Marcelo Mendonca

Staff Engineer, Data Analytics – Manufacturing 4.0


Passioned about solving the most challenging manufacturing problems through the use of technology, including Industry 4.0, Big Data Analytics, AI and Machine Learning.

I am an experienced manufacturing and data professional with 24+ years of extensive international experience obtained from several positions within Australia/Asia, South America and North America, including Manufacturing Facilities Maintenance, Manufacturing Operations, Body Shop Maintenance, Manufacturing Engineering and Manufacturing Data Analytics.

With my extensive manufacturing domain knowledge combined with data analytics experience, I am uniquely positioned to bridge the gap between manufacturing operations and technology.

I engage with a variety of business stakeholders, from senior leaders to plant floor operators, to understand the problem and translate it into practical solutions that drive action and real business outcome.

Examples of manufacturing problems I’ve been solving with technology and digital transformation:
* Increase production efficiency and throughput
* Reduce unplanned downtime events – OEE (availability)
* Reduce hidden losses such as slow cycles and small stops – OEE (performance)
* Improve vision-based quality inspection First Time Quality – OEE (quality)
* Identify and eliminate production bottlenecks in complex and asynchronous manufacturing processes
* Reduce waste of raw and consumable material
* Paint a complete picture of the manufacturing processes by integrating data from multiple sources including Enterprise Asset Management systems, SCADA systems, PLCs, Motor Drives, Vibration Sensors, Weld and Dispense Controllers, Vision Inspection Cameras. Utilizing Digital Twin concepts to establish complex relationships.

+ Manufacturing efficiency improvement
+ Industry 4.0 technologies
+ Digital Twin concepts
+ Recruiting, developing and leading teams of data analysts and data scientists
+ Big Data tools including PySpark, Hadoop, Hive, Hue
+ Machine learning/deep learning models for classification, regression, clustering, and anomaly detection
+ ML/scientific packages (e.g. pandas, numpy, scikit-learn, keras)
+ NLP and pre-trained models (e.g. Transformers, spaCy, BERT, GPT-2)
+ Advanced Analytics and Exploratory Data Analysis
+ Strong Python programming, Jupyter Notebooks, Git
+ Relational Database, SQL, ETL
+ Data visualizations and dashboards (e.g Power BI, Plotly, Matplotlib)
+ Advanced MS Excel including automation with VBA
+ Web-scrapping (BeautifulSoup, Selenium)
+ Web Applications (REST APIs, Flask, Django, GraphQL, React, NextJS)