10 Generative AI Supply Chain Use Cases in 2024

«What we are seeing is that bigger companies will do disaster recovery but usually on specific events, while smaller companies tend not to do it at all,» adds Naus. Alternative component options should become common to simplify the engineering side of the supply chain and reduce susceptibility to availability challenges, Lherault says. «We’ve built flexibility into our supply chain,» he adds, noting that typically, however, people don’t plan for something unpredictable to happen.

Instead of relying on gut feelings or historical trends alone, companies can leverage data from diverse sources to predict future demand with greater accuracy. The benefits of Machine Learning and AI can be traced in every part of the supply chain, including procurement, manufacturing, inventory management, warehousing, logistics, and customer service. Let’s dive deeper into the advantages of Machine Learning in supply chain management and Machine Learning use cases in supply chain. Production facilities generate reports on the inventory levels of raw materials, works in process and finished goods.

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. AI and its subtypes can help you transform your supply chain management tactics and minimize dependence on a single supplier.

Why should I care about supply chain digitization?

This blog post delves into the world of modern supply chain analytics, exploring its definition, key components, and the remarkable capabilities it offers. 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. After release, companies can utilize real-time monitoring along with AI to enhance their offering.

Modern data platforms typically provide advanced analytics capabilities, including AI-powered predictive modeling, optimization algorithms, and machine learning techniques. These capabilities enable supply chain companies to leverage historical data and real-time data to forecast demand, optimize inventory levels, identify supply chain risks, and automate decision-making processes. A large hydrocarbon processing company implemented an AI-based solution to optimize production schedules and minimize manufacturing costs at their large polypropylene plant. The company integrated disparate data sources like demand forecasts, customer orders, production costs, and inventory into a unified data image. Based on these data, the machine learning models predict customer demand and configure optimization algorithms to generate optimal 60-day production schedules. It improved demand forecasting accuracy by 20% and incorporated over 2 million operational constraints from 20 categories.

• IDC predicts that by 2026, 55% of G2000 OEMs will redesign their service supply chains using AI.
• Even amid the global pandemic, enterprises were focused on evolving their AI supply chain pilots into operationalization.
• These systems can dynamically allocate resources, optimize workflows, and rapidly adjust to changing conditions, leading to improved throughput and reduced fulfillment times.
• «You can’t predict everything, particularly if you look only for specific things,» Naus says.

Integrated generative AI accelerates intuitive conversations between supply chain decision makers and virtual assistants, enabling fast and fact-based actions. These innovations empower supply chain professionals to focus on complex problem resolution, the continuous improvement of our workflow designs and augmenting AI models. Adding generative AI and the power of foundational models to the existing solution is a natural step in the evolution of our supply chain capabilities. Internal and external stakeholders need fast and accurate information at their fingertips to plan, manage and direct supply chains. To drive personalized actions, insights and visibility, large volumes of data (ERP, WMS, RFID and visual analytics) need to be ingested, normalized and analyzed at high speeds. The need for agile, resilient and competitive supply chains has never been greater than today.

Data integration: different techniques, tools and solutions

Natural disasters, pandemics, geopolitical tensions, and fluctuating market demands can severely impact the supply chain. Moreover, consumer expectations for faster, more reliable delivery have never been higher, adding additional pressure on supply chain systems to perform flawlessly. The organizational design of the supply chain function can have a critical impact on overall performance; even with the right solution in place, execution can be nearly impossible if individual components of the system are not aligned.

It’s about leveraging AI and ML to automate decision-making and optimize supply chain processes, as well as enabling self-learning and self-correcting supply chain systems that can adapt to changing conditions without human intervention. Providing end-to-end supply chain visibility through the use of IoT sensors, GPS tracking, supply chain use cases and other real-time data sources. Enabling proactive monitoring and alerting to identify and respond to supply chain disruptions or performance issues in a timely manner. The MediLedger Pilot Project explored the feasibility of using blockchain technology to create an electronic interoperable system as required by the DSCSA.

Autonomous planning is a continuous, closed-loop planning approach built on a fully automated technology platform, designed to optimize S&OP processes in real time. You can foun additiona information about ai customer service and artificial intelligence and NLP. For large, complex CPG companies, autonomous planning can help supply chains function more effectively in volatile environments, and with less direct human oversight and decision making required. It combines big data (internal, external, and customer information) and advanced analytics at every step of the supply chain planning process. Applying machine learning and advanced statistical modeling techniques to forecast demand, predict supply chain disruptions, and optimize inventory levels. Leveraging historical data, market trends, and external factors to generate accurate and actionable predictions.

As highlighted in the new thought leadership paper “Building intelligent, resilient and sustainable supply chains,” the necessary transformation improvements are not just a question of manufacturing, logistics or transportation. They’re fundamentally a question of timely and accurate data, both from inside the enterprise and from the ecosystem of supply chain partners. For years, enterprise supply chains have rested on the shaky foundations of disconnected, unverifiable and untimely data. When things go wrong, enterprises turn to war rooms with often aged data and competing sources of truth.

Financial optimization in supply chain

For instance, JD Logistics has implemented AI-driven warehouses based on a network of automated conveyors and robots. 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. It allows supply chain professionals to track the location of goods during transportation and provides visibility into the conditions under which the package is being transported. With the help of sensors, retailers can monitor parameters such as humidity, vibration, temperature, etc. Organizations will continue to accelerate the electrification and automation of the logistics transport value chain – especially those that remain costly or manual, such as processing of air freight and last mile delivery.

The solution also includes a pricing recommendation engine and multi-lever simulation tool, which help businesses quickly test and implement optimal pricing scenarios to drive profitability. 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.

Let’s take a look at some inspiring examples of artificial intelligence in supply chain management. Digitized supply chains enable more insight and visibility for both you and your customers, who will be happy to see that you’re up to date on the latest technology. With technology from SPS Commerce that integrates completely into your ERP systems, suppliers have the opportunity to introduce digitization into your supply chain.

The robot is designed to be used within a three-to-five-mile radius of storage facilities. It moves to the back of a trailer and connects to a conveyor system that supplies packages. More customizations can require more components with more individual features to create them, so Fairbairn notes that the concept of «infinite personalizations» may have to be unlearned when it comes to future digital transformation. Sam Harris, principal consultant at procurement-focused Proxima, points out that planning more generally to have more resiliency and more redundancy can be leveraged to free up resources that can function as an emergency fund.

Blockchain applications are meant to support better visibility in the food supply chain. Food and Drug Administration’s Drug Supply Chain Security Act (DSCSA) Pilot Project Program. The FDA chose multiple participants, which are testing methods and technologies with potential to create an electronic, interoperable system. Ensuring the pharmaceutical supply chain works as it should has important ramifications for drug safety, and in turn, consumer safety. One of the most promising use cases for supply chain blockchain is ensuring that safety. «When a customer purchases a blockchain-enabled diamond with Brilliant Earth, all of these details are included so they can see the full chain of custody,» Gerstein said.

With Intellias, businesses aren’t just users of AI software solutions — they unlock a repository of knowledge and experience. 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.

RPA can also help bring data together from across multiple data silos, such as organizational management modules, Excel sheets and web portals. In this article, we will list and explain the top 10 potential generative AI supply chain use cases. Simform partnered with a global industrial equipment manufacturer (operating in 20 countries with over 100 product lines) to develop an AI-powered Asset Performance Management solution. With nearly 200K purchase orders and 1.1 million invoices processed annually, Accenture Procurement Plus struggled to improve the accuracy and efficiency of assigned general ledger accounts during procurement. Tasks such as document processing can be automated thanks to intelligent automation or digital workers that combine conversational AI with RPA. A supply chain is a web that interconnects business activities, making it one of the most crucial elements of any business.

• Fleur Doidge is a journalist with more than twenty years of experience, mainly writing features and news for B2B technology or business magazines and websites.
• This so-called “bullwhip effect” has been known for decades, but now the data and technology are available to finally do something about it.
• On top of that, he adds, a major Chinese factory caught fire shortly befotre the pandemic.
• The test can enable companies to not only understand how resilient their supply chain and operations are, but also to identify the weakest links and quantify the impact of those links’ failures on fulfilling their role.
• Potential applications span planning, manufacturing, product life cycle, supply chain collaboration, and track and trace.

The modernized and scalable logistics platform will significantly improve the efficiency of warehouses in over 60 countries, reducing operational overhead and warehouse downtime. This is an open question for many suppliers, distributors, manufacturers, and retailers. Amid shifting supply chain market dynamics, changing ways of working, and increasingly volatile demand, businesses are wondering how to make their supply chain less vulnerable to disruption. Machine Learning holds the answer to many well-known and emerging supply chain challenges. Technology tools such as control towers and digital twins can surface critical sub-tier supplier relationships, highlight common sub-tier suppliers, factory locations and provide clear insight into the depth of an organization’s supply chain. Large language models and natural language processing can consume data from sources like market events that might affect suppliers and traffic delays involving specific shipments, then GenAI chatbots can notify suppliers about risk.

SCMR: How should supply chains approach this process? Are there technologies that provide a pathway forward?

Low-code platforms are not just a technological upgrade; they represent a paradigm shift in how organizations approach their operations providing a pathway to a more agile and adaptable future. A supply chain is a dynamic and complex process that includes provisioning, raw material supply, warehousing and the distribution of manufactured products to consumers. Implementing software change in this environment is time consuming with a high probability of errors. From a technology perspective, the capabilities to enable low touch planning are like a control tower or its more advanced counterpart, the cognitive decision center which includes digital twin capabilities.

A chatbot can be very useful to various user departments such as sales, purchase, production others, which will access SCM databases and support queries using NLP modules. Ultimately, customer segmentation for targeted supply chain strategies enables businesses to move away from a one-size-fits-all approach, creating a more nuanced, efficient, and customer-centric supply chain. Supply chain analytics is a rapidly evolving field that holds immense potential for businesses of all sizes and industries.

The revelation in Shein’s 2023 sustainability report comes as it is understood to be planning to sell shares on the stock market. Operations leaders, like COOs and CSCOs, can slay the ghosts of transformations past by putting humans at the center. Learn how to move beyond quick efficiency gains to a cohesive AI strategy that maximizes your growth potential in a fast-changing space. Neo4j has been downloaded over 2 million times and has a large global community of developers. A modern Supply Chain is well connected by IoT devices, and all transactions are updated in real-time, hence it is possible to compute the majority of KPIs in real-time.

Currently, various strategies and optimizers are being used to generate a delivery schedule and truckload. In the future, AI/ML may be able to provide a more ‘perfect’ solution to the above problem, which balances the requirements mentioned above. A report showing very ‘odd’ product movements or production declarations will be very useful as it will help management to focus on those specific movements. However, this will obviously need labeling to be done for past periods i.e., classifying and labeling movements as ‘odd’ or ‘ok’.

AI algorithms aid companies in quickly spotting suspicious activities or patterns indicating potential fraud. Using this data, AI can also alert about possible shipment delays, enabling Chat GPT 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.

Critically, the fragmentation of data impedes the creation of a holistic view of the organization’s supply chain. To avoid being left behind, it is important for organizations to understand these trends and apply specific actions to begin their transformation sooner rather than later. This way they can create a more agile and responsive supply chain that can capture the promise of value creation, cost reduction and improved shareholder value. On this platform, any authorized user could access critical information in an intuitive way, using natural language. We were able to achieve an easy-to-access, real-time, single view of truth with immediate insights to manage the client experience, operate with resilience and react to market disruptions. Despite major disruptions and dislocations in the COVID and post-COVID worlds, IBM’s supply chain fulfilled 100% of its orders and delivered on its promises.

These promise improved predictability, enhanced gross margins and free up resources to focus on value adding activities. With the continued focus on resilience and ESG coupled with the expansion of sites, flows, and partners, the pressure on supply chain planning is increasing. Existing planning capabilities have been unable to meet the demands of a more complex, multi-tiered, more nuanced world.

Demand sensing using social media and external data is a supply chain analytics example that leverages real-time information from various sources to improve demand forecasting accuracy and responsiveness. It works by collecting data from social media, search trends, news, weather forecasts, and economic indicators, then processing and analyzing this data using NLP https://chat.openai.com/ and machine learning algorithms. These insights are integrated with existing supply chain systems to update demand forecasts in near real-time. The process involves collecting historical sales data, inventory levels, and other relevant information, then applying statistical methods and machine learning algorithms to identify patterns and predict future demand.

Some popular blockchain options for supply chain management include Hyperledger Fabric, Corda, VeChain, and IBM Blockchain Platform among others. Blockchain in the supply chain creates an immutable ledger that records every transaction and movement of goods, ensuring data accuracy and security. Additionally, blockchain enables the implementation of smart contracts, automating and streamlining various supply chain processes such as procurement, payments, and compliance. Some elements of future-ready transport and logistics networks are already in evidence such as the automation of warehouses and ports, and the increasing use of autonomous vehicles.

Supply Planning or Supply network planning optimizes production using a production capacity at a very broad level. However, further optimization and scheduling are done using an advanced optimizer, which may consider additional constraints such as sequencing or constraints specific to a production process in the industry. If it is not feasible to optimize using MILP or other optimization algorithms, then specialized approaches like genetic programming are used. SCM solutions offer configurable processes covering end-to-end supply chain operations right from the procurement of raw materials to the sale of the finished product.

One of our clients, a German-based Fortune 100 multinational engineering and technology company, needed to streamline the management of more than 400 warehouses around the globe. They partnered with the N-iX specialists to modernize and build a scalable logistics platform. N-iX works on a computer vision solution for warehouse cameras based on industrial optic sensors, lenses, and Nvidia Jetson devices. This solution will allow the client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. This product will help the client with object detection, package damage detection, OCR, and NLP for document processing.

This approach results in too much executive energy seeking to understand where the business is, and not enough time spent on the forward-looking decisions essential to driving the business. In a complex and volatile environment, CPG manufacturers can no longer rely on the supply chain planning processes of the past. Instead, they have a clear opportunity to improve financial and operational performance by implementing autonomous planning across the entire end-to-end supply chain. Capturing this potential will not be easy, particularly given that many companies have long legacies and deeply entrenched ways of working. Autonomous planning focuses on enabling critical business processes with advanced analytics and artificial intelligence. That includes S&OP, demand planning, dynamic production scheduling, inventory and replenishments, exceptions management for expedited orders or other outliers, and the integration of suppliers.

As supply chain leaders, we’ve seen unprecedented levels of disruption, challenges with our ecosystem trading partner networks, tariff wars, real wars and tremendous turnover in human capital. And just when we thought things were on their way back to a new “hybrid” normal, a curious new technology in generative AI seems poised to upend the world of operations yet again. GenAI can assess aspects of operations like supplier performance and manufacturing speed, then suggest ways to optimize procedures. The rapid shift in demand during the beginning of the COVID-19 pandemic, as consumers moved their spending away from services and toward goods, led to unpredictable supply chain operations.

This allows for more insightful and actionable recommendations to anticipate challenges and optimize operations. 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.

Now that you know AI is indeed transformative for the supply chain, your next questions may be–what are the right use cases and applications of AI in supply chain management? This blog post answers these questions and provides practical insights into the role of AI in supply chain. Watch how AI can utilize data generated from customers to create accurate demand forecasts and adjust them in real-time to make the supply chain smarter and more robust. Bots enabled with computer vision and AI/ML can be used to automate repetitive tasks in inventory management, such as scanning inventory in real time.

Warehousing companies can dynamically adjust storage rates based on available space, demand for specific storage types, and the cost of labor. Manufacturers can adjust pricing based on raw material costs, production capacity, and demand for specific products. Achieving supply chain visibility requires combining data from various sources, such as ERP systems, transportation management systems, warehouse management systems, and supplier portals, to create a unified view of the supply chain. Utilizing technologies like GPS tracking, RFID tags, and IoT sensors allows for real-time monitoring of goods in transit and within warehouses.

For example, you could write to a chatbot, “I have a problem with shipping the package.” The bot would understand the words “problem,” “shipping,” and “package” and provide a predefined answer based on these phrases. Also, Machine Learning helps program autonomous vehicles and robots, which are widely used in warehouses. With the help of guides built into the system, autonomous vehicles and robots help receive, pack/unpack, transport, and upload/unload boxes. Computer vision, in this case, helps find a free place for a box, control whether it is placed correctly, and prevent collisions between robots and vehicles in warehouses.

Our team has worked with both structured and unstructured data and will help you set up automated data collection, if needed. 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.

Still, the promise of using distributed ledger technology to support greater supply chain visibility and traceability is real. Walmart’s mandate that its suppliers use blockchain is arguably the most high-profile case. 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. Complex supply chains become vulnerable to various types of fraud, such as false or inflated invoices, non-authentic products, or forgery.

Imagine sensors constantly monitoring machinery like forklifts, conveyor belts, and automated storage systems. This data is analyzed using algorithms and machine learning to identify patterns and predict potential breakdowns. Instead of waiting for a breakdown, companies can schedule maintenance during off-peak periods, minimizing disruption to operations.