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AWS Manufacturing Atlanta

Georgia-Pacific

Customer Service Automation · Document Processing · Enterprise Knowledge Search · Real-time Analytics · Risk Management

The lack of a centralized, readily accessible knowledge base often leads to reduced machine productivity, prolonged machine downtime, and higher troubleshooting and repair costs. Making phone calls to experts and searching through binders, manuals, and maintenance records takes time and increases the risk of downtime.

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Accelerated operator onboarding
Captured the knowledge of experienced operators
Minimized machine downtime
Reduced off quality through enhanced troubleshooting

Reported impact

Business outcome

By harnessing the power of generative AI, Georgia-Pacific set a new standard for knowledge management and issue resolution in large-scale manufacturing. “We’re just beginning, but we estimate millions in potential savings annually across our facilities through the use of generative AI technology,” says Shah. Looking ahead, Georgia-Pacific plans to expand ChatGP to even more of its facilities by the end of 2024. “It was inspiring to witness our teams coming together to solve this problem,” says Shah. “There has been a remarkable business impact in a short time, primarily driven by the talented teams at Georgia-Pacific, Koch, and AWS. The products created by this team stem from their self-actualization and commitment to improving the lives of our operators.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

For nearly a century, Georgia-Pacific has been a major player in the manufacturing of paper products, packaging, and lumber. As a large-scale manufacturing operation, the company remains competitive by performing routine maintenance, optimizing manufacturing lines, and minimizing equipment downtime. The company also uses Amazon Kinesis , a fully managed service that cost-effectively processes and analyzes streaming data at scale. Learn how Georgia-Pacific, a leading manufacturer, is transforming knowledge management using Amazon Bedrock. Georgia-Pacific engaged with AWS Professional Services , a global team of experts that helps companies realize their desired business outcomes on AWS. Through Amazon Bedrock, Georgia-Pacific adopted Anthropic’s Claude, a large language model, to help the chatbot understand and respond to user queries with high accuracy and context. ChatGP adapts to the needs of each facility so that the information is tailored

Data flow

Data or events enter through Amazon Kinesis; processing runs on Amazon Bedrock for the chatbot analytics workload.

Solution architecture

2 components · 2 layers
  1. Ingest
    • Amazon Kinesis Ingests or moves data and events into the chatbot analytics pipeline.
  2. Compute
    • Amazon Bedrock Runs core compute or model execution for the chatbot analytics workload.

Evidence from the source

Georgia-Pacific engaged with AWS Professional Services , a global team of experts that helps companies realize their desired business outcomes on AWS.
Making phone calls to experts and searching through binders, manuals, and maintenance records takes time and increases the risk of downtime.
The effort resulted in ChatGP, a chatbot powered by generative AI technologies.
The lack of a centralized, readily accessible knowledge base often leads to reduced machine productivity, prolonged machine downtime, and higher troubleshooting and repair costs.
The teams collaborated to design and deploy a solution that would best meet the needs of its machine operators and maintenance technicians.
Through Amazon Bedrock, Georgia-Pacific adopted Anthropic’s Claude, a large language model, to help the chatbot understand and respond to user queries with high accuracy and context.
Transparency Evidence & methodology 60/100 reference value · 88% confidence · 2 sources

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 value60/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence88%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references 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

    Georgia-Pacific customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/georgia-pacific-optimizes-operator-efficiency-case-study/
    Source type
    Vendor-published customer story
    Published
    Sep 11, 2024
    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
  2. Georgia-Pacific

    Welcome To Georgia Pacific

    First-party
    https://www.gp.com/

    AT A GLANCE 30K+ Employees.

    Source type
    Official company website
    Supports
    • Company size

Date evidence

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

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