From 2019 through 2023, worldwide business adoption of Artificial Intelligence hovered around the 50-55% range, according to2024 McKinsey report (businesses reporting adoption of AI in at least one business function). However, the first few months of 2024 show a huge spike in AI adoption, jumping from 55% up to 72%. Perhaps more telling is that adoption of Generative AI (GenAI) has blown up, growing from 33% the previous year to 65% in Q1 2024. We expect the final 2024 report will show even higher numbers.
When we specifically focus on supply chain management (SCM), utilizing AI-powered tools to enhance and streamline processes isn’t just a good idea… it’s proving to be the ideal use case for this powerful new technology. It turns out artificial intelligence and SCM go together like proverbial peanut butter and jelly. In fact, the 2022 McKinsey Global Survey stated of all the business categories surveyed, respondents reported the highest cost benefits from using AI in supply chain management. Forty-one percent of survey participants whose companies adopted AI reported significant cost decreases specifically in SCM, with some saving over 20%.
According to a 2024 article in the Georgetown Journal of International Affairs, “Early adopters of AI-enabled supply chain management have reduced logistics costs by 15 percent, improved inventory levels by 35 percent, and enhanced service levels by 65 percent. Adopting AI tools to manage manufacturing operations can be costly, but 70 percent of the respondents from a survey of CEOs of over 150 firms agreed that AI is delivering a ‘strong ROI.’”
So, it’s abundantly clear that AI-powered tools have become essential for effective, cost-efficient supply chain management in the postmodern age. Let’s look at some specific examples.
How exactly does AI benefit the various aspects of SCM?
Companies like Amazon use powerful AI tools and algorithms to analyze historical sales data, seasonal trends, and external factors (like economic indicators) to predict demand more accurately. This helps the online retail giant optimize inventory levels for demand spikes and reduce stockouts. Sifted.com says, “For example, during the 2023 Cyber Monday sales, Amazon used AI systems to forecast a daily demand of over 400 million products and predict where the orders were coming in from based on their reserves of historical data.”
The more accurately you can complete the demand planning process, the more your business can enjoy the benefits of improved efficiency and resource allocation, lower costs/better profits, and delighted customers.
Walmart employs AI-driven systems to track inventory in real-time across its vast network of stores, distribution centers, and warehouses. This ensures that products are stocked efficiently, reducing excess inventory and minimizing waste. Walmart says their AI-powered tools are able to leverage historical data and pair it with predictive analytics, allowing the organization to strategically place high-demand and/or seasonal items across distribution/fulfillment centers and stores, optimizing the entire shopping experience for both brick-and-mortar and online Walmart.com customers. “This data management system, along with investments in our supply chain—including automated facilities, department-ready freight, Next-Gen fulfillment centers, and store-based fulfillment—combined with in-store technology used by our associates and our massive last-mile delivery network, also ensures the efficient delivery of items. Our system . . . integrates insights from all the channels we use to serve customers.”
Sifted.com points out that Amazon also uses AI systems to more rapidly and effectively restock warehouses to optimize for faster delivery during peak holiday seasons, when demand on Amazon’s delivery stations nearly doubles, increasing from handling around 60,000 packages a day (each) to over 110,000 a day.
AI tools can more quickly and accurately identify inefficiencies in the supply chain. For example, IBM says, “Drowning in data and starved for insights’ is still true in the world of supply chain management. Nearly 60% of businesses have poor visibility across their supply chain and 76% of business leaders agree that without ‘one version of the truth,’ their organization will struggle to meet its business objectives. [IBM’s AI-enabled supply chain suite] brings you one unified view across your global supply chain network with actionable insights, enabling you to better understand, prioritize, and resolve critical issues in real time. By providing common situational awareness across the supply chain, users can make more confident decisions faster.”
AI-based supply chain optimization tools can automate routine decisions, allowing supply chain leaders to achieve a new level of operational agility, efficiency, and resiliency, even when unexpected supply chain disruptions occur.
Companies like Maersk aim to leverage AI to assess risks in their supply chains, such as geopolitical instability or natural disasters. Predictive analytics help them identify potential disruptions and devise contingency plans. Maersk says, “AI can provide real-time visibility into the entire supply chain, from raw materials to finished products. This can help identify potential bottlenecks and delays, enabling companies to proactively take corrective actions. The digital twin technology (a virtual, realistic replica of a physical asset or system, like a truck, warehouse or supply chain) will help with both performance-improvement and strategic purposes. Digital twin simulations can be used to visualise the performance of supply chain operations, to manage potential disruptions, and to stress-test supply chain resilience by modelling scenarios such as changing distribution flows.”
Better management (or avoidance) of disruptions reduces risk, and reduces cost.
Companies like Siemens use AI to evaluate supplier performance through data analysis, enabling better decision-making regarding supplier selection and negotiation. This enhances collaboration, improves the supplier/purchaser relationship, ensures quality, and facilitates a more consistent, regular supply flow. As far as sourcing suppliers, Scoutbee says Siemens leveraged AI to reduce their workload by up to 90%, quickly and easily identifying new potential suppliers without requiring expert knowledge or training.
The Institute for Supply Management (ISM) says, “AI-driven . . . transparency [in the supply chain] not only reduces the risk of disruptions but also encourages suppliers to uphold higher standards in terms of quality and sustainability. AI can analyze various data points, including supplier performance metrics and compliance records, providing organizations with the insights needed to make strategic decisions about their suppliers.”
Sometimes AI isn’t just software. For example, since 2002, FedEx has employed AI-powered robots in many of their sorting facilities to automate the handling of packages, to increase efficiency and reduce human error in the sorting process. Maersk is also using automated assets like robots, picking arms, drones, forklifts, and even trucks.
AI-enabled automation of processes can benefit every step of the supply chain. ISM says, “AI is streamlining the procurement process by automating routine tasks such as order processing, invoicing, and supplier onboarding. This not only frees up valuable human resources but also reduces the likelihood of errors associated with manual data entry. By automating these processes, organizations can achieve greater efficiency in procurement, allowing their teams to focus on more strategic and value-driven activities.”
As another example, Caterpillar developers use GenAI to query massive amounts of proprietary information and get practical answers without doing a deep search or spending hours reading thousands of pages of material.
Beginning in 2012, UPS started using its award-winning On-Road Integrated Optimization and Navigation (ORION) platform and has continually updated the system. It’s now enhanced with AI, enabling the recalculation of individual package-delivery routes throughout the day as traffic conditions, pickup commitments, and delivery orders change. ORION updates directions for drivers while they are on the road, based on changing conditions and commitments, in real time. The platform provides detailed turn-by-turn directions to guide local delivery drivers, not just to addresses, but to specific package drop-off and pickup locations such as loading docks that may not be visible from the street.
Oracle says, “Logistics companies use machine learning [ML] to train models that optimize and manage the delivery routes by which components move along the supply chain. These models can prioritize shipments based on order volumes, delivery promises, contractual deadlines, customer importance, or product availability. And they can provide all nodes in the distribution network with more-accurate estimated times of arrival, identifying shipments that, if delayed, risk creating larger problems.”
AI is also employed by companies like UPS and DHL to provide real-time tracking information to customers, providing greater visibility and enabling proactive communication about potential delays. It also enables more flexible delivery and scheduling, which improves customer satisfaction.
Footwear News reports that Zappos utilizes a series of AI-driven technologies that help emulate how a human being would perform various tasks. This includes GenAI to capture customers’ context and answer their questions quickly. They are also exploring the idea of a conversational AI chatbot to help customers looking for shopping suggestions, size recommendations, or assistance with past orders. The company says AI can enhance every stage in the customer journey, including search suggestions/complementary product recommendations based on items in a customer’s cart, analyzing customer sentiment, enhancing marketing communications, and even enabling virtual try-ons in Zappos’ machine-learning augmented-reality environment. Customers who try features like the virtual-try-on return about 30 percent less than customers who don’t, according to Zappos’ chief technology officer. Imagine a 30% lower reverse-logistics rate and what that could do for your SCM strategy!
Responsible companies are concerned with reducing the environmental impact of their business activities. It’s also a good opportunity for positive public relations efforts. As one example, Unilever employs AI to analyze the environmental impact of its supply chain, helping to optimize processes and products for reduced carbon footprints and better resource management. Unilever says, “We’re using AI to help identify alternative ingredients that can strengthen the resilience of our supply chain, making our formulations more sustainable and cost-efficient, and simplifying them by reducing the number of ingredients without impacting a product’s quality or effectiveness. By optimising our design and manufacturing processes through virtual simulations, automated workflows, and data-led decision making, we’re unleashing new levels of efficiency in our operations which is in turn freeing up our experts to focus on delivering impactful innovations.”
Oracle says, “By driving operational efficiencies, AI can make supply chains more sustainable and lessen their harmful environmental impact. For example, ML-trained models can help organizations reduce energy consumption by optimizing truckloads and delivery routes so trucks burn less fuel while delivering supplies.”
UPS says its ORION system, now AI-enabled, has saved at least 10 million gallons of fuel and 100 million miles annually, significantly reducing the company’s carbon footprint and saving up to an estimated $400 million in costs per year.
Caterpillar uses machine learning and AI to enable their Condition Monitoring suite of technologies, which Cat dealers use to detect and identify issues and recommend service or maintenance based on data from the machine itself. By using AI-assisted analysis of data from sensors, Cat’s Condition Monitoring Advisors (CMAs) can predict equipment failures before they happen, minimizing downtime and repair costs in the supply chain. Caterpillar says, “In the past, our CMAs had to extract data from multiple systems, perform an analysis and make a customer recommendation. Using Gen AI, the CMAs are now presented with a concise report containing data that has been automatically prepared and summarized with a recommendation already created. The CMA can then review the report and approve or edit the recommendation. There’s still a human in the loop, but the time required to prepare data and create a recommendation is significantly reduced.”
As for quality control, AI-assisted sensors and image-recognition systems help companies like Nestlé to monitor product quality on production lines. This ensures that defective products are identified and removed quickly, enhancing quality and reducing costs. The company says that AI has enabled such improvements in production efficiencies that said some of the company’s KitKat manufacturing lines are now self-regulating. “We have loops in those lines, so they detect the product quality, they measure the attributes of the wafer, for instance, and then they regulate the process back, so, it’s a self-controlling mechanism. But we have also developed, for instance, machine learning approaches for preventive maintenance. So, that allows us to reduce [the] downtime of our lines.”
Sources:
https://gjia.georgetown.edu/2024/02/05/the-role-of-ai-in-developing-resilient-supply-chains/
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
https://sifted.com/resources/how-amazon-is-using-ai-to-become-the-fastest-supply-chain-in-the-world/
https://www.ibm.com/think/topics/ai-for-supply-chains
https://www.oracle.com/scm/ai-supply-chain/
https://www.caterpillar.com/en/news/caterpillarNews/2024/future-of-ai-at-caterpillar.html
https://www.dairyprocessing.com/articles/1333-nestle-making-use-of-ai-concept-engine
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