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AWS Healthcare United States

Merck

Predictive Maintenance

Merck wanted to reduce false rejects in pharmaceutical manufacturing so it could get life-saving drugs to customers faster while maintaining product safety.

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Reported impact

Business outcome

By using AWS services, Merck improved product availability and yield while enabling faster investigations, root-cause analysis, and corrective actions across its manufacturing operations.

  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

Merck built a manufacturing analytics and AI pipeline on AWS to reduce false rejects in pharmaceutical production. Real-time data from manufacturing processes and inspection machines is ingested with Amazon Kinesis, transformed and contextualized with AWS Glue, and loaded into Amazon Redshift for analytics and dashboards in Amazon QuickSight. The company's AI/ML platform on Amazon SageMaker combines harmonized process data with defect images transferred through AWS DataSync so teams can analyze quality signals across sites, investigate anomalies quickly, and support root-cause analysis. This architecture gives Merck a shared data foundation for near-real-time decision-making while improving yield, product availability, and response times for investigations and corrective actions.

Data flow

Merck ingests real-time manufacturing and inspection-machine data with Amazon Kinesis and AWS Glue, loads harmonized data into Amazon Redshift for dashboards in Amazon QuickSight, and uses Amazon SageMaker with defect images brought in through AWS DataSync to power quality analytics and AI/ML workflows.

Solution architecture

6 components · 4 layers
  1. Ingest
    • Amazon Kinesis Streams near-real-time events into the monitoring and anomaly-detection workflows.
    • AWS DataSync Transfers source files and images into the monitoring and anomaly-detection workflows.
  2. Compute
    • AWS Glue Transforms and prepares data used by the monitoring and anomaly-detection workflows.
    • Amazon SageMaker Builds, trains, or deploys the machine learning models behind the monitoring and anomaly-detection workflows.
  3. Storage
    • Amazon Redshift Serves as the analytics warehouse for the monitoring and anomaly-detection workflows.
  4. Serving
    • Amazon QuickSight Presents dashboards and analytics for the monitoring and anomaly-detection workflows.

Evidence from the source

Delivering innovative health with generative AI solutions at Merck | Amazon Web Services In this video, learn how Merck uses Amazon Web Services (AWS) to solve a common problem in the pharmaceutical industry—the occurrence of false rejects.
Merck uses AWS Glue and Amazon Kinesis to ingest, transform, and contextualize near real-time data, run analytics on that data and load the harmonized data into Amazon Redshift, which is then used by its analytics dashboards including...
Merck's artificial intelligence and machine learning (AI/ML) platform is built on Amazon SageMaker and uses AWS DataSync to ingest defect image data from inspection machines across sites.
Through the use of AWS services, Merck has improved product availability, increased product yield, enabled rapid response to investigations, and enabled rapid Root Cause Analysis and corrective actions all while providing significant time...
Transparency Evidence & methodology 70/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Merck customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/merck-customer-video
    Source type
    Vendor-published customer story
    Published
    Jan 1, 2023
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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