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Read also: Media Supply Chain Management<\/a><\/p>\n <\/div> \n <\/div><\/p>\n AI in supply chain management doesn\u2019t just upgrade the sector\u2019s effectiveness; it is a paradigm shift that turns coping with operational challenges into proactive strategies. McKinsey research<\/a> 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<\/a> that 25% of decisions in the supply chain industry will be made using AI-driven systems as of 2025. Insights from these systems\u2019 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:<\/p>\n 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\u2019s how AI transforms demand forecasting.<\/p>\n 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<\/a> predicted demand for the Air Jordan 11, which were the most popular running shoes of the year.<\/p>\n 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.<\/p>\n After release, companies can utilize real-time monitoring along with AI to enhance their offering. As per Deloitte report<\/a>, 43% of respondents believe AI is enhancing their products and services. For example, Walmart<\/a> 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.<\/p>\n 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.<\/p>\n 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<\/a> 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.<\/p>\n 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<\/a> launched a buyback and resell initiative that allows shoppers to sell back their used furniture. In this way, IKEA furniture becomes circular.<\/p>\n 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<\/a> solution that records the location and movement of assets throughout the chain’s stores.<\/p>\n Modern warehouses aren\u2019t 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<\/a> AI-driven systems aid in cutting warehousing expenditures by up to 15%.<\/p>\n 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<\/a> implemented AI-driven warehouses based on a network of automated conveyors and robots.<\/p>\n Warehouse robots<\/a> armed with AI can manage stock and fetch goods, assisting with load and unload operations. Gartner predicts<\/a> 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<\/a> in Amazon\u2019s 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\u2019s safer to have robots pick and pack<\/a> the goods, as they can detect potential safety hazards and reduce the number of accidents.<\/p>\n 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<\/a>. Amazon leverages<\/a> 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\u2019re ordered.<\/p>\n 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.<\/p>\n Artificial intelligence simplifies and complements the process of plotting and building optimal routes<\/a> 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<\/a> an Orion AI algorithm for last-mile tracking<\/a> to make sure goods are delivered to shoppers in the most efficient way.<\/p>\n AI-powered IoT and GPS tracking<\/a> 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<\/a> 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<\/a> that captures data through IoT devices and can determine the location of a stolen vehicle.<\/p>\n 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<\/a> AI technology to automate manual trailer loading tasks by connecting intelligent robots that can think and move quickly to pack trucks.<\/p>\n 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<\/a> algorithms to find new suppliers for Taobao and Tmail. Even further, machine-powered systems can access suppliers\u2019 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. <\/a> Read also: Machine Learning in Supply Chain<\/a><\/p>\n <\/div> \n <\/div><\/p>\n 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\u2019s policies and external requirements. For instance, Microsoft uses AI<\/a> services and data science to automate document reviews and make it easier to search throughout contracts.<\/p>\n 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<\/a>, 87% of chief supply chain officers say it\u2019s complicated to foresee and proactively manage risks, AI and supply chain can become a powerful combo in predicting and identifying potential industry-related risks.<\/p>\n 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<\/a> to prevent $25 billion in fraudulent transactions annually.<\/p>\n 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<\/a> AI to monitor supply data, supplier cycle time performance, and manufacturing time, and helps to deal with unforeseen delays with inbound deliveries.<\/p>\n Enhance efficiency with data-driven insights in planning, sourcing, and delivery<\/p>\n 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.<\/p>\n AI aids with engineering more effective packaging that uses fewer materials. For instance, Tetra Pack uses AI<\/a> 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<\/a>, thereby decreasing overstock inventory. For example, Nestl\u00e9 leverages AI<\/a> to predict demand for their goods in various countries and decrease the number of overstocked products by 10%.<\/p>\n 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.<\/p>\n 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<\/a> is helpful for businesses:<\/p>\nApplications of AI for supply chain<\/h2>\n
![\"Supply](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/10\/BP-AI-in-Supply-Chain.-Graphics-05.png\")
<\/p>\nPredicting consumer demand<\/h3>\n
Analyzing historical data<\/h4>\n
Including external data<\/h4>\n
Real-time monitoring<\/h4>\n
Simulating \u201cwhat if\u201d scenarios<\/h4>\n
Augmenting inventory management<\/h3>\n
![\"Smart](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/10\/BP-AI-in-Supply-Chain.-Graphics-01.jpg\")
<\/p>\nSmart reordering<\/h4>\n
Disposing of obsolete inventory<\/h4>\n
Inventory counting<\/h4>\n
Smart warehouse management<\/h3>\n
Organizing warehouse space<\/h4>\n
Robotic pick and pack operations<\/h4>\n
Streamlining shipment scheduling<\/h4>\n
![\"digital](\"https:\/\/intellias.com\/wp-content\/uploads\/2021\/12\/digital-mature-whitepaper.jpg\")
<\/div>\n\t\t\tExpediting logistics and transportation<\/h3>\n
Intelligent route optimization<\/h4>\n
Location tracking<\/h4>\n
Truckload management<\/h4>\n
Supplier relationship management<\/h3>\n
![\"Logistics](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/10\/BP-AI-in-Supply-Chain.-Graphics-04.jpg\")
<\/p>\nSourcing new suppliers<\/h4>\n
\nContract management<\/h4>\n
Risk management<\/h3>\n
Detecting fraud<\/h4>\n
Predicting disruptions<\/h4>\n
Sustainability<\/h3>\n
Decreasing waste<\/h4>\n
Why partner with an AI software development company?<\/h2>\n
![\"Digital](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2023\/10\/BP-AI-in-Supply-Chain.-Graphics-03.jpg\")
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