Engineering Challenges With Automation In Manufacturing

Why automation adoption is slower than people think — from someone inside it every day There’s a lot of talk about “the automation boom,” but on the factory floor, adoption is still moving slower than headlines suggest — and for good reasons. Yes, robot hardware costs are down. But integration is where the real friction lives. Here’s what we see every day in manufacturing: • Integration costs dwarf robot costs The robot may be affordable — but making it work inside an existing line, with legacy conveyors, controls, and part presentation, is where budgets stretch and timelines slip. • Legacy infrastructure is the anchor Many plants weren’t designed for modern automation. Retrofitting old systems to talk to new tech is expensive, disruptive, and risky. • Safety and certification take time Compliance isn’t optional. Safety validation, guarding, testing, and approvals can add months before a system ever runs production. • Labor shortages still exist — just in a different form Automation doesn’t eliminate labor needs; it shifts them. Skilled technicians, integrators, and programmers are harder to find than ever. • Variability kills “plug-and-play” dreams Parts aren’t perfect. Orientation isn’t perfect. Environments aren’t clean-room ideal. Real-world variability is why upstream systems — feeding, orientation, spacing, inspection — matter so much. • ROI anxiety is real If a system misses rate, jams, or requires constant tweaking, the payback window stretches fast. That hesitation slows approvals. The takeaway? Automation is coming — but it isn’t magic, and it isn’t instant. The companies winning right now aren’t chasing buzzwords. They’re: Solving upstream problems first Designing systems that work with reality, not against it Building automation that operators can trust, maintain, and scale That’s how adoption actually accelerates. Curious how others are seeing this on their shop floors. Are these the same friction points you’re running into? #Manufacturing #Automation #Robotics #IndustrialAutomation #SmartManufacturing #EngineeringReality #FactoryFloor #AutomationStrategy

Can Fully Automated Factories Become a Reality? Only 6% of manufacturers worldwide operate with minimal human intervention in production environments, according to Harvard Business Review. The aspiration for full automation is clear. The path to achieving it is far more complex. Artificial intelligence has delivered measurable gains in areas such as predictive maintenance, inline defect detection, and robotic handling. However, high-variability conditions, unstructured data inputs, and exception management still rely on human supervision and judgement. For operations leaders, this is not a binary decision between human-led and machine-led systems. It is an architectural challenge that requires segmenting processes according to their degree of stability, repeatability, and decision logic. Industry leaders are now advancing hybrid automation architectures. These systems assign deterministic, high-frequency tasks to AI, while preserving human oversight for functions that require adaptability, contextual reasoning, or escalation control. This approach is not a concession. It reflects operational realism and strategic optimisation. The objective is to elevate the role of human operators to supervisory and exception-management tiers, where their value is highest. Which specific production workflows in your environment are suited to automation, and where does human intervention remain critical? #IndustrialAI #ManufacturingStrategy #AutomationDesign #OperationalExcellence #AIIntegration #FirstStepAI

I've discussed #MES/#MOM project difficulties with hundreds of people. Here's my take on the challenges that crop up time and time again: 1. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Getting master data across systems (like ERP) to be at the right level of detail, or to be abstracted to be relevant for MES/MOM. Connecting to legacy equipment and databases. A lack of standardisation or contextual information in file formats. 2. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Operators and supervisors resist new processes. Without proper training and buy-in from the shop floor, even the best #MES system becomes unused. Onus is on leadership to set the vision and align teams. 3. 𝗨𝗻𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Companies expect immediate ROI and perfect data from day one. Manufacturing transformation takes time, and data quality improves gradually as processes mature and people use the system more effectively. 4. 𝗜𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Underestimating the internal resources needed. IT teams are stretched thin, and manufacturing engineers often lack the bandwidth to support implementation properly. Often the people needed are already the busiest people, and sometimes the relevant resources don't exist in the business at all, so hiring them and getting them up to speed is a bottleneck. 5. 𝗦𝗰𝗼𝗽𝗲 𝗖𝗿𝗲𝗲𝗽: Lack of strong leadership and governance can lead to a mentality of trying to implement every suggestion - this leads to complexity that overwhelms teams and dilutes project focus. This is worst when trying to replicate functionality from old or homegrown systems. The successful projects I've observed share common traits: they build strong teams with a clear vision, invest heavily in training, set realistic timelines, and maintain strong executive sponsorship throughout. Most importantly, they treat MES implementation as a business transformation project, not just a technology deployment 💪 What's been your biggest challenge when implementing manufacturing systems? I'd love to hear your experiences in the comments. p.s. I know about the typos - but I just loved the image so much so went with it 😂

Spirit AeroSystems's plan to use automation to fix its quality control problems highlights an awkward truth for the high-class world of aerospace manufacturing: A meaningful amount of the work that goes into building airplanes is still done by human hands -- and will be for the foreseeable future. The primary gating factors for automation in aerospace manufacturing are the scale of production and the long-shelf life of planes. The 737 took its first flight in the 1960s and the exoskeleton of the plane has stayed largely the same. The designs are so old they're difficult to automate. There's more of a push toward automation as both labor costs and the rate of quality control problems rise, just as Boeing and Airbus push their supply chains toward ever higher levels of production. Still, just because something is automated doesn't mean it's done right. The oblong fastener holes that Spirit and Boeing had to fix on the Max were drilled through an automated process. Automation “theoretically takes variability out of a process,” BofA analyst Ron Epstein said. “The good news is you’re being consistent. The bad news is you can be consistently wrong.” This week's newsletter: #automation #industrial #aerospace #aerospacemanufacturing

𝐓𝐡𝐞 $𝟗𝟎 𝐌𝐢𝐥𝐥𝐢𝐨𝐧 𝐋𝐞𝐬𝐬𝐨𝐧: 𝐖𝐡𝐲 𝐒𝐭𝐚𝐧𝐥𝐞𝐲 𝐁𝐥𝐚𝐜𝐤 & 𝐃𝐞𝐜𝐤𝐞𝐫’𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐝𝐧'𝐭 𝐖𝐨𝐫𝐤 𝐐𝐮𝐢𝐜𝐤 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞: Stanley Black & Decker, Inc.'s ambitious $90 million automation project in Fort Worth, Texas, aimed to revive the Craftsman brand by producing tools domestically with unprecedented efficiency. However, equipment issues, slow production, and the impact of COVID-19 led to the closure of the factory 3½ years after its inception. 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: 1. 𝐒𝐞𝐥𝐞𝐜𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐝, 𝐋𝐨𝐜𝐚𝐥𝐥𝐲 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐞𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐁𝐮𝐢𝐥𝐝𝐞𝐫𝐬: • SBD selected a machine builder from Belarus. Although the company demonstrated its machines could forge with minimal waste, the machines didn’t work properly when installed and were difficult to fix. • SBD had to wait weeks for overseas parts and tooling to arrive for repairs. 2. 𝐓𝐡𝐨𝐫𝐨𝐮𝐠𝐡 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: • The automation technology wasn't fully tested before scaling up, leading to persistent production issues. This was likely due to pressure to finish quickly to support the increased demand during COVID. 3. 𝐇𝐮𝐦𝐚𝐧 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐯𝐬. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: • The loss of senior experienced workers, mostly due to retirements during COVID, and over-reliance on untested automation systems underscored the value of human expertise. 4. 𝐔𝐧𝐬𝐭𝐞𝐚𝐝𝐲 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: • SBD had two CEOs and five Presidents of the Global Tools Group during this period. • Leadership focus is critical for complex projects. 5. 𝐑𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬: • Overly aggressive timelines, disrupted by the pandemic, compromised the project's success. • Setting realistic goals and being adaptable to unforeseen challenges are essential for complex projects. Leadership was critical here. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐃𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐌𝐢𝐬𝐬𝐭𝐞𝐩𝐬: Stanley Black & Decker may have learned the wrong lessons from this experience. The company's recent consideration towards manufacturing parts in Mexico, rather than further investing in automation within the USA, suggests a retreat from the challenges faced rather than a strategic approach to overcoming them. 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Snap-on gradually integrated automation into its U.S. factories, evolving from a 100-to-1 ratio of workers to robots in 2010 to an 8-to-1 ratio over twelve years. This phased approach allowed Snap-on to identify optimal roles for both humans and machines. Snap-on's CEO, Nick Pinchuk, emphasized the importance of understanding the intricacies of the product and the manufacturing process. ATI Industrial Automation supports reshoring #manufacturing with the help of #robotics and #automation.  #robotrevolution https://lnkd.in/eaGwt2V5