With Maana Knowledge Platform Industrial Companies are
Optimizing Assets and Decisions Flows Three to Ten Times faster


Maana knowledge platform turns human expertise and data into digital knowledge for employees to make better decisions faster. Maana’s knowledge platform is used by the largest industrial companies in the world to optimize assets and processes 3-10 times faster than any other technology.

Maana’s dynamic insights and recommendations are operationalized into line-of-business applications for day-to-day decisions by thousands of employees, helping to improve field service profitability, prevent equipment down-time, improve accounts receivable collections and detect cyber security breaches.

What makes Maana different?

Maana Use Cases

5 High-Impact Knowledge-Centric Technology Use Cases for Improving Field Service Efficiency


Industrial eBook

 6 High-Impact Enterprise Knowledge Technology Use Cases for Accelerating Industrial Profitability


Field Service

Field Service Profitability


Business Benefits

  • Determined factors that drive job efficiencies
  • Identified correlations between job efficiencies and field engineers
  • Recommends the right field engineer to staff each job type
  • Identified profitable & non-profitable service contracts

This Fortune 500 company wanted to increase effectiveness and profitability within its service organization. While some key performance indicators were known, a lack of data access hindered the company’s efforts to determine what factors would improve job efficiencies.

Because the information needed to plan jobs and allocate resources was housed in multiple disparate silos and applications, limited metrics were the only indicators being used to assess effectiveness. Maana crawled 10 different data sources to identify correlations between job efficiency and field engineers; recommended the best field engineers for each job; and identified how often the same equipment was repaired. This gave the company a better understanding of how to drive effectiveness and increase profitability across its service areas.

Part Order Optimization


Business Benefits

  • Reduced unused parts ordered by 67%
  • Detected policy and business rule violation
  • Identified region responsible for ordering 95% of unused parts

This Fortune 100 company wanted to improve field service efficiencies by reducing returns and labor costs associated with customer service calls.

Maana assisted field engineers in optimizing the parts ordering process by identifying and recommending which parts were most likely to repair a customer’s system and involve the minimum of training and support. Maana operationalized useful information and integrated it with the company’s current line-of-business applications. This enabled the company to reduce part orders that go unused from 33 to 11 percent, and gain additional savings from other reductions in customer service trips, part returns, and inventory and shipping costs.

Turbine Service calls


Business Benefits

  • Establish correlations between customer service tickets and trips/alarms
  • Identify what issues prompt customer service contacts
  • Determine which product lines and regions incur the most issues

This Fortune 100 company sought to better understand the underlying reasons for customer service calls. To accomplish this, the company needed to gain more insight from data that was stored across multiple disparate data sources.

Maana was deployed to identify the issues that prompted customer service requests. Maana crawled and indexed multiple data sources, such as turbine data, sensor alarm data, and event data. The project involved over 600 million alarm values and approximately 1,200 trip events. Maana used Natural Language Processing (NLP) to identify the reasons why people called customer support. Machine learning tools that were developed into applications were used to gain insights into the business problem of analyzing the alarm data and using it to predict trips. Maana found and identified correlations between historical patterns in customer support calls and trip events. The correlations showed obvious but previously unverified relationships between trips and support calls for various generator models. The insights this knowledge provided enabled the company to better understand the underlying triggers for customer service calls.

Predictive Maintenance

Turbine Shutdown Prediction on 350 Units


Business Benefits

  • Identify alarm features associated with trips
  • Gain insight into trip events
  • Acquire insight into a large volume of series data

This Fortune 100 company operates hundreds of turbines around the world, and was experiencing an average of two unplanned shutdowns per day. They sought to reduce unexpected interruptions and mitigate production loss by anticipating potential shutdowns.

Maana enabled the company to navigate volumes of controller alarm data created by 350 generators. Maana built a model to predict imminent trips using alarms. With this, Maana detected unwanted events that could lead to turbine shutdown within 24 hours of failure. This allowed the company to reduce the occurrence of unexpected shutdowns.

Pump Failure Prediction

pump failure prediction

Business Benefits

  • Validate failure hypotheses
  • Predict potential pump failures
  • Identify failure causes

This Fortune 500 company wanted the ability to anticipate the types of events and failures likely to occur with new wells.

Maana was deployed to collect asset data and predict the likelihood of equipment failure. Maana crawled and indexed multiple data sources, brought them together, and provided an interactive exploration environment. This allowed subject-matter experts to validate various hypotheses related to failures and their causes. With Maana’s machine-assisted human discovery, these experts gained a holistic view that allowed them to predict which pumps were at risk for failure.


Accounts Receivable Collections


Business Benefits

  • Improved A/R collections by 65% over the prior year.
  • Recommends daily collection call queues embedded into the GETPAID system:
    • First-time customers
    • Customers with service issues
    • Institutional customers
    • Other late invoices
  • Identified first-time invoices and weekend due dates as the root cause of late payments

This Fortune 100 company sought to optimize time spent on A/R collections, and reduce the amount of past due receivables without increasing collector headcount.

Maana crawled and mined historical data such as: open, closed and disputed invoices; collector logs; customer loyalty data; and external data, including current invoices, customer credit ratings and interest rates. Using this data, Maana identified the factors that affect late payments, and recommended actions that would maximize A/R collections. Maana’s insights were operationalized into the collection line-of-business application to optimize accounts receivable priorities, which helped the company realize a 38 percent improvement in A/R collections within the first 30 days of use.

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