{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Manufacturing Hub","title":"Ep. 230 - AI in Manufacturing with Tom Hechtman of Sepasoft | Real ROI, MES on Ignition, Sepa IQ","html":"","width":"100%","height":180,"duration":4317,"description":"Artificial intelligence is no longer just a buzzword in manufacturing. The pace of adoption has been incredible, yet the reality is far more complex than flashy headlines suggest. In this episode of Manufacturing Hub, Vlad Romanov and Dave Griffith welcome back Tom Hechtman, founder of Sepasoft, to explore how AI is actually being deployed on the plant floor, what barriers remain, and whether we are truly transforming manufacturing or simply tinkering at the edges.Tom brings decades of experience building MES solutions for manufacturers around the globe. From his early days in the Midwest working with Rockwell Automation technology to launching Sepasoft’s Ignition MES modules and now leading the development of Sepa IQ, Tom has been at the forefront of data, analytics, and system integration. His insights bridge the gap between hype and practice, helping us understand where AI creates real ROI, where it still falls short, and how to build the foundations for success.Throughout the conversation we dive into the challenges of quality improvement, predictive maintenance, scheduling optimization, and contextualizing plant data. We discuss the importance of trust in both data and AI-generated outputs, the economics of running LLMs and machine learning models, and why cybersecurity and data governance cannot be an afterthought. Drawing on the recent MIT study that revealed only 5 percent of AI projects make a measurable P&L impact, Tom helps us unpack what manufacturers need to do differently if they want to avoid being part of the 95 percent that fail.We also get an update on Sepa IQ and how customers are using it to connect plant floor data, structure it for AI and analytics, and prepare for advanced scheduling and predictive tools. From lessons learned working with early adopters to practical advice on starting small, Tom makes it clear that manufacturing AI is a journey that requires technical expertise, domain knowledge, and cultural change.Whether you are an...","thumbnail_url":"https://img.transistorcdn.com/yoKAvzBXZ3YjQTekFk7KFGXeuwJ29WgXvop3dVEfhLs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzE3MjEzLzE2MDk0/MzA1OTgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}