Data analysis and machine learning for engineering
Turn manufacturing and test data into actionable insights. We provide statistical analysis, process capability studies, and predictive models that help you understand what's happening and what to do about it.
Problems we solve
Most engineering organizations collect more data than they use. We help you extract value from what you already have.
- Manufacturing data isn't being used to improve processes
- Need process capability analysis but lack statistical expertise
- Quality issues recur without root cause identification
- Inspection data sits in spreadsheets, never analyzed systematically
- Can't predict equipment failures before they happen
- No visibility into which process variables actually matter
- Reports are manual and time-consuming to produce
What you receive
Documented, maintainable outputs your team can use and build upon.
Analysis scripts (Python/R)
Documented, reusable code that your team can run and modify
Interactive dashboards
Web-based visualizations for ongoing process monitoring
Statistical reports
Clear findings with methodology, assumptions, and recommendations
Predictive models
Trained models with performance metrics and deployment guidance
Process capability studies
Cp, Cpk, Pp, Ppk analysis with control charts and recommendations
Data pipelines
Automated workflows for data collection, cleaning, and reporting
Our approach
A structured process that keeps you informed and in control.
Data review
We assess your existing data sources, quality, and structure. We identify gaps and define what questions the data can actually answer.
Analysis design
We scope the statistical approach or model architecture. You approve the methodology before we write code.
Model development
We build, validate, and document the analysis. Results include performance metrics and uncertainty quantification.
Documentation & handoff
We deliver clean code, user guides, and training. Your team can run and maintain the solution independently.
Tools we use
We work with industry-standard tools so your team can maintain and extend what we build. No proprietary lock-in.
Standards we follow
- AIAG SPC Manual for process capability
- ISO 22514 for capability indices
- MSA for measurement system analysis
- PEP 8 / tidyverse style guides for code
Python
Language
R
Language
pandas
Data manipulation
NumPy
Numerical computing
scikit-learn
Machine learning
Minitab
Statistical analysis
JMP
Statistical analysis
Plotly / Dash
Visualization
Jupyter
Development
SQL
Data access
Representative engagements
Examples of how we've helped engineering teams extract value from their data.
Injection molding process optimization
Challenge
High scrap rates on a multi-cavity injection molding line with inconsistent part dimensions across cavities.
Approach
Collected and analyzed 6 months of process data (temperatures, pressures, cycle times) alongside CMM inspection results. Built a regression model to identify the process variables with the strongest influence on dimensional variation.
Outcome
Identified two temperature setpoints that explained 70% of cavity-to-cavity variation. Recommended setpoint changes reduced scrap rate from 8% to under 2%. Delivered Python scripts for ongoing monitoring.
Equipment failure prediction
Challenge
Unplanned downtime on CNC machining centers was disrupting production schedules and causing quality issues.
Approach
Integrated sensor data (vibration, spindle load, coolant temperature) with maintenance logs. Developed a classification model to predict failures 24-48 hours in advance.
Outcome
Model achieved 85% recall on failure prediction with a 10% false positive rate. Client implemented preventive maintenance scheduling based on model outputs, reducing unplanned downtime by 40%.
Common questions
What kind of data do you work with?
We work with manufacturing process data, inspection and CMM results, sensor and IoT data, quality records, test results, and time-series data from equipment. If it's in a spreadsheet, database, or log file, we can usually work with it.
Do we need a data scientist on staff to use your deliverables?
No. We design solutions your existing engineering team can run and maintain. Scripts include clear documentation, and we provide training on how to use and update the tools we deliver.
How much data do we need for machine learning?
It depends on the problem. Simple process monitoring might work with a few hundred data points. Predictive models typically need thousands of observations. We assess data sufficiency during the initial review and will tell you honestly if you don't have enough.
Can you work with our existing software tools?
Yes. We can integrate with common manufacturing systems (MES, ERP, SCADA), databases (SQL Server, PostgreSQL, Oracle), and file formats (CSV, Excel, JSON). We also work with cloud platforms like AWS, Azure, and GCP.
What if we don't know what questions to ask?
That's common. We often start with exploratory data analysis to understand what patterns exist in your data. From there, we can identify high-value opportunities and prioritize based on business impact.
Ready to put your data to work?
Tell us about your engineering data challenges. We'll assess your situation and outline a practical path forward.
Request engineering support