LEAN PRINCIPLES
Predictive maintenance success stories In Veeva’s “2026—The State of AI in Consumer Goods” report, the software provider surveyed 150 CPG senior quality and IT leaders and found that “predictive analytics carries the most interest, with just over half (52%) saying predictive analytics are the top priority for AI.” In addition, 9 in 10 respondents say their companies are actively using AI or are conducting trials, pilots or evaluations. With predictive maintenance, “failure events are concrete and measurable, which simplifies labeling,” according to Guerster. “From a business standpoint, maintenance ROI is easier to quantify: reduced downtime IMAGE COURTESY OF SKYLINE GRAPHICS/ADOBE STOCK and spare parts savings,” he says. “In contrast, broader process optimizaoperational KPIs [key performance indicators],” says tion requires deeper integration with quality data, Guerster. lab results, and production state modeling.” “With AI, starting small and growing from there “Production assets have a variety of sensors, seems like it has proven benefits across the board,” including vibration, temperature, current, pressure adds Oskin. “Picking a good place to start, such as and flow,” says Oskin. “Structured, high-frequency a key asset, production line, or metric, and picking a data is ideal for machine learning.” SmartSights couple of AI projects to do this week or this month.” offers solutions that trigger maintenance work A recent webinar, “Optimizing Production with AI and orders, issue alarms when SCADA anomalies are Machine Learning,” with Marc Betrand at SmartSights, detected, and provides a holistic view of the full life revealed how a customer reduced waste by using feacycle of a maintenance issue. ture importance or prescriptive analytics in diagnosing a While successes in predictive maintenance are packaging line bottleneck. Feature importance identifies well documented, scaling across multiple plants the most impactful features, leading to more efficient, remains a challenge. “The proof of concept that interpretable, and high-performing models. predicted bearing failures on one packaging line The objective for this customer was to identify works brilliantly—until winter turns into summer and the appropriate KPI, such as mean time between failthe ambient conditions change, or someone adjusts ures (MTBF) or a machine center value for a packa setpoint, or the model just quietly degrades aging line bottleneck. In this example, SmartSights’ because nobody owns its ongoing maintenance,” ABLE technology conducted a root cause analysis says Ariens. of a bundler, a wrapping unit, and a tray packer, and Ariens adds, “scaling predictive maintenance identified the highest-impact machines based on across an entire asset fleet demands exactly the same potential root causes. reusable data infrastructure and governed context In parallel, a prescriptive analytics approach also models that every other data initiative needs.” modeled the packaging line. The essential KPI for Standardizing data and ML pilots will pose chalthis bottleneck was called the effective rate, which lenges, but many industry analysts are pointing to multiplies availability by the average rate for the line AI-based tools for formulation as a promising area to produce a units-per-minute metric. “Both of the for early growth. Bottom line, corporate managers algorithms were correct, but what was misleading and boards are learning more about AI and the was that the root cause is saying operators should challenges in scaling across the enterprise. be focusing on this tray packer. However, the focus “AI was this nebulous thing and is now a real should be on the bundler,” said Bertrand. The ML thing. So, people are making that leap of faith that modeling enabled the packer to increase overspeed maybe they weren’t even doing two years ago,” capacity and speed (rate) on both machines, resolvsays Oskin. “In food and beverage, specifically, ing the machine center bottleneck. companies have a lot of cost pressures.”
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3/18/26 07:28