products and services. For example, Walmart adjusts its inventory and sales strategies in real time based on analysis of huge datasets, such as in-store transactions, and even accounts for external events like weather changes.

Simulating “what if” scenarios

This approach enables businesses to anticipate and prepare for future changes, such as rapid increases or decreases in demand, supply disruptions, and even the influence of new product launches. Maersk leverages AI to model the influence of various weather conditions on its shipping routes.

Augmenting inventory management

Smart supply chain management technology

Smart reordering

AI algorithms can forecast when stock is about to be depleted using data about customer demand, supplier lead times, and transportation expenditures. One of the supply chain AI use cases is Amazon leveraging an AI-powered service called Amazon Forecast to identify the exact amount of inventory that is needed, avoiding over-purchasing and thereby lowering warehousing costs.

Disposing of obsolete inventory

AI systems consider sales data, expiration dates, inventory levels, market trends, and even customer feedback to understand what goods are no longer in demand. Employing AI for supply chain optimization helps companies reduce waste, free up warehouse space, and decrease the costs of storing unneeded goods. For example, IKEA launched a buyback and resell initiative that allows shoppers to sell back their used furniture. In this way, IKEA furniture becomes circular.

Inventory counting

AI amplified with computer vision aids with inventory. Cameras and sensors take snapshots of goods, and AI algorithms analyze the data to define whether the recorded quantity matches the actual. One firm that has implemented AI with computer vision is Zebra, which offers a SmartLens solution that records the location and movement of assets throughout the chain’s stores.

Smart warehouse management

Modern warehouses aren’t just storage centers; they are lively hubs where every square foot counts. Artificial intelligence technology speeds up the digitization of warehouses, automating picking and packing of goods, inventory, order fulfillment, and product transportation. It also equips business leaders with deep insights into their warehouses, which leads to smart and informed decisions such as where to place goods, how to route orders, and which staff to hire. According to a McKinsey report AI-driven systems aid in cutting warehousing expenditures by up to 15%.

Organizing warehouse space

AI algorithms scrutinize the frequency of demand for goods, their dimensions, and their weight. Based on this information, the system recommends the optimal placement of goods in the warehouse to maximize space and improve pick-and-pack processes. For instance, JD Logistics has implemented AI-driven warehouses based on a network of automated conveyors and robots.

Robotic pick and pack operations

Warehouse robots armed with AI can manage stock and fetch goods, assisting with load and unload operations. Gartner predicts that more than 75% of big enterprises will use robots in their warehouses as of 2026. More than 520,000 Amazon Kiva robots are working in Amazon’s warehouses, sorting and moving goods, eliminating the necessity for people to pick goods manually. In this way, the company allows its personnel to work on more complicated and valued positions. Moreover, it’s safer to have robots pick and pack the goods, as they can detect potential safety hazards and reduce the number of accidents.

Streamlining shipment scheduling

Using aggregated data about shipments, traffic, and other historical data, AI-driven systems can predict optimal times and routes for receiving incoming deliveries and dispatching outbound shipments. Amazon leverages AI to forecast what products will be in demand and where. In this way, the company is able to increase delivery speed by shipping products on the same day they’re ordered.

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Expediting logistics and transportation

Global trade grows each year, and so do shopper expectations. This places high demands on logistics and transportation providers: in particular, expectations about quick and effective product deliveries. By harnessing artificial intelligence and supply chain management, companies can significantly enhance the efficiency of their logistics and transportation operations, leading to faster, more organized deliveries to their customers.

Intelligent route optimization

Artificial intelligence simplifies and complements the process of plotting and building optimal routes based on traffic congestion, roadwork, and other variables. This allows drivers to decrease fuel consumption and speed up delivery times. For example, UPS has developed an Orion AI algorithm for last-mile tracking to make sure goods are delivered to shoppers in the most efficient way.

Location tracking

AI-powered IoT and GPS tracking bring clear visibility about the location of goods. Using this data, AI can also alert about possible shipment delays, enabling businesses to proactively address delivery issues. The logistics company Maersk uses GPS and IoT sensors to monitor the location, temperature, and humidity of their shipments. This data is then embedded into AI systems to predict delays and ensure the cargo safety. Intellias has developed a fleet truck tracking system that captures data through IoT devices and can determine the location of a stolen vehicle.

Truckload management

AI systems analyze how a truck is loaded and evaluate loading efficiency. This optimizes trailer load so that every centimeter of space is freighted effectively. For instance, the largest freight carrier in the US – FedEx leverages AI technology to automate manual trailer loading tasks by connecting intelligent robots that can think and move quickly to pack trucks.

Supplier relationship management

Logistics and transportation in supply chain management

Sourcing new suppliers

AI algorithms are capable of swiftly processing huge amounts of data about suppliers, in particular about their delivery times, pricing, and product quality. This information can serve as a basis for creating supplier scorecards and rankings. An e-commerce and retail giant Alibaba has opted for AI algorithms to find new suppliers for Taobao and Tmail. Even further, machine-powered systems can access suppliers’ risk profiles, assessing all available information. For instance, Intellias has developed a that simplifies the search and management of suppliers, appointment booking, order placement, and fulfillment.

Contract management

AI systems, along with NLP (natural language processing) and OCR (optical character recognition), can promptly review contracts and categorize their content. This speeds up the supplier contract vetting process and guarantees that all necessary details are included. In particular, AI algorithms can verify whether a contract adheres to the company’s policies and external requirements. For instance, Microsoft uses AI services and data science to automate document reviews and make it easier to search throughout contracts.

Risk management

Environmental disasters, wars, economic recessions, regulatory restrictions, and pandemics can pose a challenge for already complicated supply chains that must process different kinds of transactions and data and involve a lot of stakeholders. A hold-up period in raw material production in one country can postpone manufacturing in another, or a regulatory restriction in one country can lead to product recalls thousands of kilometers away. While, according to IBM, 87% of chief supply chain officers say it’s complicated to foresee and proactively manage risks, AI and supply chain can become a powerful combo in predicting and identifying potential industry-related risks.

Detecting fraud

Complex supply chains become vulnerable to various types of fraud, such as false or inflated invoices, non-authentic products, or forgery. AI algorithms aid companies in quickly spotting suspicious activities or patterns indicating potential fraud. For example, Visa uses AI to prevent $25 billion in fraudulent transactions annually.

Predicting disruptions

AI systems are able to process huge amounts of data, such as news, images, market trends, and social media posts, and predict when and where potential risk events might happen. Knowing this information, companies can save money and avoid potential charges or penalties. For instance, IBM Watson leverages AI to monitor supply data, supplier cycle time performance, and manufacturing time, and helps to deal with unforeseen delays with inbound deliveries.

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Supply chains bear a significant environmental footprint, which includes carbon emissions from transporting goods, deforestation due to producing raw materials, overuse of water, and habitat destruction. AI for supply chain can aid businesses in using resources more effectively, decreasing waste, enhancing energy effectiveness, and opting for routes that minimize the carbon footprint.

Decreasing waste

AI aids with engineering more effective packaging that uses fewer materials. For instance, Tetra Pack uses AI to develop Tetra Rex packages that have a lower environmental impact than equivalent glass and plastic packaging and are recyclable. AI algorithms also aid with precisely forecasting customer demand, thereby decreasing overstock inventory. For example, Nestlé leverages AI to predict demand for their goods in various countries and decrease the number of overstocked products by 10%.

Why partner with an AI software development company?

Digital technology and AI for supply chain

AI algorithms hold enormous potential, and harnessing them efficiently necessitates deep technical skills, experience, and resources. Moreover, the journey to AI maturity encompasses not just pure technical implementation: it requires skills to work with large amounts of data, train AI algorithms, and continuously improve to arrive at a better solution.

Beyond technical experience, Intellias brings deep knowledge of how supply chain and AI align with business objectives and makes sure businesses stay clear of common pitfalls and maximize their return on investment. Here are a few reasons why collaborating with an AI software development company is helpful for businesses:

  • Access to expertise. AI software development companies have the knowledge and experience to engineer and release intelligence-driven solutions that are tailored to certain business needs, such as inventory management, customer demand forecasting, transportation optimization, evaluation of supplier performance, and disruption prevention.
  • Custom solutions. Intellias is a full-stack agile development company that has been creating custom solutions for 20 years. Software engineers who work for Intellias are agile and technology-savvy professionals who pride themselves on staying ahead of the curve.
  • Speed to market. AI software development companies deliver solutions to the market quickly so that firms are able to immediately harvest the results obtained from AI algorithms.
  • Cost-effectiveness. Creating an in-house development team usually comes with considerable investments in hiring the right people. Partnering with a dedicated AI software engineering company provides prompt access to top-notch talents.
  • Commitment to quality. Intellias is committed to engineering high-quality AI solutions that meet the specific needs of each stakeholder.
  • Scalability. An established AI software engineering company makes sure that AI services are scalable according to business needs and perform stably and effectively.
  • Support and upgrades. Collaborating with Intellias provides organizations with ongoing assistance and regular updates as well as access to new technology developments, making sure the provided services are forward-looking.

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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.


Intellias works with businesses of all sizes to engineer and deploy tailored solutions that meet their unique needs and goals. Our team is continuously polishing the AI algorithms we develop and implement the latest advancements in artificial intelligence to ensure your firm stays ahead in the supply chain domain. Our AI in supply chain management offering includes:

• Customer demand prediction
• Inventory management
• AI-powered warehouse management
• Logistics and transport optimization
• Supplier evaluation
• Risk prediction
• Fraud detection
Evaluating your firm’s readiness for implementing AI is a fundamental first step that helps to determine possible infrastructure gaps, understand current capabilities, and decide on expectations of the service. An AI business assessment not only outlines where a firm can improve but serves as a roadmap for structured and methodical technology adoption. The most common steps of business evaluation for AI are:

Key business challenges. What are the main issues your company is dealing with? Predicting customer demands? Avoiding overstock or stockouts?
Data availability. Can your firm provide all the necessary data for AI implementation? For example, do you have sales and market research data, supplier lead times, current and historical inventory levels, travel times, or other data depending on your specific use case?
Technology stack. Can your current systems be easily integrated with an AI service? Are they streamlined and modern, or do they require an overhaul?
Skills. Do you have an experienced team that is able to work on AI-related tasks?
Strategic goals. How will your firm benefit from AI services in the long term and short term? For instance, a goal might be determining the optimal inventory location to meet customer demand.
Costs. What are your budgetary constraints? Though the benefits of implementing AI algorithms are undeniable, such implementation may require considerable financial investment.
Artificial intelligence in logistics and supply chain equips businesses with insights from data that may have previously been unused. Ways in which AI can augment the supply chain include:

Forecasting shoppers’ demand by analyzing past and current sales data, seasonal factors, economic upheavals, and recessions
Predicting stockouts and overstock by analyzing data on historic and actual inventory levels, products’ turnover rates, and product shelf lifetimes.
Automation of warehouse operations by employing robots to pick and pack goods, mounting cameras and sensors for automatic inventory counting.
AI-powered route optimization by selecting the optimal routes and travel times to deliver purchases.
Selection and evaluation of the suppliers by assessing supplier lead time, performance, and manufacturing time.
Detect fraud, for example, non-authentic goods, overdue contracts, and overpriced shipping charges.
Reduce waste and make your business more sustainable by determining what goods most likely won’t be in demand on the market.
Though the buzz around AI technology is all around, it’s essential for businesses to understand the practical benefits that this technology brings to supply chain operations. The specific advantages of AI for supply chain are:

End-to-end visibility and transparency. AI algorithms provide companies a clear visibility on all the stages of the supply chain and actionable insights into how the goods move through the supply chain. This comprehensive view allows businesses to utilize predictive analytics to forecast customer demand and future market trends.
Amplified productivity. AI automates mundane and tedious tasks like inventory counting, pick and pack operations, and product reordering, and is even able to adjust prices dynamically. Furthermore, AI-optimized transportation routes enable firms to save on fuel.
Cost reduction. Machine intelligence and algorithms can precisely predict shoppers’ demand and reduce expenditures on inventory and storage.
Strong resilience. From geopolitical events to natural disasters, AI algorithms are able to determine potential hazards to supply chain operations. Even more, modeling of “what if” scenarios helps businesses be more prepared for different eventualities.
Smart decision-making. AI equips organizations with deep knowledge about customer demand and required production levels. Businesses are able to spot changes in customer preferences and adjust their product strategies accordingly.
Hyper-personalized customer service. Machine-gained insights can determine how goods may be customized to better fit the needs of individual shoppers, such as through personal product recommendations and price optimization.
A leap to any new technology contains hurdles. Before sailing off on your AI journey, you should be aware of potential obstacles you may face. Certain issues are commonly associated with AI integration:

Data availability and quality are fundamental to the work of AI algorithms. With outdated, incomplete, and unstructured data, systems will not function.
Integration with legacy systems poses a challenge, as they may not be compatible with advanced technologies and may be impossible to integrate in a well-running service.
The high cost of AI implementation is associated with investments in data collection capabilities, infrastructure, and hiring an expert team.
The knowledge gap requires employing technology-savvy professionals that can develop and release an AI solution.
Miscalculations can lead to an artificial intelligence service producing incorrect results, which is why a check is needed to avoid errors.
Cybersecurity threats can lead to data exposure. Businesses have to implement data protection mechanisms and policies to safeguard information.
At Intellias, we believe that a project’s real success is measured by its impact and the value it brings. Our goal is not just to be a technology provider but a long-term strategic partner, making sure the AI algorithms we develop continue to bring innovation and improvement to your business. Therefore, our responsibilities do not end when the AI system is live; they simply switch to the next phase.

A dedicated support team is able to tackle any issues you might have.
Regular upgrades make sure the AI system works well across different operating systems and devices.
Performance monitoring of the implemented AI solution ensures it functions steadily and seamlessly.
Ongoing checks ensure that stakeholder feedback is considered and implemented.
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