AI in Supply Chain: Top Use Cases and Applications With Examples

Additionally, contracts for indirect materials and transportation should be reviewed for requote and new contracts every few years. To mitigate constant disruption, COOs are transitioning linear supply chains to a fully networked digital ecosystem. Companies are making their supply chains more cost-efficient, resilient and sustainable in an increasingly uncertain world. Discover how EY insights and services are helping to reframe the future of your industry.

Their adoption will expand as organizations commit to emissions reduction targets and battery technology evolves to extend distance limits for electric trucks, buses and delivery vehicles. Across media headlines, we see dark warnings about the existential risk of generative AI technologies to our culture and society. Yet as supply chain innovators, we know there is a rich history of applying technologies to continuously optimize operations. Is generative AI likely to drive an “extinction event” for supply chains as we know them?

AI algorithms are capable of swiftly processing huge amounts of data about suppliers, in particular about their delivery times, pricing, and product quality. 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. Modern warehouses aren’t just storage centers; they are lively hubs where every square foot counts.

Most SCM solutions implement traditional algorithms and optimization as part of their backend logic and rarely use AI/ML algorithms. In fact, the examples of applications of AI in the supply chain can go as far as your imagination does. I’ve gathered 28 examples on how to boost the supply chain with artificial intelligence in an earlier article. Keeping track of the flow of goods in the supply chain on a system such as Food Trust helps participants track the temperature information and potentially settle any disputes, Gopinath said. As part of that mission, Tony’s Chocolonely teamed up with Accenture to develop and pilot a working private blockchain prototype that its supply chain partners in Ivory Coast successfully tested in the field.

You can prepare to fill your stores in advance and prevent excesses of goods or important parts for manufacturing. Generative AI can analyze large volumes of data, including credit history, financial statements, and market information, to assess the creditworthiness of suppliers, partners, or customers. This helps supply chain stakeholders to manage financial risks, make informed decisions about extending credit, and identify potential defaults or disruptions in the chain. By processing large volumes of data, including historical supplier performance, financial reports, and news articles, generative AI models can identify patterns and trends related to supplier risks. This helps businesses evaluate the reliability of suppliers, anticipate potential disruptions, and take proactive steps to mitigate risk, such as diversifying their supplier base or implementing contingency plans. For example, a digital twin can serve as the foundation of a supply chain stress test, such as the one Accenture and MIT have developed.

«In my research, I haven’t really been able to find a very clear-cut case that said, ‘yes, we can correlate sales lift to [using blockchain],'» Laborde said. «There is research that shows that the more transparent a company is about their products, [that] directly correlates with an increase in [consumers’] purchases,» Laborde said. Artificial intelligence simplifies and complements the process of plotting and building optimal routes based on traffic congestion, roadwork, and other variables.

The fundamental nature of supply chain is evolutionary, and it has been that way since our craft was born out of the Toyota Production System in the 1950s. «Business leaders should look to add automation to offer local [supplier] options to supply chains to tighten them and lower costs,» Le Clair said. Finding new ways to boost supply chain management efficiency is more critical than it’s ever been. Generative AI models can analyze factors such as customer demand, competitor pricing, and market conditions to generate optimal pricing strategies. These strategies can help businesses maximize revenue, profit margins, and market share while maintaining a competitive edge. The global supply chain has been continuously evolving, striving to achieve the most significant advantages in efficiency, cost reduction, and customer satisfaction.

Suggested approaches include a rule-based or heuristics or some other AI/ML algorithm, which will analyze the cumulative status of the supply chain (e.g., to date in the month) and amend the supply or production plan for the coming days/weeks. The CPG industry has long relied on traditional processes to manage supply chains and operational performance, but the pandemic has upended many (if not most) of these efforts. Consumer sentiment has changed dramatically, with a marked shift to value and a greater focus on essential products. In many markets, concerns about physical stores have accelerated growth in online shopping. Purchasing loyalty has diminished, as consumers have become more willing to try new brands. All of these changing consumer needs and market dynamics put significant pressure on CPG companies to find better ways of planning.

This way, trucks can be diverted at any time on their way when a more cost-effective route is possible. From ESG to robots and the metaverse, supply chain leaders have new challenges to prepare for. Organizations will need to intensely focus on mining relevant, clean and well-governed data if they want to make the most of their new technology investments. Data will also be crucial as organizations are pressured to meet evolving ESG and Scope 3 commitments.

Even amid the global pandemic, enterprises were focused on evolving their AI supply chain pilots into operationalization. But, suddenly, another evolution of AI seized the spotlight — generative AI, popularized by ChatGPT — and upended our notions of what’s possible. Ultimately, inventory optimization through predictive analytics is one of those supply chain analytics examples that enable companies to achieve more efficient and cost-effective processes. Logistics companies can adjust their shipping rates based on fuel prices, traffic conditions, and demand for specific routes.

C. Manufacturing

Furthermore, predictive maintenance allows for more accurate forecasting of spare parts needs, minimizing stockouts and reducing inventory costs. Route optimization for transportation networks involves designing and improving efficient routes to move goods cost-effectively. By optimizing transportation routes, businesses can minimize expenses such as fuel costs, labor costs, and vehicle maintenance costs, resulting in increased profitability. Cognitive supply chain is a new concept growing in popularity thanks to these technologies.

Intelligent automation layers AI on top of RPA and can help prepare a request for quotation package and allow access to a wider set of vendors. As we stand on the cusp of a new era in supply chain management, the question isn’t whether to adopt AI or not. However, integrating AI into your processes and systems efficiently requires a technology partner with deep knowledge and experience of AI in supply chains.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. Fairbairn contrasts the previous «just-in-time» standard, which saw companies still producing to demand without holding large volumes of inventory, with the current approach to holding larger stock to reduce risk.

This is why companies that are looking to increase their spending on and use of these technologies should focus their initial efforts to get the biggest return on their investment. We think three use cases, in particular, make the most sense as starting points—all of which can play a significant role in helping companies maximize relevance, resilience and responsibility. Accenture’s Solutions.AI for Pricing usess advanced AI and machine learning algorithms, including deep learning and game theory, to optimize pricing strategies in real-time. It offers capabilities like base-price optimization, discount personalization, and deal margin optimization across multiple industries.

On top of that, he adds, a major Chinese factory caught fire shortly befotre the pandemic. «Moving all the manufacturing from North America or Europe eastwards means you still have to ship everything back,» Mohamed says. «Globalization has impacts, and when calamities or issues come up, everyone looks for localized support.» Sign up today to receive our FREE report on AI cyber crime & security – newly updated for 2024. In recent years this has been especially apparent, with the lack of diversity in component suppliers and design alternatives laid bare amid the pandemic and wider economic downturns.

Generative AI in Manufacturing Industry: 5 Use Cases in 2024

AI algorithms can also automate and streamline critical warehouse operations, such as order picking, packing, and shipping. These systems can dynamically allocate resources, optimize workflows, and rapidly adjust to changing conditions, leading to improved throughput and reduced fulfillment times. Moreover, the portal allowed Ducab to digitize and streamline various supplier management tasks, such as certificate tracking and profile updates. These and more AI features in the portal, have helped the company eliminate manual processes from their supplier relationship management operations.

With fresh constraints on the near to medium horizon on aspects of the supply chain from shipping to materials sourcing, the IT industry stands reminded of its vulnerability to global shocks. It must also be remembered that the process is what will deliver the desired results—not the technology. Technology, however, is important and can be a differentiator if it’s leveraged correctly. Only then should an organization select and deploy a technology that supports and enhances the process. Organizations that fail to establish processes then deploy technology often end up with a system that merely does the wrong thing faster.

AI also enables personalization, allowing route optimization to be tailored to individual preferences and needs, such as delivery time windows, customer instructions, and vehicle characteristics. AI systems can provide up-to-the-minute information on traffic conditions by processing vast amounts of data from GPS, traffic cameras, and mobile apps. This allows route optimization algorithms to dynamically adjust routes and avoid congestion, saving time and reducing fuel consumption. AI systems can autonomously learn which visual features are essential for quality inspection by analyzing large datasets of good and bad product samples. This self-learning capability, enabled by deep learning algorithms, allows the AI to adapt to a wide range of quality scenarios without the need for extensive manual programming by experts.

You can also check our data-driven list of supply chain software to find the option that best fits your business. AI-powered tools such as RPA can also help automate routine supplier communications like invoice sharing and payment reminders. Automating these procedures can help in preventing silly hiccups caused, for example, by failing to pay a vendor on time and having a negative knock-on effect on shipment and production. 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.

Businesses can use SRM analytics to assess supplier performance, identify risks, inform negotiations, and make strategic decisions about supplier selection and development. This approach enables companies to improve supplier performance, https://chat.openai.com/ reduce costs, mitigate risks, and align supplier capabilities with long-term business goals. Predictive maintenance is a game-changer for supply chains, using data to anticipate equipment failures before they occur.

This way, the machine can teach itself over time, improving the accuracy of its algorithms. IDC predicts that by 2026, 55% of G2000 OEMs will redesign their service supply chains using AI. This means that over half of these major manufacturers will leverage Artificial Intelligence to transform their service operations. Each day millions and millions of date records are generated across the supply chain from multiple systems. The proliferation of digital technologies, IoT devices, and advanced tracking systems have compounded the problem. This wealth of data has given rise to greater silos of data within the organization which in turn has led to disconnected data sets.

Supply chain & operations

In recent years, we have all witnessed the transformation of the traditional linear supply chain into digital supply networks (DSNs). With the help of technologies such as IoT, Artificial Intelligence, and Machine Learning, it is possible to transform traditional linear supply chains into connected, intelligent, scalable, customizable digital supply networks. If you deal with complex, multi-party transactions, require transparency, and need to enhance trust among participants, blockchain can be a valuable tool. It is particularly helpful when there’s a need for traceability, compliance, and risk reduction.

Harness the power of data and artificial intelligence to accelerate change for your business. Real-time access to supplier data can enable companies to hold suppliers accountable for where and how they’re sourcing materials—allowing brands to cut off a supplier that’s not meeting ethical or sustainable standards. Most companies couldn’t see beyond a few major suppliers—they were effectively flying blind—so they couldn’t know which suppliers were shut down or where orders were in the pipeline. It was especially difficult due to the global nature and complexity of most supplier bases. The solution integrates data from 17 different internal systems and external sources, processing over 1 million data points daily.

Organizations’ supply chain departments can use an RPA bot to check inventory levels and initiate a purchase order when supply levels dip below a specified threshold. Most companies have a purchase order template or online ordering process set up with their vendors, and the structured nature of purchase order information lends itself to automation. RPA bots can also generate notifications to customers if there’s a delay, enhancing customer experience with practice and real-time order updates, she said. RPA is particularly useful in managing cross-border shipments that may require various additional customs, storage and inspections processes that need to be coordinated. Maintaining equipment is an important aspect of supply chain management, and RPA — working with other technologies — can help by facilitating predictive maintenance efforts. AI can analyze various types of risks, such as currency fluctuations, interest rate changes, or geopolitical events, and generate insights to help businesses develop risk mitigation strategies.

Generative AI models can analyze demand patterns, lead times, and other factors to determine the optimal inventory levels at various points in the supply chain. By generating suggestions for reorder points and safety stock levels, AI can help businesses warehouse management by minimizing stockouts, reducing excess inventory, and lowering carrying costs. Generative AI creates models that can analyze large amounts of Chat GPT historical sales data, incorporating factors such as seasonality, promotions, and economic conditions. By training the AI model with this data, it can generate more accurate demand forecasts. This helps businesses better manage their inventory, allocate resources, and anticipate market trends. A digital twin can be created for the end-to-end supply chain or for specific functional areas for targeted improvements.

This solution leverages advanced AI to optimize picking processes, adapt to real-time warehouse conditions, and generate data for improving layouts, staffing, and inventory management. The AI-driven robots are designed to enhance efficiency while complementing human workers, aiming to create a smarter, safer, and more reliable supply chain. For cost optimization, AI models analyze historical pricing data, market trends, and supplier performance to recommend optimal sourcing strategies. These systems can predict future price fluctuations, suggest the best time to make purchases, and even automate routine procurement tasks. 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.

AI-powered supplier relationship management solutions leverage machine learning, natural language processing, and data analytics to help organizations select and manage the right suppliers for their products and services. Real-time updates can help create better inventory management practices and customer service with the aid of accurate delivery estimates and updates. But this real-time data also allows businesses to make informed decisions quickly for improved decision-making. It identifies bottlenecks and inefficiencies immediately while ensuring all stakeholders can access the same information, promoting transparency and accountability throughout the supply chain.

Applying this meant Alcatel-Lucent often managed to deliver products even when supplies were tightest, partly through investing more in its inventory to compensate for component shortages from the outset, he says. Therefore it’s critical to look beyond simply globally procuring the best quality for the lowest price, building in resilience and enough redundancies and localization to cover your bases when something goes wrong, he says. That was just weeks after a report released by Swiss advocacy group Public Eye said excessive overtime was still common for many workers in Shein’s supply chain. The company has been criticised for the conditions faced by workers at factories in its supply chain. However, if you’re currently evaluating your existing ERP system and in the market for a new back-end system or looking for a better, more cost-effective document exchange process, it’s a great opportunity to adopt something totally new.

From demand forecasting and inventory optimization to risk mitigation and supply chain visibility, we’ll examine a range of real-world use cases that showcase the transformative power of modern supply chain analytics. By the end of this post, you’ll be equipped with the knowledge and inspiration to harness the power of data and revolutionize your supply chain operations. Leveraging data analytics has become a critical differentiator for any business that seeks to optimize its supply chain operations. Modern supply chain analytics, a transformative approach that harnesses the power of data-driven insights, has become a true game-changer in the field.

Before moving forward with GenAI applications in the supply chain, supply chain leaders should consider which GenAI capabilities align with company objectives and assess applicable benefits and limitations. Big enterprises such as Wayfair, UPS, Unilever and Siemens move to automate more of their supply chains with AI as the coronavirus pandemic disrupts business operations. Robotic process automation can help companies automate supply chain and logistics workflows. RPA can help companies build a more resilient supply chain in the wake of COVID-19 by bringing automation to supplier relationships. RPA can streamline these aspects of the order management process, said Prasad Satyavolu, chief digital officer for manufacturing, logistics and energy at utilities at Cognizant, an IT consultancy based in Teaneck, N.J. In these cases, RPA bots monitor orders and update the order handover details across all relevant systems, Hung said.

But capturing these benefits is a journey, not a one-time transaction, and it entails thinking beyond technology to include process redesign, talent, performance management, and other aspects of operations. S&OP is a cross-functional business process that aligns supply and demand to optimize overall performance. It involves forecasting sales and demand, planning production and resource requirements, balancing inventory levels and supply chain constraints, and integrating financial and operational plans.

Top 10 Use Cases: Supply Chain Management

The “machine” learns, thinks and executes repetitive tasks while allowing supply chain professionals to focus on high impact business events. GenAI models with data such as historical weather patterns, traffic maps and fuel prices can identify routes for optimal travel and highlight potential upcoming disruptions as well as alternate routes if needed. Doing so can help shipping stay on schedule and improve customer service, since orders won’t be delayed. When companies combine RPA software with machine learning, it can gather data from vendors and customers, run simulations and analyze alternatives.

These same tools can help organize the data from vendor documents, allowing technicians to compare it. RPA bots can also help perform background «due diligence» tasks, such as running credit and compliance checks, to streamline the vendor selection process. «If an organization has limited ability to aggregate, consolidate and correlate data, decision-making is constrained at best,» Satyavolu said.

AI/ML Use Cases for Supply Chain Management (SCM)

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. For instance, it’s still critical to effectively manage inventory levels to optimize capital tied up in materials and source materials from reliable suppliers at competitive prices while also maintaining quality.

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. From a business perspective, Machine Learning provides valuable insights that simplify and accelerate decision-making. Machine Learning uses complex algorithms to suggest optimal solutions to business leaders so that they can make well-informed decisions. Machine Learning applications in supply chain are revolutionizing how retailers and suppliers work. As a branch of Artificial Intelligence, Machine Learning in supply chain uses data to train a computer model adjust to conditions without being programmed to do so.

Global Fortune 500 companies and government organizations are developing GenAI tools with partners to map and navigate complex supplier networks. These tools make it easier to plan for alternative suppliers in the event of a disruption and offer product tracing platforms to meet regulatory or ESG requirements. Enabled by data and technology, our services and solutions provide trust through assurance and help clients transform, grow and operate. In addition, KPIs will likely need to be defined for the entire supply chain organization, with everyone incentivized to strive for the right target behaviors.

Key variables like lead times, capacity, demand, and costs are incorporated into these models. Using analytics tools, businesses simulate how different scenarios would affect their supply chain and analyze the potential consequences on key performance indicators. Inventory optimization through predictive analytics is a data-driven approach to managing stock levels in supply chain management. This method uses advanced analytics techniques to forecast demand and determine optimal inventory levels, reorder points, and order quantities.

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. AI enabled sales and operational planning (S&OP) and integrated business planning (IBP) applications will help eliminate the gap between supply chain planning and execution. Low touch planning will take large swaths of manual work out of the end-to-end planning process and leverage the power of advanced analytics to answer deeper questions with minimal human intervention.

Since AI-powered forecasts can help maintain optimal inventory levels, carbon emissions attached to storage and movement of excess inventory can be reduced. Smart energy usage solutions can also reduce carbon emissions related to warehouse energy consumption. And to enhance your supply chain visibility, check out our data-driven list of Supply Chain Visibility Software. Since these systems do not tire, they can help improve productivity and accuracy in production lines.

To manage this uncertainty, many companies opt for price elasticity analysis for raw materials. It helps them understand how price changes affect the demand or supply of materials essential to a business. This approach involves analyzing historical data on prices and quantities to calculate elasticity coefficients, which measure the sensitivity of demand or supply to price fluctuations. A modern data platform is easily scalable, so it leverages advanced data integration techniques and technologies like data lakes and data warehouses. This is where the power of ELT (Extract, Load, Transform) data integration comes into play, particularly advantageous in the logistics context. This agility is crucial for enabling real-time analytics and other advanced analytical techniques that can provide a modern boost to your logistics analytics capabilities.

All in all, AI in supply chain has the potential to transform the industry holistically, from planning, sourcing, and procurement to quality control and supply chain automation. AI-powered tracking systems provide granular, real-time visibility into the movement of goods across the supply chain. If a shipment of perishable goods is delayed due to a port congestion, AI can automatically recalculate delivery times, assess the risk of spoilage, and suggest alternative routing or storage solutions to minimize losses. AI-powered spend analysis tools can rapidly categorize and analyze vast amounts of purchasing data across an organization. These systems use NLP and machine learning algorithms to automatically classify spend data into standardized categories, regardless of how individual vendors or departments may label items. This granular categorization allows procurement teams to identify consolidation opportunities, negotiate better contracts, and uncover maverick spending.

Facilitating seamless collaboration and information sharing among all supply chain stakeholders is critical for smooth end-to-end performance. Modern data platforms can facilitate secure data sharing and collaboration among supply chain partners, enabling them to share information, coordinate activities, and make joint decisions based on a shared understanding of the supply chain. Advanced security and access control features ensure the protection of sensitive supply chain data. Analyzing historical data to understand past performance, identify patterns, and uncover insights about the supply chain’s operations. Amsterdam-based Tony’s Chocolonely chocolate company represents one business that is working to help end child labor and modern slavery in cocoa supply chains as well as to help create a slave-free chocolate industry. Here are seven real-life use cases of how blockchain has the potential to improve supply chain management.

For instance, in the supply chain, ML helps identify fraudulent transactions, prevent credential abuse, accelerate fraud investigations, and automate anti-fraud processes. Moreover, with ML, supply chain professionals can automate the process of monitoring whether all parts and finished products meet quality or safety standards. Generative AI (GenAI) is a subset of AI that has the potential to revolutionize supply chain management, logistics and procurement. Software engines powered by GenAI can process much larger sets of data than previous forms of machine learning and can analyze an almost infinitely complex set of variables. GenAI can also learn —and teach itself — about the nuances of any given company’s supply chain ecosystem, allowing it to refine and sharpen its analysis over time. Storing extra product costs companies more money, so reducing excess stock could cut down on costs.

Our experts help you identify the right use case, select and fine-tune the right AI model, and deploy the solution efficiently. Learn how AI is reshaping supply chain planning, sourcing, procurement, and logistics operations, along with real-world examples of successful supply chain solutions powered by AI. Sustainability is a growing concern of supply chain managers since most of an organization’s indirect emissions are produced through its supply chain. The global furniture brand Ikea has also developed a demand forecasting tool based on AI, which uses historic and new data to provide accurate demand forecasts. Only a third of companies ushering in AI-driven transformation perform a diagnostic audit before rolling out the technology. 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.

On the consumer level, the GenAI process consists of inputting a command or question into a text, image or video field, which prompts the AI to generate new content. GenAI models are typically trained on large-scale data sets, and when a user inputs fresh data, the application uses the new data and its previously learned knowledge to create new content. RPA bots and AI are behind-the-scenes essential personnel during COVID-19, working virtually alongside supply supply chain use cases chain workers and sustaining goods and services lifelines. Automations for routine and repetitive manual tasks, such as load matching with transport availability and order management, can be difficult to implement directly into the existing ERP. Here are seven ways companies are weaving RPA into logistics and supply chain workflows. For more information on such technologies, you can check our article on the AI uses cases for supply chain optimization.

Even when supply chain transformation initiatives consider the implications of data, they often do it too late in the process, as a hygiene issue. This limits improvements to the realm of visibility, rather than surfacing actionable insights, making it harder to achieve operational success and realize value. If you want to transform supply chains, you must internalize this truth before you start. Clean, connected data will be the foundation of next-generation supply chain operations. Additionally, if you want accurate and timely data, you need to collaborate across enterprise boundaries. In a world where disruptions and complications are inevitable, strong supply chains are more essential than ever before.

However, technologies such as Machine Learning and AI can help you at all stages of supply chain management. ML algorithms will correctly forecast demand, improve logistics management, help you reduce paperwork, and automate manual processes. As a result, you will get end-to-end visibility into your supply chain while ensuring it works more efficiently, requires fewer operational costs, and is less vulnerable to disruptions. Businesses are bringing artificial intelligence into their supply chains to cut costs, speed up distribution, and get ahead of potential disruptions. Leveraging advanced analytics and decision intelligence, AI supply chain management helps companies make faster and more accurate decisions at strategic, operational, and tactical levels.

As an example of how these efforts can add up, consider how IBM Consulting recently helped IBM Systems transform the global supply chain that supported their USD 10 billion server business. When you integrate AI capabilities into your supply chain, you’re eliminating hours of manual work. Plus, AI can analyze valuable data that enables you to discover new focus areas and processes that could be optimized.

The system processes a variety of data inputs, including historical delivery patterns, real-time traffic updates, and weather forecasts. By analyzing this diverse data set, the AI can predict potential delays, identify optimal routes, and suggest proactive adjustments to delivery schedules. Moreover, ML models can leverage historical patterns and external factors like weather to anticipate traffic bottlenecks and suggest alternative routes before they become problematic.

• Companies are making their supply chains more cost-efficient, resilient and sustainable in an increasingly uncertain world.
• «In my research, I haven’t really been able to find a very clear-cut case that said, ‘yes, we can correlate sales lift to [using blockchain],'» Laborde said.
• It enabled the automation of supplier pre-screening and self-registration, ensuring that only qualified suppliers get added to the database.
• To capitalize on the true potential from analytics, a better approach is for CPG companies to integrate the entire end-to-end supply chain so that they can run the majority of processes and decisions through real-time, autonomous planning.
• Zara has improved its online order fulfillment speed and efficiency by leveraging AI and robotics.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. The insights and services we provide help to create long-term value for clients, people and society, and to build trust in the capital markets. A better approach will be segmenting SKUs using clustering (e. g. K-Means) and then applying different strategies to each segment. However, the interpretation of segments (clusters) has to be done manually by business analysts/data scientists. Maybe in the future, an AI-based algorithm will be available which will provide a better and more interpretable solution to the clustering problem.

Thanks to recent updates that make it simpler to use and more effective in realizing value, organizations are now forced to determine how these advances will impact their sector or risk disruption. All of these processes use historical information and machine-learning methodologies to create a clear view of the entire supply chain, so that COOs can optimize for specific variables. For example, an ideal solution would maximize product availability and production capacity, while also lowering the total cost to serve. In addition, it would be able to model potential future scenarios, with predictive planning to simulate the impact on the supply chain, along with the specific implications of various mitigation measures. Despite the initial investment required, the long-term benefits in cost savings, risk reduction, and strategic advantage often make it a worthwhile endeavor for companies looking to build more resilient and efficient supply chains. The potential benefits include improved forecast accuracy, reduced inventory levels, fewer stockouts, increased agility in responding to market changes, significant cost savings, and potential revenue growth.