Purpose-built for Maximo/Maximo Application Suite
PredictNOW
Aquitas Weibull Solution Accelerator — forecasting the likelihood of asset failures
Weibull Analysis, developed in 1937 by a Swedish engineer and mathematician Ernst Weibull, forecasts the likelihood of failures within a specific timeframes and empowers you to plan maintenance more efficiently, reducing downtime and costs.
Introducing PredictNOW by Aquitas Solutions, a predictability tool that collects data on asset failures to make your organization’s asset management smarter and more proactive. PredictNOW utilizes the Weibull distribution model, to help you understand the probability of an asset failure in a given timeframe. Unlock the potential of anticipatory maintenance with Aquitas’ Weibull-powered Accelerator!
What is PredictNOW?
PredictNOW utilizes the Weibull distribution statistical model that helps us understand how long things last and how often they might give us a surprise. It is not about guessing; it’s about understanding the average lifespan and foreseeing the pattern of surprises over time. The Weibull failure curve is characterized by its “U” shape, also known as the bathtub curve, which represents the failure rate of the asset over time. It starts with a high failure rate, decreases over time, and increases again when the asset fails more frequently. This is an easy first step to using more advanced MAS solutions – Monitor, Health and Predict!
How Weibull Works for you
- Configuration Steps: Collects data on asset failures such as reported dates and types of maintenance and utilizes this data to create a reliability profile for each asset.
- Interpreting the Results: Provides clear visuals, Probability Density Function (PDF), and Cumulative Distribution Function (CDF) for easy understanding, and highlights the likelihood of failures within specific time intervals.
- Benefits: Makes asset management smarter and more proactive and helps in scheduling maintenance when it's most needed, optimizing resources and costs.
Get started with PredictNOW today!
PredictNOW is simple to deploy! It works on both past and present data – there is no need for massive amounts of data to begin using! PredictNOW reduces the entry barrier (less cost, quick deployments) for maximizing ROI, makes users comfortable with using new age technologies like AI to succeed in Industry 4.0, and expands the horizons of EAM solutions through a focus on outcome-based values.
PredictNOW’s key features include:
- Weibull Parameters Insight
- Weibull Distribution Curves
- Mean Time Between Failure (MTBF) Assessment
- Crow-AMSAA Reliability Growth Model
- Outlier Elimination
Why choose our Weibull solution?
- Proven Accuracy
- User-Friendly
- Configurable
- Cost-Effective
- Outlier Elimination
Frequently Asked Questions
PredictNOW is not a competitor to AI-based solutions but rather a complementary tool that helps organizations build a data-driven maintenance strategy. It provides immediate value with minimal setup and acts as a stepping stone toward more sophisticated AI-driven predictive maintenance solutions.
PredictNOW differs from AI-based predictive maintenance solutions in the following key ways:
Methodology:
- PredictNOW uses statistical models like Weibull distribution, Crow-AMSAA, and confidence interval estimation to analyze past failure data.
- AI-based solutions rely on machine learning (ML) and deep learning models that process large datasets, often including IoT sensor data, to predict failures.
Data Requirements:
- PredictNOW only requires historical Corrective Maintenance (CM) work orders stored in IBM Maximo to generate failure forecasts.
- AI models typically require real-time sensor data, failure logs, operational parameters, and environmental conditions to improve accuracy.
Interpretability & Transparency:
- PredictNOW provides clear, explainable results using well-established reliability engineering principles.
- AI models operate as black boxes, meaning their predictions may be difficult to interpret without additional explainability techniques.
Implementation Complexity:
- PredictNOW is plug-and-play, does not require extensive configurations, and can be used without AI expertise.
- AI-based solutions require training data, model tuning, and integration with IoT platforms, making them resource-intensive to deploy.
Use Case Maturity:
- PredictNOW serves as a first step in predictive maintenance, helping organizations gain insights into failure probabilities and optimize maintenance planning.
- AI-based solutions are more advanced but require a mature data ecosystem and higher investment in technology and skills.
PredictNOW is designed to analyze a wide range of assets across various industries. The solution is best suited for assets that:
- Have Historical Failure Data – PredictNOW relies on historical Corrective Maintenance (CM) work orders stored in IBM Maximo. Assets with a consistent history of failures are ideal candidates.
- Experience Wear and Tear Over Time – Assets that degrade due to usage, aging, or operational stress can benefit from Weibull analysis and reliability modeling.
Examples of suitable assets:
- Rotating Equipment: Pumps, motors, turbines, compressors
- Fixed Equipment: Heat exchangers, boilers, cooling towers
- Electrical Systems: Transformers, circuit breakers, switchgear
- Facility Assets: HVAC units, elevators, conveyors
- Fleet & Vehicles: Trucks, forklifts, railcars
- Industrial Machinery: CNC machines, robotic arms, production lines
Assets That May Not Be Ideal:
- New Assets with No Historical Failures: Without past failure data, PredictNOW cannot generate accurate failure probability estimates.
- Assets with Random Failures: If failures are due to unpredictable external factors rather than inherent reliability trends, PredictNOW’s statistical approach may have limited effectiveness.
The ROI timeline for PredictNOW depends on multiple factors, including the asset failure frequency, maintenance costs, and operational impact of unplanned downtime. However, most organizations see tangible benefits within 3 to 12 months.
Immediate Cost Savings (0-3 months)
- Reduction in unplanned failures by identifying high-risk assets early.
- Optimized maintenance planning, avoiding excessive PM.
- No need for expensive sensor installations or third-party integrations, ensuring low deployment costs.
- Quick deployment (4-8 hours) means businesses start benefiting almost immediately.
Operational Efficiency Gains (3-6 months)
- Improved resource allocation, reducing wasted labor hours.
- Less emergency work leading to fewer disruptions in maintenance scheduling.
- More accurate parts and inventory planning, avoiding excess stock or shortages.
- Data-driven decisions enable better maintenance prioritization.
Long-Term Strategic Impact (6-12 months and beyond)
- Reduction in asset lifecycle costs by minimizing early failures and extending asset lifespan.
- Training the team on predictive analytics, shifting from reactive to proactive maintenance culture.
- Justifying future AI-driven enhancements based on early statistical forecasting results.
