Reported impact
Business outcome
Unilever can explore more candidate materials in less time, accelerate sustainable product innovation, and push digital science farther upstream in the R&D funnel.
- Productivity Improvement
- Scalability
- Speed to Market
MicrosoftConsumer GoodsAI and Machine LearningData PlatformProductivity ImprovementScalabilitySpeed to Market2024 MicrosoftMS#0078d4Consumer GoodsUnited KingdomUnilever needed a faster way to explore huge materials and chemistry search spaces for product R&D without relying solely on slow, resource-intensive physical experiments.Products used by over 3.4 billion people dailyAug 23, 20245Azure Quantum Elements · Azure high-performance computing · Microsoft Azure0 2024-08-23 5
Unilever
Unilever needed a faster way to explore huge materials and chemistry search spaces for product R&D without relying solely on slow, resource-intensive physical experiments.
Products used by over 3.4 billion people daily
Screen tens of thousands of materials
Thousands of computational simulations in the time it would take to run tens of laboratory experiments
Solution
Unilever is using Azure Quantum Elements as the computational core of its DataLab environment to accelerate materials discovery in research and development. Researchers query scientific information through Microsoft Copilot for Azure, then use Azure high-performance computing and Azure Quantum Elements to run thousands of simulations far faster than physical lab work alone could support. The outputs from those simulations are fed back into models that screen tens of thousands of candidate materials and explore chemical reactions before laboratory teams commit to experiments. By making simulation and natural-language exploration part of the discovery funnel, Unilever turns its proprietary science data and R&D expertise into a scalable digital workflow. The platform helps scientists search broader molecular spaces, refine formulations for brands such as Dove and TRESemmé, and move sustainability and product-innovation work forward with materially faster iteration.
Data flow
Researchers query scientific problems through Microsoft Copilot for Azure, Azure Quantum Elements and Azure high-performance computing run simulations, and the resulting data is fed into models that screen materials and guide the next set of experiments.
Solution architecture
- Compute
- Azure Quantum Elements Runs the advanced scientific simulations that drive Unilever’s discovery workflow.
- Microsoft Azure Hosts the broader DataLab environment and supporting digital research infrastructure.
- Azure high-performance computing Provides the compute power needed to execute simulations at much larger scale than laboratory-only methods.
- Serving
- Microsoft Copilot for Azure Lets researchers query scientific information in natural language inside the digital R&D workflow.
Architecture clues
- DataLab is described as the digital counterpart to Unilever’s physical laboratories
- Natural-language querying is combined with simulation outputs for model refinement
- Scaled simulations are moved to the front of the discovery funnel
Evidence from the source
Unilever is bringing its digital vision to life through the transformational DataLab—its digital counterpart to the company’s physical laboratories—with the help of Microsoft Azure.
Unilever scientists can use the data gathered from these simulations to fine-tune models that screen tens of thousands of materials.
With Copilot in Azure, Azure high-performance computing, and the advanced simulation capabilities of Azure Quantum Elements, Unilever can query scientific information using natural language, performing thousands of computational simulations in the time it would take to run tens of laboratory experiments.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value66/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence15/203 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.microsoft.com/en/customers/story/1805371430718336282-unilever-azure-consumer-goods-en-united-kingdomMicrosoftFirst-party
Unilever customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Aug 23, 2024 · Microsoft · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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