List of SAS Asset Performance Analytics Customers
SAS Institute
100 SAS Campus Drive,
Cary, 27513-2414, NC,
United States
Cary, 27513-2414, NC,
United States
1 800-727-0025
Professional Services
13500
$3.5B
Since 2010, our global team of researchers has been studying SAS Asset Performance Analytics 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 Asset Performance Analytics for Asset Performance Management 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 Asset Performance Analytics for Asset Performance Management include: Western Digital, a United States based Manufacturing organisation with 40000 employees and revenues of $9.52 billion, NAM, a Netherlands based Oil, Gas and Chemicals organisation with 1700 employees and revenues of $420.0 million, Mack Trucks, a United States based Manufacturing organisation with 2200 employees and revenues of $300.0 million, SPG Dry Cooling Belgium BV, a Belgium based Manufacturing organisation with 175 employees and revenues of $48.0 million and many others.
Contact us if you need a completed and verified list of companies using SAS Asset Performance Analytics, 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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Mack Trucks | Manufacturing | 2200 | $300M | United States | SAS Institute | SAS Asset Performance Analytics | Asset Performance Management | 2017 | n/a |
In 2017, Mack Trucks implemented SAS Asset Performance Analytics as part of an Asset Performance Management initiative to operationalize predictive maintenance and remote diagnostics for its connected fleets. The deployment used SAS Asset Performance Analytics together with SAS Advanced Analytics and SAS AI solutions to process streaming IoT telemetry and fault code data for GuardDog Connect, Mack Trucks’ telematics service.
The implementation centralized functional capabilities typical of Asset Performance Management, including real time IoT ingestion, fault detection and severity ranking, anomaly detection and machine learning driven predictive models, and automated diagnostic recommendations. SAS Asset Performance Analytics produced recommended action plans and supported remote software updates when issues were software related, enabling faster diagnosis and prescriptive guidance to field agents and technicians.
Operational integrations included direct ingestion of vehicle sensor and telematics streams, routing of diagnostic results to Mack’s 24/7 Uptime Center agents, and handoff of repair instructions and parts requirements to dealer service locations. The scope reported in the program covers more than 70,000 Mack connected vehicles, and the relationship between Mack Trucks, SAS Asset Performance Analytics, and Asset Performance Management supported connected vehicle services, dealer operations and engineering feedback loops.
Governance and workflow changes centered on severity based routing of incidents, agent-driven remediation plans, and analytics-driven adjustments to maintenance planning and product design. Across the Volvo Trucks and Mack Trucks program supported by SAS, monitored faults experienced a 70% reduction in diagnostic time and a 25% reduction in repair time, and Mack Trucks cites improved uptime and a very high Net Promoter Score for its connected services.
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NAM | Oil, Gas and Chemicals | 1700 | $420M | Netherlands | SAS Institute | SAS Asset Performance Analytics | Asset Performance Management | 2010 | n/a |
In 2010 NAM deployed SAS Asset Performance Analytics to enable predictive maintenance and outage prediction across its Groningen gas field operations. The Asset Performance Management implementation centralized sensor ingestion and analytics for 20 fully automated production locations, consolidating data from more than 90,000 sensors into scalable analytics workflows.
EOM designed and implemented the architecture and associated business processes so that NAM data scientists and process engineers could analyze current and historical sensor data and deploy analytical models for continuous monitoring. The implementation uses SAS Asset Performance Analytics alongside SAS Data Management, SAS SPD Server, SAS Enterprise BI Server, SAS Visual Analytics, and SAS Enterprise Guide to stage data, support model development, and deliver operational reporting. Intelligent ETL processes ingest raw telemetry from OSIsoft PI, process it into a data warehouse, and populate data marts used by analytical teams, supporting the management of approximately 80 billion observations.
Functional modules and capabilities include time series ingestion, data quality cleansing, KPI generation, model training and deployment for continuous surveillance, and alarm workflow handling. Equipment performance is expressed as KPIs between 0 and 100 and visualized over time in PF curve views customary to maintenance practitioners. Information products delivered through Web Report Studio and Visual Analytics dashboards feed an Event Based Surveillance alarm application that triggers follow up workflow and automatic email notifications via the Wikker SAS portal.
Governance and data quality controls were formalized using Ralph Kimball's An Architecture for Data Quality, with business rules and data quality rules applied during ETL to mitigate the impact of broken sensors and equipment maintenance on analytics. NAM elected to have its chemical process engineers perform analytics and model construction, with training and operational support provided so models could be embedded into day to day maintenance workflows. EOM's implementation contributed to Wikker's success in supporting preventive maintenance of NAM gas production sites.
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SPG Dry Cooling Belgium BV | Manufacturing | 175 | $48M | Belgium | SAS Institute | SAS Asset Performance Analytics | Asset Performance Management | 2017 | n/a |
In 2017 SPG Dry Cooling Belgium BV implemented SAS Asset Performance Analytics as its central Asset Performance Management capability to predict and optimize the performance of its air cooled condensers and to support energy production forecasting for power plants. The deployment established advanced analytics as a core function for aftersales and engineering, enabling the company to move from equipment vendor interactions to long term operational partnerships with power plant operators.
The implementation centered on a digital twin and predictive analytics stack within SAS Asset Performance Analytics, including asset health modeling, forecasting of plant capacity, and predictive maintenance workflows. Models consume large volumes of sensor telemetry and operational parameters to capture environmental drivers such as ambient temperature and wind, and they are configured to scale across multiple condenser installations to improve prediction precision.
Operationally the solution ingests IoT sensor streams from SPG Dry Cooling installations at power plants around the world and aggregates fleet level data to support site level forecasts and cross site benchmarking. Analytics outputs are used by operations and maintenance teams to inform cleaning schedules, to forecast 24 hour capacity, and to provide engineering feedback for future condenser design improvements.
Governance and process adaptation included close collaboration between thermal engineering and data science teams to translate domain physics into robust analytic models and to embed analytical outputs into maintenance planning and customer advisory processes. The SAS Asset Performance Analytics implementation explicitly targeted improved power plant efficiency, reduced unplanned outages through better maintenance planning, and enhanced forecasting of power plant capacity, while also creating a new feedback loop for product development and customer engagement.
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Manufacturing | 40000 | $9.5B | United States | SAS Institute | SAS Asset Performance Analytics | Asset Performance Management | 2018 | n/a |
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FAQ - APPS RUN THE WORLD SAS Asset Performance Analytics Coverage
SAS Asset Performance Analytics is a Asset Performance Management solution from SAS Institute.
Companies worldwide use SAS Asset Performance Analytics, from small firms to large enterprises across 21+ industries.
Organizations such as Western Digital, NAM, Mack Trucks and SPG Dry Cooling Belgium BV are recorded users of
SAS Asset Performance Analytics for Asset Performance Management.
Companies using SAS Asset Performance Analytics are most concentrated in Manufacturing and Oil, Gas and Chemicals, with adoption spanning over 21 industries.
Companies using SAS Asset Performance Analytics are most concentrated in United States, Netherlands and Belgium, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of SAS Asset Performance Analytics across Americas, EMEA, and APAC.
Companies using SAS Asset Performance Analytics range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 25%, large organizations with 1,001-10,000 employees - 50%, and global enterprises with 10,000+ employees - 25%.
Customers of SAS Asset Performance Analytics include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified SAS Asset Performance Analytics customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Asset Performance Management.