Managing a transport operation involves working with numerous shipments, carriers, routes, and incidents. When each carrier uses its own systems and formats, operations teams can end up spending a significant portion of their time consulting portals, cross-referencing information, and manually updating files.

In this context, a transport management software It can help centralize information and offer a more complete view of what's happening in operations. However, technology applied to transportation can go beyond simply displaying data. Artificial intelligence allows for the detection of incidents, the analysis of information, and helps teams move from alert to resolution.

Cadenity proposes this approach through an agentic AI platform for logistics and supply chain, designed for companies that work with multiple carriers and need to reduce manual tracking and reactive incidents.

Centralize information from different carriers

One of the main challenges of multi-carrier operations is information fragmentation. Each carrier may use different terminology and provide shipment data in their own formats. This makes it difficult to get a unified view when the team needs to check the status of the entire operation.

Cadenity allows you to centralize data from different carriers and standardize it into a common taxonomy. Currently, the platform can automatically ingest information from DHL, MRW, and other carriers using CSV files.

In this way, a transport management software It can become a central hub for tracking shipments without relying on multiple portals. Cadenity brings together shipments, carriers, routes, and incidents in a single view, providing real-time visibility into operations.

This centralization also allows for working with more homogeneous information. Instead of interpreting the states of each carrier individually, the data is normalized to facilitate analysis and monitoring.

Real-time visibility to detect incidents

Visibility is especially important when incidents occur that could affect deliveries. A delay, a partial delivery, a return, or a POD-related issue requires rapid notification so the team can analyze the situation.

Cadenity offers automatic incident detection in seconds and allows users to receive alerts related to different operational situations. In addition, the platform can display indicators such as on-time deliveries, incidents by carrier and route, carrier rankings, performance maps, and comparisons with previous periods.

Therefore, a transport management software It should not only allow users to check the location of a shipment, but it can also help identify patterns and problems within operations, giving teams a more complete overview.

Cadenity also allows you to generate scheduled Excel reports with information on incidents, delays, and snapshots, without requiring the team to prepare them manually. Alerts can also be emailed to designated team members.

Integration without migrations or changes in operations

Implementing a new technology solution can be complex when a company already uses different systems. For this reason, Cadenity is committed to a zero-disruption model: working on the existing infrastructure without replacing current systems.

The platform can connect with carriers and use formats already handled by companies, such as CSV. It can also integrate ERP exports and work alongside the TMS. Cadenity indicates that the visibility and detection phase can be operational in days, without lengthy integration projects.

This allows incorporating a transport management software progressively. Instead of proposing a complete replacement of existing technology, Cadenity builds upon current systems to add visibility, intelligence, and automation capabilities.

Connections to direct carrier APIs, certain ERP and TMS systems, such as SAP TM, are incorporated during the activation phase according to the technology stack and operational priorities of each client.

From the detection of an incident to its resolution

Detecting a problem is only the first step. Once an issue is identified, the team needs to gather information, analyze the alternatives, and decide how to proceed. In a traditional operation, this process can involve calls, emails, queries in different systems, and copy-and-paste tasks.

Cadenity addresses this process through its AI Orchestration Center. The AI detects the incident, gathers available context, analyzes the situation, and generates resolution options with information on their confidence level, financial impact, and effect on the SLA. The operator then reviews and approves the option before the action is executed.

Human approval is a core element of the system. AI proposes solutions, but the final decision always rests with a person. Furthermore, all actions and decisions are recorded through an auditing system that provides complete traceability.

Currently, Cadenity indicates that the AI Orchestration Center manages issues such as delays, partial deliveries, returns, POD gaps, and SLA risks. Autonomous execution via direct carrier APIs is being implemented progressively on a customer-specific basis.

View operations using artificial intelligence

Another of Cadenity's capabilities is Caden, its AI agent specializing in logistics. It is designed to understand terms related to shipments, OTD, incidents, routes, and carriers, and allows users to make queries using natural language in Spanish and English.

With Caden, teams can directly query their shipping data without using SQL or exporting files. For example, they can check the main delivery issues, compare average delivery times by destination, carrier, and service, or request a ranking of carriers based on on-time delivery percentage.

Thus, a transport management software It incorporates a new way of accessing information. The user does not need to know how the data is structured to ask a question about the operation.

In addition, Caden can generate executive summaries of the dashboard with three prioritized insights and maintains the context of the conversation to facilitate follow-up questions. It can also be integrated as a widget into other internal client tools.

A more connected and autonomous transport management

The evolution of transport management hinges on providing teams with centralized information and enabling them to act on it more quickly. To achieve this, simply gathering data is not enough: it also requires standardizing the data, detecting incidents, and facilitating its analysis.

Cadenity combines these elements into a platform that offers connectivity with existing carriers and systems, real-time visibility, and artificial intelligence capabilities to analyze and coordinate incident resolution.

In this sense, a transport management software It can become a tool for reducing manual tasks and improving operational control. Information is centralized, incidents can be detected earlier, and teams have tools to analyze them without constantly relying on manual processes.

Furthermore, Cadenity's approach keeps the human operator within the decision-making process. Artificial intelligence detects, analyzes, and proposes, while decisions and actions remain under human supervision.

Ultimately, the combination of connectivity, visibility, and artificial intelligence allows us to move towards more integrated transport management. transport management software Cadenity proposes this evolution without requiring migrations or changes to existing systems, making it easier for companies to incorporate new capabilities gradually.

La entrada Software de gestión de transporte: más visibilidad y control con IA se publicó primero en Cadenity.

Managing a supply chain with multiple carriers involves handling large amounts of information, constantly tracking shipments, and reacting to incidents that can affect operations. When teams have to consult different portals or manually track the status of a delivery, a significant portion of their time is spent searching for information instead of analyzing it and making decisions.

In this context, a logistics software It allows for the centralization of information and improved visibility of operations. The evolution of these platforms also incorporates artificial intelligence to analyze data, detect incidents, and help teams move toward their resolution.

Cadenity proposes this approach through an agentic AI platform for logistics and supply chain, aimed at companies that work with multiple carriers and need to reduce manual tracking and reactive incident management.

The challenge of managing operations with multiple carriers

When a company works with different carriers, information can be scattered across various systems and portals. Each carrier may provide data on shipments, statuses, or incidents, but compiling this information to obtain a comprehensive overview takes time.

Furthermore, detecting an issue doesn't always mean the problem is resolved. A delay, a partial delivery, a POD gap, or an SLA risk may require the operations team to investigate what happened and contact the relevant carrier.

Therefore, having a logistics software Gathering information from different sources provides a more complete view of the supply chain. Cadenity centralizes shipments, carriers, routes, and incidents in real time and standardizes the data into a common taxonomy.

A single view for the entire supply chain

One of Cadenity's key pillars is visibility. The platform allows users to view the main elements of operations in a single view, eliminating the need to constantly switch between separate portals for each carrier.

In addition, Cadenity automatically detects certain issues, including delays, POD gaps, and missed scans. This allows the team to identify areas requiring attention sooner.

The platform also allows users to work with delivery-related indicators. Caden can answer queries about the percentage of on-time deliveries from different carriers or compare average delivery times by destination, carrier, and service.

Thus, a logistics software It is not limited to storing information, but can facilitate its consultation and help teams to better understand what is happening in operations.

Connectivity without changing existing systems

Implementing a new technology platform can raise concerns when a company already has an ERP, TMS, and various transportation systems. Cadenity is committed to a zero-disruption approach to ensure that platform adoption doesn't require migrating data or modifying existing processes.

The platform connects with carriers and systems using formats that companies already handle, including CSV, ERP, and TMS. According to Cadenity, the visibility and detection phase can be active in days, without the need for lengthy integration projects.

This possibility allows incorporating a logistics software progressively. The company can start with visibility and incident detection and later activate new capabilities according to its priorities.

Furthermore, integration with ERP and TMS systems is activated progressively according to each client's needs. This allows Cadenity to be integrated into existing operations without replacing the systems already part of daily functioning.

From detecting an issue to working on its resolution

Detection is only one part of incident management. Receiving an alert about a delay doesn't eliminate the problem. The team needs to analyze what happened, assess the alternatives, and decide how to proceed.

Cadenity addresses this aspect through its AI Orchestration Center. The platform enables a progression from detection to coordinated resolution. Artificial intelligence detects, analyzes, and proposes options, while the final decision always remains with a human.

This human-in-the-loop approach allows for a combination of automation and supervision. Every action requires human approval, and decisions are recorded with complete traceability.

According to Cadenity, the AI Orchestration Center can manage issues such as delays, partial deliveries, returns, POD gaps, and SLA risks. Autonomous execution capabilities are activated progressively based on customer needs.

Therefore, a logistics software AI-based technology can help teams reduce manual tasks without eliminating control over decisions that affect operations.

Caden, an AI agent specializing in logistics

Caden is Cadenity's AI agent specializing in logistics. It is designed to understand the specific vocabulary of logistics operations, including shipments, OTD, incidents, routes, and carriers.

Furthermore, it allows for queries using natural language without requiring SQL knowledge or file exports. This makes it easier for teams to directly access information related to their work.

For example, Caden can answer questions about the main delivery issues, compare average times by destination, carrier and service, or show carrier rankings based on the percentage of on-time deliveries.

Thus, the logistics software It incorporates a conversational layer that facilitates access to data and allows users to consult operational information more easily.

Artificial intelligence applied to logistics allows for the combination of data, visibility, and analytical capabilities on a single platform. Cadenity is committed to this model through a solution connected to existing systems and carriers, designed to keep the human operator involved in the decision-making process.

Ultimately, the evolution of logistics management is not just about having more data, but about being able to access it quickly, detect what is happening, and use artificial intelligence to help teams take action. logistics software as Cadenity proposes this transition towards a more connected, visible and autonomous management, while maintaining human control.

La entrada Software de logística: cómo la IA está transformando la gestión de las operaciones se publicó primero en Cadenity.

In this scenario, the Agentic AI in European logistics It emerges as the definitive evolutionary leap, leaving behind traditional and static software systems to give way to autonomous systems capable of reasoning, proposing and executing actions in complex contexts.

The difference between classic automation and agentic systems

During the last decade, automation in the transport sector has been based on rigid rules of the type «"If A happens, then do B"» (if-this-then-thatWhile this approach is useful for simple mechanical tasks, it breaks down in the face of the real volatility of the supply chain, where a delay at a customs hub or a local transport strike requires evaluating multiple variables simultaneously.

Unlike traditional systems, agentic AI stands out for three differentiating capabilities:

• Adaptive autonomy: It does not depend on pre-configured rules; it understands the ultimate goal (for example, “Minimize delivery costs by guaranteeing the SLA before 6:00 PM”) and evaluates the best path in real time.
• Understanding the multimodal context: It can simultaneously process structured data from a CSV delivery note, real-time weather updates, and the historical behavior of a specific carrier in a given province.
• Interaction and execution capacity: Agents don't just display a bar chart on a dashboard; they interact with the digital ecosystem to prepare for the resolution of a problem from start to finish.

[Traditional Software] ➔ Displays a delayed packet on a map. [AI Agent] ➔ Detects the delay, searches for alternative stock, calculates the cost impact on three different carriers, and drafts a solution proposal for the operator.

The specific challenges of the supply chain in Europe

Cadenity's vision on the implementation of the Agentic AI in European logistics It addresses three critical challenges of the community market:

1. Endemic multi-carrier fragmentation

Unlike more unified markets such as the United States, operations in Europe require combining global carriers with specialist local operators in each country (France, Germany, Benelux, Spain, etc.). Centralizing and standardizing this information without compromising the agility of local partners is an insurmountable technical challenge for traditional, rigid platforms.

2. The need for "Zero Disruption" solutions«

European companies can't afford 18-month technology consulting projects or massive migrations of their core ERP or TMS systems to start seeing results. Agent AI must be able to "ride on top" of existing infrastructure, absorbing standard formats like CSV files or lightweight API connectors to deliver value from week one.

3. The linguistic and operational factor in customer service

Managing cross-border incidents requires multilingual interaction. An intelligent agent capable of resolving queries and writing technical explanations in both Spanish and English, directly and natively based on the shipping data, drastically reduces operational barriers for pan-European teams.

Towards a proactive and uninterrupted supply chain

The ultimate goal of integrating intelligent agents into the heart of the operations strategy is not to build completely autonomous and isolated systems, but to enhance the resilience of the organization.

By delegating to AI the massive tracking, translation of complex taxonomies, and technical formulation of operational solutions, companies can proactively mitigate the impact of any disruption in transportation. Agentic AI in European logistics It has ceased to be a future projection and has become the standard tool for operations teams that will lead the market in the coming years.

What do you think of these approaches for launching the blog? If you need to make any adjustments to keyword density, headings, or the visual style of the blocks to fit the design you're developing, let me know and we'll refine it.

La entrada Cómo la IA está transformando la logística europea – visión de Cadenity se publicó primero en Cadenity.

Most traditional tools of supply chain They are merely informative. Cadenity changes this paradigm through the AI Orchestration Center in logistics, an infrastructure designed not only to monitor, but to coordinate the autonomous resolution of incidents under human supervision.

From passive alert to coordinated real-time resolution

When a delivery goes awry—for example, a recipient is unavailable for a critical B2B delivery—the standard procedure involves 25 to 40 minutes of phone calls and emails. The coordinator must verify the order details in the ERP system, compare alternative delivery options, and send manual instructions to the carrier.

He AI Orchestration Center in logistics It automates this entire intermediate sequence by connecting detection with action through a structured flow in six key phases.

[Detect] ➔ [Analyze] ➔ [Generate Options] ➔ [Approve (Human)] ➔ [Execute] ➔ [Learn]

The 6 phases of the intelligent operations flow

1. Detect: The AI engine proactively identifies the deviation (delay, SLA breach, or cost overrun) the moment the carrier records the event.
2. Analyze: The system autonomously gathers the complete context: the shipment history, the customer's SLA contractual conditions, and the historical patterns of that specific route.
3. Generate options: Instead of leaving a blank screen, the platform calculates 3 or 4 viable resolution alternatives (e.g., different smart redirection points or service changes), assigning each one a confidence indicator and breaking down its estimated financial impact.
4. Approve (The human factor): The AI suggests, but the operator decides. The specialist reviews the pre-calculated options and validates the optimal one with a single click. This step guarantees absolute control over the operation.
5. Execute: Once approved, the action is propagated and executed in a coordinated manner across the relevant systems, eliminating the need to manually duplicate data between platforms.
6. Close and learn: The incident is archived, keeping full traceability (resolution times and costs saved), feeding the predictive model to refine future recommendations.

A non-negotiable design principle: "Human-in-the-Loop"«

One of the most frequent questions Chief Operating Officers ask when evaluating intelligent automation technologies is the risk of losing control over pricing or logistics. Could AI make a wrong decision that drives up last-mile costs?

The answer in Cadenity's architecture is a resounding "no." The principle Human-in-the-Loop It ensures that human approval is mandatory in the production phase. The technology acts as a high-performance copilot, eliminating the need for research and data preparation, and allowing the operator to make the final decision with all the strategic information on screen in under 3 minutes.

Strategic benefits for Supply Chain management

Adopting an AI-powered orchestration center transforms the department's key performance indicators (KPIs):

• Radical reduction of mean time to resolution (MTTR): Incident management times drop from 40 minutes to just 2 or 3 minutes.
• Customer SLA protection: Early detection and the immediate proposal of solutions such as Smart Redirect They avoid unnecessary returns and failed second deliveries.
• Full traceability and auditing: Every action, recommendation, and approval is recorded in an unalterable log, providing a unified source of truth for internal operational audits and reviews with clients and investors.

The future of efficient logistics is not about adding more staff to manage multi-carrier complexity; it's about equipping the current team with the orchestration tools needed to multiply their problem-solving capacity.

La entrada Qué es el AI Orchestration Center y por qué importa al Director de Operaciones se publicó primero en Cadenity.

The hidden cost of this approach isn't just measured in licenses or servers; it's measured in your team's most valuable asset: time. How can a AI agent in logistics operations Can we transform this reactive scenario into a real competitive advantage?

The hidden cost of "copy and paste" between carrier portals

A traffic coordinator's day-to-day work usually starts the same way: downloading CSV files from DHL, MRW, or other regional carriers to consolidate shipment visibility. Each carrier uses its own terminology; what one calls "In Transit," another might call "Central Hub" or "Departure from Branch.".

This lack of standardization forces professionals to dedicate between 2 and 3 hours daily exclusively to:

• Manual cross-referencing of data between ERP exports and carrier portals.
• Manual normalization of states to be able to extract reliable KPIs.
• Creation of weekly OTD reports (On-Time Delivery) for the address.

This purely reactive approach means that transport incidents are only detected when the end customer calls to claim their order, increasing friction and overloading the customer service department.

The AI Agent Revolution: Real Automation from Day One

The introduction of a AI agent in logistics operations It breaks completely with this manual work model. By implementing intelligent automation platforms like Cadenity, data ingestion from multiple carriers is done automatically and transparently.

Frictionless taxonomy ingestion and standardization

Instead of processing files individually, the system absorbs the data and instantly translates it into a common, unified taxonomy (for example, structured into global categories and specific states). The team no longer jumps between portals; it has a single, operational source of truth.

Natural language queries: Goodbye to SQL and overworked analysts

One of the biggest bottlenecks disappears when any team member, from operations to customer service, can query the system directly in natural language. Phrases like «"Which carrier has experienced the most delays on routes to France this week?"» o «"Show me the partial deliveries from MRW yesterday."» They provide immediate analytical answers, eliminating the need to generate complex reports or wait for a technical analyst to become available.

[Traditional Mechanics] -> CSV Download -> Manual Normalization -> Pivot Table -> 40 min [Cadenity Approach] -> Natural Language Query to AI Agent -> 5 seconds

Measurable impact: 30 hours recovered for value-added tasks

When you automate data ingestion, status normalization, and the generation of scheduled reports (which are automatically sent to the team's email every morning), the impact on the timer is dramatic.

Teams that migrate from spreadsheet-based workflows to an AI-optimized environment recover an average of 30 hours per week per team. This time is not eliminated from the organization; it is directly reinvested in:

1. Strategic negotiation with carriers based on real performance rankings and heatmaps compliance.
2. Proactive resolution of complex exceptions before they affect the customer's SLA.
3. Cost optimization through in-depth analysis of transportation deviations and penalties.

Logistics automation does not seek to replace human judgment, but to free it from the administrative burden so that it can focus, once and for all, on optimizing the supply chain.

La entrada Del Excel al agente IA: cómo los equipos de operaciones recuperan 30 horas a la semana se publicó primero en Cadenity.