With a 20-year partnership under our belt, when our client pledged to go carbon neutral by 2050, we saw a valuable opportunity to use our operational knowledge to help achieve this vision.
The company's extensive use of diverse industrial automation equipment across 60 global factories for high-end plastics production contributed to its carbon footprint. Without regular maintenance of machines, operational efficiency took a hit. Plus, it led to an increase in energy consumption and emissions. The result? A less-than-perfect product quality.
The client wanted to redesign monitoring equipment maintenance, minimize machine downtime, and quickly test products on the factory floor to maintain product quality. But this was a complex task. To ensure good quality, the machines in the production environment needed 5,000–6,000 predefined operational parameters. These machines generated 100,000 data points every few seconds – data that could help monitor the production process better and support engineers in maintaining quality benchmarks, getting better process visibility, and proactively supporting incidents (or issues). Instead, its team got pulled into fixing incidents rather than understanding these data points and focusing on quality.