Custom manufacturing analytics solutions:
Acerta Professional Services
Solving unique manufacturing challenges with machine learning and AI
Do you have a manufacturing problem you’re not sure how to solve?
Most of our customers use our predictive quality analytics software solution, LinePulse, to improve part quality, reduce scrap and rework, and remove production bottlenecks. However, we also have a Professional Services team that solves unique manufacturing challenges with machine learning and artificial intelligence (ML/AI).
When you engage with the Acerta Professional Services team, our experts will work with you on a customized manufacturing analytics project to help you solve a unique problem.
We’ll analyze and recommend solutions for the problem by leveraging our domain expertise in automotive engineering, data science, and ML/AI.
Our custom manufacturing analytics
solutions can help you:
Predictive failure analytics with Nissan, Japan
Our extensive, proprietary anonymized database of automotive products, processes, and failure modes gives our data scientists a head start on any dataset. For example, we completed a custom platform of machine learning models for Nissan.
Together with Nissan Research Center in Japan, we developed failure prediction analytics to detect subtle anomalies in vehicle data, predict potential failures, and estimate the remaining distance the vehicle can travel before it needs to warn the driver.
How an Acerta Professional services project works:
1. Project Discovery
Our experts in automotive manufacturing analytics and data science listen to the problem you are trying to solve, and help determine whether there is enough data available to get the answers you need.
We define a Scope of Work for the project, and determine how your data will be shared and received. Once the data is delivered, we take a first look and ask any clarifying questions to ensure we are interpreting it correctly.
2. Machine learning model creation
Acerta’s data science team explores the data and determines an appropriate machine learning model. We present this initial model and discuss whether the results are leading in the right direction.
3. Model refinement
The data science team takes feedback and tweaks the model to take any more contextual information into account, and make it as precise and accurate as possible.
4. Model deployment
Not every data science project includes deployment of a live model. When this is necessary, we determine the most convenient way to deploy the model for your team to use on an ongoing basis.
We deploy the model and train your team how to run it, and how to interpret the results.
Final documentation, recommendations, or reports for the project are delivered as necessary according to the Scope of Work.
If a model was deployed, an agreed-upon number of check-ins are set up on a regular basis to ensure that the model is working correctly, and we answer any lingering questions.
What we provide
A ready-to-use machine learning model that leverages your data to accomplish a target goal (such a predicting a parts failure or optimizing a specific process)
Development of a complete, deployable solution that leverages your data and can be available to you afterward
Evaluation of your dataset and recommendations for improvement of data quality
Case studies: example manufacturing analytics solutions
Predicting suspension failure with machine learning
Diagnosing engine failure modes with AI
Tell us about your manufacturing challenges