List of SAS/QC Software Customers
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Since 2010, our global team of researchers has been studying SAS/QC Software customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SAS/QC Software for Analytics and BI from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SAS/QC Software for Analytics and BI include: Murata Manufacturing, a Japan based Manufacturing organisation with 77581 employees and revenues of $15.75 billion, USG Corporation, a United States based Distribution organisation with 6900 employees and revenues of $3.30 billion, Kia America, a United States based Automotive organisation with 5000 employees and revenues of $1.50 billion and many others.
Contact us if you need a completed and verified list of companies using SAS/QC Software, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Kia America | Automotive | 5000 | $1.5B | United States | SAS Institute | SAS/QC Software | Analytics and BI | 2014 | n/a |
In 2014, Kia America implemented SAS/QC Software within its Analytics and BI stack to operationalize reliability engineering and warranty forecasting functions. Kia America deployed SAS/QC Software to support Weibull reliability analyses, failure-rate forecasting and quality dashboarding for initial quality and warranty reserve processes in the United States.
The implementation configured SAS/QC Software capabilities for Weibull life-distribution modeling and SPC-style reliability analysis, and automated failure-rate forecasting workflows aligned with quality engineering practices. Those modules were used to generate standardized analytic outputs for root-cause investigations and failure-mode trending, embedding statistical process control and reliability modeling into repeatable quality workflows.
SAS/QC Software was instrumented alongside broader SAS analytics and AI capabilities, including Visual Analytics, to feed interactive quality dashboards and warranty forecasting models. Operational coverage focused on warranty and quality engineering teams within Kia America in the United States, with analytic outputs consumed by initial quality review and warranty reserve processes.
Governance centered on model standardization and repeatable root-cause workflows, integrating SAS/QC Software outputs into established quality review and warranty forecasting processes. According to published materials, the program accelerated root-cause analysis and improved initial quality and warranty reserve processes for Kia America.
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Murata Manufacturing | Manufacturing | 77581 | $15.7B | Japan | SAS Institute | SAS/QC Software | Analytics and BI | 2016 | n/a |
In 2016, Murata Manufacturing implemented SAS/QC Software in an Analytics and BI deployment to support production quality analytics across its global factory footprint. The implementation drew on SAS Production Quality Analytics to consolidate sensor and process data across multiple factories and to focus analytics efforts on production yield improvement.
SAS/QC Software was configured to support statistical process control and reliability analyses, including control charting, process capability studies, and failure analysis workflows aligned with SPC practices. The setup included automated data ingestion into statistical engines and scheduled charting and reporting for production engineering and quality teams.
The deployment centralized factory sensor streams and plant process data repositories into a single analytics environment, enabling cross-factory benchmarking and accelerated root cause analysis. Operational coverage spanned production quality, manufacturing engineering, and operations functions across multiple sites.
Governance emphasized standardized SPC workflows and centralized data stewardship to ensure consistent quality monitoring and traceability during rollout across factories. The SAS case study notes that a one percent yield improvement was estimated to equate to roughly 50 million dollars in savings.
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USG Corporation | Distribution | 6900 | $3.3B | United States | SAS Institute | SAS/QC Software | Analytics and BI | 2017 | n/a |
In 2017, USG Corporation deployed SAS/QC Software as part of a broader SAS analytics implementation. The deployment, classified under Analytics and BI, extended predictive quality and raw-material formulation optimization across many plants in the United States.
The implementation included SAS Model Manager, Grid Manager, and Visual Analytics, configured to support predictive modeling workflows, model lifecycle governance, and interactive analytics for quality teams. SAS/QC Software usage is inferred for statistical process control, reliability analysis, and production-quality analysis based on the manufacturing quality and predictive-modeling use cases described in the SAS customer story.
Grid Manager provided distributed processing to scale model training and scoring across plant datasets, while Model Manager established model version control and promotion workflows for production scoring. Visual Analytics delivered operational dashboards and reporting to manufacturing and R&D teams to monitor formulation experiments and process variability.
Operational scope centered on manufacturing and quality functions across US plants, with rollout focused on embedding predictive models into formulation and production decision processes. The implementation aimed to predict product quality and optimize raw-material formulations, and the customer account reports reduced downtime and improved quality outcomes.
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