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Hall 4, Booth F05
At LogiMAT 2026 (March 24 - 26, Hall 4, Booth F05), Logivations GmbH, an international technology and consulting company based in Munich, will present the latest innovations and applications of its W2MO Logistics Suite. This represents a major technology leap: Generative AI is now fully embedded in the Digital Twin – secure, controllable, and directly integrated into operational and planning workflows.
New: W2MO Extended End-to-End with an MCP Server – LLMs as an Integrated Part of the Digital Twin
With the addition of an MCP Server (Model Context Protocol), all applications of the W2MO Logistics Suite are enhanced with Generative AI. W2MO uses Large Language Models (LLMs) such as Claude, ChatGPT and Gemini in clearly defined roles – for analysis, modeling, optimization,
A key component is data security: it can be controlled at a granular level,
Key Benefits
Logivations Supply Chain Engineering: Scenario Manager + LLM Assistance via MCP
User-friendly and highly interactive: scenarios can be easily created, compared, and evaluated.
New: LLM assistance via the MCP Server, for example to
AI identifies goods, parcels, and load carriers and reduces manual effort by around 30%. Depending on the available data, identification is performed using barcodes, QR codes, text/labels, dimensions, or visual appearance – fully automated and secure.
New: Recognition logic, assignment rules, and exception handling can be defined and documented in natural language (e.g. “if the code is unreadable, use dimensions and visual features; otherwise use the barcode first”).
Warehouse Capacity and Structure Optimization: More Space through AI-Based Layout Planning
Optimizing the arrangement of areas, travel paths, and racks can often increase warehouse capacity by more than 20% using modern AI algorithms. The Digital Twin supports the practical placement of aisles, racks, areas, and travel paths.
Currently: large-scale rollouts across more than 180 warehouses.
New: Constraints and site-specific rules (e.g. “fire safety distances”, “restricted areas”, “clearance widths”) can be defined and maintained more easily through LLM assistance via the MCP Server; layout alternatives are derived more efficiently, including clear justifications.
AI Algorithms for All Logistics Challenges: LLMs Can Directly Leverage Algorithms in the Digital Twin
The portfolio of AI algorithms includes, among others, product placement, space management, tour building, order sequencing, 3D packing, workforce planning, and much more.
Scenarios can be compared interactively, and layouts as well as processes can be continuously improved.
New: Via the MCP Server, the LLM can directly access the algorithms available in the Digital Twin – suggesting suitable optimizers, deriving parameters, interpreting results, and steering iterations in a targeted way.
Real-Time Digital Twin, Forklift Control System, Fleet Management, RTL & RS: Rules in Natural Language, Decisions Are Traceable
Events from the real world are detected and reflected in the Digital Twin. Real-time digitization creates a precise digital representation of reality: the positions of stocks, space utilization, people, and forklifts are known, and bookings can be automated. This enables a comprehensive control system for forklifts as well as AGVs and AMRs from different manufacturers.
New: Individual rules can be defined in natural language and transferred into the Digital Twin via the MCP Server (e.g. prioritization logic, exception handling, service level rules) – including clear explanations for transparency and traceability.
Hall 4, Booth F05
At LogiMAT 2026 (March 24 - 26, Hall 4, Booth F05), Logivations GmbH, an international technology and consulting company based in Munich, will present the latest innovations and applications of its W2MO Logistics Suite. This represents a major technology leap: Generative AI is now fully embedded in the Digital Twin – secure, controllable, and directly integrated into operational and planning workflows.
New: W2MO Extended End-to-End with an MCP Server – LLMs as an Integrated Part of the Digital Twin
With the addition of an MCP Server (Model Context Protocol), all applications of the W2MO Logistics Suite are enhanced with Generative AI. W2MO uses Large Language Models (LLMs) such as Claude, ChatGPT and Gemini in clearly defined roles – for analysis, modeling, optimization,
A key component is data security: it can be controlled at a granular level,
- which information is transmitted to the LLM, and
- what the LLM is allowed to change in the Digital Twin (e.g. only generate suggestions, vary parameters, create scenarios, define rules – or explicitly make no model changes).
Key Benefits
- Better decisions: Data validation reduces modeling errors; blind spots and bottlenecks are identified faster.
- Booster for non-professionals: Simulations can be operated using natural language.
- Higher transparency: Results and recommendations are provided with traceable explanations.
- Faster and more cost-effective projects: Savings of 50–80% of project time are possible.
- Better results: Optimization approaches aligned with real-world constraints (space, processes, personnel, service levels).
- More efficient projects: Less data preparation and modeling effort; expert capacity is freed up.
Logivations Supply Chain Engineering: Scenario Manager + LLM Assistance via MCP
User-friendly and highly interactive: scenarios can be easily created, compared, and evaluated.
New: LLM assistance via the MCP Server, for example to
- generate scenario variants in natural language (“What happens if volume increases by +15% and the cut-off changes?”),
- validate input data and explain results, and to prepare them in a management-ready format.
AI identifies goods, parcels, and load carriers and reduces manual effort by around 30%. Depending on the available data, identification is performed using barcodes, QR codes, text/labels, dimensions, or visual appearance – fully automated and secure.
New: Recognition logic, assignment rules, and exception handling can be defined and documented in natural language (e.g. “if the code is unreadable, use dimensions and visual features; otherwise use the barcode first”).
Warehouse Capacity and Structure Optimization: More Space through AI-Based Layout Planning
Optimizing the arrangement of areas, travel paths, and racks can often increase warehouse capacity by more than 20% using modern AI algorithms. The Digital Twin supports the practical placement of aisles, racks, areas, and travel paths.
Currently: large-scale rollouts across more than 180 warehouses.
New: Constraints and site-specific rules (e.g. “fire safety distances”, “restricted areas”, “clearance widths”) can be defined and maintained more easily through LLM assistance via the MCP Server; layout alternatives are derived more efficiently, including clear justifications.
AI Algorithms for All Logistics Challenges: LLMs Can Directly Leverage Algorithms in the Digital Twin
The portfolio of AI algorithms includes, among others, product placement, space management, tour building, order sequencing, 3D packing, workforce planning, and much more.
Scenarios can be compared interactively, and layouts as well as processes can be continuously improved.
New: Via the MCP Server, the LLM can directly access the algorithms available in the Digital Twin – suggesting suitable optimizers, deriving parameters, interpreting results, and steering iterations in a targeted way.
Real-Time Digital Twin, Forklift Control System, Fleet Management, RTL & RS: Rules in Natural Language, Decisions Are Traceable
Events from the real world are detected and reflected in the Digital Twin. Real-time digitization creates a precise digital representation of reality: the positions of stocks, space utilization, people, and forklifts are known, and bookings can be automated. This enables a comprehensive control system for forklifts as well as AGVs and AMRs from different manufacturers.
New: Individual rules can be defined in natural language and transferred into the Digital Twin via the MCP Server (e.g. prioritization logic, exception handling, service level rules) – including clear explanations for transparency and traceability.
