Test Plan Optimisation

Create a more efficient test plan you can trust

Reduce test steps.  Increase coverage.  Build confidence in a smarter test strategy.

Reduce Tests to Run_Kautex_laptop
BAE Systems

Find the most important test conditions, eliminate the rest.
  Make the most of your test time.

Test too much and you waste time confirming what you already know. Test too little and risk missing performance issues. The schedule, product quality, and ultimately your confidence in the test plan depends on finding the balance.  With Monolith, you can find the most efficient test plan without sacrificing coverage or quality for your next design. 

Run the most important tests and skip the rest
error detection
Optimize resources spent on costly test rigs and facilities
catch bad data
Validate your designs faster with fewer prototype iterations


"The Monolith Team understood what it means to work with genuine engineering problems in artificial intelligence: the needed flexibility and knowledge." 


Joachim Guilié
R&D Technical manager and Senior Data Scientist at Michelin


Multiple tools to optimise your test plan

With the array of tools Monolith provides for exploring and analysing your data, you can visualise your test conditions in multiple plots and charts to quickly identify regions of your design space that are untested.

Apply statistical plots to understand distribution of values in your current test plan to find duplicates or weak spots. For high-dimensional design spaces, use complex plots to find combinations of test parameters that are not in your test plan.

In addition to visualization tools, you can use statistics to quantify gaps in your test plan to ensure you don’t miss important conditions or parameter values.  


 Once you train a model using test data, you can apply a variety of tools in Monolith to learn more about your design to guide you on ways to improve your testing strategy.

Using sensitivity analysis, you can understand which parameters have the greatest influence on output parameters and should therefore be a high priority in your test plan.

By running forward predictions with your model, you can identify which test conditions have little or no impact on test results and can be reduced or eliminated from your test plan.

By running optimization tools against your model, you can find the best combination of values to meet performance requirements and get further insight into which conditions are most important to test. 


With Next Test Recommender (NTR), you can apply an iterative active learning approach to evaluate your model to find the most important test conditions for your test plan.

Using data from a prototype of similar product design, Next Test Recommender applies multiple combinations of modelling algorithms to explore the product design space and identify the most important conditions for testing.

Feed your current test plan into NTR to discover the most efficient order of tests to run.  Find and remove tests that are redundant or don't reveal any new information about your design. Or start with an initial set of tests and use NTR to guide you on which conditions to test next for a more efficient plan.

When applying NTR to actual test plans, it is not uncommon to reduce the number of test steps by 30%, 50%, or even 70% from the original plan based on machine learning models.  

New Feature

Next Test Recommender (NTR): AI-Powered Test Plan Optimisation

Learn how our AI software's latest feature enables users to train and assess machine learning models. It offers valuable recommendations for optimal test conditions to apply in the next round of testing. NTR assesses previously gathered data to suggest the most effective new tests to conduct. 

No code software

AI built by engineers for engineers


  • Avoid wasted tests due to faulty data
  • Build just the right test plan - no more, no less 
  • Understand what drives product performance and failure
  • Calibrate non-linear systems for any condition 
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Trusting test data 


It's vital to understand that testing every possible scenario is not feasible. Over-testing confirms what's already known, while under-testing risks failing certification or missing issues. To optimize testing efforts, identify critical performance components and prioritize tests accordingly.

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self learning models for AI
How we solve it:

Revolutionised testing 


Using self-learning models that get smarter with every test, Monolith identifies the input parameters, conditions, and ranges that most impact product performance so you do less testing, more learning, and get to market faster. 

Key use cases 

test Data Validation
Test Data Validation
root-cause analyiss
Root-Cause Analysis 
System calibrtaion
AI for simplifying validation testing 


4 applications for AI in validation test


AI has a significant impact on validation testing in engineering product development. You can reduce testing by up to 73% based on battery test research from Stanford, MIT, and Toyota Research Institute. Learn more with Monolith:
4 validation cases white paper battery testing
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Battery White Paper 


Accelerate EV Battery Development with AI-Guided Test 


EV batteries are incredibly complex to design and simulate because of their challenging mix of electrical, chemical, and thermal characteristics. As a result, OEMs are investing massive amounts of time and money in the physical testing of batteries.  With self-learning AI models built from test data, engineering teams can speed up battery development and ensure batteries meet high standards for performance, reliability, and safety.  Learn more with Monolith:
*A commissioned study conducted by Forrester Consulting on behalf of Monolith

It is imperative to get to market faster.

You need to get to market faster with revolutionary new EV batteries but you can't rely on your current methods of physical testing and simulation. 

In our 2024 study, 58% say AI is crucial to stay competitive. Here are other highlights:


  • 64% believe it's urgent to reduce battery validation  
  • 66% say it's imperative to reduce physical testing
  • 62% indicate virtual tools don't fully ensure designs meet validation requirements

Ready to get started?

BAE Systems