While AI in Manufacturing is perceived as a gamechanger globally, Plant Monitoring-specifically machine health & process efficiency with a potential of up to $50Million annual savings is a use case that manufacturers desire to optimize particularly with AI.

But while choosing an AI strategy for Plant Monitoring, a manufacturing leader faces two choices:

  1. Generic AI Platforms that enable a DIY approach with pre-built data connectors, transformations, machine learning models to build your own analytics
  2. A Purpose-built AI app/solution with in-built domain expertise & process understanding to solve a specific problem.

Both of these come with their own pros & cons. So how do you choose one over the other?

We can help you make the right choice by comparing & contrasting both of these with respect to relevant parameters like Value Proposition, Core Functionality, Availability, Costs, ROI, and Resource Requirements for plant monitoring through this whitepaper crafted by our team. of manufacturing domain experts.

Also learn when to use the one over another, leveraging our extensive experience from working with leading process manufacturers across oil & gas, petrochemicals, utility companies globally.

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Don’t take only our word for it

Companies using UptimeAI are saving millions with timely alerts & near-zero downtime

“We were monitoring limited equipment in isolation. UptimeAI’s approach enables us to look at the interrelations & scale the plant”– Oil & Gas, Executive Asia

“Building a full plant monitoring application on an IoT platform is too difficult. In contrast, ‘AI Plant Expert’ offers higher ROI & quick scale-up with an out-of-box, comprehensive solution.”– Power, CIO Asia