Laboratory software
Definition
Laboratory software refers to digital applications that support experimental, analytical and quality-related workflows in industrial laboratories. These include systems for capturing experimental data, managing formulations and samples, connecting analytical instruments, evaluating material properties and documenting testing and development processes.
Extended explanation
In industrial research and development, laboratory software is more than a digital filing system. It represents the relationships between raw materials, formulations, process parameters, measured values and material performance. Particularly in formulation development, polymer development, coating chemistry, battery research or quality assurance, data is generated from many sources: balances, rheometers, spectrometers, testing machines, environmental chambers, LIMS, ELN systems and often also Excel files. Laboratory software helps to structure this information traceably, make series of experiments comparable and base development decisions on consistent data.
Depending on the area of application, laboratory software may include functions of an electronic lab notebook, a laboratory information management system, formulation management, a material database or an analysis platform. What matters is not the system name, but whether experimental data is preserved in its technical context: which raw material batch was used, which process conditions were present, which test method was applied and which target variable was achieved?
Key aspects
- Data context: Laboratory software should link measured values with formulations, samples, batches, process parameters and test conditions so that development data remains interpretable later on.
- Instrument and data integration: Relevant systems connect analytical instruments, testing devices, spreadsheets, databases and existing laboratory infrastructure without the need to transfer critical information manually.
- Formulation and material development: For iterative series of experiments, laboratory software supports the comparison of variants, target variables, constraints and performance criteria.
- Quality assurance: In QA laboratories, it facilitates the structured documentation of tests, specifications, approvals and deviations along defined laboratory processes.
- AI-supported evaluation: Well-structured laboratory data forms the basis for pattern recognition, design of experiments, property predictions and data-driven optimisation.
Relevance for LabV
For LabV, laboratory software is a central component of digital R&D environments, because material and formulation data only becomes usable when it is consistent, rich in context and available for evaluation. Many industrial laboratories still work with separate files, instrument-specific exports and manual transfers. As a result, relationships between experiment, measurement and result can easily be lost.
In the LabV context, laboratory software is particularly closely linked to Material Intelligence: experimental data should not only be documented, but also made accessible for comparisons, analyses and AI-supported development work. This applies both to early screening experiments and to recurring quality tests and the evaluation of complex material properties.
| Criterion | LIMS | Excel | Material Intelligence Platform (LabV) |
|---|---|---|---|
| Primary purpose | Sample management, test workflows, result approval and compliance | Flexible data entry, calculations and basic analysis | Connects raw material, formulation, process and test data as a basis for development decisions |
| Strengths | Standardised processes, traceability and regulatory compliance (e.g. audit trail) | Quick setup with flexible spreadsheets and calculations | Makes relationships between raw materials, formulation variants and test results visible across many experiments |
| Data structure | Sample- and results-based | Cells, worksheets and files; user-defined structures and links | Structured by raw material, formulation, process parameter and test characteristic; linked and machine-readable |
| Analysis | Predefined reports, specification checks, some trend analysis | Formulas, charts and pivot tables; data consolidation usually manual | Compares formulation variants across the entire dataset; an AI assistant identifies patterns and deviations, while the technical assessment stays with the team |
| Integration | Instrument integration and ERP interfaces (e.g. SAP QM) are common; effort depends on the system | File imports and exports; further connections via interfaces or add-ons | Connects testing instruments and existing systems such as ERP and MES; measurement data flows directly into the dataset |
| Target users | QA/QC teams, laboratory managers and compliance professionals | Individuals and teams across all laboratory functions | R&D and technical service teams working with formulation data |
| Fit and limitations | Strong for high sample throughput and standardised test workflows; less flexible for iterative development work | Maintenance effort increases as data volumes and file versions grow | Suited to development work with many formulation variants, raw material changes and recurring test cycles |
Typical areas of focus; features and integrations vary depending on the solution and configuration.
FAQ
How does laboratory software differ from Excel-based laboratory workflows?
Excel can document individual experiments, but it quickly reaches its limits when dealing with linked formulations, instrument imports, version statuses, sample histories and comparable series of experiments. Laboratory software creates structured data models and reduces manual transfer errors.
Is laboratory software only relevant for regulated quality laboratories?
No. R&D laboratories also benefit when experimental data, analytics and material properties are captured systematically. Especially in iterative development projects, the reusability of historical data is an important technical advantage.
What data is typically managed in laboratory software?
This includes formulations, raw materials, samples, batches, process parameters, measured values, test methods, specifications, observations, evaluations and metadata from analytical instruments or test rigs.
Synonyms & related terms
Related terms include laboratory information system, LIMS, ELN, electronic laboratory documentation, formulation management, material database, laboratory data management, research data management and laboratory automation.
Internal links
LIMS | ELN | Laboratory data management | Material Intelligence | Formulation development