Processing's Weekly Mixer: Data hurdles hinder life sciences manufacturers amid AI adoption, and more
Welcome to the latest installment of Processing's Weekly Mixer, which highlights recent content from EndeavorB2B brands relevant to process manufacturers.
This week's entry features content from Pharma Manufacturing, Automation World, Chemical Processing and Plant Services, as well as this week's content from Processing.
Data hurdles hinder life sciences manufacturers amid AI, automation adoption
With the advent of artificial intelligence (AI), life sciences manufacturers recognize now more than ever the importance of digital maturity. However, many companies’ digital strategies are still developing alongside their adoption of these rapidly growing technologies, and even those who are actively implementing AI, automation, and connected systems are facing challenges about integrating these platforms into their operations.
A report from Rockwell Automation, The New Operating Model for Life Sciences Manufacturing: Moving from Compliance to Continuous Readiness, found that while more than half of manufacturers (58%) have already deployed smart manufacturing technologies — either at scale or across portions of their operations — many are also navigating common challenges around data accessibility, system integration, governance, and operational complexity.
While 90% of life sciences manufacturers say digital transformation is now business-critical, the report revealed that only 32% of organizations effectively use more than half of the data they collect, making it harder to scale technologies like AI which rely on connected, trusted, and contextualized data.
The bottom line is that AI adoption among life sciences manufacturers has accelerated faster than governance, infrastructure, and organizational readiness, with fragmented data, limited interoperability between manufacturing systems, and uneven digital maturity remaining as barriers to success.
At the same time, regulatory agencies are increasing expectations around cybersecurity, data integrity, digital records, AI governance, and continuous quality oversight. Regulators are ultimately asking manufacturers to place greater emphasis on transparency, traceability, and the ability to demonstrate control across increasing digital operations, according to the report.
“Manufacturers used to treat regulatory readiness as a project — something they ramped up for,” Matt Weaver, vice president of global industry-life sciences at Rockwell Automation, said in a statement. “Now, it has to be a daily discipline built into how the facility runs. The companies connecting their data, securing systems and validating AI deployments today are the ones that will be ready for what comes next from regulators or from the market.”
The path to smarter predictive asset management and the efficiencies that follow
Kaminski explains that condition monitoring converts machine data into actionable maintenance insights, helping manufacturers keep assets operating efficiently and avoiding failures. He recommends beginning with the most critical, failure-prone, or difficult-to-replace equipment, such as compressors, pumps, and connecting systems.
The discussion highlights Banner’s Snap Signal ecosystem, which connects diverse sensing devices—including vibration, temperature, voltage, current, pressure, and humidity sensors—into a streamlined data network that can incorporate non-Banner products. Vibration, temperature, and power data provide early warnings of mechanical or electrical problems, while differential-pressure monitoring helps optimize filter replacement.
Kaminski says modern systems enable planned maintenance, reduced unplanned downtime, longer asset life, and lower consumable and energy costs. He also describes simpler deployment through overlay networks, wireless options, dashboards, and edge controllers.
Looking ahead, he expects easier-to-use systems that support both basic monitoring and automated maintenance workflows.
Listen to the conversation below.
eChemExpo lunch and learn — Steam traps: small valves, big losses
Predictive maintenance strategies can extend bearing life and catch problems before failure
Bearing failure can cause costly downtime. In order to avoid this, bearings must be properly lubricated and free of contamination. Replacing them can be a challenge, especially if your maintenance techs work on older machines that may not have needed new bearings in a long time. Predictive maintenance can reduce that downtime. Plant Services spoke to Alex Thompson, director of product management at SKF, about how predictive maintenance can extend bearings’ lives.
Many organizations have a “fix and replace” attitude toward maintenance, says Thompson. This reactive maintenance is a habit that needs to be changed in order to improve bearing life.
Instead of handling each problem as it comes up, get into a habit of relying on equipment and regular inspection.“Let's install some equipment that's going to monitor it for me, instead [of] doing additional spot checks,” Thompson said. “A lot of times, we need to identify what is the most critical piece of equipment, because those are really the things that that we can't allow to fail in a plant.”
