AWSManufacturingIT Operations AutomationAI and Machine LearningApplication ModernizationGenerative AI / LLMCost ReductionCustomer ExperienceRevenue GrowthRisk ReductionScalabilitySpeed to Market2025AWSAWS#ff9900ManufacturingGlobalIn the 2010s, Jabil introduced digital manufacturing, 3D printing, robotics, automation, and Internet of Things (IoT) solutions to enhance efficiency and meet changing customer needs. While it took time to build…The company reduced deployment times by 67–83 percent and data processing times by 74 percent, accelerating…Apr 15, 20256IT Operations AutomationAmazon EKS · Amazon Q · Amazon Q Business02025-04-156
In the 2010s, Jabil introduced digital manufacturing, 3D printing, robotics, automation, and Internet of Things (IoT) solutions to enhance efficiency and meet changing customer needs. While it took time to build maturity, standardization, and overcome resistance to change, cloud adoption is now widely embraced, delivering tangible value to the company." With extensive global operations spanning more than 100 manufacturing facilities in more than 25 countries, Jabil faced challenges with large volumes of machine data siloed at individual sites.
↗The company reduced deployment times by 67–83 percent and data processing times by 74 percent, accelerating project delivery and improving operational efficiency.
↗This change unlocked a 23 percent cost savings compared to standard AWS Glue ETL pricing, even with a significant increase in the processing volume for serial numbers.
Reported impact
Business outcome
Jabil’s collaboration with AWS has delivered impactful results. During its first EBA engagement, Jabil tackled a critical business challenge by automating manual processes for shop floor applications. Key reported metrics include This change unlocked a 23 percent cost savings compared to standard AWS Glue ETL pricing, even with a significant increase in the processing volume for serial numbers.; The company reduced deployment times by 67–83 percent and data processing times by 74 percent, accelerating project delivery and improving operational efficiency..
Cost Reduction
Customer Experience
Revenue Growth
Risk Reduction
Scalability
Speed to Market
Solution
Learn how Jabil unlocked more value from its data and launched AI-driven solutions to streamline global operations, reduce costs, and improve customer satisfaction. Jabil , a global leader in engineering, supply chain, and manufacturing solutions with over 100 global sites and 140,000 employees, collaborated with Amazon Web Services (AWS) on a multi-year transformation to enhance efficiency and reduce costs. The company reduced deployment times by 67–83 percent and data processing times by 74 percent, accelerating project delivery and improving operational efficiency. To define and execute its data strategy, Jabil engaged AWS experts in a multi-year program aimed at driving data-driven decision-making and efficiency. Through collaboration with AWS Professional Services , data strategists, and technologists, Jabil modernized its material sourcing application stack.
Data flow
Source data enters the Jabil workflow through its application services, stored or indexed in Amazon Redshift, processed with Amazon EKS, and delivered through Amazon Q Business, Amazon Q.
Solution architecture
4 components · 3 layers
Compute
Amazon EKSRuns containerized workloads and Kubernetes-based application services.
Storage
Amazon RedshiftStores analytical data and powers warehouse-style querying for reporting or ML features.
Serving
Amazon Q BusinessProvides a conversational assistant over enterprise content and business context.
Amazon QProvides conversational AI assistance for employees or end users.
Architecture clues
In a follow-up EBA engagement supported by AWS experts, Jabil focused on enhancing its Data Dispatcher application by transitioning to AWS Glue Flex , a serverless data integration service.
In another project, AWS assisted in enhancing Jabil’s shop floor control system on Amazon Elastic Kubernetes Service (Amazon EKS).
Since its launch, the solution has provided employees globally with real-time access to over 1,700 policies, manufacturing specifications, and troubleshooting documents in various languages, significantly reducing averag
Evidence from the source
In the 2010s, Jabil introduced digital manufacturing, 3D printing, robotics, automation, and Internet of Things (IoT) solutions to enhance efficiency and meet changing customer needs. While it took time to build maturity, standardization, a
Jabil’s collaboration with AWS has delivered impactful results.
The company reduced deployment times by 67–83 percent and data processing times by 74 percent, accelerating project delivery and improving operational efficiency.
This change unlocked a 23 percent cost savings compared to standard AWS Glue ETL pricing, even with a significant increase in the processing volume for serial numbers.
Through collaboration with AWS Professional Services , data strategists, and technologists, Jabil modernized its material sourcing application stack.
To define and execute its data strategy, Jabil engaged AWS experts in a multi-year program aimed at driving data-driven decision-making and efficiency.
Review how this reference was scored and trace each normalized claim back to its documented source.
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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 value95/100
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 impact25/25
2 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
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
6 direct source quotes retained.
Date provenance8/10
The publication date comes from publisher-controlled metadata.
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