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NPI: A How To Guide for Engineers & Their Leaders
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Leading from the Front
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Marcel Tremblay: The Olympic Mindset & Engineering Leadershippopular
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Anurag Gupta: Framework to Accelerate NPI
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Kyle Wiens on Why Design Repairability is Good for Business
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Nathan Ackerman on NPI: Do The Hard Thing First
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JDM Operational Excellence in NPI
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Building the Team
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Quality is Set in Development & Maintained in Production
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3 Lessons from Tesla’s Former NPI Leader
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Maik Duwensee: The Future of Hardware Integrity & Reliability
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Reject Fake NPI Schedules to Ship on Time
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Leadership Guidance for Failure to Meet Exit Criteria
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Screws & Glue: Getting Stuff Done
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Choosing the best CAD software for product design
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Screws vs Glues in Design, Assembly, & Repair
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Best Practices for Glue in Electronics
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A Practical Guide to Magnets
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Inspection 101: Measurements
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OK2Fly Checklists
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Developing Your Reliability Test Suite
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Guide to DOEs (Design of Experiments)
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Ten Chinese phrases for your next build
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NPI Processes & Workflows
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Production: A Primer for Operations, Quality, & Their Leaders
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Leading for Scale
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Proven Strategies for Collaborating with Contract Manufacturers
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Greg Reichow’s Manufacturing Process Performance Quadrants
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8D Problem Solving: Sam Bowen Describes the Power of Stopping
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Cut Costs by Getting Your Engineers in the Field
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Garrett Bastable on Building Your Own Factory
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Oracle Supply Chain Leader Mitigates Risk with Better Relationships
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Brendan Green on Working with Manufacturers
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Surviving Disaster: A Lesson in Quality from Marcy Alstott
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Ship It!
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Production Processes & Workflows
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Failure Analysis Methods for Product Design Engineers: Tools and Techniques
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Thinking Ahead: How to Evaluate New Technologies
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How to Buy Software (for Hardware Leaders who Usually Don’t)
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Adopting AI in the Aerospace and Defense Electronics Space
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Build vs Buy: A Guide to Implementing Smart Manufacturing Technology
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Leonel Leal on How Engineers Should Frame a Business Case for Innovation
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Saw through the Buzzwords
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Managed Cloud vs Self-Hosted Cloud vs On-Premises for Manufacturing Data
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AOI, Smart AOI, & Beyond: Keyence vs Cognex vs Instrumentalpopular
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Visual Inspection AI: AWS Lookout, Landing AI, & Instrumental
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Manual Inspection vs. AI Inspection with Instrumentalpopular
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Electronics Assembly Automation Tipping Points
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CTO of ASUS: Systems Integrators for Manufacturing Automation Don't Scale
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ROI-Driven Business Cases & Realized Value
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Webinars and Live Event Recordings
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Build Better 2024 Sessions On Demand
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Superpowers for Engineers: Leveraging AI to Accelerate NPI | Build Better 2024
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The Motorola Way, the Apple Way, and the Next Way | Build Better 2024
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The Future of Functional Test: Fast, Scalable, Simple | Build Better 2024
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Build Better 2024 Keynote | The Next Way
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Principles for a Modern Manufacturing Technology Stack for Defense | Build Better 2024
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What's Next for America's Critical Supply Chains | Build Better 2024
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Innovating in Refurbishment, Repair, and Remanufacturing | Build Better 2024
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Leading from the Front: The Missing Chapter for Hardware Executives | Build Better 2024
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The Next Way for Reducing NPI Cycles | Build Better 2024
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The State of Hardware 2025: 1,000 Engineers on Trends, Challenges, and Toolsets | Build Better 2024
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Scaling Manufacturing: How Zero-to-One Lessons Unlock New Opportunities in Existing Operations | Build Better 2024
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Design for Instrumental - Simple Design Ideas for Engineers to Get the Most from AI in NPI
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Webinar | Shining Light on the Shadow Factory
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CTO of ASUS: Systems Integrators for Manufacturing Automation Don't Scale
Estimated reading time: · copy linkThe manufacturing industry is undergoing technological transformation, but progress has been slow. As part of my series to uncover little-discussed insights from technology leaders in manufacturing, I recently sat down with Tai-Yi Huang, CVP and CTO of ASUS, a top consumer electronics brand and manufacturer. Huang leads the charge in ASUS’s adoption of new technologies, focusing on the development and early validation of an AI engine. While both Huang and I agree that manufacturing provides many high-leverage applications for AI, widespread adoption of the technologies on the market has been slow. Huang shared his perspective: “We don’t see more AI in factories because the systems integration effort is too heavy.”
Technology vendors develop and provide new products like smart cameras, robot arms, or analytics software. The buyers are the brands or manufacturers who want to incorporate that technology into their process. To do that, they employ a middleman, the systems integrator.
Systems integrators specialize in knowing about all available technologies and delivering combinations of them with some custom engineering to provide a complete (but often non-reusable) turnkey solution for the manufacturer’s specific need. If the systems integrator selects a smart camera as part of the solution, they will also be the ones to do custom programming of the camera to work for the use case.
The problem is that every use case requires a new, custom setup (not to mention that if changes are made to the process or parts, these setups have to be updated), making it very slow and expensive. As a result, it only makes sense to use systems integrators, and the automation they provide, for the most painful applications in the upper echelon of ROI. There are countless other potential applications in the visual inspection space alone where the cost of the technology isn’t prohibitive, but the cost of integrating it is. As Huang said, “The systems integrator effort is not scalable.”
Since systems integrators have been the main method that technology vendors use to deliver new technologies for decades, they are designed with them in mind. Many technology products for manufacturing optimization prioritize delivery of advanced functionality over ease-of-use and intuitive design. Complex user interfaces mean manufacturers often cannot make minor adjustments or updates to their automated solutions without calling in an integrator and paying their fees. It also means that manufacturers are sometimes not getting everything they could be from the technologies they’ve purchased because additional setup would be expensive. Since the setups are not reusable, in some industries where the product lifecycle is short, these expensive lines full of equipment are essentially "disposable" and retain no value to the purchaser after the program is done.
The pervasive use of systems integrators also points to larger issues that affect everything from automation to AI. Most importantly, it points to a lack of readily available manufacturing data. Data is the lifeblood of any AI engine -- ASUS and Instrumental alike. While many manufacturing processes are partially automated in the United States, most tracking and assembly processes in low-cost labor counties are still manual – including paper and pencil notations and manually generated reports. Needless to say, these paper or low-resolution MES (manufacturing execution systems) tracking systems are usually not good candidates for AI analysis. When paper and manual reports compiled in Excel are part of the foundational data system for a manufacturing process, it’s difficult to build anything more advanced on top, let alone apply AI-based powers like predictive analytics in manufacturing. Even different sites owned by the same manufacturer may use different data systems. Huang explained, “Everything is unique, so each data solution is unique.”
So how do we unblock technology adoption to bring about the dividends and manufacturing optimization of Industry 4.0? Huang believes AI will play a large role. The opportunity is tantalizing: AI can help to bridge the gap. Today, we have applications that require custom setups by systems integrators. Tomorrow's AI allows for generalized technologies that might set themselves up as they learn about an application from data – with little to no systems integration efforts.
The industry needs fundamental changes in the complex ecosystem required to get technology on manufacturing lines. Technology solutions must decrease deployment friction and prioritize flexible solutions, such as self-learning AI or adaptable machine learning manufacturing technologies. Manufacturing companies must also take fundamental steps towards digitization and remove data extraction hurdles. Manufacturers should start by investing in technologies that capture readily available and often overlooked data, like images, and favor options that can deliver sophisticated manufacturing data analytics and concrete ROI.