Octave’s Mission: Building Systems That Don’t Break

At Hexagon’s recent global leadership event Hexagon LIVE, Mattias Stenberg, who was appointed in October 2024 to lead Octave, a strategic spinout from Hexagon, delivered a keynote that was both grounded and far-reaching. His talk offered a clear-eyed assessment of the state of critical infrastructure today, and what needs to change if we want our most essential systems to function reliably in the future.

Stenberg, who has been president of Hexagon’s Asset Lifecycle intelligence division since 2017, wasn’t there to announce a product line or unveil another dashboard. His message was more fundamental: we’ve inherited systems built to break. Octave’s mission is to build ones that don’t.

A New Kind of Intelligence for the Real World

Octave is not just another brand. It’s designed as an intelligence layer for the infrastructure that keeps society running. Think power grids, manufacturing systems, highways, water networks, emergency operations centers. The kind of systems where failure isn’t just inconvenient, it’s dangerous, costly, or even catastrophic.

At the core of Octave’s approach is a simple shift in mindset: stop reacting, start anticipating. Octave doesn’t aim to flood teams with more data or more disconnected tools. It aims to turn existing data into foresight, to embed real-time, contextual intelligence into the systems we rely on every day.

The name “Octave” itself is no accident. Inspired by music, it reflects a higher level of coordination and elevation, not just making noise, but orchestrating decisions. In practice, that means integrating AI directly into physical systems so they can detect issues, adapt, and respond long before human operators are even aware a problem is coming.

The Problem: Everything Breaks

Stenberg talked directly – “Things break,” he said, invoking the second law of thermodynamics with a hint of humor.

He pointed out examples we all recognize from the news:

• The Francis Scott Key Bridge collapse, a structural failure that could have been prevented with early detection.
• The Deepwater Horizon disaster, caused by system blind spots and poor integration between safety layers.

He pointed out that in each case, the data existed. The systems failed because they weren’t connected in the right way and lacked the intelligence to interpret early signals.

Octave’s Vision: Failure Is Not Inevitable

Too many organizations accept breakdowns as part of doing business. Delays, downtime, asset failure, they’ve been normalized. Octave’s entire premise is to challenge that mindset.

If systems can sense changes in their environment, process those signals intelligently, and act on them in time, then many failures can be avoided. But that level of performance requires embedded, context-aware intelligence, not generic AI models running in a separate system. Octave’s agents aren’t passive tools waiting for prompts. They’re active participants in system behavior.

Already, Hexagon has started deploying this approach across sectors, from manufacturing and logistics to public safety and energy. The early returns, he stated, are promising: less downtime, better coordination, and more resilient performance.

The Digital Transformation Disconnect

To underline the need for a new approach, Stenberg shared data from a recent C-suite survey conducted by Hexagon’s Asset Lifecycle Intelligence division,:

• Only 1 in 5 companies say they are realizing the full value of their digital transformation investments.
• 76% report using more tools and dashboards than ever, but feel less aligned and less in control.
• One executive summed it up: “More dashboards. More complexity. Less actual visibility.”

This reflects a deeper truth. Digital transformation alone doesn’t guarantee better performance. In fact, if not managed carefully, it can add noise, create silos, and obscure decision-making.

What Octave is offering is a reset: cut the clutter, connect what matters, and turn data into decisions that move the needle.

AI: Less Hype, More Usefulness

Stenberg then discussed the current AI hype cycle.

Yes, AI holds enormous potential, but most AI systems today are untrained, detached from operations, and built in isolation. They’re promising demos in a lab, not solutions in the field.

Octave takes the opposite path. Its AI agents are trained for specific environments, tied into operational systems, and continuously learning. These aren’t showpieces, they’re in the loop, helping machines and humans respond to signals in real time.

The Mirror World: Not Just a Twin, But a Partner

The term “digital twin” has been used in nearly every industry over the past few years. But as Stenberg pointed out, many of these twins are little more than static visualizations, nice to look at, but lacking predictive value.

Octave, Stenberg said, will be redefining the concept. In its vision, the Mirror World is a living, learning model of a physical system, constantly updated, deeply embedded, and able to act. If your digital twin can’t help you see trouble coming, it’s just a digital museum.

The goal is to spot patterns early, detect small signals before they become big problems, and support decisions that prevent, not just mitigate, failure.

Voices from the Field: What Must Not Break

Stenberg’s keynote also featured a panel of customers speaking directly about the pressures they face. Each one brought a unique perspective, but all centered on resilience and visibility.

Donald Lhoest (Carmuese): Global logistics operations require consistent, trustworthy intelligence. When teams are remote and distributed, “data needs to empower, not isolate, them.”

Colonel Mark Shelley (Lee County Sheriff’s Office): “Public safety depends on integrated, real-time intelligence. There’s no margin for error.”

Wade McNabb (Lixil): Factory downtime is a major risk. But human error is just as dangerous. “We need better training systems, and better insight into how our customers actually use our products.”

Joe Bonnet (Worley): “We run 10,000 projects at once. Complexity itself is the problem. If we can eliminate failure points, we can focus on innovation.”

Proof in Practice: The Öresund Bridge

To bring the message home, Stenberg closed with a case study:

The Öresund Bridge, connecting Sweden and Denmark, is equipped with a real-time digital twin. The system detected subtle vibration patterns, signals no human would have caught on their own.

Engineers investigated and discovered microcracks forming. Intervening early prevented what could have been a major infrastructure failure. That’s not theory. That’s the Mirror World in action.

Final Message: Time to Rethink What We Accept

Stenberg’s final words were a challenge to the industry.

“We’ve inherited systems that were built to break. Octave’s job is to build systems that adapt, and don’t fail silently.”

It is not about layering more dashboards or adding complexity. It’s about designing smarter from the start, systems that can think, respond, and evolve.

The post Stop Reacting, Start Anticipating: Hexagon’s Octave Vision for a Smarter and More Resilient Industrial and Commercial Infrastructure appeared first on Logistics Viewpoints.

In 2025, two ownership transition strategies are taking center stage in the manufacturing and supply chain sectors: strategic mergers and acquisitions (M&A) and employee stock ownership plans (ESOPs). While M&A is a vehicle for growth, it’s equally driven by founder-owner succession, generational transitions, and liquidity needs. At the same time, ESOPs are gaining momentum as a compelling alternative for owners seeking to preserve legacy while rewarding employees.

M&A: Supply Chain Resilience Drives Deal Activity

Global M&A activity in the manufacturing sector rose 5% in 2024, reaching $160 billion in total deal value. But beneath the headline numbers is a notable shift: more business owners are turning to M&A not to expand, but to exit. Private equity firms and strategic buyers are actively acquiring mid-sized, founder-led companies with strong customer relationships, operational know-how, and long-standing reputations in their regions.

In the supply chain and industrial services sectors, succession planning is now one of the top drivers of M&A activity. Many founders are ready to retire but face limited internal options. For these owners, a sale to a private buyer or PE-backed platform offers liquidity but often raises questions around keeping the core team and brand intact.

PE groups are fueling this trend—accounting for nearly 60% of all supply chain transactions in late 2024. Many are pursuing buy-and-build strategies and are eager to acquire specialized, profitable operators in logistics, distribution, contract manufacturing, and related segments.

ESOPs: Preserving Legacy While Enhancing Performance

ESOPs are becoming increasingly popular alternatives to traditional M&A, especially in the manufacturing and logistics sectors—where employee ownership is most prevalent. In fact, manufacturing accounts for 21% of all ESOP companies and holds 42% of the total assets among ESOP-owned firms, making it the leading industry for this structure.

For many owners, ESOPs offer a unique path: a tax-efficient sale that rewards employees, preserves company culture, and maintains operational continuity. Legislation like the proposed Employee Ownership Fairness Act of 2025 could further expand access by increasing contribution limits and simplifying compliance.

ESOP-owned companies are often more resilient, with lower turnover, stronger employee engagement, and better long-term performance—particularly valuable in industries facing labor shortages and margin pressure.

Where the Strategies Meet: Hybrid Approaches Are Emerging

An emerging trend is the combination of ESOPs and M&A. Some business owners opt to sell a minority stake through an ESOP while simultaneously bringing on outside capital or pursuing strategic acquisitions. This blended approach can offer liquidity, talent retention, and growth capital—all without ceding full control.

In both cases, the key is planning. Owners who wait until they’re ready to retire may miss out on the most favorable terms. Whether selling to a third party or transitioning ownership internally, a proactive process—backed by valuation expertise, tax planning, and strategic advice—can significantly enhance outcomes.

Ownership Transitions Are Shaping the Industry’s Future

The logistics and manufacturing sectors are entering a new era—one defined not just by technological transformation, but by ownership transition. As more founders consider stepping back, the decisions they make today will define their company’s future.

ESOPs, often considered niche, are a viable solution for founders who value continuity and culture. And in some cases, the best path may involve elements of both.

For business owners in the supply chain ecosystem, the time to evaluate exit options is before you need them. Whether through sale, succession, or employee ownership, the most successful transitions are those that are intentional, informed, and aligned with long-term goals.

Planning For a Thoughtful Transition

Ownership transitions—whether through a third-party sale, ESOP, or a hybrid approach—are complex decisions with long-term implications for both the owner and the business. In today’s environment, having a qualified advisor can make a meaningful difference in how those decisions are structured and executed.

If you’re considering a transition, it’s worth starting the conversation early. The right advisory team can help you evaluate your options objectively, navigate the nuances, and ensure the strategy you choose aligns with your long-term goals—for your business, your legacy, and your people.

By Robert Reavis, Director, ButcherJoseph & Co.

As a Director at ButcherJoseph & Co., Robert has advised a diverse group of middle market companies on mergers and acquisitions, capital raising, and strategic advisory assignments. Robert specializes in complex recapitalizations, capital markets activities, and real estate related transactions. Robert began his investment banking career at Société Générale, working in Paris for the consumer, retail, and luxury M&A group. His investment banking experience includes cross-border M&A advisory engagements for publicly listed and privately held companies across a broad range of industries. Robert earned a Bachelor of Arts with honors in economics and international studies from the University of Chicago as well as an international diploma from Institut d’Études Politiques de Paris (“SciencesPo”).

The post How Traditional M&A and ESOPs Are Reshaping The Manufacturing and Supply Chain Industry appeared first on Logistics Viewpoints.

For years, supply chain professionals have talked about visibility, resilience, and efficiency. The tools we have used, ERP systems, spreadsheets, and siloed databases, have served us well, however as complexity increases, and the margin for error narrows, there has been a growing recognition that patchwork systems are no longer enough.

What is needed is a practical, scalable way to unify supply chain data across systems, make it useful in real time, and apply intelligence, whether from algorithms, machine learning models, or a trained human eye, to act on it quickly. That is exactly the direction taken by InterSystems and their customers, as detailed at InterSystems READY 2025 in Orlando, Florida.

Data Fabric Studio: One Place to Start

At the center of the discussions was the InterSystems Data Fabric Studio, a cloud-based system designed to integrate and organize data from multiple sources. Not just for IT departments or data scientists, but also for the people who manage daily operations, procurement leads, planners, and inventory analysts.

This product connects directly to systems like Snowflake, Kafka, AWS S3, and relational databases. It allows users to build and automate workflows (called “recipes”) that clean, reconcile, and move data into consistent formats, without having to code from scratch.

In short, it helps turn fragmented operational data into something trustworthy, structured, and ready for use across departments.

Use Case: Supplier Data Integration Across ERPs

One session focused on a familiar problem: integrating supplier data spread across two disconnected ERP systems. Each system used different IDs for the same suppliers, different formats for purchase orders, and different rules for reconciliation.

Using Data Fabric Studio, the team:

• Mapped and validated key identifiers (like DUNS numbers) across systems.
• Flagged inconsistencies in supplier names and standardized the records.
• Created lookup tables and transformation rules to automate future loads.
• Set a schedule to refresh this data daily, no manual uploads are needed.

The takeaway: fewer errors, faster onboarding, and one consistent view of supplier performance.

Forecasting, Not Just Reporting

Several sessions went far beyond integration, since once data is unified, it becomes possible to do more with it, like improve forecasts or detect early signs of trouble.

One method shown was to create snapshots of data tables at regular intervals, such as open purchase orders at the start of each week or inventory by location at shift change. These snapshots could then feed planning tools without requiring repeated rework or new queries every time someone asks for an update.

It is not classical predictive AI, but it is the kind of practical structure that supports accurate forecasting and decision-making.

AI Integration

Many AI projects fail not because of the models themselves, but because the data going into them is disorganized or outdated. InterSystems’ position, with which ARC strongly agrees is that data must be AI-ready, structured, validated, and governed, before AI can be reliably applied.

For those who are ready, Data Fabric Studio includes native support for vector search and retrieval-augmented generation (RAG). This means it can:

• Embed semantic search into procurement or customer service workflows.
• Feed large language models with accurate, up-to-date information drawn from verified data.
• Support natural language interfaces, including assistants that generate SQL or explain trends.

One example came from Agimero, a European firm that used vector search to streamline parts procurement. They built a semantic layer into their sourcing tool, which helped reduce turnaround time and freed up staff. Time to deploy? Less than a week!

Lessons from Healthcare That Apply Here

A keynote from the AI for Healthcare track may seem unrelated at first, but the core lesson was broadly applicable: data doesn’t have to be clinical to be useful in diagnosis. In one case, the team used shopping data to detect early signs of ovarian cancer based on changes in food purchases.

Now let’s translate that into supply chain language.

What if sudden shifts in supplier invoicing patterns indicated financial stress? What if internal communications flagged increasing lead times before they hit the dashboard? The tools now exist to explore those questions, not just log them.

The point is it is time to examine where such signals might live in your own systems.

A Modular Approach That Does Not Lock You In

Another strength of the Data Fabric Studio is its modular design. You can start with basic data ingestion and cleaning, then layer on adaptive analytics, natural language assistants, or domain-specific modules (e.g., for supply chain, finance, or healthcare) when and if they make sense.

Unlike some vendor ecosystems, this one doesn’t insist you move everything into a new system. It works alongside existing data warehouses, ERP tools, and planning platforms. That flexibility matters, especially for organizations that cannot afford multi-year migrations.

Vector Search and RAG: Where It Fits

The integrated vector search capabilities shown during the sessions were grounded, not speculative. One demonstration showed how a company used it to improve search across 400 million records of biological data. The same tools were used in supply chain use cases, surfacing similar suppliers, matching part numbers across catalogues, or enabling text-based queries across historical documents.

These systems don’t replace human judgment, but they make pattern recognition faster, and reduce the time spent digging through dashboards and reports to get to the relevant piece.

Scalability Is not Optional

For companies working on a global scale, performance is key. Sessions with Epic (the healthcare software company behind MyChart) showed how InterSystems IRIS, the underlying engine behind Data Fabric Studio, supports hundreds of millions of real-time transactions.

Why mention this in a supply chain context? Because once data becomes foundational to operations, slow queries and manual workarounds no longer are enough. The infrastructure must keep pace.

InterSystems have built their offerings with that in mind, whether for healthcare, finance, or logistics.

What stood out across all the sessions was not hype, there was a clear theme:

• Organize your data first.
• Reconcile it across systems.
• Use automation to reduce repeat work.
• Add intelligence gradually, where it supports decisions.
• Prioritize infrastructure that can scale.

If you have worked in supply chain for any length of time, that list is no surprise, however seeing those steps pulled together in one single system, accessible to both developers and business users, is important and unique.

Digital Transformation is about doing what works, better, faster, and with less friction, and that is exactly what was seen at InterSystems READY 2025.

The post InterSystems READY 2025 – Modern Supply Chains, Practical Data Strategy, and Tools That Work appeared first on Logistics Viewpoints.

The United States stands at a critical juncture, confronting a surge in electricity demand driven by the rapid expansion of data centers and the broader electrification of its economy. This demand spike coincides with a worldwide imperative to transition toward cleaner energy sources. However, a complex and at times contradictory web of federal policies is creating significant headwinds. While the Inflation Reduction Act (IRA) offers powerful incentives to build a domestic clean energy supply chain, a concurrent strategy of imposing steep tariffs on imported components, particularly from China, is creating a policy paradox. This report will analyze how these conflicting measures, intended to foster long-term industrial strength, are raising the immediate cost of the cheapest sources of new power—solar, wind, and batteries—thereby threatening to increase electricity prices and delay the nation’s ability to meet the urgent power needs of data centers and a newly electrified society.

The Conflicting Signals of US Energy Policy

The current U.S. approach to the energy sector is characterized by two powerful but opposing policy levers: punitive tariffs and conditional incentives. This creates a volatile and uncertain environment for developers of renewable energy and storage projects.

The Tariff Wall Against Clean Energy Components

The U.S. has enacted a series of escalating tariffs, primarily under Section 301 of the Trade Act of 1974, targeting a wide range of Chinese goods essential for the energy transition. Lithium-ion batteries, a cornerstone technology for both electric vehicles (EVs) and grid stability, have been a primary focus. In 2024, the tariff on Chinese EV lithium-ion batteries rose from 7.5% to 25%. For non-EV batteries, such as those used in grid-scale storage systems, tariffs are also slated to increase to 25% by 2026. These duties are compounded by additional levies, leading to combined tariff rates on grid batteries of approximately 65%, with projections they could exceed 80%.

The immediate consequence of this tariff wall is a sharp increase in the price of these components in the U.S. market. This directly drives up the capital expenditures for renewable energy projects, complicating deal structures and introducing new financial risks. Because the U.S. battery energy storage system (BESS) industry is heavily reliant on Chinese imports, these tariffs have a particularly disruptive effect, leading to project delays and investment uncertainty.

The Inflation Reduction Act’s Conditional Incentives

In contrast to the punitive nature of tariffs, the 2022 Inflation Reduction Act (IRA) was designed to catalyze a domestic clean energy manufacturing renaissance through substantial subsidies. The Section 45X Advanced Manufacturing Production Credit, for instance, offers lucrative tax credits for domestically produced battery components, including $35 per kilowatt-hour (kWh) for battery cells and $10/kWh for battery modules.

However, these powerful incentives come with significant strings attached. To qualify for consumer tax credits like the $7,500 Clean Vehicle Credit, products must meet stringent sourcing requirements for battery components and critical minerals. Crucially, the IRA includes a “Foreign Entity of Concern” (FEOC) exclusion rule, which, starting in 2024, disqualifies any vehicle containing battery components from entities in China, Russia, Iran, or North Korea from receiving the credit.

This creates a policy paradox. The federal government is simultaneously subsidizing the clean energy industry while taxing its most critical and cost-effective inputs. For a project developer, this means navigating a landscape where the benefits of IRA credits may be partially or wholly negated by the increased costs imposed by tariffs. This dynamic forces companies to re-evaluate their supply chains, seek alternative suppliers that are often more expensive or have limited capacity, and contend with significant investment uncertainty.

The Direct Impact on Clean Power Costs

While the global trend for clean energy technologies has been one of rapidly falling costs, U.S. policy is creating a notable divergence, artificially inflating the price of the very technologies needed to decarbonize the power grid affordably.

The Rising Cost of Grid-Scale Battery Storage

Grid-scale battery storage is essential for a modern, reliable power grid. It solves the intermittency problem of wind and solar power by storing excess energy and dispatching it when needed, thereby enhancing grid stability. Lithium-ion batteries, particularly the Lithium Iron Phosphate (LFP) chemistry, have become the preferred choice for these applications due to their high efficiency and the fact that costs have declined 80-90% over th past ten years. .

However, U.S. tariffs are directly countering this deflationary trend. With the U.S. power industry facing an average tariff rate of 38% on electrical equipment, the cost of deploying BESS has risen significantly, deterring investment. This is especially damaging given that the cost of battery packs, which had been falling dramatically for over a decade, is a primary driver of the economic viability of storage projects. While technological advancements continue to push global battery prices down, U.S. trade policy is forcing domestic project costs in the opposite direction, slowing the deployment of this critical grid-balancing technology.

The Ripple Effect on Solar and Wind Projects

The cost pressures extend beyond batteries. Import tariffs are driving up capital expenditures for solar panels and wind turbines as well, complicating the economics of new renewable energy projects. Globally, wind and solar represent the cheapest sources of new electricity generation and are expected to provide 70-90% of all new power in the next 5 years. New grid power in the US was about 93% renewable in 2024. By artificially inflating their costs in the U.S., these policies blunt their competitive edge and slow the pace of their deployment. The result is a more expensive energy transition, where the cost savings that should be realized from adopting cheaper renewable sources are instead eroded by trade policy.

Consequences: Project Delays and Unmet Power Demand

The combination of higher costs and supply chain disruptions is creating a bottleneck in the deployment of new clean power resources. This bottleneck comes at the worst possible time, as new sources of electricity demand, particularly from data centers, are placing unprecedented strain on the nation’s grid. While current policies are pushing fossil power, no new coal plants will be built and the cost and schedule for new natural gas power plants has increased substantially with increased costs for steam and gas turbines and a shortage if engineering, procurement, and construction (EPC) manpower to build them.

The Data Center and Electrification Dilemma

The boom in artificial intelligence and cloud computing is fueling a massive build-out of data centers, which have immense and unrelenting power requirements. This, combined with the general electrification of transport and buildings, is creating a surge in new power demand that many utilities are struggling to meet. Clean energy, particularly solar-plus-storage projects, is the ideal solution to quickly power these new loads without increasing emissions. While recent government support for nuclear power is a longer-term option and while firms like Meta, Google, Amazon, and Microsoft have entered into alliances with new SMR and advanced reactor suppliers, new nuclear power will take a long time to get on-line and it is highly likely that new unproven reactors will have delays and cost increases.

However, U.S. policy is hindering this solution. The reliance of data centers on lithium-ion batteries for backup power and grid services means that tariffs are directly increasing their construction costs by mid-to-high single digits. More broadly, the delays and cost increases for utility-scale solar and battery projects make it harder for utilities to bring new, clean generation online in time to meet requests for new data center connections. This could force delays in the tech sector’s expansion or, perversely, lead to a greater reliance on fossil fuel “peaker” plants to meet the demand.

The impact on broader electrification is also significant. Tariffs on batteries and other components are contributing to a 10% or more increase in the price of EVs for American consumers, hindering the transition away from internal combustion engines. The complexity of the IRA’s sourcing rules further limits which vehicles qualify for consumer credits, acting as another drag on adoption.

Supply Chain Disruption and Canceled Projects

The strategic goal of reshoring the battery supply chain is a long-term endeavor. In the short-to-medium term, the primary effect of the current policy mix is disruption. Forced to seek alternatives to the dominant Chinese supply chain, U.S. companies face a market with a limited number of global suppliers and insufficient domestic capacity.

This disruption has tangible consequences. Between 2024 and 2025, canceled battery projects in the U.S. amounted to an estimated $9.5 billion, while new project announcements totaled only $1.175 billion. This investment chill, driven by cost uncertainty and supply chain instability, directly translates to a slower build-out of the manufacturing capacity and energy infrastructure needed for the transition.

Conclusion and Outlook

The United States is pursuing two parallel but conflicting policy goals: the rapid, affordable decarbonization of its economy and the strategic, long-term reshoring of its clean energy supply chain. While the latter is a valid national security and economic objective, the current strategy of combining high tariffs with complex, restrictive incentives is creating a policy paradox that jeopardizes the former.

By raising the cost of solar, wind, and battery storage, these policies are slowing the deployment of the cheapest and cleanest sources of new power. This threatens to inflate electricity prices for consumers and businesses and risks leaving the nation unable to cleanly and affordably meet the surging power demands of data centers and broader electrification. The ultimate success of this strategy will depend on how quickly a cost-competitive domestic supply chain can be established. In the interim, the U.S. faces a period of higher costs, project delays, and a potential slowing of its energy transition, highlighting the profound tension between the urgent need for clean energy deployment and the strategic desire for supply chain security.

The post The Policy Paradox: How US Tariffs and Tax Credits Risk Inflating Power Costs and Delaying the Energy Transition appeared first on Logistics Viewpoints.

Supply Chain leaders (CSCOs) are responsible for driving strategic growth, fostering innovation within their supply chains, and ensuring long-term value creation. However, many companies are now confronted with challenges such as abrupt tariff increases, supply chain instability, and delays in the delivery of critical components. Recent data from Yale’s budget analysis highlights the growing economic burden of tariffs. With the average tariff rate reaching 22.5%—the highest since 1909—consumers have faced a 2.3% price increase, while 75% of businesses report financial strain due to these trade barriers. These figures underscore the ripple effects tariffs have on both households and the broader economy

These issues are not anomalies but indicators of deeper structural vulnerabilities in global operations. Addressing them requires more than traditional playbooks. It demands strong leadership, adaptive strategies, and the strategic use of advanced technologies including optimization and decision intelligence.

So how did we get here?

For years, globalization made supply chains efficient but brittle. Companies outsourced manufacturing, hollowed out domestic capacity, and grew dependent on long, opaque networks across the globe.

Enter trade wars, pandemics, energy shocks, and geopolitical standoffs. With a single policy shift, you could be staring at:

• 25% or more tariffs on key inputs
• Supplier insolvency in a sanctioned country
• Delays that dramatically reduce quarterly margins

Supply chains globally are under great duress and uncertainty.

What Should CSCO’s Do Now?

Supply Chain leaders can’t rewind history, but they can choose how to lead forward. The Cynefin Framework (Figure 1) gives them the playbook. It helps them recognize the context their organizations are in. Adopting the wrong decision model in the wrong situation makes things worse.

Figure 1. Cynefin Framework

Here’s how to respond:

If you’re in CHAOS (e.g., surprise tariff, blocked shipments):

Act. Build buffers, reroute shipments, activate emergency playbooks. Speed trumps elegance. Stabilize, then assess.

If you’re in COMPLEXITY (e.g., redesigning supplier networks):

Probe. Run small pilots. Test nearshoring. Explore new trade partners. Learn what works. Then scale.

If you’re in COMPLICATION (e.g., reclassifying products or modeling cost):

Analyze. Use expert-led modeling, compliance audits, and financial forecasts to define the best course of action.

If you’re in CLARITY (e.g., stable tariff classification or bonded zone use):

Standardize. Codify what works. Automate. Monitor for exceptions.

Most companies oscillate between these zones. A CSCO’s job is sense the environment, then lead their team accordingly — decisively in chaos, experimentally in complexity, analytically in complication, and systematically in clarity. This combination of multi-purpose tools to manage the business can also be referred to as orchestration. AI and Optimization are great tools to help CSCO’s orchestrate their supply chains.

Figure 2. Supply Chain Orchestration

How AI and Optimization Can Help CSCOs to Orchestrate their supply chains?

AI Helps Companies Sense and Learn

• Detect new patterns (e.g., supplier delays in one region start cascading)
• Forecast disruptions using external signals (weather, policy changes, lead-time spikes)
• Identify anomalies and shifts faster than humans can spot manually

Optimization Helps Companies Respond and Solve

• Determine best-fit supplier mixes under different tariff scenarios
• Recalculate cost-to-serve as conditions change
• Score and compare resilience vs. cost tradeoffs for network redesign
• Reroute shipments in real time to minimize cost or risk

Below are a few practical use cases for orchestrating your supply chains where AI and Optimization in combination can be a potent weapon.

Practical use cases for supply chain orchestration (relevant to the tariffs/trade wars)

A CSCO’s Roadmap for Success

Many organizations were caught unprepared as the global operating environment underwent rapid and fundamental shifts. The path forward is not a return to previous norms, but the development of a new kind of leadership capability that is grounded in contextual awareness and adaptive execution – we call this as “The Adaptive Supply chain”.

To rebuild effectively, leaders must:

• Accurately assess the nature of the challenge using frameworks such as Cynefin
• Make decisions that are aligned with the complexity of the context
• Enable their teams with advanced technologies, including AI and optimization, that support scenario-based planning and agile response

This is how organizations regain stability and resilience and become adaptive. Not through excessive planning, but by combining strategic insight with orchestration. We have outlined key strategies for companies to become adaptive through this white paper – “6 strategies for building adaptive supply chains”.

By Nari Viswanathan – Sr. Director, Product Segment Marketing, Coupa

Nari is currently Sr. Director of Product Segment Marketing at Coupa, where he brings products to markets in the areas of Direct Material Procurement and Supply Chain Design and Planning. Over the past 20 years, Nari has held VP and Director of Product Management, Research and Marketing roles at Aberdeen Group, River Logic, Steelwedge and E2open. He has significant experience building products from the ground up and managing the P&L for a product suite. He is a proven B2B marketer with expertise in content marketing, competitive intelligence, and positioning. He has published numerous thought leadership articles, whitepapers, blogs and delivered dozens of webinars during his career. Nari Viswanathan is a six times SDCExec Supply Chain Pro to Know award winner. Nari holds a master’s degree in Manufacturing Systems Engineering at the University of Wisconsin-Madison and a bachelor’s degree in Mechanical Engineering at the Indian Institute of Technology, Chennai.

The post How to Navigate the Chaos of Trade Disruption with the Cynefin framework, AI and Optimization. appeared first on Logistics Viewpoints.

From May 19 to 21, 2025, CeMAT Southeast Asia and LogiSYM Asia Pacific were co-located for the second consecutive year at Singapore EXPO, creating a comprehensive platform for the region’s logistics and supply chain community. Now in its second edition, CeMAT Southeast Asia showcased a broad spectrum of intralogistics, automation, and material handling solutions, reflecting the sector’s continued growth and transformation. Meanwhile, LogiSYM Asia Pacific, a well-established event with over a decade of history, brought together professionals from supply chain, logistics, and shipping sectors for strategic discussions on resilience, digitalization, sustainability, and global trade dynamics. Together, the two events bridged operational technologies with strategic insights, offering attendees a well-rounded perspective on the industry’s future direction.

The event also celebrated industry success and leadership through the annual LogiSYM Awards, recognizing outstanding companies driving innovation and excellence across multiple categories. Geek+ was honored as Best Warehouse Robotics Company for its smart warehouse automation solutions.

The exhibition floor reflected a vibrant logistics automation ecosystem, featuring a diverse range of companies across multiple sectors. Storage automation and AS/RS solution providers such as AutoStore, Swisslog, and Kardex were present, alongside intralogistics, supply chain, and warehouse automation specialists including SSI Schaefer, Dematic and Körber Supply Chain. Material handling and forklift equipment manufacturers, including Hyster-Yale, rounded out the comprehensive showcase of integrated solutions shaping the  future of supply chain operations.

Cubic storage solutions from AutoStore and Kardex continued to attract interest, particularly for their ability to maximize space utilization and support high-throughput operations. These AS/RS systems leverage dense, three-dimensional grid configurations to achieve high storage density and fast picking speeds, making them especially well suited for dynamic e-commerce, retail, and micro-fulfillment environments.

With the increasing adoption of AI technologies, AutoStore introduced its latest innovation, CarouselAI—an AI-powered robotic piece-picking solution. Seamlessly integrated within the AutoStore grid system, CarouselAI uses machine learning and advanced vision technologies to automate the identification, grasping, and placement of individual items, significantly enhancing picking productivity and accelerating fulfillment.

A wide variety of mobile warehouse robots were actively demonstrated across the exhibition hall, spanning traditional platform-type Autonomous Mobile Robots (AMRs) and goods-to-person systems. Chinese robotics companies featured prominently, including Geek+, Syrius Technology, Seer Robotics, and Bluesword.

Geek+ unveiled several of its latest developments, including the PopPick Lite workstation—an efficient and modular variant of its flagship goods-to-person solution. Designed to boost flexibility, picking speed, and scalability, PopPick Lite caters to fast-moving warehouse environments and space-constrained operations.

New exhibitors added fresh momentum to the mobile robotics segment. Libiao Robotics demonstrated its T-Sort sorting robots, designed for high-speed parcel distribution in e-commerce fulfillment centers. Libiao Robotics also showcased AirRob, a novel autonomous tote-handling system that operates more like a mobile robotic. Mounted on lightweight rails affixed to racking systems, AirRob moves both vertically and horizontally to retrieve totes from storage and hand them off to ground-level robots for further transport.

Kole Robotics, another new participant, presented its range of AMR solutions for retrieval of totes at elevated height. The company also showcased an integrated robotic picking arm, expanding its offerings to support automated case and item-level picking—ideal for operations seeking to automate high-mix, low-volume workflows without committing to large-scale fixed automation.

Pudu Robotics, traditionally known for its service robots, made a notable pivot into industrial logistics with the launch of its Pudu T300 model. This autonomous delivery robot is designed to streamline internal transport tasks within warehouses and distribution centers, signaling a growing convergence between service robotics and industrial automation.

Another refreshing innovation came from Lead Logistics, which introduced a Cantilever AGV designed for precise material handling applications. Equipped with a cantilever arm capable of transporting coil-like goods, the system offers a promising solution for specialized needs in manufacturing sectors such as lithium battery, electronics, and semiconductors.

SSI Schaefer, in collaboration with Hai Robotics, showcased a climbing robot engineered to ascend racking systems and perform automated storage and retrieval tasks. This solution supports high throughput while maximizing vertical space usage—meeting the demands of warehouses with significant height and storage density requirements.

Singaporean robotics companies also made their mark at the exhibition. Xsquare Technologies and Botsync demonstrated goods-to-person AMRs and fleet management software tailored for facilities facing spatial constraints, such as urban logistics hubs. Botsync emphasized its interoperability-focused approach, designed to seamlessly integrate mobile robots into existing warehouse infrastructure with minimal retrofitting.

Running concurrently, LogiSYM Asia Pacific 2025 provided a strategic complement to CeMAT’s technology-driven focus. Over two days, the forum addressed key macroeconomic and operational challenges facing the logistics sector. A major theme this year was the global tariff environment, particularly the impact of evolving U.S. trade policies on sourcing strategies, inventory flows, and distribution networks amid rising costs. Additional discussions explored supply chain resilience, digital transformation, and sustainability.

A consistent takeaway from both CeMAT Southeast Asia and LogiSYM Asia Pacific 2025—as in previous years—is the growing maturity of Southeast Asia’s logistics and supply chain landscape. The region is no longer just adopting imported technologies but is now strategically integrating them to tackle local challenges such as fragmented infrastructure, labor shortages, and mounting pressure for cost-effective operations. This shift is supported by a growing pool of domain specialists who offer advisory expertise across various aspects of implementation and integration. At the same time, the increasing adoption of artificial intelligence is driving the development of more advanced robotic applications throughout warehouse and fulfillment environments.

Naturally, this progress is attracting more new entrants and greater activity in the robotics and automation space in the region. Companies like Kardex and Libiao Robotics have recently established new offices in Southeast Asia, including Singapore, to tap into the region’s fast-growing logistics market. Meanwhile, regional initiatives like the Singapore–Johor Special Economic Zone aim to improve cross-border logistics, strengthen connectivity, and support the development of integrated supply chains—reinforcing Southeast Asia’s position as a rising logistics hub on the global stage.

The post CeMAT Southeast Asia & LogiSYM Asia Pacific 2025: Robotics Drive Forward as Supply Chains Strategize appeared first on Logistics Viewpoints.

This is the first part of a five-part series on AI-to-AI communication. In Part 1, we will discuss the necessity for artificial intelligence to communicate with itself and the implications of this capability. Subsequent parts will cover protocols for AI conversations, the importance of context in multi-agent AI interactions, the impact of these technologies on enterprise and governance, and the ethical considerations and standards for AI-to-AI coordination.

The supply chain and logistics industry involves complex systems. From global procurement and multimodal transportation to inventory management and demand forecasting, operations require coordination of materials, people, and data. Artificial intelligence is being integrated into these processes to improve decision-making and efficiency.

A key development in this space is machine-to-machine intelligence. This refers to AI systems communicating directly with each other to coordinate tasks. Known as AI-to-AI (A2A) communication, this capability is becoming important for managing supply chain operations.

Why Supply Chain AI Must Communicate Internally

No single AI model can manage all aspects of supply chain operations. Organizations now use multiple specialized models:

• Demand forecasting models using historical and market data
• Procurement models for supplier evaluation
• Computer vision models for warehouse inspections
• Route optimization models for transportation planning

These models often need to work together. For example, when a shipment is delayed, the system may need to update forecasts, notify suppliers, and revise delivery schedules. Doing this manually is inefficient. A2A allows AI systems to coordinate these tasks without human intervention.

A2A Is More Than API Integration

While software systems already communicate through APIs, A2A goes further. It allows AI models to share:

• Intent: What the model is trying to achieve
• Context: What has already been processed
• Constraints: Operational limits and requirements
• Confidence: Estimated reliability of the information

In practice, this may involve:

• A maintenance model detecting a likely equipment failure
• Alerting a scheduling model to adjust labor
• Querying a parts inventory model to prioritize repairs

These interactions require more than data sharing. They require mutual understanding of task objectives and operational logic.

Use Cases for A2A in Logistics

1. Disruption Response
When a delay or incident affects the supply chain, A2A allows AI systems to update forecasts, reroute shipments, and reallocate resources in real time.

2. Multi-Agent Planning
Digital twins of supply networks include several models. These need to synchronize their simulations to provide accurate results.

3. Autonomous Procurement
An AI model tracking material prices may trigger a contract negotiation model to adjust supplier terms and inform an inventory optimizer to evaluate buffer stock levels.

In these examples, human users set parameters, but AI systems perform the necessary coordination.

Key Elements of A2A

Effective A2A communication depends on:

• Semantic Interoperability: Shared definitions for common terms
• Task Attribution: Identification of model capabilities and roles
• Context Sharing: Transfer of decision history and rationale
• Role Recognition: Awareness of model functions and decision authority

These elements ensure AI agents can collaborate effectively.

Development of the A2A Protocol

The A2A protocol is currently being shaped through collaborations among leading AI developers and standards organizations. Entities such as OpenAI, Anthropic, and Google DeepMind are exploring foundational designs. These efforts often align with initiatives from the Frontier Model Forum and early policy discussions from national AI safety institutes. While no universal standard has yet been adopted, work is progressing toward creating interoperable frameworks that can support regulated and enterprise-scale AI communications.

Connection to Model Context Protocol (MCP)

For A2A to be reliable, AI systems must also maintain a shared record of their interactions. The Model Context Protocol (MCP) addresses this by providing a standard for recording task history, tools used, and decisions made.

In logistics, MCP enables:

• Traceability: Documenting why a decision was made
• Continuity: Allowing handoffs between planning and execution systems
• Auditability: Supporting compliance and performance review

A2A and MCP together support scalable, collaborative AI workflows.

Outlook for the Industry

AI systems in the supply chain will continue to expand. These systems will increasingly need to collaborate. Inventory management models will communicate with procurement agents. Compliance models will alert logistics scheduling systems. AI-driven control towers will involve coordinated efforts from multiple AI tools.

Future improvements will depend not just on the capabilities of individual models but on how well they can interact.

In Part 2, we will explain the technical standards behind A2A communication and how AI systems can operate on shared protocols.

Next Up:

Part 2: Understanding A2A: Protocols for AI Conversations
How Language Models Learn to Speak the Same Language

The post The Rise of Machine-to-Machine Intelligence Why AI Needs to Communicate with Itself—and What Happens When It Does appeared first on Logistics Viewpoints.

Supply chain leaders today are bombarded with noise—constant chatter about AI, rising complexity, uncertainty, disruptions, trade policies, and geopolitics. Simultaneously, the landscape of supply chain planning is shifting due to both rapid advancements in technology and workforce changes. This evolving reality often makes supply chain management feel like an impossible balancing act, where leaders must play both firefighter and superhero at the same time.

Where should you focus your attention to drive the most meaningful impact? How do you strike a balance between your most pressing priorities today while delivering on the promise of digital transformation for tomorrow–all while doing more with less? This is why targeted automation is so important.

Automation in supply chain planning is not new, but traditional automation efforts have often been narrowly focused on cost reduction and efficiency. While these goals remain important, supply chain leaders must now think bigger, broader, and bolder about automation to be able to find and drive business value.

With a targeted and value-centric approach to automation, you can move beyond simply cutting costs to now create new opportunities for business growth, build resilience, and create competitive differentiation.

Instead of treating automation as a checkbox item, organizations must embed it into their core strategy, leveraging it across various dimensions of planning. This includes augmenting human decision-making, enhancing data-driven insights, and ensuring that automation delivers measurable improvements to key business objectives.

Make Automation a Strategic Priority

Supply chain planning is often hindered by manual processes and siloed operations, making it difficult to orchestrate decisions and effectively scale. Intelligent automation, powered by AI and machine learning, transforms these processes enabling companies to:

• Level Up Planners: By automating repetitive and labor-intensive tasks, businesses can reallocate their workforce to higher-value activities.
• Quickly Analyze Large Data Sets: AI can process and compare vast amounts of information in real time, identifying patterns and optimizing decision-making in ways humans simply cannot.
• Reduce Bias in Planning Decisions: Algorithms provide objective insights based on data rather than gut instinct, leading to better accuracy and consistency.
• Boost Productivity with Decision Automation: Explainable and prescriptive recommendations help teams act with confidence while reducing manual intervention.

Augment and Automate with the User in Mind

Companies must accelerate their automation efforts by identifying where AI can drive stakeholder value while ensuring ease of use and accessibility.

Consider the example of a large consumer goods manufacturer and distributor managing more than 80,000 locations. This company leverages AI and automation across multiple layers of its supply chain, including forecasting, replenishment, and transportation logistics. By integrating data from point-of-sale (POS) systems, IoT sensors, traffic, and weather forecasts, the company can:

• Automate Demand Sensing: AI dynamically adjusts order recommendations in response to demand fluctuations.
• Minimize Manual Forecasting Adjustments: For this manufacturer, planners don’t touch the forecast, focusing instead on strategic oversight.
• Optimize Transportation Routes: AI-powered analytics suggest alternative delivery routes to minimize delays and reduce costs.
• Enhance Decision-Making: By combining machine learning insights with prescriptive recommendations, the company reduces waste and improves supply chain efficiency.

In this use case example, the manufacturer harnesses targeted automation at the forecasting, replenishment and transportation layer. This demonstrates how targeted automation is helping to go beyond optimizing individual processes to transform the entire supply chain network, creating a seamless, intelligent ecosystem that continually adapts to real-world changes.

Demystifying AI for Better Adoption and Decision-Making

Despite the clear benefits of automation, many organizations struggle with adoption. To ensure success, supply chain leaders must prioritize transparency and usability. The explainability of AI predictions is crucial to building trust in automated recommendations. AI in supply chain should not operate as a black box; instead, it must offer clear, interpretable insights that allow planners to understand the rationale behind each decision. Increased transparency and explainability enables trusts which leads to higher adoption rates across organizations.

Performance tracking is another critical element in fostering adoption. Organizations should continuously monitor key metrics such as automation adoption rates, decision quality, and user engagement. These value-add metrics help teams assess the impact of automation while providing opportunities for continuous improvement, ensuring that the technology evolves in alignment with business needs.

By taking these steps, companies can foster greater trust in AI and advanced automation to ensure that supply chain teams fully leverage the potential of these technologies.

As the supply chain landscape continues to rapidly evolve, companies must rethink their approach to automation focusing on where it can bring value. It is no longer just about incremental improvements—automation must be embedded into the fabric of supply chain operations to drive end-to-end orchestration and long-term business resilience.

The call to action is clear: Think broader, bigger, and bolder about automation. In an era where doing more with less is critical, targeted automation is the key to unlocking sustained value and maintaining a competitive edge.

About the Author:

Matt Hoffman is the Vice President of Product and Industry Solutions at John Galt Solutions. Matt specializes in delivering transformational from analysis through execution across a diverse range of clients in manufacturing, distribution, and retail. Matt is committed to ensuring that processes drive solution adoption, resulting in measurable outcomes. Throughout his career, Matt has successfully led software implementations utilizing best-in-class supply chain planning systems, execution systems, and merchandising planning systems.

The post Do More with Less: Think Bigger and Bolder about Automation appeared first on Logistics Viewpoints.

 Ultra-Wideband (UWB) is a radio frequency technology operating across a wide spectrum from 3.1 to 10.6 GHz. It functions by transmitting extremely short bursts of radio energy, typically lasting only a few nanoseconds. This pulse-based transmission enables precise distance measurement through techniques such as Time-of-Flight (ToF) and Time-Difference-of-Arrival (TDoA). ToF measures the time taken for a signal to travel between two UWB devices, while TDoA calculates location based on the differences in arrival times of a UWB signal at multiple fixed reference points. UWB technology is standardized under IEEE 802.15.4, with amendments 802.15.4a and 802.15.4z specifically enhancing its ranging capabilities with added security and robustness.

UWB systems provide highly accurate, real-time positioning data, particularly effective in indoor environments where Global Positioning System (GPS) signals are often unavailable or degraded. This capability renders UWB valuable in sectors requiring spatial awareness, including manufacturing, healthcare, and logistics.

Technical Characteristics

• Frequency Range: 3.1 GHz to 10.6 GHz. The wide bandwidth allocated to UWB allows for the transmission of very short pulses, which is fundamental to its precise ranging capabilities. Regulatory bodies, such as the Federal Communications Commission (FCC) in the United States, permit UWB operation within this spectrum under specific power limits to ensure compatibility with other radio services.
• Pulse Duration: Nanosecond-scale. The brevity of these pulses minimizes the impact of multipath interference, a common challenge in indoor environments where signals reflect off multiple surfaces. This characteristic enables UWB to resolve closely spaced signal paths, contributing to its high accuracy.
• Location Accuracy: Typically 10–30 cm. This level of precision is achieved through the ability to timestamp UWB signals with sub-nanosecond resolution, directly translating to highly accurate distance calculations.
• Core Standards:
  • IEEE 802.15.4: This foundational standard specifies the physical layer (PHY) and media access control (MAC) for low-rate wireless personal area networks (LR-WPANs).
  • IEEE 802.15.4a: This amendment introduced precise ranging capabilities to the standard, primarily through the analysis of the UWB signal’s Channel Impulse Response (CIR). This allows for high-resolution time measurements essential for accurate distance determination.
  • IEEE 802.15.4z: This amendment further enhanced UWB ranging by adding secure time-of-flight measurements and improving robustness. It includes cryptographic protection of ranging measurements to mitigate vulnerabilities such as spoofing and relay attacks, thereby increasing the integrity and trustworthiness of location data.
• Industry Ecosystem:
  • The FiRa Consortium is an industry alliance dedicated to promoting interoperability and the widespread adoption of UWB technology across various applications. Member companies include Samsung, Bosch, Cisco, and NXP, among others.

UWB systems exhibit greater resilience than signal strength–based solutions like Bluetooth Low Energy (BLE) or Radio Frequency Identification (RFID), particularly in environments characterized by high levels of interference or the presence of metallic obstructions. This resilience is attributed to UWB’s wide bandwidth and low power spectral density.

Comparison with Other Tracking Technologies

Deployment Considerations

Verified Real-World Implementations in Logistics

These use cases demonstrate UWB’s application and measurable impact within supply chain logistics:

• Warehouse Optimization – Pozyx’s UWB solution was implemented at Bonduelle, a processed vegetable producer, to address the challenge of locating pallets in their large fresh salad factory. By leveraging real-time UWB tracking of pallets, the company achieved a 3% increase in warehouse efficiency. This precision in localization reduced manual search times, resulting in hundreds of hours saved annually per warehouse.
• Employee and Forklift Tracking in Warehouses – Navigine deployed a UWB-based real-time tracking system across a 10,000 m² logistics warehouse. Employees and forklifts were equipped with UWB tags, enabling their precise location tracking. This implementation led to a 4% increase in daily task completion per employee and a 3% increase in overall warehouse productivity through optimized routes and workflow monitoring. Furthermore, the system integrated a collision prevention feature, enhancing worker safety within the operational area.
• Real-time Goods Receipt and Transport Optimization – TB International collaborated with Inpixon/INTRANAV to integrate a smart warehouse module incorporating both RFID and UWB technologies. This multi-RTLS approach enabled precise localization with UWB and item identification with RFID. The system automated goods receipt processes, provided digital work instructions for sorting operations, and optimized transport orders for forklifts based on real-time location data. These improvements collectively resulted in a nearly 40% increase in operational efficiency, including scannerless storage and retrieval processes.

Standards and Ecosystem

• IEEE 802.15.4 This is the foundational standard for low-rate wireless personal area networks (LR-WPANs), upon which UWB operates. Key amendments to this standard have specifically evolved UWB’s capabilities:
  • 802.15.4a: This amendment introduced specific provisions for high-resolution ranging and location capabilities for UWB. It defines mechanisms for more accurate time-of-flight measurements by analyzing the UWB signal’s Channel Impulse Response (CIR).
  • 802.15.4z: This amendment builds upon 802.15.4a, focusing on secure UWB ranging and enhanced robustness. It integrates cryptographic techniques to protect ranging measurements from manipulation and improves the reliability of ranging in challenging radio environments.
• FiRa Consortium The FiRa Consortium is an industry alliance established to ensure interoperability among UWB devices from various manufacturers. Its activities include the development of common technical specifications, the establishment of certification programs, and the promotion of UWB technology for secure ranging and precise location. This concerted effort contributes to the growth and diversification of the UWB ecosystem, facilitating broader adoption across industries.

Limitations of UWB

• Higher initial hardware and installation cost: Compared to technologies like BLE or passive RFID, UWB systems typically incur higher upfront costs. This is due to the specialized nature of UWB transceivers, antennas, and the precise calibration required for anchor placement during installation.
• Tag size and cost may not suit very small or low-value items: The size and unit cost of current UWB tags, driven by component size and battery requirements, can render them impractical for tracking extremely small or disposable, low-value items where cost per tag must be minimal.
• Performance may be affected in environments with dense physical obstructions: While generally robust, UWB signal propagation can experience attenuation or severe multipath effects in environments with numerous dense metallic structures or thick concrete walls. This may necessitate a denser deployment of anchors to maintain desired accuracy.
• Integration with business software systems is necessary for full ROI: The raw location data generated by a UWB RTLS requires processing and integration with existing enterprise systems (e.g., WMS, ERP, MES) to transform it into actionable insights and enable automated workflows. This integration process can represent a significant portion of the total project cost and complexity.

Ultra-Wideband technology provides precision in indoor asset tracking capabilities. Its technical characteristics, supported by IEEE standards and fostered by the FiRa Consortium, position UWB as a solution for applications requiring accurate, real-time spatial awareness. From logistics terminals to industrial sites, UWB facilitates advanced automation, enhances safety protocols, and contributes to operational efficiency. Verified implementations in supply chain logistics underscore its application in optimizing material flow, improving productivity, and ensuring worker safety.

The post Ultra-Wideband Technology: Redefining Precision in Asset Tracking appeared first on Logistics Viewpoints.

Sustainability isn’t just a corporate goal anymore—it’s a supply chain requirement. Schneider Electric’s new Environmental Data Program gives logistics professionals unprecedented access to carbon and sustainability metrics, covering over 70% of its product turnover. This move empowers companies to make more informed sourcing decisions, comply with ESG standards, and build greener operations.

As Craig Resnik writes,

Schneider Electric launched its Environmental Data Program to increase transparency around product-level environmental impact. The initiative builds on the legacy Green Premium label and supports regulatory compliance and sustainability planning.

Key details include:

• 14 environmental data attributes are now disclosed, which is up from five and covers additional aspects, such as carbon footprint, energy efficiency, packaging, and recycled content.

• 110,000 commercial references are currently included, with a target of expanding coverage to 155,000 references by the end of 2025.

• This expansion increases coverage from 70 percent to 80 percent of Schneider Electric’s product turnover.

• The program helps customers to better track, understand, and reduce their own environmental impact through more data-informed decisions.

• It responds to evolving regulations, such as the draft EU Green Claims Directive, which raises standards for transparency and accountability.

• The timing aligns with broader global challenges, including 2024 being the hottest year on record and the circularity rate of the global economy remaining at an estimated 7.2 percent.

Environmental data transparency is becoming more central to both decarbonization and competitiveness, providing the foundation for more responsible and resilient operations.

See how better data is driving better environmental choices across the supply chain. Read the full article on ARC Advisory Group. https://www.arcweb.com/blog/schneider-electric-launches-environmental-data-program-advance-product-transparency

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