Carlos Hernández
About Carlos Hernández
As an automation professional, my goal is to lead digital transformation initiatives that connect and integrate industrial systems on the manufacturing floor to improve business operations and decision making strategies. I have over 9 years of experience working within the industrial manufacturing space across a number of different verticals. My experience is rooted in solving customers’ needs, working alongside production managers, subject-matter experts, and all necessary stakeholders to deliver true value-add solutions that produce meaningful results. Many of the automation projects that I’ve quoted, sold and executed on have been designed with line optimization, waste reduction, yield improvement and operational efficiency in mind. I see exponential value in empowering others – whether it be business unit managers, operations personnel, or maintenance technicians – with an understanding of how properly functioning automation controls allow for a better functioning company. As an automation engineer, I make it a mission to share my domain knowledge with others, cross-training and developing those with less experience. Working as part of an organization, I actively pursue daily wins that strategically build towards much larger and impactful wins, and I am the type of person that keeps the larger picture in mind. I am hands-on when problem solving, I lead from the middle and I am unquestionably accountable for my work. I am a passionate person, highly self-motivated, and happiest when putting feet to pavement getting things done. I consider myself an automation professional that can bridge the industrial manufacturing stack (machines, PLCs, HMIs, and sensors) with desired business outcomes. My goal is to peer into everyday production and design problems through the lens of connectivity, leveraging the analysis of operational data to help companies meet the challenges and opportunities brought on by the 4th industrial revolution. I'm passionate about M2M communications, Machine Vision, Model Predictive Control, and Machine Learning applications at the Edge.