List of SAS/OR Customers
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Since 2010, our global team of researchers has been studying SAS/OR 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/OR 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/OR for Analytics and BI include: Duke University Health System, a United States based Healthcare organisation with 23462 employees and revenues of $4.84 billion, Mission Health, a United States based Healthcare organisation with 12000 employees and revenues of $1.80 billion, Oberweis Dairy, a United States based Leisure and Hospitality organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using SAS/OR, 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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Duke University Health System | Healthcare | 23462 | $4.8B | United States | SAS Institute | SAS/OR | Analytics and BI | 2013 | n/a |
In 2013, Duke University Health System implemented SAS/OR in an Analytics and BI initiative to model operations at Duke Children’s Hospital. The deployment used SAS Simulation Studio, a component of SAS/OR, to construct a discrete event simulation of the neonatal intensive care unit to analyze staffing, patient flow and operational trade-offs.
The implementation built a discrete event simulation model that encoded patient arrivals, length of stay distributions, unit capacity and staffing schedules to enable scenario based experimentation. Configuration prioritized scenario analysis and capacity planning workflows, supporting comparisons of staffing rosters and patient throughput under varied demand assumptions. These capabilities align with Analytics and BI use cases for predictive operational modeling and what if exploration.
Operational scope was focused on the NICU, supporting clinical operations and workforce planning for neonatal services rather than enterprise analytics. The SAS/OR deployment produced simulation outputs that were used by clinical and operational planners to inform NICU layout and staffing decisions. No external system integrations are documented in the source material.
The project produced validated operational insights that contributed to a 2016 Journal of Perinatology study and helped earn regional recognition for analytics driven improvements in NICU planning and staffing. Governance centered on using validated simulation outputs to inform planning decisions and research dissemination rather than automated transactional workflows.
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Mission Health | Healthcare | 12000 | $1.8B | United States | SAS Institute | SAS/OR | Analytics and BI | 2014 | n/a |
In 2014 Mission Health implemented SAS/OR to develop a discrete event simulation for its neonatal intensive care unit. The deployment, categorized under Analytics and BI, engaged US based clinicians working with SAS and Duke to translate clinical workflows and staffing policies into a production simulation. The implementation specifically targeted evaluation of staffing policies, length of stay, costs and clinical outcomes within the NICU.
The simulation was built using SAS Simulation Studio, part of SAS/OR, and leveraged scenario modeling and stochastic process simulation capabilities commonly used in Analytics and BI simulation workflows. Models encoded staffing schedules, patient arrivals, bed utilization and care pathways, and were fed by clinical and operational data to run scenario comparisons and sensitivity analyses. SAS/OR was configured to support parameter sweeps and what if scenario orchestration to surface nonintuitive system behaviors tied to operational policies.
Operational coverage centered on NICU clinical operations in the United States, with project governance conducted jointly by Mission Health clinicians, SAS and Duke clinical researchers. The US based work informed operational decision making and demonstrated counterintuitive relationships between length of stay and cost, findings that were reported in a 2016 peer reviewed study and in SAS insights articles. The effort exemplifies use of SAS/OR within Analytics and BI to align simulation modeling with clinical staffing governance and operational policy evaluation.
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Oberweis Dairy | Leisure and Hospitality | 10 | $1M | United States | SAS Institute | SAS/OR | Analytics and BI | 2016 | Zencos |
In 2016, Oberweis Dairy implemented SAS/OR within its Analytics and BI environment to optimize home delivery routing and driver utilization across its US operations. The implementation centered on analytical optimization rather than transactional systems, using SAS/OR alongside SAS analytics to address routing complexity for direct-to-consumer deliveries.
The deployment used SAS analytics and SAS/OR routines to cluster delivery destinations and to solve traveling salesman style routing problems, enabling route optimization, destination clustering, and scheduling capability for driver runs. Configurations included optimization model specification, constraint handling for route feasibility, and iterative scheduling workflows implemented in SAS code.
The US implementation was delivered in partnership with systems integrator Zencos and was applied across regional distribution centers, where optimization outputs were embedded into distribution center scheduling and dispatch workflows. Operational scope focused on home delivery operations and driver scheduling, aligning optimization results with daily route planning processes.
Governance and operationalization were supported by documented SAS code and procedures, as described in the SAS Global Forum 2017 paper that accompanies the implementation. The documented implementation reported operational benefits including reduced delivery distance and improved scheduling, reflecting measurable routing and scheduling improvements after deployment.
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