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Supply Chain AI: 25 Current Use Cases (and a Handful of Future Ones)
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1 an agoon
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When it came out, ChatGPT seemed like magic. It has led supply chain vendors to discuss how they currently use artificial intelligence. Further, virtually every supplier of supply chain solutions is eager to explain the ongoing investments they are making in artificial intelligence.
Any device that can perceive its environment and can take actions that maximize its chance of success at some goal is engaged in some form of artificial intelligence. AI is not a new technology in the supply chain realm; it has been used in some cases for decades. More recently, many other cases have emerged.
Optimization is used in supply planning, factory scheduling, supply chain design, and transportation planning. In a broad sense, optimization refers to creating plans that help companies achieve service levels and other goals at the lowest cost. In mathematical terms, optimization is a mixed-integer or linear programming approach to finding the best combination of warehouses, factories, transportation flows, and other supply chain resources under real-world constraints.
Machine Learning occurs when a machine takes the output, observes its accuracy, and updates its model so that better outputs will occur. Demand planning engines have natural feedback loops that allow the forecast engine to learn. The forecast can be compared to what actually shipped or sold.
Since ML began being used in demand forecasting in the early 2000s, ML has helped greatly increase the breadth and depth of forecasting. Now, ML forecasting is not just monthly or quarterly; weekly and even daily forecasting is now possible. We have moved from product-level forecasts at a regional level to stock-keeping unit forecasts made at the store level. More recently, demand planning applications based on machine learning have improved forecasting by incorporating competitor pricing data, store traffic, and weather data.
We are no longer just forecasting demand but also when trucks and factory machinery are likely to break down (predictive maintenance), the optimal amount of inventory to hold and where it should be held (inventory optimization), and labor forecasting in the warehouse. This type of forecasting can forecast the number of employees required to perform estimated work down to the day, shift, job, and zone level. ML can also be used to generate labor standards for warehouse workers.
ML techniques like clustering, data similarity, and semantic tagging can automate master data management. Without accurate data, companies face the garbage in, garbage out problem.
In terms of supply planning, if key parameters (like supplier lead times) are no longer correct, then the planning becomes suboptimal. ML is being used to keep key parameters and policies up to date. It is also being used to predict whether an SKU believed to be in stock at a store is actually out of stock.
Supply chain risk solutions use ML and other forms of AI to predict which suppliers are included in a company’s multi-tier supply chain. This is becoming increasingly necessary as customs will hold up shipments at the port if it believes the shipment contains products made with slave labor from China, even if those components came from their supplier’s supplier’s supplier and represent a minuscule portion of the total cost of the product. Shippers’ end-to-end supply chain predictions are based on applying AI to OpenWeb searches, import/export records, data from sourcing platforms like ThomasNet, federal logistics records, and other data. These predictions accelerate a company’s ability to verify how its extended supply chain is constructed. Customs uses the same technology to determine which shipments should be denied entry.
Natural Language Processing is used to classify commodity classification for use in imports and exports and in real-time supply chain risk solutions.
The Harmonized System is a commodity classification coding taxonomy that forms the basis upon which all goods are identified for customs. It is used by customs authorities worldwide. Using the right product classification allows companies to pay the correct tariffs. Paying the right tariffs is necessary to avoid government fines and calculate the true landed cost of products. The problem is that there is an incredible gap between how products are described commercially and how they are expressed in the national customs tariff schedules. This has resulted in error rates as high as 30%. The combination of natural language processing and expert systems has been used to automate and significantly improve the classification process.
Real-time risk solutions also use natural language processing to read online publications and other data sources, make sense of what they read, contextualize the data into information, and report supply chain disruptions caused by weather, geopolitical events, and other hazards in near real-time. Every step in that value chain has search terms associated with it. The names of the suppliers, carriers, logistics service providers become search terms. Those search terms are paired with terms signaling a problem – those terms might be “bankruptcy,” “plant fire,” “port explosion,” “strike”, and many, many other terms. So, the term “Haiphong” when combined in an article with the phrase “port fire” would generate an alert.
Reinforcement Learning is a form of machine learning that lets AI models refine their decision-making process based on positive, neutral, and negative feedback. For example, if you want to train a vision system to recognize a dog’s image, you will start by using humans to look at tens of thousands of images of animals. The humans label the pictures as dog, not dog, or unclear. The computer is then presented with those images. The system would say, “this is a dog” or “this is not a dog” and it learns whether its conclusion was correct.
Drones use this form of AI to improve inventory accuracy in a warehouse. Reinforcement learning allows the drone to recognize warehouse racks, pallets, and cases and get close enough to inventory to scan the barcodes. Similarly, reinforcement learning has been applied to security camera footage in the warehouse to ensure workers are following standard operating procedures.
Simultaneous localization and mapping (SLAM) allows a vehicle to construct and update a map of an unknown environment while simultaneously keeping track of the vehicle’s location within it. This technology allows mobile robots to move autonomously through a warehouse.
Drones and autonomous mobile robots using SLAM are in an early adoption stage for last-mile deliveries. Autonomous trucks will revolutionize logistics.
Autonomous trucks are not yet feasible, but we are probably just a couple of years out from being able to transport goods from a distribution center to a retail facility autonomously.
Causal AI is a technique in artificial intelligence that builds a causal model and can make inferences using causality rather than just correlation. Cause-and-effect relationships in an extended supply chain can be an intricate web that is difficult to unravel, but these relationships govern business operations. A causal model graph represents a network of interconnected entities and relationships, enabling the system to understand how various factors influence each other to create an optimized outcome. By leveraging causal knowledge and data graphs, Causal AI can navigate complex business scenarios, anticipate outcomes, and recommend optimal courses of action. Georgia-Pacific has demonstrated an application of Causal AI to improve touchless commerce dramatically. The solution was used to detect and correct both common and uncommon order errors or discrepancies in near real-time.
GenerativeAI is the new kid on the block. GenAI can generate text, images, videos, or other data using generative models. Some warehouse management suppliers are exploring using GenAI to generate end-of-shift reports or talking points used at standup meetings at the beginning of a shift.
Several supply chain application vendors are investing in GenAI to improve their user interfaces. The idea is that a user will make a request, and the system will take them directly to the answer they seek. GenAI can also help interpret complex charts and planning outputs. If a planning system indicates that a plan shows high costs or an inability to achieve targeted service levels, GenAI can help explain the upstream constraints driving that outcome.
Planning vendors are also interested in using GenAI to solve the black box problem. The black box problem occurs when planners don’t understand how the planning engine produced the plan it did. If they don’t understand it, they don’t trust it, and they then produce a much less optimal plan using Excel.
In the longer term, GenAI will help some planning vendors generate autonomous plans. When disruptions constantly occur, there is no time to constantly create and analyze scenarios on how to react best. Autonomous planning can improve a company’s supply chain agility. However, it is worth noting that a few planning suppliers can already generate autonomous plans based on ML and attribute-based planning rather than having to rely on GenAI.
The post Supply Chain AI: 25 Current Use Cases (and a Handful of Future Ones) appeared first on Logistics Viewpoints.
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Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution
Published
2 jours agoon
20 mars 2026By
Walmart’s new patents and digital shelf rollout point to a more tightly integrated model linking demand forecasting, pricing, and store-level execution.
Walmart has secured two patents related to automated pricing and demand forecasting, drawing attention to how large retailers are evolving their pricing and execution capabilities.
One patent, System and Method for Dynamically Updating Prices on an E-Commerce Platform, covers a system that can dynamically update online prices based on changing market conditions. A second, Walmart Pricing and Demand Forecasting Patent Classification, relates to demand forecasting technology designed to estimate what customers will buy and recommend pricing accordingly. At the same time, Walmart is expanding digital shelf labels across its U.S. stores, replacing paper labels with centrally managed electronic displays.
Individually, none of these elements are new. Retailers have long used forecasting models, pricing tools, and store execution processes. What is notable is the combination.
Walmart now has three capabilities aligned:
Demand forecasting tied to predictive models
Price recommendation based on that demand
Store-level infrastructure capable of rapid execution
That combination reduces the operational friction historically associated with pricing in physical retail.
Pricing Moves Closer to Execution
Traditional store pricing changes required coordination across multiple steps: analysis, approval, printing, distribution, and manual shelf updates. That process introduced delay and inconsistency.
Digital shelf labels materially change that constraint. Prices can be updated centrally and executed across stores with significantly less manual intervention.
This does not change the underlying logic of pricing decisions. Retailers have always adjusted prices based on demand, competition, and margin targets. What changes is the speed and consistency of execution.
As a result, pricing moves closer to real-time operational control.
Implications for Supply Chain Operations
Pricing is not an isolated commercial function. It directly influences demand patterns, inventory flow, replenishment timing, and markdown activity.
When pricing becomes faster and more responsive, those linkages tighten.
Three implications are clear:
1. Increased Execution Speed
Retailers can align pricing decisions more quickly with current demand conditions, reducing lag between signal and action.
2. Stronger Dependence on Forecast Accuracy
When pricing recommendations are driven by predictive models, the quality of demand sensing becomes more consequential. Forecast errors can propagate more quickly into sales and inventory outcomes.
3. Closer Coupling of Merchandising and Supply Chain
Pricing decisions influence demand. Demand impacts inventory, replenishment, and store execution. Faster pricing cycles compress the distance between these functions.
Centralization and Control
Walmart has positioned its digital shelf label rollout as an efficiency and accuracy initiative. Centralized price management improves consistency between systems and store execution while reducing labor tied to manual updates.
That positioning aligns with the operational realities of large-scale retail. At Walmart’s footprint, even small improvements in execution efficiency translate into material cost and accuracy gains.
At the same time, the shift toward algorithm-supported pricing introduces standard enterprise control requirements. Organizations need clear governance around how pricing recommendations are generated, reviewed, and executed, particularly as systems become more automated.
A Broader Technology Pattern
Walmart’s patents are best understood as part of a broader shift in supply chain and retail technology.
AI and advanced analytics are moving closer to operational decision points. Forecasting models are no longer confined to planning environments; they are increasingly connected to systems that can act.
In this case, that connection spans:
Demand sensing
Price recommendation
Store-level execution
The result is a more tightly integrated operating model in which commercial decisions and supply chain execution are linked through software.
What This Signals
The significance of Walmart’s move is not tied to public debate over surge pricing scenarios. The underlying development is structural.
Retailers now have the ability to connect demand forecasting, pricing logic, and execution infrastructure into a faster decision loop.
For supply chain leaders, that represents a clear direction:
Execution is becoming more digital, more centralized, and more tightly coupled to predictive models.
The companies that benefit will be those that can align forecasting, pricing, and operational execution within a controlled, coordinated system.
The post Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution appeared first on Logistics Viewpoints.
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Supply Chain and Logistics News March 16th-19th 2026
Published
2 jours agoon
20 mars 2026By
This week’s installment of Supply Chain and Logistics news includes stories about record increases in oil prices, Rivian’s autonomous taxis, and much more. Firstly, the Trump administration has issued a 60-day waiver of the Jones Act, a century-old regulation that requires goods moved between US ports to be transported by US-built vessels, etc. Additionally, this week Uber & Rivian announced a partnership for Rivian to build 50,000 autonomous robotaxis by 2031 with over a billion dollars in investment from Uber. Schneider Electric and EcoVadis announced a partnership to target emissions in the health care sector. Lastly, DHL announces 10 warehousing sites to be used for data center manufacturing capacity, and Mind Robotics raises 100 million in series A funding.
Your Biggest Stories in Supply Chain and Logistics here:
Trump Administration Issues Pause on Century-old Maritime Law to Ease Oil Prices
The Trump administration has issued a 60-day waiver of the Jones Act. This century-old regulation typically requires goods moved between US ports to be carried on vessels that are US-built, US-owned, and US-crewed. However, with oil prices surging toward $100 a barrel due to escalating conflict in the Middle East, the suspension aims to ease logistics for vital commodities like oil, natural gas, and fertilizer. While the move is intended to lower costs at the pump and support farmers during the spring planting season, it has sparked a debate between those seeking immediate economic relief and domestic maritime unions concerned about the long-term impact on American shipping and labor.
Uber and Rivian Partner to Deploy up to 50,000 Fully Autonomous Robotaxis
Uber and Rivian have announced a massive strategic partnership that signals a major shift in the future of autonomous logistics and urban mobility. Under the terms of the deal, Uber is set to invest up to $1.25 billion in Rivian through 2031, a move specifically tied to the achievement of key autonomous performance milestones. The primary focus of this collaboration is the deployment of a specialized fleet of fully autonomous R2 robotaxis, with an initial order of 10,000 vehicles and an option to scale up to 50,000 units. From a supply chain perspective, this represents a significant commitment to vertical integration; Rivian is managing the end-to-end production of the vehicle, the compute stack, and the sensor suite, including its in-house RAP1 AI chips, while Uber provides the scaled platform for deployment. Commercial operations are slated to begin in San Francisco and Miami in 2028, eventually expanding to 25 cities globally by 2031.
Schneider Electric and EcoVadis Announce Partnership to Decarbonize Global Healthcare Supply Chains
Schneider Electric, a major player in the digital transformation of energy management and automation, and EcoVadis, a provider of business sustainability ratings, have announced a strategic partnership aimed at accelerating decarbonization within the healthcare industry. “Energize” is a collective initiative to engage pharmaceutical industry suppliers in climate action. The collaboration focuses on addressing Scope 3 emissions, those generated within a company’s value chain, which often represent the largest portion of a healthcare organization’s carbon footprint. By combining Schneider Electric’s expertise in energy procurement and sustainability consulting with EcoVadis’s supplier monitoring and rating platform, the partnership provides a structured pathway for pharmaceutical and medical device companies to transition their global suppliers toward renewable energy.
Mind Robotics, a Rivian spin-off, raises $500 million in Series A Funding
RJ Scaringe, CEO of Rivian, is positioning his new $2 billion spin-off, Mind Robotics, as a technological solution to the chronic shortage of manufacturing labor in the Western world. By developing a “foundation model” that acts as an industrial brain alongside specialized mechatronic bodies, the company aims to move beyond the rigid, fixed-motion plans of traditional robotics toward systems capable of human-like reasoning and adaptation. Scaringe emphasizes that while these machines must perform with human-level dexterity, they don’t necessarily need to be humanoid in form; instead, the focus is on creating a data-driven “flywheel” within Rivian’s own facilities to lower production costs and help domestic manufacturing remain globally competitive.
DHL is significantly scaling its data center logistics (DCL) footprint in North America, announcing the addition of 10 dedicated sites totaling over seven million square feet of warehousing capacity. This expansion is a direct response to the explosive demand for AI-driven infrastructure and the specific needs of hyperscale and colocation data center operators. By offering specialized services like rack pre-configuration, white-glove handling of sensitive IT hardware, and warehouse-to-site transportation, DHL is positioning itself as an end-to-end partner in a sector where 85% of operators express a preference for a single logistics provider. This move not only addresses the logistical complexities of moving high-value components like GPUs and cooling systems across global borders but also underscores the critical role of integrated supply chains in maintaining the build speed of the digital backbone.
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How to Capitalize Quickly to Address Hyperconnected Industrial Demand
Published
2 jours agoon
19 mars 2026By
This first in a blog series offers a review of discussion that occurred during ARC Advisory Group’s 2026 Industry Leadership Forum. Specifically, it details a keynote conversation held with senior executives from Rolls-Royce, BTX Precision, and MxD.
The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production
Industrial leaders have been talking about tearing down workflow and data silos for decades. Yet here we are again. For most, the reality is that most operations and supply chains today typically don’t indicate much progress. A few leaders have figured out how to use digital tools to scale and build pathways forward, a whopping 12.9% according to our latest data (yes, that’s sarcasm). However, even as they struggle to coordinate, orchestrate, and innovate across their operations and enterprise, much less tightly collaborate outside their four walls. In a digital world, this continued capability gap, the inability to closely link market signals to responsive production and external supply chains, is very quickly becoming a liability.
Recently, at the 30th Annual ARC Industry Leadership Forum in Orlando, I had the privilege of leading a keynote discussion entitled The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. As part of that, I moderated an excellent conversation that included Global Commodity Executive Greg Davidson of Rolls-Royce, CEO Berardino Baratta of MxD, and CRO Jamie Goettler of BTX Precision.
In this four-part series, we will explore that conversation fully, digging into how the “fabric of market demand” has fundamentally changed, and why structural modernization, both human and technological, is no longer just an option. It is an industrial imperative that will increasingly determine who wins in disrupted markets.
Why Legacy Workflow Will Actually Get Modernized
If we examine the present through the lens of the past, the fundamental laws of supply and demand haven’t really changed. What has changed is the hyperconnectivity of the world and our compressed time to both reward and volatility.
The hard truth is that legacy linear workflows simply do not work in hyperconnected, digitally-driven environments, which are non-linear by nature. As our industrial environments become more digital, they naturally open up countless new ways for how things can get done and how risk can enter the organization. As a result, disruption has shifted from a rare event to a fairly continuous and pervasive reality. In this new reality, responsiveness differentiates you from the competition, and lag time kills.
To survive and thrive in non-linear environments, tighter, integrated ecosystems are required, where silos are actively torn down or redesigned so that barriers to value can be continuously identified and quickly eliminated. At the core, this concept is unfolding around data access, contextualization, and sharing. It provides the urgency behind the need for building industrial data fabrics.
This rewiring certainly extends beyond operations and enterprise processes, enabling the entirety of the supply chain to be judged on its collective responsiveness to the market, all the way down to the individual company level. In this scenario, data can quickly point out laggards who limit value. As the orchestrators of these supply chains identify these limitations on value, they quickly break off and discard the connection and move on without these weak links.
Pillars of the New Fabric of Demand
To achieve necessary level of operational and supply chain responsiveness, the roles of every entity within an ecosystem must be rethought. In the subsequent three blogs of this series, we will take a deep dive into the three distinct pillars that make up this modern architecture, but I’ll begin by laying them out here:
The Market Signal is the catalyst of the entire ecosystem. It dictates the “what” and the “when,” defining what value, success and risk look like in real-time. In blog 2, I’ll explore how to move from reactive assumptions to proactively capturing the market signals that actually matter.
The Demand Architect is moving beyond traditional order-taking. The Demand Architect designs and orchestrates the ecosystem, aligning external partners as true extensions of the enterprise. In blog 3, I’ll discuss the structural agility required to lead this response, rather than just manage a process.
The Agile Partner is the engine of execution. The Agile Partner links supply chain dynamics directly to the shop floor, differentiating themselves through their responsiveness to the market signal. In the final blog in the series, I’ll tackle how data transparency and trust become technical requirements, not just buzzwords, without exposing mission-critical IP.
Building the Modern Industrial Enterprise
Legacy workflows cannot survive in a non-linear world. Industrial organizations must re-architect operations and ecosystems for real-time responsiveness and secure, transparent collaboration. To do so, they will need to:
Improve the measurement of responsiveness: Efficiency and margin-squeezing are important, but they aren’t game-changers. Your competitive edge now relies on how quickly you can adapt to market signals.
Embrace transparency over secrecy: Modern collaboration requires providing a contextualized “lens” into production status without compromising proprietary IP or cybersecurity. Industrial data fabrics are key.
As always, view technology as a tool, not an outcome: Industrial data fabrics are needed to break silos and AI to manage complexity and improve accuracy and speed of decisions. However, the age-old adage remains true. Just because you can apply AI to something doesn’t mean you should. It must be grounded in measurable Value on Investment (VOI), not just return.
The New Fabric of Demand Blog Series
This is the first in a series of four on The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. Over the coming days, I’ll publish a perspective from each of the three pillars of the new fabric of demand:
Pillar 1: The Market Signal
Pillar 2: The Demand Architect
Pillar 3: The Agile Partner
By Mike Guilfoyle, Vice President.
For more than two decades, Michael has assisted organizations, including numerous Fortune 500 companies, in identifying and capitalizing on growth opportunities and market disruption presented by the effects of digital economies, energy transition, and industrial sustainability on the energy, manufacturing, and technology industries.
The post How to Capitalize Quickly to Address Hyperconnected Industrial Demand appeared first on Logistics Viewpoints.
Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution
Supply Chain and Logistics News March 16th-19th 2026
How to Capitalize Quickly to Address Hyperconnected Industrial Demand
Walmart and the New Supply Chain Reality: AI, Automation, and Resilience
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