Honeywell launched Forge Plant Cognition on Oct. 26 with Dangote Industries, pairing NVIDIA computing with Dell Technologies infrastructure to give plant teams predictive analytics — early warning on downtime, energy use and production problems before they stop a line, using data from the systems already running on the floor.
The timing matches where the industry already is. A 2026 report from Augury and IndustryWeek found 57 per cent of manufacturers now use predictive maintenance in some form, even as many say they lack the internal resources to run it well. Prediction has gone mainstream; the people to run it have not.
Which is why the order of operations matters. A prediction is only as good as the signal underneath it: sensors installed where failures actually start, machines connected into one place, and readings clean enough to trust. Skip that layer and the dashboard is confident, polished and wrong.
The algorithm is the last ten per cent of predictive maintenance. The first ninety is sensors, connections and data somebody trusts.
That first ninety per cent is integration work — the discipline of a systems integrator: fitting sensing and monitoring onto equipment of different ages and vendors, and making the readings land where maintenance teams actually work. It rarely announces itself, and it decides whether the announcement works.
If your plant is pricing prediction platforms, audit the signal first: list the machines whose failure hurts most, check what each one reports today, and close the sensing gaps before signing for software that assumes they are closed.