Head of AI

Discover how INX International used AssetWatch and MaintainX to turn predictive maintenance insights into reliable, real-time action across global sites. If you've ever been on the plant floor when a machine failure stops production, you'll recognize this story. For years, INX International dealt with delayed equipment health data and limited ways to use it to prevent breakdowns. Root cause analysis was time consuming, and maintenance stayed reactive. That changed when INX integrated AssetWatch and MaintainX into their operations. As a key Industry 4.0 initiative, they built a global reliability model that links condition monitoring to maintenance. In its webinar, How INX Connects Predictive Maintenance to Action, INX's VP of Operational Excellence, Chris Rodgers, walks through how they're closing the gap between detection and action. A global manufacturer ready for change. Walk into any grocery store, and you'll see INX's work everywhere. If there's color on the packaging, particularly aluminum soda cans, INX makes the ink for it. INX is one of the largest manufacturers of high-performance inks and coatings in the world. But for all its innovation in print chemistry, INX faced a problem familiar to many manufacturers: day-to-day reactive chaos on the plant floor. Consumed by emergency repairs, INX operations teams didn't have much time to trace problems to their source. This meant they were making repeated repairs where an engineering fix could have eliminated the root cause. INX's equipment mix was a complicating factor: some assets had been running for 50 years, while others were brand new. Data visibility was inconsistent, limiting the potential for predictive insight. Rodgers and his team knew they needed to build a foundation for Industry 4.0 reliability - one that connected their frontline technicians, operators, and managers around real-time data. The proof: two critical asset saves with early detection. Simple three-roll mill repair with no production losses. INX first partnered with AssetWatch for AI-powered condition monitoring. At the Charlotte, NC plant, AssetWatch sensors detected a bearing issue on one of the site's most critical machines - a large, slow-turning three-roll mill. "Without the three-roll mills, our Charlotte facility does not get their product out the door," Rodgers said. "There's not a lot of backup equipment." After an alert from their AssetWatch CME, the team shut the machine down before the problem cascaded into catastrophic failure. * A planned, scheduled repair prevented an emergency shutdown. * There was no disruption to production schedules. * Rolls were resurfaced instead of replaced - saving 3 - 4x the cost. In another case, a recently placed AssetWatch sensor caught a dangerous temperature spike in an air compressor enclosure. INX's maintenance team was alerted by their AssetWatch CME before the compressor overheated. Within hours, based on their CME's recommendations, technicians cleaned the enclosure and replaced filters. The temperature soon returned to normal, avoiding a full outage that would have shut down multiple machines. "This was a very big savings for us, because if the air compressor goes out, you're down," Rodgers explained. "A lot of equipment is down, a lot of time is lost, a lot of cost to be able to get that machine back up and running quickly so that the plant can get back up and running." These examples illustrate the real power of predictive maintenance when it's actionable - thanks to precise, real-time data, plus AI analytics and ongoing expert support. The turning point: connecting insight to Action. To create an integrated loop from detection to resolution, INX added MaintainX to its Industry 4.0 initiatives. Combining real-time condition monitoring with MaintainX's maintenance and asset management capabilities has transformed INX's maintenance operations. * AssetWatch monitors critical equipment for vibration and temperature anomalies. * When an issue arises, a MaintainX work order is automatically generated, complete with asset history, parts inventory, and real-time tracking of MTTR, MTBF, and other key metrics. * Technicians receive diagnostics, photos, and prescriptive recommendations from their dedicated AssetWatch condition monitoring engineer (CME). * Once the repair is done, the data syncs back to AssetWatch, informing the CME, training the AI in both platforms, and improving future insights. This "closed loop" integration now underpins INX's entire reliability initiative. Technicians have everything they need on their mobile device or desktop - prioritized work orders, asset history, step-by-step procedures, parts availability, and CME guidance. Empowering the front line, changing the culture. Technology alone didn't drive INX's transformation. People did. INX built a reliability culture grounded in training, collaboration, and ownership. Operators were trained in autonomous maintenance and given direct input into reliability processes. Before, an operator filled out a paper "red tag" when there was an equipment issue. The supervisor would manually input the data into the legacy CMMS and hand a physical copy to the maintenance team. * Every asset has a QR code * Operators scan it on their phone or tablet to open a work request instantly * Maintenance sees it in MaintainX in real time, with context and photos attached This shift - from reactive maintenance to shared reliability ownership - became one of INX's most impactful wins. Scaling reliability across sites. With Charlotte as the pilot, INX moved fast. Using standardized templates, architecture, and support from AssetWatch and MaintainX, the company replicated its success across U.S. sites within months. The rollout included integration with Oden Technologies, a high-level MES that monitors real-time asset utilization data. This means INX is now evolving from calendar-based maintenance to usage- and condition-based maintenance, much like changing a car's oil after 5,000 miles instead of every six months. Lessons learned from INX's reliability journey. 1. Choose critical equipment wisely. Start where failure hurts most - both "class A" machines and hidden assets like compressors and ventilation. 2. Standardize data and architecture. Build templates early. Every site will move faster with a proven integration model. 3. Train relentlessly. As Rodgers put it: "Train the maintenance staff, train the operators, and train the management. As soon as you think everybody's been trained, train them again." Adoption only happens when confidence builds. 4. Partner deeply. By leveraging AssetWatch's engineering expertise and MaintainX's intelligent maintenance workflows, INX kept internal resources lean while accelerating results. 5. Make reliability everyone's job. When operators, engineers, and maintenance staff share ownership, reliability becomes part of daily culture. Curious about INX's results? What started as a pilot in Charlotte has become a model for global reliability. By connecting AssetWatch's AI-powered anomaly detection with MaintainX's digital work execution, INX built a predictive maintenance system that actually works in practice - for people, not just machines. Want to learn how INX transformed maintenance and reliability? Catch the full replay of How INX Connects Predictive Maintenance to Action. Watch on-demand here.

Westerville, OH - August 12, 2025 - AssetWatch, a leading provider of AI-powered predictive maintenance solutions for manufacturers, has been named to the 2025 Inc. 5000 list, ranking No. 526 nationally - its second time earning the distinction.