ARC ADVISORY GROUP | EXPERT VIDEO INSIGHTS

Supply chain technology is advancing rapidly. Understanding what matters, what works, and where to invest requires more than keeping up with the latest announcements.

Supply chain and logistics leaders face an increasingly complex technology landscape. Artificial intelligence, warehouse automation, cybersecurity, connected vehicles, digital twins, and new approaches to supply chain planning are changing how organizations manage operations, evaluate investments, and prepare for disruption.

Keeping pace with these developments requires more than product announcements and technology demonstrations. Decision-makers need informed perspectives that explain how emerging capabilities work, where they create operational value, and what organizations should consider before investing.

To support that need, Logistics Viewpoints is highlighting its
Supply Chain & Logistics Video Library: Expert Insights from ARC Advisory Group,
a resource designed to make analyst perspectives and industry discussions more accessible to supply chain professionals.

The objective is straightforward: help supply chain leaders translate rapidly evolving technologies into better-informed business and operational decisions.

Bringing Supply Chain Research to Video

For years, Logistics Viewpoints and ARC Advisory Group have provided research and commentary on transportation management, warehouse operations, supply chain planning, industrial automation, and enterprise technology.

Video adds another way to explore that research. Analyst discussions, technology briefings, and industry interviews can help executives examine important developments without having to begin with lengthy research reports or technical documentation.

The objective is not simply to explain what a technology does. It is to examine the business problems it addresses, the challenges associated with implementation, and the implications for supply chain performance.

Exploring the Technologies Reshaping Supply Chains

The video resources reflect several areas of increasing importance across logistics and supply chain management.

01. Artificial Intelligence and Decision Intelligence

Artificial intelligence is changing how organizations approach forecasting, inventory management, transportation planning, and operational exception management. As AI becomes more deeply integrated into enterprise systems, questions concerning governance, interoperability, data quality, and human oversight are becoming equally important.

02. Warehouse Automation and Connected Operations

Robotics, automated storage and retrieval systems, warehouse software, and increasingly interconnected execution technologies are reshaping distribution operations and fulfillment strategies. Understanding how these technologies integrate with existing systems is critical to evaluating their operational and financial impact.

03. Cybersecurity and Operational Resilience

Cybersecurity and operational resilience have become essential considerations as supply chain networks grow more digitally interconnected. The increasing dependence on cloud platforms, connected equipment, logistics partners, and shared operational data introduces new risks that organizations must understand and manage.

04. Sustainability, Transportation, and Digital Twins

Other important areas include sustainability, energy management, connected transportation, and digital twins. Each presents opportunities to improve operational performance while introducing questions about investment requirements, organizational readiness, and long-term value.

From Technology Awareness to Better Decisions

One of the persistent challenges facing supply chain executives is distinguishing technological possibility from operational practicality.

A promising application of AI may still depend on substantial improvements in data integration. A warehouse automation investment may require changes to facility design, workforce processes, and supporting software. A digital twin may provide sophisticated simulation capabilities but offer limited value without accurate, current operating data.

These considerations are central to ARC Advisory Group’s research approach: understanding technology in the context of the operations it is intended to improve.

For technology buyers, analyst video discussions can provide useful context during early research and supplier evaluation. For operations leaders, they offer perspectives on industry developments that may influence future strategy. Technology suppliers can also benefit from a broader understanding of the challenges and priorities facing their customers.

Extending the Logistics Viewpoints Research Experience

The video library complements the broader Logistics Viewpoints research offering, including technology analysis, market commentary, white papers, interviews, and ARC Advisory Group’s MarketMap research.

Different resources serve different purposes. Written research provides opportunities for detailed examination of technology markets and operational challenges. Video can make complex subjects more approachable, communicate key findings, and introduce questions that warrant further investigation.

Together, these formats help supply chain professionals move from general technology awareness toward more informed strategic and operational decisions.

Explore the Supply Chain & Logistics Video Library

The pace of supply chain technology development shows little sign of slowing. New capabilities continue to emerge, but their practical value depends on how well they address the operational challenges businesses actually face.

The Logistics Viewpoints video library provides another avenue for examining those developments through the perspective of industry analysts and subject-matter experts.

Explore Expert Supply Chain Insights

Discover perspectives from ARC Advisory Group on the technologies, trends, and strategic decisions shaping the future of logistics and supply chain management.

Explore the Video Library →

The post Introducing the Supply Chain & Logistics Video Library: Expert Insights from ARC Advisory Group appeared first on Logistics Viewpoints.

Blue Yonder has announced Release 2026.3, its latest product update, introducing advancements in supply chain intelligence, AI-driven decision-making, connected planning and execution, and coordination across trading partners.

The release focuses on AI that explains its reasoning and operates within governed guardrails, role-based workspaces, and a more unified supply chain operating model.

Orchestrator and Platform: One Place to See, Ask, Decide, and Act

Blue Yonder Orchestrator becomes a unified operating environment for supply chain work, with interactive briefings that allow users to ask questions of personalized Executive Summaries, updated navigation and mobile experiences, and a hands-free Voice-Only Mode.

Blue Yonder Mission Hub provides a self-service environment for provisioning, configuration, onboarding, migration, and observability, with contract-driven deployment automation. Service extensibility for warehousing allows customer-specific logic to run as platform services rather than customized code.

Cognitive Solutions for Planning: Faster, More Confident Plans

Retail planning enhancements include AI-assisted planogram design in Space Planning, a Forecasting Agent that explains forecasts, an audit trail for forecast overrides, and conversational scenario modeling in Merchandise Financial Planning.

Weekly labor forecasts now extend up to 52 weeks and incorporate weather data. Manufacturing planning adds validated forecasting models, scenario conflict management, dynamic supply constraints in Integrated Business Planning, and disruption simulation within scheduling workflows.

Cognitive Solutions for Orders and Returns: One Platform for Every Order and Return

Cognitive Order Management is now available as a unified platform supporting both B2C and B2B operations.

The solution includes a persona-driven console with more than 100 configurable cards, Fulfillment and Customer Service Agents that recommend and execute governed actions, and native B2B capabilities including Excel bulk uploads, supplier purchase order management, and bulk return merchandise authorization processing.

The platform is offered as a single SKU with modular add-ons. Returns management now operates on the Blue Yonder Platform, with expanded third-party drop-off options and brand-level routing and policy controls.

Cognitive Solutions for Execution: Smarter Operations from Warehouse to Road

Warehouse enhancements include a flexible integration layer connecting robotics and automation from multiple vendors, explainable multi-objective slotting recommendations, and a Warehouse Ops Agent that detects advance shipping notice discrepancies, flags perishables, and forecasts trailer shortages. An Allocation Shorts capability is also available in beta.

Transportation enhancements include a new award decision dashboard providing procurement visibility into spending and carrier concentration. MileMaker is now available alongside PC*MILER, while load-building improvements allow planners to adjust stacking orientations for the same SKU.

The Logistics Emission Calculator is also embedded into transportation and network decisions.

Blue Yonder Network and Services: Coordinated Action Beyond Go-Live

Blue Yonder Network continues evolving from a connectivity layer toward intelligent orchestration, introducing multi-party orchestration, Network Intelligence Agents that recommend and automate next steps, and expanded in-transit risk visibility across transportation lanes.

Blue Yonder is also expanding Surround Services, with AI, machine learning, and data science expertise available to customers to support continuous model optimization.

The release reflects Blue Yonder’s continued investment in connecting supply chain planning, execution, and trading partner collaboration through AI-enabled capabilities.

For additional information, visit the Blue Yonder Release 2026.3 announcement.

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The transportation management systems (TMS) market is entering a period of significant change as artificial intelligence moves beyond analytical assistance toward more automated execution. Suppliers are introducing AI agents, intelligent freight procurement, automated exception management, and capabilities that connect transportation planning with operational decisions. Yet a distinction is emerging between the pace of innovation and the rate at which transportation organizations can deploy these capabilities effectively.

The numbers provide perspective. Descartes’ 2026 Transportation Management Benchmark Survey, based on responses from 300 senior transportation decision-makers in the United States and Canada, found that just 19% of shippers and 15% of logistics service providers (LSPs) reported using AI at scale. At the same time, 82% of shippers expected to increase transportation management IT spending over the next two years. The contrast suggests substantial investment appetite but considerable work still required to convert innovation into operating performance.

For transportation executives, the question is no longer simply whether AI can help with planning, carrier management, freight audit, or exception resolution. It is how these capabilities can be incorporated into existing operations while preserving reliability, controlling costs, and delivering measurable returns. For software suppliers, implementation economics may become as important as technological sophistication.

A Market Pursuing Different Paths to Intelligent Transportation

Recent announcements illustrate that AI-enabled transportation management is not developing around a single architectural model. Established vendors are extending existing applications, while others are consolidating platforms or placing transportation intelligence within broader enterprise and logistics networks.

At its September 2026 Insight conference, Trimble announced agent-ready modernization of its TMW.Suite, TruckMate, Innovative, and Fuel Dispatch carrier TMS platforms. Browser-accessible interfaces and cloud-hosted architectures preserve underlying operational logic while making room for AI-enabled workflows. Trimble Arc Agent addresses activities including order entry, contract intake, and freight RFQ preparation, with final approval retained by transportation personnel.

The significance lies in protecting investments that customers have made over years of configuration, integration, and process development. Replacing a carrier TMS can carry considerable expense and execution risk. Modernization within established workflows offers a potential route to new capabilities without requiring a wholesale operational reset.

FreightWise took a different approach with its October 2026 launch of Kuebix AI. Following its acquisition of Kuebix, the company is bringing its transportation technology onto a single platform and roadmap for small and midsize shippers. The offering combines less-than-truckload, truckload, and parcel execution with carrier connectivity, analytics, and integrations. Its AI-supported capabilities include multistop routing, service-time planning, and freight audit exception resolution. Customer migrations are underway, so the benefits of the consolidated architecture will depend in part on how smoothly those transitions proceed.

Oracle’s October announcement points to another direction: governed agentic applications embedded in enterprise processes. Its Fusion Claw-powered Shipment Load Planning Workspace evaluates consolidation opportunities, equipment capacity, timing, and service constraints to develop load recommendations for planner review and approval. Importantly, the workspace supports supply planning decisions; it does not itself execute transportation or replace Oracle Transportation Management. The strategic proposition is tighter coordination between enterprise planning data and decisions that eventually influence transportation execution.

Blue Yonder’s Logistics Ops Agent emphasizes decision support within transportation operations. It helps identify unroutable shipments, uncover backhaul opportunities, assess disruptions, and explain recommended actions in natural language while leaving transportation professionals in control. The approach combines operational context with more accessible analysis rather than treating an AI agent as a substitute for established optimization and execution processes.

project44’s Intelligent TMS, introduced in 2025, represents a network-centered approach. The platform brings freight procurement, planning, execution, visibility, and freight audit together using the company’s logistics data foundation. It can serve as a standalone TMS or operate alongside an incumbent platform, giving enterprises more than one path to adoption.

These are different market segments and technology layers, not five interchangeable products. Trimble’s carrier systems, FreightWise’s shipper-focused platform, Oracle’s planning workspace, Blue Yonder’s enterprise transportation capabilities, and project44’s integrated offering address overlapping but distinct requirements. Their announcements establish strategic direction and claimed functionality, not a uniform basis for comparing independently verified production results.

The Implementation Gap Is an Enterprise Challenge

The Descartes findings help explain why adoption remains uneven. Data quality was identified as a barrier to scaling AI by 33% of shippers and 45% of LSPs, while integration complexity was cited by 39% and 38%, respectively. These are survey respondents’ reported obstacles, not measurements of any individual supplier’s implementation performance.

Nevertheless, the findings identify a structural constraint. A system recommending carriers, consolidating loads, or revising a transportation plan needs dependable information about rates, capacity, delivery commitments, equipment, and operating restrictions. When those inputs are fragmented or inconsistent, even sophisticated recommendations can produce poor operational decisions.

The stakes also change as AI moves from administrative assistance toward action. Extracting information from transportation documents is a relatively bounded task. Changing a carrier assignment, revising a load plan, or responding to a service exception can introduce contractual, financial, and customer-service consequences. Reliable automation therefore depends on business rules, permissions, approval boundaries, exception handling, and auditability, not simply the intelligence of the underlying model.

Incremental deployment can be economically sensible. Organizations may achieve meaningful value from automating order entry, freight audit, communications, or monitoring before extending decision-making authority across a network. The opportunity for suppliers is to make those transitions easier through simpler integration, transparent recommendations, configurable controls, and support for existing processes.

What Transportation Buyers Should Measure

Transportation executives should broaden their software evaluation criteria without abandoning traditional TMS fundamentals. Functional breadth, optimization, multimodal coverage, carrier connectivity, and integration remain essential. AI adds new questions about implementation effort, decision reliability, oversight requirements, usage costs, and the consequences of automated actions.

A product demonstration can show that a workflow is technically possible. It says less about how the workflow behaves across thousands of transactions, difficult exceptions, and variable operating conditions. Buyers should ask suppliers for production evidence: exception rates, the frequency of human intervention, implementation effort, control mechanisms, and results achieved in comparable environments. They should also distinguish features available today from capabilities still on a product roadmap.

Commercial terms warrant similar scrutiny. AI capabilities may be included in subscriptions, priced by usage, or separately licensed. A pilot that looks inexpensive may have different economics when processing volume grows. The relevant measure is total cost to operate the capability, including integration, monitoring, support, and corrective intervention.

Most importantly, benefits should be assessed through transportation outcomes rather than counts of automated tasks. Shippers might track freight cost per shipment, tender acceptance, planner productivity, on-time delivery, and exception resolution costs. LSPs may focus on operating expense per load, revenue per employee, carrier-management productivity, and customer service performance. Improvements should be measured against a defined baseline and over enough time to capture operational variability.

A platform with sophisticated AI but extensive customization requirements may be less attractive for some organizations than one offering narrower functionality with faster deployment and predictable performance. The appropriate choice depends on network complexity, existing technology, and operating maturity, not the number of AI features on a slide.

Deployment Economics Will Shape the Next Competitive Phase

The TMS market is moving toward more intelligent transportation operations, but the transition will not be uniform. Large enterprises must balance innovation with continuity across complex systems and trading-partner relationships. Smaller shippers and service providers may place more immediate value on speed to implementation, integration simplicity, and demonstrable productivity gains.

Suppliers that align their technology and commercial models with these realities will have an advantage. Preserving useful customer configurations, improving data accessibility, reducing integration effort, and introducing automation within governed boundaries can be as important as adding capabilities. Buyers, meanwhile, should insist that the degree of autonomy matches the reliability of the data, the maturity of the workflow, and the cost of a wrong decision.

The Descartes survey points to a market where AI investment intent is strong but scaled deployment remains limited. That gap is an opportunity, not an indictment of existing technology. It shifts attention from what AI can demonstrate to what transportation organizations can reliably implement and measure. The next generation of TMS leaders will not necessarily be determined by how many AI features they introduce, but by their ability to make intelligent transportation execution practical, dependable, and economically valuable at scale.

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