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.
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Applications of AI for supply chain
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 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.
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
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.
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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.
Sustainability
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?
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.
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.
