The many facets of PdM: Amway uses AI and ML models to streamline maintenance service cases
The medley of predictive maintenance (PdM) strategies for improving machine health is growing larger and more powerful, whether using classic portable tools for non-critical asset inspection rounds and on-site problem verification and troubleshooting, or advanced technologies such as the IIoT, cloud, and AI and ML algorithms.
Leaders and analysts who go on record by documenting improvements gained from predictive maintenance initiatives provide a window into the immense potential of today’s enabling technologies. This article is one of seven diverse case studies that illustrate some of the many PdM methods and applications employed today.
The other case studies include:
- Analyst foresees AI/ML driving widespread adoption of prescriptive maintenance
- Oil and gas supermajor uses AI predictive analytics
- Midstream energy company uses IIoT strategy with integrated CMMS
- Self-driving truck company uses CMMS, BI tooling, and mobile app
- Tire manufacturer uses 24/7 wireless vibration monitoring system
- Thermal battery manufacturer uses Generative AI-driven data operations platform
- Mining company uses industrial edge data platform and SCADA system
Challenge: Frequent HVAC failures at a critical Amway facility in India drove the need to create a single system of record to improve transparency into daily building operations, streamline maintenance service cases, and balance indoor air comfort against energy consumption.
Solution: Leveraging its long-term partnership with Honeywell, Amway chose to deploy Honeywell Forge Performance+ Predictive Maintenance to modernize its facility management capabilities with a data-based strategy. Close collaboration between the partners enabled the Amway team to identify underutilized and fragmented official data across their current systems, and aided Honeywell in quickly modeling analytical rules that fit Amway’s operational conditions.
Results: Performance+ Predictive Maintenance helped to solve data fragmentation with a dashboard presenting a single source of truth, and allowed Amway to realize a 15% increase in “excellent” rated comfort performance zones over a three-month period. AI and ML models can forecast trends, detect anomalies, and enable root cause analysis, while also learning and adapting to changes in the HVAC systems. The advanced algorithms and automated workflows allow users to quickly identify service cases and proactively address HVAC issues to reduce downtime and extend asset life.
