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Resolve Process Risks Using Autonomous Early Detection

NMM is revolutionizing risk detection in process industries by autonomously identifying earliest indicators of problems, facilitating proactive measures. Our first-of-their-kind products  Dynamic Risk Analyzer™ (DRA), Batch DRA™, ARC DRA™ – boost operational efficiency, reliability, and safety, and are redefining industrial risk management.


“Analysis of loss events  after DRA was deployed – shows that 18 potential plant trips were avoided because process engineers could detect process anomalies, allowing early intervention to prevent the trips.”

Conference Publication, PETRONAS LNG


Proactive, Resilient Industrial Operations

Developed specifically for the Process and Energy Industries, our DRA product suite provides advanced warnings about hidden process issues, typically days or weeks ahead of traditional alarms or tools. Customers report significant benefits within just a few days of system use.

Reduced Unexpected Shutdowns and Process Failures

DRA products are designed to uncover hidden problems, so you can stay focused on finding solutions, not hunting for problems.

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Increased Uptime and On-Stream Efficiency

DRA products provide peripheral vision on issues developing on the sidelines, enabling you to address new risks and their drivers.

Improved Process Safety and Reliability

What makes DRA products unique are their ability to rip through the entire spectrum of process data using proprietary AI algorithms, uncovering problems, long before humans or traditional tools can.



Pioneering a New Era of Autonomous Early Risk Detection

We are a one-of-its-kind early risk detection company. Our products help companies elevate their operations performance, increase bottom lines, and foster sustainability by averting process problems at plant operations. Powered by our expertise in autonomous machine learning, our unique approach has earned a reputation as a disruptive technology that provides ‘peripheral vision’ to detect hidden risks.

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Dynamic Risk Analyzer

Dynamic Risk Analyzer™ (DRA) is a first-of-its-kind advanced warning and risk detection software platform  for continuous processes. It uses our autonomous machine learning algorithms that identify process problems at initiation stage – enabling operating personnel to take proactive corrective actions and prevent losses.



Batch DRA

Batch DRA is a software designed for early risk detection in batch processes. It uses our proprietary AI for autonomous problem detection, is self-regulating, scalable, and secure. It boosts efficiency and productivity by providing timely alerts and requires minimal manual intervention, leading to significant operational improvements.



ARC DRA™ is a software designed for early risk detection in industrial processes with repeating cycles, such as Cokers (Refineries). It enhances operational efficiency by running cycles more effectively, increasing the number of excellent cycles, and addressing issues early on, leading to improved productivity and reduced downtime.


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Industries We Work With

Our current focus is Oil and Gas, Chemicals, Energy, and Manufacturing. We have global presence as our product is already taken up by industry leaders. However, our technology can be applied to any industry, where time-series data is collected continuously or periodically.

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  • How long does it take to set up DRA?
    Setting up DRA is quick and efficient. The installation process can be completed in just one day, and the system becomes fully functional within two weeks. This includes integrating DRA with existing data sources, completing initial data configuration, and performing historical back-calculations using its machine learning technology. This streamlined setup process allows customers to rapidly start benefiting from DRA's early risk detection capabilities.
  • Are there any customer presentations showcasing DRA?
    Yes, our customers have made several presentations on the use of DRA at various conferences around the world. Here are some of the presentations: a) “Enhancing Proactive Organizational Culture and Improving Process Performance Using AI/Machine Learning: Celanese Experience,” Reuters Downstream USA Conference, 2024 (Upcoming Presentation). b) “Dynamic Risk Analyzer and Fault Tree Analyzer as a Predictive Monitoring Tool,” IChemE Webinar, 2023. c) “Improved Process Performance and Safety via Autonomous AI: Dyno Waggaman Experience,” AIChE Ammonia Safety Conference, 2022. d) “A Study of Process Anomalies Leading to Plant Trips and the Effectiveness of a Software Tool in Trip Prevention,“ International Conference on Production, Energy and Reliability, 2021. e) “Process Anomaly Detection using Machine Learning Technology,” Asian Downstream Summit, 2019. f) “Sustaining Superior Performance through Influential Digital Transformation,” Asian Downstream Summit, 2019. g) “Autonomous Risk Detection for Improved Plant Performance: Lessons Learned and Case Studies,” Refining and Petrochemicals World, 2019. h) “Utilising Big Data Analytics for Optimal Performance and Cost Savings: PETRONAS Experience in the Implementation of DRA,” Asian Downstream Summit, 2018. i) “Early Detection of Process Risks: Improving OEE, Safety and Reliability,” Asian Downstream Summit 2017. j) “Early Detection of Process Risks for Improved Safety, Reliability and Operational Excellence,” AFPM Reliability and Maintenance Meeting, 2017. k) “Predicting Process Risks for Improved Safety and Operational Excellence: Breakthrough Technology and Case Studies,” AIChE Ammonia Safety Conference, 2016.
  • What has been the customer feedback on DRA?
    Our customers praise DRA for its user-friendly interface, comprehensive analytical capabilities, easy implementation, and cost-effectiveness. Their testimonials highlight DRA’s impact in preventing unplanned slowdowns and shutdowns, thereby saving significant costs, and maintaining high plant reliability. Users appreciate DRA's ability to produce essential insights without requiring ongoing maintenance, reflecting its effectiveness and utility in real-world settings.
  • How quickly can someone learn to use DRA?
    Learning to use DRA is designed to be quick and straightforward, thanks to its user-friendly interface. Most new users can become proficient with just two hours of training. This efficient training schedule ensures that teams can start using the system effectively almost immediately after installation, enabling them to quickly take advantage of DRA's early risk detection capabilities.
  • What makes DRA unique?
    DRA is a one-of-its-kind system with autonomous, self-optimizing machine learning algorithms that eliminate the need for model building and ongoing maintenance. It uniquely identifies risks at their inception, processes vast amounts of data rapidly, and delivers actionable insights. DRA is known for its rapid installation, seamless integration, and minimal training needs. Additionally, it operates securely within company firewalls, significantly reducing cybersecurity risks. Its scalability across various units and facilities without integration challenges further sets it apart, making it a highly effective solution for proactive risk detection in process industries.
  • What makes DRA easy to integrate into existing systems?
    DRA is designed for ease of integration. It can be installed in a day and becomes operational within two weeks without requiring extensive IT support or changes to existing infrastructure. Its compatibility to operate independently within company firewalls enhances its security and ease of deployment.
  • How is DRA different from self-service analytics?
    DRA differs significantly from self-service analytics primarily in its specialization and autonomy. DRA focuses on early risk detection using proprietary, self-optimizing unsupervised machine learning algorithms. It autonomously identifies anomalies and potential failures without requiring user intervention for model tuning or maintenance. In contrast, self-service analytics tools cater to a broad range of users and applications, requiring more input, customization, and manipulation from users to generate insights. DRA's deep operational analysis enables real-time, proactive risk management, which is crucial for preventing disruptions in plant operations.
  • In a few words, how does DRA contribute to increased plant uptime?
    By providing early detection of risks and analyzing thousands of tags rapidly, DRA allows engineers to focus on solving rather than finding problems, significantly boosting operational efficiency and reducing downtime.
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