Manufacturing Happy Hour · 31 Aug 2021 · 45m
Anna-Katrina Shedletsky, CEO and co-founder of Instrumental, explains how her platform uses AI and aggregated product data to optimize manufacturing and reduce waste. Drawing from her experience leading product design teams at Apple, including the high-risk Apple Watch program, she details how Instrumental helps companies close feedback loops in development and production. She highlights specific use cases, such as using unsupervised learning to correlate visual inspection data with functional test failures, to improve yield and accelerate time-to-market. Additionally, Shedletsky discusses her Women in STEM Mentorship Program and Instrumental's recent recognition on the Inc. 5000 list.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulationInstrumental
Instrumental's AI performs unsupervised discovery of anomalies in manufacturing data, allowing engineers to identify root causes of failures without pre-defining what to look for.
“we're actually doing unsupervised, um, discovery of of anomalies in the data. So what that means is you don't have to tell us what you're looking for or even where to look.”
Anna-Katrina Shedletsky · Hear it at 26:32
Engineering & simulationInstrumental
The platform correlates functional test failures with visual inspection images to find hidden root causes, such as connector angles, that manual inspection missed.
“instrumental went and compared the testation data from that population and looked at the images to see, is there something different about these images relative to the entire population that was not selected and actually ranked what it based on correlating correlations to that population.”
Anna-Katrina Shedletsky · Hear it at 26:32
Engineering & simulationInstrumental
The AI identifies similar defects across a population, helping engineers prioritize whether an issue is systemic or isolated, thereby speeding up failure analysis.
“you can use the AI to actually find things that look similar to ones you already know, so you can, you can then find like, oh, there's 10 that all have scratches in this area and no definitive lay that like in your population, that's how many you have.”
Anna-Katrina Shedletsky · Hear it at 26:32
Engineering & simulationInstrumentalramped to stability weeks faster
Using Instrumental in development allows products to ramp to stability weeks faster by solving production problems during the design phase, saving hundreds of thousands of dollars per program.
“one of them over 10 programs, who told us that they were able to see, um, that their products essentially ramped to stability weeks faster, if they use instrumental in development.”
Anna-Katrina Shedletsky · Hear it at 31:14
Running the companyInstrumental60% of time on non-engineering tasks
The platform aggregates product data to reduce the 60% of time engineers spend on non-engineering tasks like communication and travel, effectively tripling the engineering team's output.
“We have customers that tell us it's like tripling the size of your engineering team, um, to just have access to the data means you're spending a lot less time.”
Anna-Katrina Shedletsky · Hear it at 31:14
companies scaling up
Other shows on the same ground, closest first.
Anna-Katrina Shedletsky, CEO and founder of Instrumental, discusses how her company uses AI to accelerate New Product Introduction (NPI) and improve manufacturing efficiency. She highlights a case study with Meta, where Instrumental's technology saved 900 engineering weeks annually across seven programs by identifying defects earlier in the EVT phase. Shedletsky explains the concept of the 'shadow factory' (rework and bone piles) and how reducing its size frees up capacity. She also advises manufacturers to focus AI adoption on solving specific, high-impact business problems rather than chasing trends, noting Instrumental's nine-year journey from startup to an established player in the contract electronics space.
Anna-Katrina Shedletsky, CEO and founder of Instrumental, discusses how the pandemic accelerated digital transformation in electronics manufacturing, validating her prediction of five years of innovation in 18 months. She explains Instrumental’s cloud-based platform that collects engineering data from across the supply chain to identify defects and improve first-pass yield, moving away from opaque, just-in-time models toward resilient, data-driven collaboration. Shedletsky highlights the shift from Capex to Opex for manufacturing technology and the critical need for transparency between brands and Contract Manufacturers (CMs) to solve problems faster.
Daniel First, founder and CEO of Axion, explains how AI is transforming quality and innovation roles in manufacturing by turning overwhelming streams of customer, device, and service data into actionable insights. He argues that quality teams are shifting from plant-floor oversight to strategic partners across engineering, marketing, and supply chain, using AI to accelerate product evolution and break down silos. First highlights specific examples, such as Shark Ninja’s ice cream machine and power systems in mines, to illustrate how unexpected customer usage drives innovation and how AI agents can detect root causes faster than humans.
Peter Gutschi, a consultant supporting companies like ESCATEC and startups, explains how AI is moving beyond hype to drive real efficiency in manufacturing, from shop floor quality management to supply chain planning. He highlights specific technologies such as Luminovo's platform for calculating CO2 footprints and Cybord's component inspection tools, which handle data volumes impossible for humans to manage in real time. Gutschi argues that this enhanced visibility allows EMS providers to accurately commit to delivery dates and prevent the inventory overhangs seen in past crises like the 2000 tech wreck.
Dr. Prasad Akella, Founder and CEO of Drishti, explains how his company uses AI-powered video analytics to empower manufacturing workers rather than replace them. He details how Drishti’s technology captures production line video to create searchable 'video birth certificates' for products, enabling root cause analysis and quality control. Akella highlights specific applications like action recognition for error detection, optimizing line balancing, and using video to accelerate training and improve employee retention by identifying and leveraging 'brilliant outliers' on the floor.
The summary, chapters and topics are generated from the episode’s audio by this directory, not written by Manufacturing Happy Hour. The episode itself belongs to its publisher.