Reimagined Data Drives a Predictive Maintenance Success Story 4 min read Client Snapshot Profile A U.S.-based market leader in commercial and industrial laundry systems. Situation After three years of work, the client’s predictive maintenance platform couldn’t perform in production because field data wasn’t reliable. It needed to predict issues early so customers could fix problems before equipment failed. Work Performed Protiviti identified previously unused data with the potential to impact maintenance, developed and trained five new AI models/algorithms based on that data, and designed an ML operations framework, all within a 12-month timeframe. Outcome/Benefits The company is first in the industry to embed predictive maintenance capabilities in the digital platform it offers servicers, opening a significant new digital revenue stream. Emergency repairs plunged 85% and better scheduling extended equipment life by 45%. A global equipment manufacturer spent years developing a proprietary predictive‑maintenance platform, but the solution couldn’t scale in production. It performed well in the lab yet failed to deliver reliable insights in variable field conditions, and its rigid design, requiring new hardware and software, made it unsustainable.A smarter, data‑driven approachProtiviti was brought in to assess viability and chart a new path. The team analyzed equipment failures, identified root causes, and quantified unplanned‑downtime costs, shaping a value‑based model‑selection approach focused on early failure prediction and repair readiness.Leveraging seven years of previously unused edge‑device data, Protiviti redesigned the machine‑learning models, expanded data inputs, and enabled real‑time integration for earlier, more accurate detection. Alert thresholds and maintenance workflows were restructured to shift teams from reactive repairs to proactive interventions. Reimagining the client’s existing data as a strategic asset demonstrated the power of AI to drive business value. Creating intelligence that worksTransforming large volumes of raw data into predictive‑ready assetsSelecting models based on measurable valueBuilding and tuning models using best practice ML and AI capabilitiesDesigning user experiences that help teams interpret predictionsImplementing MLOps for scale and sustainabilityWithin 12 months, Protiviti deployed five production‑grade AI models, starting with high‑value leak detection. The result: higher service efficiency, reduced downtime, stronger engineering feedback loops, and a scalable, data‑driven foundation—without additional proprietary infrastructure.First in the industryThe enhanced solution identified failures four to eight weeks in advance, reducing emergency repairs by 85 percent. Planned interventions extended equipment life by 45 percent and increased productive operating hours. The project transformed previously untapped data into a strategic advantage, proving the business value of AI even with messy, imperfect inputs . Being first in the industry to launch a digital product of this kind, the client turned predictive maintenance into a true market differentiator and a brand‑new revenue stream. 85% Reduction in emergency repair events 45%Improvement in equipment life 4-8 weeksAdvance failure detection Topics Business Performance Data, Analytics and Business Intelligence Digital Transformation Artificial Intelligence We recommend these resources Manufacturing and Distribution Protiviti partners with leaders to help them achieve greater confidence in these dynamic and ever-changing environments, through our manufacturing and distribution solutions. We aim to better understand the unique strengths, risks, and opportunities of your organization and its future goals. Artificial Intelligence Services Protiviti works with clients to design AI that’s innovative to drive optimal results, put transparent controls in place, and champion user adoption, ensuring that AI brings measurable value while maintaining the highest standards of safety and accountability.
