List of SAP Predictive Asset Insights Customers
SAP
Dietmar-Hopp-Allee 16,
Walldorf, 69190,
Germany
Walldorf, 69190,
Germany
49 6 2277-47474
Professional Services
110650
$42.3B
Since 2010, our global team of researchers has been studying SAP Predictive Asset Insights 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 SAP Predictive Asset Insights for Enterprise Asset Management, 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 SAP Predictive Asset Insights for Enterprise Asset Management, Asset Performance Management include: ENGIE, a France based Utilities organisation with 96454 employees and revenues of $85.78 billion, Petrobras, a Brazil based Oil, Gas and Chemicals organisation with 41778 employees and revenues of $16.67 billion, Swiss Federal Railways, a Switzerland based Transportation organisation with 34200 employees and revenues of $11.88 billion, Swiss Federal Railways SBB, a Switzerland based Transportation organisation with 33500 employees and revenues of $9.50 billion, Norwegian Public Roads Administration, a Norway based Government organisation with 7000 employees and revenues of $5.75 billion and many others.
Contact us if you need a completed and verified list of companies using SAP Predictive Asset Insights, 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.
The SAP Predictive Asset Insights customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
Ansaldo Energia | Manufacturing | 3200 | $1.2B | Italy | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2020 | n/a |
In 2020 Ansaldo Energia implemented SAP Predictive Asset Insights to support Enterprise Asset Management,Asset Performance Management across its manufacturing and services operations. The initiative targeted optimization, integration, and standardization of global processes while creating situational awareness on the health and performance of plant equipment and field assets.
The deployment leveraged SAPs digital core to connect manufacturing, supply chain, and warehouse management systems, and used the SAP Business Technology Platform to integrate field sensor data flows with back-end systems and enable IoT-driven business processes. Functional capabilities centered on asset health monitoring and predictive analytics, product performance insight tied to manufacturing quality, and supply chain and warehouse simplification.
Operational coverage extended across lines of business and group companies, with an explicit focus on supplier quality data sharing and accelerating services portal onboarding for new customers. Governance changes emphasized standardized global processes and instrumented data flows to increase visibility across suppliers and internal inventory, enabling tighter coordination between maintenance, manufacturing, supply chain, and customer service functions.
Outcomes documented for the SAP Predictive Asset Insights implementation included greater process integration across lines of business and group companies, an expectation of 40% more suppliers digitally sharing quality control data, and 80% less time and effort to onboard new customers in the services portal. The program also produced simplified supply chain and warehouse management, improved product performance insight to support quality and customer service, and an expected substantial increase in services revenues, supported by SAP Innovation Services and SAP Services and Support expertise.
|
 |
BITZER Kuehlmaschinenbau GmbH | Manufacturing | 3800 | $1.0B | Germany | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2021 | n/a |
In 2021 BITZER Kuehlmaschinenbau GmbH implemented SAP Predictive Asset Insights to build an asset network and enable a shift from selling compressors to offering compressor-enabled services, deploying capabilities aligned with Enterprise Asset Management,Asset Performance Management. The implementation was positioned to give customers real-time operational visibility and predictive service capabilities across the compressor lifecycle, and to support BITZER’s goal of helping customers digitalize their systems.
The deployment integrated SAP Predictive Asset Insights with sensor telemetry and analytics to deliver tailored customer alerts, status reports, and predictive maintenance workflows. Functional capabilities implemented include sensor-based condition monitoring, predictive service orchestration, lifecycle asset tracing that records product history from initial order through operation, and tools that allow customers to adjust machine parameters informed by BITZER know-how.
Architecturally the solution is built on SAP Business Technology Platform and consumes SAP Internet of Things sensor readings, while SAP Integration Suite provides application and process connectivity and SAP Asset Intelligence Network accelerates SaaS-based deployment. The BITZER Digital Network connects all BITZER products to the cloud so the SAP Predictive Asset Insights environment can present a complete real-time overview of a customer’s product portfolio and enable cross-system data flows between devices, cloud services, and enterprise processes.
Governance and rollout followed an agile approach supported by SAP Services and Support, with implementation focused on breaking down information and departmental silos to operate as a single company oriented to customer outcomes. Explicit outcomes documented include increased energy efficiency as customers optimize parameters, reduced compressor downtime through predictive service capabilities, a strengthened basis for partners to offer refrigeration as a service, and improved customer satisfaction as customers adopt digital product and service models.
|
 |
DFS Deutsche Flugsicherung | Professional Services | 5600 | $1.3B | Germany | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2021 | n/a |
In 2021, DFS Deutsche Flugsicherung GmbH deployed SAP Predictive Asset Insights to enable near-real-time monitoring of asset condition and performance. The deployment used SAP Predictive Asset Insights as part of an Enterprise Asset Management,Asset Performance Management initiative to support maintenance planning, equipment reliability, and asset availability for air navigation services.
The implementation leveraged built-in machine learning in SAP Predictive Asset Insights to process large volumes of measurement data and surface leading indicators and failure modes. Functional capabilities implemented included condition-based monitoring, advanced analytics for anomaly detection, and operational maintenance support through the SAP Asset Manager mobile app to assist technicians during on-site activities.
The solution integrated with SAP Internet of Things to ingest sensor telemetry, providing near-real-time access and analysis of sensor data from remote assets, for example more than 40 IoT sensor readings every other minute from a control tower location. Data enrichment of back-end IT systems enabled the service organization and maintenance teams to observe equipment behavior remotely and prioritize field activities based on predictive insights.
Governance and rollout included a pretest phase that delivered new insights for condition-based monitoring and accelerated problem determination, enabling a deliberate shift from time-based to condition-based maintenance events. The program prepared the business for 2022 maintenance activities by addressing equipment longevity, uptime, scheduling, and spare parts availability while keeping equipment locations secure, and it boosted the service team’s ability to understand and respond to complex events and interdependencies. Reduced equipment failures and increased lifetime value of critical infrastructure assets were reported as value-driven results.
|
 |
ENGIE | Utilities | 96454 | $85.8B | France | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2014 | n/a |
In 2014, ENGIE implemented SAP Predictive Asset Insights to improve responsiveness and asset availability for its remote solar operations. The deployment targeted Enterprise Asset Management,Asset Performance Management use cases to address long travel times, high maintenance costs, and limitations in detecting and diagnosing downtime events.
The implementation centered on a digital twin model and automated alerting, using SAP Predictive Asset Insights to autogenerate alerts when abnormal behavior was detected. Functional capabilities included remote recognition of production losses, automated measurement of the impact of maintenance activities on plant performance, and the ability to apply integrated machine learning algorithms to refine anomaly detection and alert precision.
Architecturally the solution paired SAP Predictive Asset Insights with SAP Enterprise Product Development to simulate weather variations and instrument the digital twin, while SAP Business Technology Platform provided integration middleware and connectivity. SAP S/4HANA was used to connect operational telemetry to financial and enterprise systems, enabling a unified view of asset health and work order implications across ERP and industry cloud components.
Operational scope included ENGIE Energía Chile solar farms and field maintenance teams, where autogenerated alerts and remote diagnostics reduced the need for travel and accelerated fault remediation. Governance and workflow changes emphasized event-driven maintenance, systematic evaluation of maintenance impact on plant performance, and rapid operationalization of alerts, with the project completed in seven months.
Outcomes reported by ENGIE included reliable alerts from the digital twin, improved asset uptime through faster fault correction, and lower CO2 emissions by avoiding unnecessary maintenance travel. The program documented productivity gains of 35 percent on maintenance, travel, and diagnosis activities, an 11 percent reduction in production losses tied to faster replacement of broken fuses, and 100 percent accurate remote recognition of production losses, encapsulated in the statement Our digital twin gave us useful insights on how to be more efficient, the results were exciting: 100% accurate remote recognition of production losses and a systematic evaluation of the impact of maintenance activities on plant performance.
|
 |
ENGIE Australia & New Zealand | Utilities | 1200 | $480M | Australia | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2022 | n/a |
In 2022, ENGIE Australia & New Zealand deployed SAP Predictive Asset Insights as a core capability within an Enterprise Asset Management,Asset Performance Management program tied to its renewable energy operations. The implementation was anchored on SAP S/4HANA Cloud as the ERP platform harmonizing systems used by ENGIE entities worldwide, providing a single transactional backbone for asset and maintenance data flows.
The technical architecture layered SAP Business Technology Platform to host analytics and integration services, SAP IoT to connect field sensors to photovoltaic equipment, and SAP Integration Suite to orchestrate real-time telemetry and historical maintenance records. SAP Intelligent Asset Management was used to build a Photovoltaic plant engineering model inside Enterprise Product Development, Asset Central was configured to associate physical sensors with equipment, and SAP Analytics Cloud provided end-user reporting and visualization.
SAP Predictive Asset Insights was deployed to ingest Big Data from sensor streams and maintenance logs, and to compare theoretical PV performance calculations based on temperature, solar radiation, and wind speed against actual measured output for anomaly detection. The solution scope focused on solar farms and PV plant operations, enabling predictive, preventive, and corrective maintenance workflows to be informed by sensor-driven condition monitoring.
Governance involved embedding the PV engineering model into asset master data, linking sensor identifiers to Asset Central records, and routing PAI anomaly signals into maintenance planning processes so engineering and operations teams could triage alerts. ENGIE engaged SAP technologies to accelerate its growth roadmap and to operationalize intelligent maintenance, with SAP Predictive Asset Insights serving as the anomaly detection and predictive maintenance engine for its renewable asset fleet.
|
 |
|
Manufacturing | 7000 | $1.1B | Germany | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2020 | n/a |
|
|
|
|
Government | 7000 | $5.8B | Norway | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2020 | n/a |
|
|
|
|
Oil, Gas and Chemicals | 41778 | $16.7B | Brazil | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2024 | n/a |
|
|
|
|
Professional Services | 1500 | $40M | Italy | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2020 | n/a |
|
|
|
|
Utilities | 4000 | $4.5B | United States | SAP | SAP Predictive Asset Insights | Enterprise Asset Management,Asset Performance Management | 2018 | n/a |
|
Showing 1 to 10 of 12 entries
Buyer Intent: Companies Evaluating SAP Predictive Asset Insights
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating SAP Predictive Asset Insights. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating SAP Predictive Asset Insights for Enterprise Asset Management, Asset Performance Management include:
- Hecht Kugellager, a Germany based Distribution organization with 28 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated |
|---|---|---|---|---|---|---|
 |
Hecht Kugellager | Distribution | 28 | $8M | Germany | 2025-11-17 |
FAQ - APPS RUN THE WORLD SAP Predictive Asset Insights Coverage
SAP Predictive Asset Insights is a Enterprise Asset Management, Asset Performance Management solution from SAP.
Companies worldwide use SAP Predictive Asset Insights, from small firms to large enterprises across 21+ industries.
Organizations such as ENGIE, Petrobras, Swiss Federal Railways, Swiss Federal Railways SBB and Norwegian Public Roads Administration are recorded users of
SAP Predictive Asset Insights for Enterprise Asset Management, Asset Performance Management.
Companies using SAP Predictive Asset Insights are most concentrated in Utilities, Oil, Gas and Chemicals and Transportation, with adoption spanning over 21 industries.
Companies using SAP Predictive Asset Insights are most concentrated in France, Brazil and Switzerland, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of SAP Predictive Asset Insights across Americas, EMEA, and APAC.
Companies using SAP Predictive Asset Insights range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 66.67%, and global enterprises with 10,000+ employees - 33.33%.
Customers of SAP Predictive Asset Insights 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 SAP Predictive Asset Insights customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Enterprise Asset Management, Asset Performance Management.