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How AI Can Help Tame Warehouse Complexity
Published
9 mois agoon
By
Growing Complexity
The complexity of running the warehouse only continues to increase. Supply chain leaders face macro-challenges such as the pressure for sustainability, labor shortages and the effects of inflation on operating margins.
Layer on the daily micro disruptions that every warehouse experiences regardless of size – unexpected or delayed deliveries, inventory shortages, quality issues and scheduled labor or equipment mismatches versus real-time needs—and suddenly running the warehouse becomes unmanageable.
Backwards Solutions
Logisticians and warehouse operators have worked to rise to the challenge; however, there is only so much complexity that a warehouse team can effectively manage using many of today’s siloed, rules- and history-based warehouse management solutions. Too often, the challenge is met by throwing added resources at problems to close the gap. And unfortunately, those resources may not actually add much value.
For example, slotting and picking usually consume more than half of warehouse labor costs. Studies estimate that a typical warehouse picker spends over 50% of their shift just in travel time winding their way across the warehouse to or from a product pick. Warehouses also struggle with being over or understaffed and rarely strike the balance of what is “just right” for the day’s staffing needs.
This is because the data that the warehouse is working on includes historical receipts, demand, picks and shipments. When it does look into the future, it too often uses a forecast or plan that can be weeks, if not months, out of date.
AI Helps Close the Complexity Gap
Acknowledging that warehouse complexity is not going away, what can be done?
We envision AI and Machine Learning as the path to close the complexity gap and move the warehouse to the next level of efficiency and effectiveness.
AI and Machine Learning can take signals from historical data, blend them with real-time updates, future forecasts, predictive learning models and even include signals from other connected solutions. Together, these signals provide the warehouse team with recommendations that can optimize activity across the warehouse for today and into the future.
AI and Machine Learning can be the partner that the warehouse team has been looking for to simplify their complexity. We can already see ways that this can transform warehouse operations.
Agentic AI
Every warehouse team has that one person who understands the warehouse and brings an unrivaled depth of experience that helps the warehouse run.
Now, take that knowledge and expertise and imagine it on an interactive device and in your team’s hands 24/7/365.
Agentic AI can incorporate all those signals and provide recommendations in real-time to your warehouse team on the most effective use of their time. What trailer needs to be worked next and why? What is the best use of your slotting team’s time to reduce pick travel time and improve efficiency?
All while providing your entire team with clear, understandable explanations of why the recommendation was made and how it can help your team and operations.
AI in the Yard
AI and Machine Learning can help to reduce complexity in the warehouse yard.
Today, trailers are often prioritized for unloading mainly to support facility service levels and to avoid detention charges.
With AI and Machine Learning, Warehouse Management can utilize real-time signals to “see” the contents of each trailer, the status of pending orders against those contents and their priority against every other trailer in the yard.
AI and Machine Learning can provide real-time recommendations based on available scheduled resources, select the highest priority trailers to be worked next and anticipate the best dock door to most efficiently slot the product or cross dock for immediate shipment.
All to reduce complexity in the yard and the receiving dock.
AI Within the Facility
Slotting and picking are among the most labor-intensive activities within the facility. Most slotting decisions are made literally in the moment based on available slots. The results are products distributed across the warehouse based on thousands of disconnected slotting decisions.
With the introduction of AI and Machine Learning to the process, warehouse management can analyze each individual slotting decision, including signals such as history, volume estimates, pick affinity, optimal task and travel time, as well as available locations. Then, balance them against configured guardrails such as efficiency and movement costs to determine slotting locations that continuously optimize the warehouse for tomorrow’s needs, instead of just today’s availability.
The result is reduced complexity, which decreases pick travel time and increases pick efficiency since the slotting decisions have been continuously optimized over time to place products for faster picking.
AI and Resource Management
Accurately forecasting and then managing warehouse resources in real-time is a significant part of a warehouse leader’s work.
In today’s warehouse, resource forecasts are typically based on a combination of historical data and demand forecasts that usually do not accurately reflect what is needed in real-time. So, resource forecasting too often results in the warehouse being chronically over or understaffed.
When forecasting resources with AI and ML, solutions can incorporate historical data but also the latest real-time forecasted data to more accurately project resource needs days or months in advance, improving staffing and scheduling at a much more granular level.
But even the most intelligent resource forecast needs to be adjusted when today’s emergency intrudes. With AI-powered visibility, the warehouse management solution can ingest all these various signals, see the priority of workflows across the entire building and then shift available resources in real time from lower-priority work to mitigate today’s emergency.
The warehouse management solution is continuously updated and can re-prioritize tasks in real time, ensuring that all work across the facility stays on track and reducing the complex firefighting work that warehouses complete daily.
All these recommendations can be reviewed and approved by the warehouse team based on clear and understandable explanations from the solution’s AI.
Conclusion
All of this sounds like science fiction. But it is all very real.
Software providers are seeing more logistics and supply chain clients that need and inquire about AI and Machine Learning capabilities as part of their warehouse management or other solution sets.
To address the growing complexity and macro- and micro-disruptions, the warehouse team needs a trusted partner that can learn, improve and understand their facility and its capabilities.
A trusted partner that can help to reduce complexity and keep the facility working efficiently, not just today but every day.
AI and Machine Learning is ready to be that partner.
Steve Ross is part of Blue Yonder’s Solution and Industry Marketing team focusing on Supply Chain Execution.
With almost 30 years of operations, logistics, and e-commerce experience. Steve is an advocate for how technology can help free up the warehouse and store teams from the obstacles that legacy processes, solutions, and thinking have put in their path.
Steve believes in that Blue Yonder’s industry-leading supply chain platform and cognitive capabilities can be the partner the warehouse team needs to simplify their workload and improve efficiency.
Before joining Blue Yonder, Steve held multiple leadership roles, serving as the “translator” between the Logistics, Operations, Planning, and Buying teams. Steve has led various omnichannel implementations and has a background in Lean Six Sigma and project management.
Steve has an MBA from Roosevelt University, and a BA from the University of Arizona.
In his spare time, you will often find him in his kitchen baking sourdough bread or something sweet.
The post How AI Can Help Tame Warehouse Complexity 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.
Song of the Week:
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How to Capitalize Quickly to Address Hyperconnected Industrial Demand
Published
3 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.
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