Customer Success – Guiding technology adoption and value realization

This is an example of how our Customer Success team provides valuable support to all our product customers, facilitating technology adoption and value realization.

In this case, we had the opportunity to collaborate with a crushing plant reliability team, assisting them in adopting our products and systematizing the performance analysis of their equipment.

Initially, the team faced the challenge of measuring process availability, due to the complexity of the crushing process. With many inputs and outputs, it was hard for them to define a single critical line where process uptime could be measured. This made the availability definition a source of daily controversy with Operations.

To tackle this issue, the team implemented RMES Suite. With its digital models, RMES Suite captures process configurations and measures both equipment and system-level KPIs. This enabled the team to propose an availability metric well aligned to the process throughput and its bottlenecks, which was well received by the operations teams.

To ensure that improvements were sustained over time, our Customer Success team worked closely with the reliability team. They identified the key factors that needed to be addressed to ensure technology adoption. Among them, two factors stood out: firstly, the team needed to ensure data quality; and secondly, they needed to build additional capabilities to use the analysis tools effectively.

Our Customer Success team works as an expert advisor to our customers, tailoring specific plans to support different phases of product use, including technology adoption, value realization, new use cases, and long-term collaboration. At the core of this approach is our STAIR model <link to STAIR>, which concentrates our experience, raising industrial processes to their full potential.

The initiatives implemented by the reliability team included best practices in data coverage monitoring, targeted failure mode catalog optimization, product training, and discussing performance analysis reports. After a year, they reached full data classification coverage, optimizing data classification categories and routinary bottleneck analysis from online data.