Loady nominated for ifoy awards 2023

Mannheim, June 11, 2026. For many companies, the digitalization of logistics requirements begins with a tedious task: information from SAP comments, Excel files, emails, and individual customer specifications must first be structured and transferred into a digital data model.

With a new AI-powered onboarding solution, Loady now significantly reduces this effort. Initial projects show that up to 80 percent of existing data sets can be automatically processed and integrated into Loady's standardized structure. Users then only need to review, supplement, and approve the results.

“In many companies, relevant logistics information is fundamentally available – but often incomplete, outdated, inconsistently maintained, or even contradictory. This is precisely where we step in: We reduce the one-time initial effort and help companies work faster with reliable, structured logistics data,” says Elzbieta Wiankowska, co-founder and COO of Loady. “With over 20,000 product data sets and logistics requirements from thousands of real transport processes, we now have the specialized data foundation to automate this task efficiently and with high quality.”

The data prepared in this way remains entirely under the control of its owners. Via standard interfaces, it can then be synchronized with existing ERP, TMS, yard, or telematics systems or driver apps and shared with business partners.

“We see Loady as a universal connector for logistics requirements,” explains Dennis Jantos, co-founder and CTO of Loady. “Logistics operates with a multitude of different systems. Our goal is for information to be available where it is needed, regardless of software boundaries.”

Companies that do not use direct system integration can alternatively provide the information via the multilingual web platform. Smaller and medium-sized freight forwarders particularly benefit from this, as dispatchers and drivers can access current loading and unloading requirements without additional IT projects.

The new onboarding solution is an important component of Loady's AI strategy. AI methods are already being used at various points on the platform to support users in processing and maintaining logistics requirements. In the future, further functions will improve data quality, detect inconsistencies, extract information from documents, and proactively assist users in maintaining logistics master data.

“AI is not an end in itself for us,” says Jantos. “It helps us overcome the biggest hurdle in digitalization: the path from unstructured information to usable data. Data onboarding is an important milestone in this process. In the long term, we want to support companies throughout the entire data lifecycle – from acquisition and quality assurance to automated provision in operational processes.”

With this, Loady aims not only to make logistics requirements digitally available but also to keep them permanently up-to-date, usable across systems, and accessible to all stakeholders along the supply chain.

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About Loady

Founded in 2023 in Mannheim, Germany, Loady lays the foundation for the next generation of digital logistics processes. By standardizing and digitally providing logistics requirements, Loady enables their cross-system use along the supply chain – from freight procurement, commissioning, and dispatching to delivery and control. In this way, logistics knowledge becomes a digital resource for efficient collaboration, automation, and the use of AI. Already today, Loady contains logistics requirements for over 20,000 products and 500 sites in Europe, North America, and Latin America, available in 17 languages.

Loady has received a research grant for the development of the electronic Previous Load Restriction Check (ePLR). The funding supports an approach that addresses a long‑standing, unresolved core issue in chemical logistics: the systematic and reproducible application of previous load restrictions in road transport.

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Previous load restrictions are a key component of quality assurance—and at the same time one of the most frequent causes of operational disruptions. In practice, they regularly lead to transports being cancelled, processes being interrupted, or, in the worst case, quality risks. Despite their relevance, many companies still do not manage them systematically, but rely on fragmented data sources and manual decision‑making.

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The ePLR Check directly addresses this gap. Its goal is to translate previous load restrictions into a structured, machine‑readable data model for the first time and to derive a digital decision logic from it. Two central challenges are tackled: the unambiguous identification of chemical products despite inconsistent naming, and the mapping of complex, company‑specific rule sets—including the consideration of loading histories.

The innovative value lies in the combination of these elements: semantic classification of substances, modelled chemical properties, and a dynamic, version‑controlled rule set. This transforms what has so far been an interpretative assessment into a systematic decision—automated, consistent, and scalable. Existing approaches, by contrast, rely largely on static matrices or expert knowledge and do not adequately reflect the actual complexity.

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The certification authority confirms both the novelty and the technical challenges of the project, underscoring its research and development character.

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The ePLR Check will be deployed productively for the first time in 2026 at two major European chemical sites. This transforms a previously fragmented topic into an integrable digital process—with immediate impact on process stability, risk reduction, and cost control.

December 3, 2025 - Loady's new position paper analyzes the current situation with fragmented data and demonstrates how structured previous load restriction data can improve inspection processes in the chemical, feed, and food industries, leading to gains in efficiency and sustainability.

Unified data model replaces manual checks.

Loady structures previous load restrictions within a chemically and logistically consistent data model. Each substance is uniquely identified by CAS and EC numbers, synonyms, and multilingual designations. Rules can be defined at the substance or group level and return clear statuses. Companies manage their restrictions centrally in Loady and can import existing lists in a structured manner. The platform offers API interfaces for synchronization with ERP and QHSE systems, as well as export functionalities. The use of structured previous load restriction data reduces manual inspection efforts by up to 80%, prevents rejections through valid dispatching, and lowers CO₂ emissions by reducing multiple trips and unnecessary cleanings.

The solution is used at three levels: Dispatchers, shippers, and cleaning companies check whether product combinations are permissible and which cleaning procedures are required. Forwarders and shippers perform structured Previous Load Restriction Checks (ePLR) by entering up to five previous loads and immediately receiving validated results. Pilot customers integrate the ePLR logic into transport and yard management systems for automatic checks before dispatch or gate release.

Future integration into electronic cleaning certificates

Loady is working on integrating the ePLR logic into electronic cleaning certificates such as ECLIC's eECD or Bulkvision certificates. In the future, cleaning certificates could be automatically checked against valid previous load restrictions – the cleaning station transmits the previous product and cleaning code, Loady validates the information and records the inspection result directly in the certificate. The whitepaper is available for free download at the following URL: Link