The challenge manufacturers face

Manufacturing leaders are operating in an environment where operational complexity is growing faster than operational visibility. Production lines span multiple generations of equipment, each with its own data formats, tooling, and maintenance logic. At the same time, many critical assets were never designed with modern analytics in mind, yet they remain central to output, safety, and revenue.

The business impact is cumulative and quiet, until it isn’t.

Downtime costs are rising, but so is the cost of over-maintaining. To avoid production interruptions, teams often default to conservative maintenance schedules, excess spare-parts inventory, and reactive firefighting. This protects short-term output but erodes margins, ties up working capital, and distracts skilled engineers from higher-value work.

The core challenge is not technology adoption; it is decision confidence. Manufacturers need to know which signals matter, when to act, and what business outcome will follow. Without trusted, interpretable analytics that connect equipment behavior to operational and financial impact, predictive maintenance remains an experiment rather than a strategy.