Challenges in Chemical Research and Development
Research and development in the chemical industry is under pressure; energy and raw materials are becoming more expensive, regulations are becoming stricter, and sustainability goals are moving closer.
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Limits of Traditional LIMS and Laboratory Solutions
At the same time, data from formulations, recipes, tests and inspections remain in silos. LIMS ensures standard processes and traceability in quality assurance but reaches its limits in exploratory phases of R&D, especially where iterative processes are involved. Laboratory software and tools such as ELN or ubiquitous Excel help in specific cases, but do not create a consistent, context-rich and searchable data foundation. Material Intelligence precisely addresses this gap with an integrated view of data, workflows and AI.
Material Intelligence is the approach that structures and fully captures laboratories, process and test data and enables their long-term use. Secondly, projects, samples, resources and instruments are managed as digital workflows so that decisions are made on a shared information basis. Thirdly, AI accesses these data directly: for automated evaluations, variant comparisons or as a basis for predictive models.
In practice, this R&D software is not a standalone solution but connects all relevant systems and instruments. Instruments from various manufacturers — including all NETZSCH analysis instruments — can be integrated; measurement data from thermal analysis, e.g., Differential Scanning Calorimetry, DSC and Thermogravimetry, TGA, are ingested, formats are standardized and stored centrally; optionally, ERP, MES or production-related systems are integrated. This creates a robust foundation from the development order through to archiving.
The LabV® helps
• organize formulations, processing and manufacturing data in one place.
• manage all resources, from inventory to equipment, ensuring consistent results.
• analyze results and compare material performance.
• exploit the power of AI & ML to make data-driven decisions.
Greater Efficiency in the Lab: From Daily Work to Real-World Example
On this basis, R&D software supports day-to-day work: context-aware search, documentation, plausibility checks and transparent comparisons across formulation variants. Material Intelligence uses this structure to make patterns visible and to test hypotheses in the development project in a targeted manner.
A practical example from the plastics industry illustrates the effect. When mixing time and temperature are recorded together with measurements such as DSC data in a central system, deviations become apparent immediately; iterations in R&D become shorter, decisions more robust. In practice, up to 40% more efficient workflows and up to 50% fewer experiments are achievable because histories are available and duplicate work is reduced. AI can additionally propose variants with expected properties.
Step-by-Step Implementation – and What Comes Next?
For the introduction, a step-by-step approach is recommended: prioritize critical data sources, harmonize formats, model initial workflows, gather feedback and only then expand the scope. Unlike a LIMS or other traditional solutions, implementing LabV®is possible with a manageable financial and staffing effort. More user-friendliness is also provided by modern platforms with clear interfaces, traceable workflows and a short learning curve. This is what makes them acceptable for everyday use by everyone.
The next sensible step is not a system change, but clarity: Which data sources need to be consolidated? Which workflows are causing delays today? And which metrics measure progress — for example, time to the first functional prototype, the share of structured datasets, the rate of duplicate experiments? On this basis, Material Intelligence can be introduced step by step —vendor-agnostic, compatible with the existing IT infrastructure — and then deepened where the practical benefit in everyday work is greatest.
