Acerta

‘Twas the night before production, when all through the plant, Not a robot was whirring, no worker’s complaint. The weld guns were hung by the conveyors with care, In hopes that smooth cycles soon would run there. The line techs were nestled all snug in their beds, While visions of uptime danced in their heads. The forklifts were parked, the warehouse was still, Awaiting the morning for pallets to fill. When out in the bay there arose such a clatter, The night shift ran out to see what was the matter. Away to the dock they flew like a flash, Tripping on pallets, avoiding a crash. The moon on the hood of a freshly built car, Gave the luster of steel to machines near and far. When what to their wondering eyes did appear, But a semi of parts and eight cases of beer. With a savvy old driver, so lively and quick, They knew in a moment it must be St. Nick. More rapid than press lines his orders they came, He whistled and shouted and called them by name: "Now Stamping! Now Welding! Now Paint and Assembly! On Testing! On Shipping! On QA’s checklist spree! To the end of the line! To the lot near the wall! Now dash away! Dash away! Dash away all!" As sparks from a grinder in free flight will soar, When they meet with a surface, erupt with a roar, So out to the conveyors the workers they flew, With the sleigh full of parts and St. Nicholas too. And then, in a twinkling, they heard on the floor The prancing and pacing of boots by the door. As they turned from their stations and spun all around, Down the main aisle St. Nicholas came with a bound. He was dressed all in coveralls, from his head to his feet, And his hands bore the stains of grease, oil, and heat. A bundle of tools he had flung on his back, And he looked like a tech as he opened his pack. His eyes, how they twinkled! His laugh, how it boomed! His cheeks were like brake lights, his nose like perfume. His droll little mouth was drawn up like a bow, And his beard was as white as assembly line snow. The stump of a wrench he held tight in his grip, And he gave a quick nod as he tightened a clip. He had a broad face and a round little belly, That shook when he laughed like a loose ball of jelly. He was jolly and skilled, a right clever old elf, And they laughed when they saw him, in spite of themselves. A wink of his eye and a twist of his spanner, Soon gave them to know it was time for good manner. He spoke not a word but went straight to his task, And filled every station, no question to ask. And laying his finger aside of his nose, With a nod to the foreman, up the freight lift he rose. He sprang to his semi, to his team gave a cheer, And away they all drove as the morning drew near. But they heard him exclaim, ere he drove out of sight, "Happy production to all, and to all a good night!"

When you are investing in new operational technology, evaluate how it can scale. Ask yourself: 🔷 Can the solution adapt easily as you grow or change your manufacturing processes? 🔷 Is it easy to role out on additional production lines, or at multiple different facilities? 🔷 Can the software play nice with other parts of your OT stack? 🔷 Does the pricing model charge you by seat or by data volume, and if so, does that impact the ROI? #manufacturingtech #otstack #operationaltechnology

"Around 30 minutes per day" "20 hours" "A work of 3-4 weeks took only one week" "Several hours in total" "4-8 hours" "1 hour daily" "More than three days" We've started getting in the habit of asking our users, "how much time is LinePulse saving you on a routine basis?" The above quotes from process, quality, manufacturing engineers (and one program manager) are fascinating to us. Often, we focus on the time and cost savings from some of the BIG problems that can be solved or avoided with LinePulse. A few hours daily might not seem like much at first glance... but it doesn't take a math guy to figure out how these time savings can add up quickly when every engineer is using the platform. #manufacturinganalytics #automotivemanufacturing

The old way of looking at quality in automotive was indirectly, through process optimization measured by OEE. But there's another way to think about it: focusing not just on the machines, but on the actual quality of your parts in production. https://hubs.li/Q02YQt3l0

Quality and process engineers, does this look familiar? As much as we love Excel for so many of our essential business tasks, it just wasn't designed to process the vast amounts of data that need to be processed by process and quality engineers when they are trying to solve their production issues. While Excel was designed for maximum flexibility for any situation, LinePulse was designed specifically for high-volume discrete manufacturers. We don't limit how much data you can ingest with LinePulse, because our priority is that you have all the data you need to get the most accurate insights possible. If you want to say goodbye to error messages like these, swing on by our website and book a LinePulse demo to get started.

Yesterday our customer Brandon Sheldrake, Global LMMS Manager of Linamar Corporation joined us for a (virtual) fireside chat at our annual winter summit. Our whole team benefitted from hearing Brandon's perspective on the changes he's seen in manufacturing across his career, the future of traceability, and what manufacturers are looking for in their ML/AI analytics platforms.

Here's a little snapshot of Acerta. We're deployed on over 300 manufacturing lines in 12 countries. Currently, LinePulse analyzes over 21.6 million events per day. And we're trusted by a pretty spectacular group of automotive customers. This deep experience in automotive is what really sets us apart. Our secret sauce of manufacturing-specific data handling, feature engineering and curated algorithms has been proven over and over to provide the right insights that speed up decision-making and reduce KPIs like scrap and rework. #manufacturinganalytics #predictivequalityanalytics

What’s the cost of being reactive in manufacturing? Beyond the visible impact—defects, scrap, and rework—there’s an invisible toll: missed opportunities to improve. Every defect contains valuable lessons about how processes can be optimized. But traditional systems often miss these lessons, focusing only on detection rather than understanding the root cause. Predictive quality changes this. By creating a closed-loop system where every defect feeds back into smarter predictions, manufacturers can continuously improve, rather than perpetually react. Quality isn’t just about reducing defects; it’s about building a system that learns from them. #predictivequality #manufacturinganalytics #continuousimprovement

Bust out the champagne! 🍾 Acerta's security team has hit a major milestone! We've received our SOC 2 Type 1 audit report and we've passed with flying colors. This industry-standard cybersecurity certification demonstrates our commitment to provide a secure, reliable, and confidential platform for our manufacturing customers. https://hubs.li/Q02ZgVMD0

In manufacturing, "within spec" doesn’t always mean “fits perfectly.” Take vehicle side mirrors, for example. Their final assembly involves multiple sub-components—some made internally, others from suppliers. Even if a supplier’s mirror housing meets specifications, being on the high end of its tolerances might cause it to misalign with other parts, like injection-molded interior components. This is what we call a classic *stack-up of tolerances* problem. Individually, everything is “right,” but together, the fit just doesn’t work. It’s a reminder that in complex assembly processes, it’s not just about meeting specs—it’s about understanding how parts interact. We've built LinePulse to help solve problems just like this. #manufacturingquality #qualityengineering #automotivemanufacturing #processcontrol