-
NPI: A How To Guide for Engineers & Their Leaders
-
Leading from the Front
-
Marcel Tremblay: The Olympic Mindset & Engineering Leadership
-
Anurag Gupta: Framework to Accelerate NPI
-
Kyle Wiens on Why Design Repairability is Good for Business
-
Nathan Ackerman on NPI: Do The Hard Thing First
-
JDM Operational Excellence in NPI
-
Building the Team
-
Quality is Set in Development & Maintained in Production
-
3 Lessons from Tesla’s Former NPI Leader
-
Maik Duwensee: The Future of Hardware Integrity & Reliabilitypopular
-
Reject Fake NPI Schedules to Ship on Time
-
Leadership Guidance for Failure to Meet Exit Criteria
-
-
Screws & Glue: Getting Stuff Done
-
Choosing the best CAD software for product design
-
Screws vs Glues in Design, Assembly, & Repair
-
Design for Instrumental - Simple Design Ideas for Engineers to Get the Most from AI in NPI
-
Best Practices for Glue in Electronics
-
A Practical Guide to Magnets
-
Inspection 101: Measurements
-
A Primer on Color Matching
-
OK2Fly Checklists
-
Developing Your Reliability Test Suite
-
Guide to DOEs (Design of Experiments)
-
Ten Chinese phrases for your next build
-
-
NPI Processes & Workflows
-
-
Production: A Primer for Operations, Quality, & Their Leaders
-
Leading for Scale
-
Greg Reichow’s Manufacturing Process Performance Quadrants
-
8D Problem Solving: Sam Bowen Describes the Power of Stopping
-
Cut Costs by Getting Your Engineers in the Field
-
Garrett Bastable on Building Your Own Factory
-
Oracle Supply Chain Leader Mitigates Risk with Better Relationships
-
Brendan Green on Working with Manufacturers
-
Surviving Disaster: A Lesson in Quality from Marcy Alstott
-
-
Ship It!
-
Production Processes & Workflows
-
Failure Analysis Methods for Product Design Engineers: Tools and Techniques
-
-
Thinking Ahead: How to Evaluate New Technologies
-
How to Buy Software (for Hardware Leaders who Usually Don’t)
-
Adopting AI in the Aerospace and Defense Electronics Space
-
Build vs Buy: A Guide to Implementing Smart Manufacturing Technology
-
Leonel Leal on How Engineers Should Frame a Business Case for Innovation
-
Saw through the Buzzwords
-
Managed Cloud vs Self-Hosted Cloud vs On-Premises for Manufacturing Data
-
AOI, Smart AOI, & Beyond: Keyence vs Cognex vs Instrumentalpopular
-
Visual Inspection AI: AWS Lookout, Landing AI, & Instrumental
-
Manual Inspection vs. AI Inspection with Instrumentalpopular
-
Electronics Assembly Automation Tipping Points
-
CTO of ASUS: Systems Integrators for Manufacturing Automation Don't Scale
-
-
ROI-Driven Business Cases & Realized Value
-
Today, most consumer electronics devices are still assembled by hand, but significant advances in automation have been made in the last decade. With the continued investment in manufacturing in the United States and other high labor-cost countries, automation is an important part of the production process. Here are the tipping points to keep an eye on to identify whether automation is worth the cost for your program and organization.
- Operator Availability: Assembly line work is repetitive and demanding, and the global labor market is changing. Whether you are in low-cost labor markets or high-cost ones, there are not enough manufacturing workers to go around. The worse this problem gets, the stronger the argument for automation.
- Operator Cost: The cost of labor is rapidly increasing due to operator availability. Inflation is also playing a role. This makes it easier to justify the ROI of automation investments.
- Downtime: Humans need rest. Unionized labor needs more rest. The calculation of lost productivity from downtime can play into the upside for automation.
- Human Logistics: To employ tens of thousands of operators, housing, transport, and food must be arranged. Where will the living quarters go? How far away from the line are those quarters? How will the workforce move back and forth?
- Superior Hardware: The human machine has remarkable sensory (eyes) and motor hardware (fingers) that has served us well for millennia. Reasonably priced machines have finally started to catch up. High-resolution cameras and industrial machine vision systems are affordable, capture much more than just visible light, and can store data perpetually. Machine manipulators have improved as well, though most solutions must still be custom: using off-the-shelf building blocks, there are many integrators eager to build solutions for customers seeking tighter tolerances, strict SOP compliance, and a log of critical data.
- Data Outputs: When compared to machines, humans are glacial at data output -- we can speak, type, or write. We will also inadvertently inject errors into the data. On the other hand, machines can reliably communicate data at the speed of light.
- Recall and Processing: A human has limited recall ability when processing thousands of daily units. A machine can save all of the data (full manufacturing traceability) and run algorithms on its data to provide useful information -- either by brute force or perhaps through…
- Intelligence: This has long been seen as the defining human quality: the capacity to learn, evaluate new situations, develop insights, and make decisions in new ways. Our intelligence also makes humans easy to “program”: we can apply years of prior experience to be retrained to perform a new task in minutes. In the last decade, making smarter machines has come a long way -- the gap between human and machine intelligence is narrowing. This is the area of greatest opportunity for manufacturing optimization in the coming decade.
Humans are quickly losing their edge in intelligence, the last bastion of superiority over machines. Manufacturers will have no choice but to move towards automation as this tipping point plays out in manufacturing.
This change also carries great opportunity: there is still significant opportunity for improvement in manufacturing. Advances in robotics and AI may give society unprecedented manufacturing capability and efficiency. It will also change the dynamics of the global manufacturing market, where the greatest differentiator in the cost of manufacturing will be proximity to the supply chain and the cost of the land -- not the labor.