Disruptions aren’t the exception anymore, they’re part of how supply chains run. Whether it’s a ransomware attack, a weather disaster, or a labor strike, most companies have faced at least one serious disruption in the last few years. In this environment, crisis response and business continuity planning can’t be treated as check-the-box activities. They need to be operational disciplines.

Many companies have documented response plans, risk registers, and compliance frameworks. Fewer have continuity plans that are practical, current, and executable under pressure.

Crisis vs. Continuity, Where the Line Falls

Crisis management is the immediate response to something unexpected and high impact. Think: system outage, factory fire, or port closure. It’s about triage, fast decisions, and clear communication.

Business continuity, on the other hand, is the longer game. It focuses on preparing for disruptions before they happen, by finding critical dependencies, defining recovery procedures, and testing whether the response can actually work. It’s less about the single moment and more about supporting operational flow under strain.

Both are essential. One is about reacting quickly. The other is about recovering well.

What Disrupts Supply Chains Today?

• Cyberattacks, especially ransomware, can lock up WMS, TMS, and ERP systems
• Transportation breakdowns, including port congestion, strikes, and capacity collapses
• Supplier failure, whether due to insolvency, quality problems, or force majeure
• Extreme weather events that shut down infrastructure or delay deliveries
• Public health crises, which affect labor, travel, and regulation
• Geopolitical tensions that lead to sanctions, trade barriers, or sudden embargoes

Each of these triggers touches multiple parts of the business, from logistics and procurement to IT, legal, and customer service.

What a Real Continuity Plan Includes

Strong business continuity planning goes well beyond a binder on a shelf. It should be woven into how the supply chain is run. Key components include:

1. Identify What’s Critical

Figure out what can’t go down without serious consequences, suppliers, routes, systems, SKUs, or even customers. Use metrics like lead-time sensitivity, margin contribution, and order volume to prioritize.

2. Assess and Rank Risks

Not all risks are equal. Use a consistent framework to evaluate likelihood and impact. Conduct business impact analyses (BIAs) to quantify financial exposure and operational delays. Align those results with leadership priorities.

3. Set Recovery Targets

Define how quickly you need to bounce back (RTO) and how much data you can afford to lose (RPO). These should reflect your actual risk tolerance, not generic estimates.

4. Build Playbooks

Don’t just write down broad guidelines. Create clear, step-by-step instructions for specific scenarios, like losing a distribution center, a WMS failure, or a Tier 1 supplier going offline. Include:

• Immediate actions and escalation paths
• Alternate suppliers, routes, or sites
• Contact lists and comms templates
• Roles for legal, IT, HR, and customer support

5. Assign Roles and Train Teams

Clarity matters in a crisis. Who makes the call to shift production? Who talks to customers? Who activates backup systems? Roles should be clearly assigned and regularly tested. Desktop walkthroughs and simulated events go a long way.

6. Support It With Technology

Continuity planning relies on prompt, reliable data. That means:

• Backups and tested recovery procedures
• Emergency communication tools
• Visibility platforms that track suppliers, carriers, and exceptions
• Systems that feed real-time risk signals into dashboards or control towers

Where Companies Fall Short

Even with awareness on the rise, there are common execution gaps:

• Plans are owned by audit or risk, not operations
• Tier 2 and Tier 3 supplier visibility is minimal
• Decision authority is unclear during crises
• Playbooks are outdated or too generic to be useful
• Teams haven’t practiced what’s in the plan

When a real disruption hits, these gaps slow down the response and extend recovery time.

Real-World Examples

• Maersk (2017): A ransomware attack crippled IT systems across its global logistics network. Recovery required offline backups and manual workarounds, highlighting the value of tested, non-digital contingency plans.
• Texas Freeze (2021): Supply of semiconductors and plastics ground to a halt. Companies with dual sourcing and buffer inventory got back on track faster.
• COVID-19: Exposed overreliance on single suppliers and manual systems. The firms that adjusted quickly had flexible fulfillment models and real-time visibility across their networks.

What Stands Out Across the Board

• Continuity planning is an ongoing process, not a document.
• Plans work when they’re grounded in operations and tied to genuine business impact.
• Execution depends on clear governance: who decides, what they use to decide, and how fast they can act.
• Technology helps, but it’s only as good as the people and processes around it.
• The organizations that test, update, and rehearse their plans recover faster and protect customer relationships better.

In today’s environment, disruptions are a given. The difference is how well you respond. Companies that treat continuity as a living capability, not just a plan, gain a real edge. They don’t just survive disruption. They stay focused, make faster decisions, and come out stronger.

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As logistics gets smarter, so does the packaging. We have moved beyond the days when a box was just a box. Today, smart packaging, enabled by sensors and connectivity, lets companies monitor their shipments in real time. This technology helps track where things are, what condition they are in, and whether they have been handled properly.

That said, most real-world deployments are focused. We are not talking about every box on every truck reporting back constantly. Instead, companies track specific conditions, things like temperature, humidity, shock, and location, on higher-risk shipments like pharmaceuticals, fresh food, and expensive electronics.

How It Works

A smart packaging setup usually includes:

• Sensors that measure temperature, humidity, tilt, or shock
• Communication tech like LTE-M, NB-IoT, BLE, or RFID
• A data platform to collect and display sensor data
• Rules that trigger alerts when something goes wrong

In practice, most companies attach reusable sensor tags to crates or pallets. Embedding them in individual packages is still too expensive and complex for most operations.

Where It is Being Used

In cold chain logistics, smart packaging is used to make sure temperature-sensitive items like vaccines stay within safe ranges. These systems log data all the way from origin to destination, giving proof that the product was managed properly. During the COVID-19 rollout, vaccine shipments used temperature-tracking containers for this exact reason.

In electronics and industrial parts, sensors can show if a shipment was dropped, tilted, or exposed to static, helping to catch damage early and handle disputes with carriers or insurers.

Some retail and luxury brands are experimenting with smart packaging for theft prevention and authenticity. Tags can flag unexpected openings or verify products at the point of sale. But adoption here is still limited due to cost and privacy concerns.

System Integration Matters

Sensor data is not very helpful on its own. It becomes useful when it is connected to systems like a TMS or ERP, so alerts are tied to specific shipments, customers, or SKUs. For example, a temperature spike during a customs delay might trigger an alert, if the systems are connected. Otherwise, it is just another unread data point.

To work well, these systems need solid APIs and consistent data formats. Otherwise, you run into problems combining data from different vendors or dealing with mismatched time zones and units.

Dashboards Are Everywhere, But Often Disconnected

Most platforms include dashboards showing shipment conditions and alerts. That is helpful, but unless the dashboards tie into your actual workflows, their value is limited. Few companies have wired this data into automatic rerouting, claims filing, or SLA enforcement. That is still a gap.

Cost, Value, and What to Watch

These devices are not cheap. A basic sensor tag might run from $5 to $60 depending on features and connectivity. There are also costs for cellular data, software, and retrieving and reusing the devices.

Return on investment usually comes from reduced spoilage, fewer rejected shipments, easier audits, or stronger service-level compliance. But if you are shipping low-value goods or general freight, the math often doesn’t work. Most companies start with targeted trials on high-risk lanes or products.

The Fine Print: Limits and Tradeoffs

• Connectivity is not guaranteed everywhere, especially across borders or in rural areas.
• Battery life varies. Devices might last a week or two and then need recharging or replacement.
• Data overload is a risk. Without filters, users can get swamped with unnecessary alerts.
• Compliance matters. In pharma, for example, sensor data needs to be audit-ready and securely stored for years.

Final Thoughts

Smart packaging is a good fit for certain logistics challenges, especially when regulation, product sensitivity, or cost of failure is high. But it is not plug-and-play. For this tech to really deliver, it needs to be well-integrated, well-targeted, and part of a clear operational plan.

It is not about chasing trends. It is about knowing where it fits, and where it does not.

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Digital infrastructure is now integral to logistics execution. Supply chain networks depend on structured data, exchanged through APIs, middleware, and telemetry, to coordinate across facilities, regions, and partners. Three enabling capabilities stand out: artificial intelligence (AI), cybersecurity, and real-time monitoring. While each presents unique benefits, their value depends on disciplined implementation and integration into business-critical workflows.

AI Deployment in Operational Context

Artificial intelligence has become a common feature in supply chain systems, though the depth of adoption varies widely. Among Tier 1 retailers and logistics service providers, AI is embedded in planning, inventory control, and exception resolution. Smaller enterprises, however, often remain limited to off-the-shelf forecasting tools or point solutions without broader system integration.

Forecasting and Replenishment Logic

Short-horizon demand forecasting has shifted from batch to continuous models. Large retailers such as Walmart have implemented machine learning to generate daily updates at the SKU-store level. These models leverage structured data sets, POS sales, historical trends, promotions, and weather, to adjust replenishment targets. Improvements in fill rate and inventory turnover are typically incremental but statistically significant when applied at scale.

That said, model accuracy is sensitive to data freshness, SKU volatility, and the presence of external noise (e.g., shifting macroeconomic indicators). In many mid-sized firms, forecast models remain under-optimized due to poor signal-to-noise ratios or data latency across systems.

Inventory Placement and Fulfillment Optimization

Amazon’s forward-deployment model is often cited as a benchmark. The company dynamically positions inventory within its fulfillment network using projected demand heat maps and transportation cost models. This approach reduces lead time and minimizes cross-country shipments, but it requires high system interoperability and robust handling of demand spikes and regional anomalies.

For firms lacking this infrastructure, stock centralization remains the norm, with AI used primarily to flag replenishment exceptions rather than rebalance across nodes.

Exception Management

Exception detection, whether for late shipments, order imbalances, or route deviations, is a common entry point for AI in logistics. Rule-based systems are giving way to models that identify anomalies using pattern recognition. These alerts can trigger escalations, route adjustments, or proactive customer notifications. While effective in controlled environments, integration into enterprise workflows remains uneven, especially where legacy ERPs or outdated TMS platforms persist.

Cybersecurity in a Distributed Digital Environment

Cybersecurity risk in logistics has shifted from a hypothetical concern to an operational constraint. Logistics IT environments, spanning cloud platforms, control systems, and third-party APIs, face a growing set of threat vectors. Recent events have underscored this risk.

Notable Incidents and Sector Implications

In 2022, Toyota suspended operations at multiple plants following a supplier-side breach. The disruption had knock-on effects across its domestic and international supply chain. In 2017, Maersk’s encounter with NotPetya malware required a full infrastructure rebuild and delayed cargo worldwide.

These cases reflect a broader pattern: as digital dependency increases, operational exposure scales with it. Cyber resilience has become a board-level concern in firms with large logistics footprints.

Access Control and Network Security

The application of Zero Trust principles is expanding across logistics organizations. Identity verification, role-based access control, and device-level authentication are now prerequisites in platforms with external connectivity. Enterprise firewalls and EDR platforms have been supplemented by behavior-based threat detection, particularly in environments where remote access or multi-site coordination is required.

While effective, such systems require consistent patching, configuration management, and staff training. Small-to-mid-size logistics providers often struggle to maintain coverage across all assets.

API Exposure and Integration Security

Modern logistics depends heavily on APIs, for shipment booking, status updates, customs clearance, and document exchange. These interfaces, if not secured, can expose sensitive data or create denial-of-service vectors.

Best practice includes TLS encryption, token-based authentication (e.g., OAuth2), and throttling. However, compliance varies. Many legacy integrations operate on outdated standards, especially in sectors where digital transformation is ongoing but incomplete.

Real-Time Monitoring and Sensor-Driven Visibility

The gap between scheduled updates and real-world movement has prompted widespread deployment of sensors, telematics, and real-time data feeds. This visibility enables logistics managers to identify deviations early and act accordingly.

Asset Location and Route Monitoring

GPS and cellular trackers are now embedded in high-value shipments and leased container fleets. These devices report location data in regular intervals, often augmented by geofencing logic to detect unplanned route deviations or idle time.

However, benefits depend on data integration. In firms where telematics platforms are not connected to TMS or order management systems, alerts remain siloed and underutilized.

Environmental Monitoring in Sensitive Freight

Cold chain logistics, chemical shipments, and electronics distribution increasingly rely on real-time temperature, humidity, and shock sensors. These devices provide direct feedback to control towers or customer portals, enabling corrective action if handling parameters are breached.

In pharmaceutical logistics, for example, real-time monitoring is often mandated for regulatory compliance. The data is used not only for response but for audit and documentation purposes in the event of spoilage claims or carrier disputes.

Fleet Telematics and Driver Behavior

Fleet operators collect telematics data across engine metrics, route adherence, and driver behavior (e.g., acceleration, idling, braking). This data supports fuel optimization, maintenance scheduling, and compliance reporting.

However, telematics systems require data governance and standardization. Without consistent timestamping, unit-level normalization, and fault-tolerant connectivity, insights can be degraded or delayed, reducing their value for real-time decisions.

Integration and Data Governance: Core Enablers

The utility of AI, security tools, and real-time monitoring hinges on how well data is structured and systems are integrated. Without governance, these systems generate more noise than signal.

Data Model Consistency

Organizations often struggle with inconsistent identifiers for orders, products, carriers, and facilities. This leads to failed joins in data pipelines and manual reconciliation in reporting.

Master data governance, including data dictionaries, naming conventions, and controlled vocabularies, helps ensure that telemetry data, order events, and AI outputs can be correlated and acted upon in real time.

Interoperability Across Platforms

Data normalization across ERP, WMS, TMS, and IoT systems is essential for analytics and automation. Middleware layers or integration platforms-as-a-service (iPaaS) are used to create consistent data streams and enable real-time orchestration.

Without this layer, AI-generated forecasts or exception alerts are disconnected from execution systems, resulting in inefficiencies or delays in response.

Compliance and Audit Requirements

Supply chain data increasingly falls under regulatory scope, GDPR, CTPAT, FDA 21 CFR Part 11, and others. Secure audit trails, data lineage tracking, and system-of-record clarity are required for compliance and investigation.

Organizations must ensure that their data capture processes and integration workflows align with both industry standards and legal obligations.

Strategic Observations

• AI improves forecast precision and response agility, but only when tied to structured, recent, and trustworthy data.
• Cybersecurity maturity now defines whether a firm can maintain uptime and data integrity under active threat.
• Real-time monitoring improves situational awareness but requires closed-loop feedback with execution systems to deliver measurable impact.
• Integration gaps remain a primary barrier to value realization.

Firms with the highest return on investment in these areas tend to treat data as infrastructure, not just as an IT or analytics function.

Supply chain performance now depends on the maturity of three systems: intelligent planning, secure infrastructure, and live monitoring. Each requires not only technology investment but also organizational discipline in governance and integration. These capabilities are not universal yet, but for firms operating at scale or in regulated sectors, they are already operational requirements. Continued success will depend on an organization’s ability to align data quality, system design, and process accountability.

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Autonomous systems are becoming an integral part of modern logistics infrastructure. The convergence of robotics, artificial intelligence, and sensor technologies is enabling new levels of automation in both warehouse operations and last-mile delivery. These systems are no longer in the prototype phase; they are in active deployment across multiple industry sectors.

Warehouse Robotics: Systematic Redesign of Core Functions

Warehouse operations have historically relied on manual labor for tasks such as picking, sorting, inventory management, and material handling. Robotic systems now perform many of these tasks with higher consistency and fewer interruptions.

• Autonomous Mobile Robots (AMRs) navigate dynamic environments using LiDAR, cameras, and SLAM (Simultaneous Localization and Mapping). They adapt to changing layouts and perform zone picking and goods-to-person operations.
• Automated Guided Vehicles (AGVs) follow predefined routes and are well-suited for repetitive, fixed-path material transport.
• Collaborative Robots (Cobots) are used near humans and support tasks such as assembly, packing, or workstation help.
• Autonomous Case-handling Robots (ACRs) manage heavier loads and multiple cases per cycle.
•  Automated Storage and Retrieval Systems (ASRS) increase vertical density and reduce space requirements.

These systems are often implemented incrementally, allowing organizations to scale according to operational requirements. Robotics-as-a-Service (RaaS) models further reduce capital investment barriers.

Inventory Management: Measurable Efficiency Gains

Autonomous drones have demonstrated quantifiable improvements in inventory tracking accuracy and labor efficiency.

• Langham Logistics used Gather AI drones to improve inventory accuracy from 97% to over 99.9%, while reducing cycle count time tenfold.
• NFI decreased annual inventory count hours from 4,400 to 800 using autonomous drones, scanning three times more locations.
• GNC deployed Corvus One drones, achieving 99.9% accuracy and reallocating labor from cycle counting to higher-value tasks.

These drones work independently, integrate with WMS or standalone exports (CSV, XLS), and reduce the need for lifts or cold-storage exposure.

Material Handling: Predictable Throughput and Safety Benefits

Autonomous systems manage repetitive or hazardous tasks with consistent output. Robots such as Amazon’s Proteus help in sorting and transporting items within distribution centers. These systems keep 24/7 operation without performance degradation due to fatigue or environmental conditions.

Safety improvements include a reduction in workplace injuries, particularly in cold storage and high-traffic forklift zones. Companies report productivity increases as manual labor is redirected toward supervisory or exception-based roles.

Last-Mile Delivery: Use Case-Specific Autonomy

Autonomous last-mile systems fall into two categories: aerial drones and ground-based delivery robots.

• Wing, Amazon Prime Air, and Walmart are conducting drone trials for small-package deliveries. These drones reduce delivery time by bypassing road congestion and reaching remote areas.
• Starship Technologies and Nuro deploy ground vehicles equipped with cameras, radar, and computer vision to navigate urban environments. Starship reports over 5 million completed deliveries.

The economics of drone delivery suggest potential cost per package as low as one or two dollars once multi-drone operation scales are achieved. However, drone payload capacity remains limited. Ground robots can manage larger or heavier items and are more suited for urban sidewalks and campus logistics.

Technical Infrastructure: Requirements for Scalable Deployment

Autonomous systems rely on a foundation of integrated technologies:

• Artificial Intelligence (AI) for navigation, recognition, and decision-making.
• Sensor arrays including LiDAR, radar, and ultrasonic inputs for obstacle detection.
• SLAM for environmental mapping and localization in real time.
• 5G connectivity for low-latency coordination, high-density device integration, and private network security.
• IoT frameworks for telemetry, fleet management, and system diagnostics.

Without reliable connectivity and sensor fusion, performance degradation or safety risks increase, especially in dynamic environments.

Regulatory and Legal Considerations

The current regulatory environment is incomplete:

• In the U.S., FAA Part 107 rules limit drone flights to within visual line of sight (VLOS). BVLOS operations require waivers or special exemptions.
• The NHTSA has issued a framework for automated vehicles, but state-by-state variation still applies.
• In the EU, upcoming legislation including the Machinery Regulation, Product Liability Directive, and AI Act will increase liability and documentation requirements for robotics manufacturers and operators.

Issues such as fault attribution, software safety, and cybersecurity introduce complexity. For example, if a warehouse robot or delivery drone causes damage, finding whether fault lies with the equipment manufacturer, AI developer, or operator is still a legal challenge.

Workforce Transition

Automation does not eliminate labor, but it changes the nature of required skills. Roles increasingly involve monitoring, diagnostics, maintenance, and exception handling. This shift requires retraining, especially for roles that previously relied on physical labor.

Reduced exposure to hazardous conditions, improved cycle times, and fewer injuries are cited as measurable benefits. However, workforce planning must include retraining pathways to avoid skill redundancy.

Deployment Outlook

Adoption will depend on four variables:

1. Cost efficiency at scale compared to manual operations.
2. Regulatory clarity enabling reliable deployment across jurisdictions.
3. Integration with existing infrastructure and logistics platforms.
4. Operational consistency under real-world conditions.

Autonomous drones and robotics are no longer speculative technologies—they are operational tools being implemented across logistics networks to meet evolving cost, labor, and service demands. Their deployment in warehousing improves inventory accuracy, reduces manual workload, and increases throughput. In last-mile delivery, autonomous systems provide alternatives to human couriers in constrained environments. However, their integration requires careful consideration of infrastructure, regulatory alignment, and workforce implications. Organizations that approach adoption methodically—prioritizing reliability, interoperability, and risk mitigation—will be best positioned to realize long-term operational efficiencies and resilience in an increasingly automated supply chain environment.

Learn More! Download the Executive Summary of our Autonomous Mobile Robots (AMR) Research Here: 

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Las Vegas, NV – May 2025

I had the opportunity to attend Momentum 2025 in Las Vegas, Manhattan’s annual user conference focused on supply chain and commerce innovation. The sessions provided clear insights into the company’s strategic direction, technology roadmap, and leadership transition—highlighting a focus on platform unification, practical AI deployment, and long-term operational alignment.

Agentic AI: Expanding Automation Across the Supply Chain

A major focus this year was the introduction of Agentic AI, a suite of digital agents integrated into the Manhattan Active® Platform. These intelligent agents, powered by large language models (LLMs), operate within Manhattan’s microservices-based architecture. Designed to support real-time, autonomous decision-making, the agents help reduce manual tasks and improve responsiveness across operational workflows.

• The initial agents announced include:
  • Intelligent Store Manager – Supports daily store operations
  • Labor Optimizer Agent – Dynamically adjusts workforce assignments
  • Wave Inventory Research Agent – Assists with inventory investigation
  • Contextual Data Assistant – Enables conversational insights and data access
  • Virtual Configuration Consultant – Aids in system configuration and setup

These agents are already in production and are being used to coordinate task management and respond to thousands of user queries daily.

Agent Foundry: Custom Agent Development

To support customization and extensibility, Manhattan introduced Agent Foundry, a toolkit that allows customers and partners to build their own AI agents. The environment supports emerging interoperability standards, such as A2A and MCP, and integrates with third-party platforms, including Google Agentspace.

This approach allows organizations to design intelligent agents tailored to their processes and objectives, accelerating time-to-value while maintaining alignment with Manhattan’s architecture.

Platform Updates and Strategic Themes

The keynote reinforced the company’s commitment to its cloud-native, API-first Manhattan Active Platform. Recent updates include:

• Over 156 million API calls processed daily
• A quarterly release cadence with new feature deployments
• A unified suite covering supply chain execution, commerce, and planning
• Built-in tools for no-code, low-code, and custom code development
• Integrated forecasting, labor optimization, and inventory planning modules

Eric Clark, Manhattan’s new President and CEO, highlighted the platform’s single-codebase architecture as a differentiator—enabling faster updates and seamless data integration across functions. He emphasized continuity in Manhattan’s direction, citing the quality of the team and the strength of the customer base as key factors behind his decision to join the company.

Practical Implementation: Customer and Product Examples

Brian Kinsella, SVP of Product Management, discussed real-world applications of unified supply chain execution, noting that over two dozen customers are moving toward platform-wide adoption. He introduced Enterprise Promise and Fulfill, a new solution that supports enterprise-wide inventory visibility and real-time order orchestration.

Customer adoption stories included Duluth Trading Company, which shared a case study on its $60 million investment in warehouse automation and its use of Manhattan solutions to improve order accuracy, fulfillment speed, and labor efficiency.

Ecosystem Expansion and Accessibility

Ann Ruckstuhl, SVP and CMO, closed the event by announcing that Manhattan Active Solutions are now available on the Google Cloud Marketplace, making the platform more accessible to enterprise customers. She also shared updates on new partnerships with Google and Shopify, aimed at enhancing AI-powered commerce and deployment flexibility.

Momentum 2025 underscored Manhattan’s consistent focus on delivering unified, scalable systems to meet the evolving demands of supply chain and commerce operations. From leadership continuity to AI agent deployment, the company is investing in technology that support greater automation, interoperability, and operational clarity.

It was encouraging to see how customers are already applying these solutions in production, and how the company is enabling broader innovation through accessible development environments and ecosystem collaboration. I look forward to seeing how these technologies continue to support transformation across the industry.

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As artificial intelligence (AI) becomes more integrated into supply chains, companies are focusing on how it can support human workers. Most effective AI implementations today are designed to improve decision-making, reduce routine tasks, and increase operational efficiency through human-in-the-loop systems and decision support tools.

This article outlines how organizations like Amazon, Walmart, and Toyota are using AI to assist their workforce, and it identifies key steps for ensuring successful adoption.

Human-in-the-Loop Systems: AI as a Support Layer

In supply chain operations, AI is rarely deployed to act independently. Instead, it provides recommendations that people review and act on. This is known as a human-in-the-loop (HITL) model.

For example, Amazon uses AI to optimize delivery logistics. During the 2024 holiday season, it reduced unnecessary package movement and shortened delivery distances by leveraging AI to strategically position inventory closer to customer locations. Warehouse and transportation staff still manage fulfillment decisions, but AI provides improved visibility and supports faster planning.

In this HITL model, warehouse employees, dispatchers, and planners remain responsible for reviewing system recommendations. The AI contributes by identifying patterns and recommending changes based on real-time data, but decisions remain with people.

Decision Support and AI Copilots in Retail

AI is also being used in decision support systems that help employees respond more effectively to demand shifts and operational risks.

Walmart has implemented AI to enhance inventory forecasting. By analyzing historical data, local shopping patterns, and external signals like weather, its systems recommend changes to inventory mix and replenishment. Store and warehouse staff then use this information to make restocking decisions.

These tools help employees act more efficiently but do not eliminate their involvement. The AI acts as a copilot, improving responsiveness without automating the entire process.

Workforce Enablement: Toyota’s Internal AI Tools

Toyota has taken a different approach by enabling its factory workers to develop AI tools themselves. Using a platform built on Google Cloud’s infrastructure, Toyota employees can build and deploy machine learning models to monitor equipment performance, reduce downtime, and improve quality control.

This approach has reduced manual labor and helped teams resolve local operational issues without external support. Toyota estimates that the initiative saves more than 10,000 hours of labor annually.

Toyota’s model shows how organizations can train operational staff to work directly with AI tools. Rather than relying only on central IT teams, this approach enables domain experts to create targeted solutions for their own areas.

Adoption Challenges and How to Address Them

Despite growing interest in AI, adoption can face internal resistance. Common concerns include job displacement, lack of clarity about how AI works, and fear of making mistakes based on algorithmic recommendations.

To address these challenges:

• Position AI as a support tool: Make it clear that AI is intended to assist workers, not replace them.
• Provide relevant training: Train staff to understand what AI is doing, how to use its output, and when to intervene.
• Ensure transparency: Use AI systems that explain their reasoning or show the data behind their recommendations.
• Start with small-scale pilots: Demonstrate value in limited use cases before expanding across the organization.

Amazon, Walmart, and Toyota each took different approaches to adoption, but all emphasized transparency, training, and control. These are essential to building trust and ensuring that new systems are used effectively.

The Role of Leadership

Executive support is necessary to scale AI across a supply chain. Leaders need to ensure that AI deployments align with business goals and that staff are prepared to work with new tools.

Key actions include:

• Setting clear expectations about the purpose of AI.
• Investing in training and support.
• Measuring both system performance and user engagement.
• Encouraging collaboration between data teams and operational units.

Without leadership alignment, even well-designed AI systems are unlikely to achieve sustained use.

Evolving Roles and Responsibilities

As AI becomes more common in supply chain operations, job roles will change. Many repetitive tasks, such as manual data entry and static forecasting, can be handled by software. At the same time, new responsibilities are emerging—such as validating AI outputs, handling exceptions, and interpreting recommendations in context.

In all three examples—Amazon’s logistics AI, Walmart’s inventory systems, and Toyota’s factory platforms—people remain essential. AI is being used to improve accuracy and speed, but responsibility for outcomes still rests with human teams.

AI in the supply chain is not about full automation. It’s about equipping people with tools that help them work more effectively. Companies that approach AI with a focus on workforce support—through human-in-the-loop systems, decision support tools, and accessible platforms—are seeing tangible benefits in speed, accuracy, and productivity.

Organizations considering AI adoption should focus on transparency, training, and alignment with operational workflows. The examples from Amazon, Walmart, and Toyota show that when AI is implemented with the workforce in mind, it becomes a practical tool for improving supply chain performance.

The post People + AI: Augmenting the Supply Chain Workforce appeared first on Logistics Viewpoints.

At this year’s keynote, Manhattan Associates outlined its current strategic direction, underscoring platform unification, AI integration, and leadership transition. The presentations provided a clear account of the company’s continued investment in product development and operational capabilities, alongside practical use cases from customer deployments.

Leadership Transition and Strategic Continuity

Eddie Capel, Chairman of the Board opened the event by reaffirming Manhattan Associates’ ongoing focus on innovation, partnership, and adaptability. His comments reflected a long-term orientation: technology and strategy are expected to evolve in parallel with shifts in the global supply chain environment. Capel’s remarks highlighted the need for organizations to remain operationally synchronized—across systems, partners, and functions.

He emphasized that tools must speak the same language to support efficient, intelligent operations. Capel also referenced the increasing importance of modular, interoperable systems and the company’s work to reduce friction across supply chain processes. His introduction of Eric Clark as the incoming President and CEO framed the leadership change as an extension of Manhattan’s current trajectory rather than a pivot.

Eric Clark: Emphasis on People, Platform, and Market Responsiveness

Eric Clark, President & CEO opened by acknowledging the legacy of the organization and thanking Eddie Capel and the broader team for their support. He explained that his decision to join Manhattan was shaped by three main factors: the quality and depth of the team, the pace of platform innovation, and the resilience of the customer community. In his view, these components form the foundation for long-term operational and commercial success.

Clark cited the Manhattan Active Platform as a differentiated asset in the enterprise technology market. Built as a unified suite rather than integrated point solutions, the platform includes end-to-end functionality across supply chain execution, commerce, and planning. He stressed the strategic advantage of managing all functions on a single codebase, which allows for faster updates, stronger data integration, and reduced implementation complexity.

Recent examples included the rollout of a new inventory planning application and the introduction of a hybrid AI-powered demand forecasting engine. Clark noted that these developments were designed to meet emerging customer needs, particularly in markets experiencing unpredictable demand and inventory constraints.

He also introduced the company’s new agentic AI strategy—referring to intelligent software agents that can make autonomous decisions and manage specific operational tasks. According to Clark, these agents are being deployed to reduce manual intervention and enable more adaptive, responsive systems in real-time environments.

Brian Kinsella: Practical Application of Platform Unification

Brian Kinsella, Senior Vice President, Product Management, focused on the measurable outcomes associated with unified applications. He reported that over 25 customers are currently moving toward unified supply chain execution—leveraging consistent data models, processes, and system behaviors across distribution, transportation, and fulfillment functions.

Kinsella detailed recent functionality developed to support these goals. Examples included dynamic trailer door assignments, shipment planning enhancements, drag-and-drop yard visualization tools, and integrated labor planning modules. Each feature was tied to a larger goal: removing operational silos and enabling system-wide coordination.

He introduced a new solution called Enterprise Promise and Fulfill, built to address enterprise-wide inventory visibility and order fulfillment orchestration. Unlike traditional order management systems, this application incorporates live data from across the network and supports multi-node order allocation and dynamic lead time calculation.

Kinsella also stressed that unification is not only a design goal but a practical enabler of speed, accuracy, and efficiency for customers seeking to simplify multi-system architectures.

Sanjeev Siotia: AI Agents and System Architecture

Sanjeev Siotia, Executive Vice President & CTO, provided a technical update on the Manhattan Active Platform. He explained how the company’s API-first, microservices-based architecture serves as the basis for current and future enhancements. Each service operates independently while integrating seamlessly, allowing for more resilient deployments and easier scaling.

Siotia introduced the concept of agentic AI in greater detail. These agents are software entities powered by large language models, capable of coordinating tasks, making decisions, and adapting in real time based on context. This capability marks a shift from traditional rule-based systems to more flexible, learning-based automation.

He offered specific examples, including a labor optimization agent designed to monitor warehouse workflows and adjust assignments dynamically. These agents are already in production, contributing to operational task management and responding to thousands of user queries.

To support broader adoption, Siotia announced the launch of Agent Foundry—a toolkit for customers to create and deploy their own AI agents. He also noted a strategic collaboration with Google to support interoperability between Manhattan’s AI agents and external platforms, ensuring customers can extend capabilities as needed.

Customer Case Study: Duluth Trading Company

AJ Sutera, Senior Vice President, Chief Technology & Logistics, of Duluth Trading Company, presented a customer case study highlighting the retailer’s digital transformation over the past several years. He outlined the company’s approach to rethinking fulfillment, digital strategy, and  channel operations, particularly as part of a major warehouse automation investment.

Sutera described a $60 million project to build an automated fulfillment center in Adairsville, Georgia, launched in September 2023. The initiative was supported by partners including Manhattan Associates, Summit Advisory Services, and others. According to Sutera, the facility has improved operational metrics related to order accuracy, fulfillment speed, and labor efficiency.

His remarks focused on change management as much as technology—emphasizing the need for new skills, clearer roles, and accountability across teams during the transformation process. He credited strong partnerships and coordinated execution as key factors in the project’s success.

Ann Ruckstuhl: Platform Availability and Ecosystem Partnerships

Ann Ruckstuhl, Senior Vice President & CMO, concluded the session by discussing Manhattan’s go-to-market ecosystem. She confirmed that Manhattan Active Solutions are now listed on the Google Cloud Marketplace, increasing availability and simplifying access for enterprise buyers.

Ruckstuhl discussed the company’s partner strategy, including collaboration with Shopify and Google to expand AI-enabled solution delivery. These partnerships are intended to support more flexible deployment options and help customers build integrated commerce experiences.

She encouraged attendees to explore current capabilities through the Unity Pavilion and hands-on demonstrations, which included AI agent use cases, platform configurability, and roundtable discussions on sector-specific applications.

Manhattan Associates used the keynote to communicate its continued investment in unified system design, AI functionality, and customer enablement. The leadership transition from Eddie Capel to Eric Clark was framed as a handoff aligned with the company’s existing direction.

With live deployments of unified applications, practical AI agents in use, and new tools for customer-led development, the company is positioning itself for scalable execution across complex supply chain environments. The focus remains on reducing system fragmentation, simplifying operations, and improving business responsiveness in a dynamic global market.

The post Manhattan Associates’ Strategic Vision – Unifying Platforms Integrating AI and Leading the Future appeared first on Logistics Viewpoints.

In the past, tracking a shipping container across continents or monitoring the temperature of a pharmaceutical package in a rural warehouse came with trade-offs: cost, power drain, or unreliable coverage. Asset visibility was reserved for high-value goods, while the rest of the supply chain operated on estimates, paper trails, and phone calls.

This is changing. Two cellular technologies—LTE-M and NB-IoT—are now reshaping long-distance asset tracking. Designed specifically for low-power, wide-area connectivity, they are not flashy or fast, but they are practical. And that practicality is unlocking a new standard of visibility across logistics networks.

A Functional Divide: What Makes LTE-M and NB-IoT Different?

Both LTE-M (Long Term Evolution for Machines) and NB-IoT (Narrowband Internet of Things) were developed under the 3GPP standard. They’re not general-purpose wireless technologies. They’re built for a narrow job: to allow simple devices to send small data packages across long distances with minimal power usage.

Still, they serve different purposes.

LTE-M supports voice (VoLTE), real-time mobility, and bandwidth up to 1.4 MHz. That makes it suited for assets in motion—trucks, railcars, shipping containers. Devices stay connected as they cross cell towers, even at highway speeds.

NB-IoT, on the other hand, is optimized for stationary or slow-moving assets. It operates on just 180 kHz of bandwidth, uses even less power than LTE-M, and excels at indoor or underground penetration. That makes it ideal for warehouse environments, shipping pallets, or cold storage units.

In both cases, devices can last up to 10 years on a battery, waking only to transmit data at defined intervals.

On the Ground: Where These Technologies Are Already Working

In Germany, Deutsche Telekom uses NB-IoT to track reusable transport packaging—low-value but often misplaced. By tagging these items with NB-IoT sensors, they can recover more assets and reduce losses.

In the United States, Roambee uses LTE-M to track pharmaceutical shipments. Their sensors capture not just GPS data, but also temperature, humidity, and light exposure—essential data for compliance and quality control.
Reference

Meanwhile, Sierra Wireless now Semtech offers LTE-M modules built into trackers used across North American freight networks. They enable cross-border asset visibility without requiring complicated roaming workarounds.
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These are operational deployments—quietly streamlining real supply chains today.

From Fragmented Data to Structured Insight

For decades, asset tracking systems lived in silos—fleet telematics in one system, warehouse sensors in another, handheld barcode scans in a third. LTE-M and NB-IoT allow these devices to transmit consistent, time-stamped data that can be ingested directly into ERP, WMS, or TMS platforms.

This leads to several operational changes:

• Shipment ETAs can now be calculated using real-time location data.
• Route deviations, temperature excursions, or tampering events can trigger alerts instantly.
• Idle asset time, loss rates, and turn rates can be tracked quantitatively rather than through assumptions.

These technologies give the supply chain a memory. Not just precise locations—but how long they’ve been there, under what conditions, and whether any anomalies occured.

Strategic Implications: More Than Better Tracking

The shift to LTE-M and NB-IoT is not just about technical improvement. It represents a change in how companies define what is worth tracking.

1. Lowering the Threshold

When connectivity was expensive and battery life short, only high-value goods justified GPS trackers. LPWA (low power wide area) tech brings that threshold down. Companies can now afford to track plastic pallets, returnable containers, or temperature-sensitive packaging. These items were once considered disposable or unmonitored; now, they are part of the data flow.

2. Preparing for 2G/3G Sunset

Many tracking systems still rely on 2G or 3G cellular modules. As networks phase out legacy services, companies are being forced to choose a replacement. LTE-M and NB-IoT offer a forward-compatible path that avoids the higher costs of full LTE or 5G broadband connections.

3. Shifting from Status to Intelligence

In the past, knowing an asset’s last known location was sufficient. Today, organizations are using real-time sensor data to move from reactive to predictive operations. Cold chain breaches, delivery delays, or maintenance needs can be identified before they create service failures or lost revenue.

4. Expanding Global Logistics

LTE-M supports broader international roaming than NB-IoT, but both are becoming more accessible globally as carriers standardize their infrastructure. For multinational operations, this means fewer gaps and less complexity in deploying one global asset tracking framework.

Constraints and Deployment Realities

These technologies are not without limitations:

• Roaming for NB-IoT is still fragmented, limiting its use in cross-border applications.
• Latency for NB-IoT can be high, making it unsuitable for urgent alerts or rapid two-way communication.
• Device provisioning and firmware updates must be managed remotely, especially for assets deployed in hard-to-reach areas.

As a result, organizations must carefully evaluate their device requirements, data latency tolerance, and regional coverage before choosing between LTE-M and NB-IoT.

Supply chains are being redefined not by high-profile innovations, but by infrastructure technologies like LTE-M and NB-IoT that enable quiet, scalable change. These technologies do not replace the need for planning or coordination, but they reduce uncertainty. They give supply chain managers access to verified data instead of estimates. They allow decisions to be made with more context, and fewer assumptions.

In practical terms, LTE-M and NB-IoT make it feasible to track every pallet, every package, and every trailer—not just those deemed high-value. That’s not a breakthrough, it is how resilient, modern supply chains operate today.

The post How LTE-M and NB-IoT Are Revolutionizing Asset Tracking in Global Supply Chains 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.

 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.

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