CASE STUDY

IIoT case study: ofi stabilizes quality and improves yield

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This case study showcases how a global leader in the food and beverage industry, olam food ingredients (ofi), uncovered process optimizations using IIoT.

Ofi wanted to stabilize and fine-tune one of their product lines in an effort to improve overall yield. The teams were manually collecting data and performing calculations, but they couldn’t get the level of detail they needed to significantly improve their production processes.

Ofi also wanted to give employees valuable analytics tools to help them be more effective at solving current (and future) challenges. 

As part of their digital transformation journey, ofi brought in Braincube to help them get a clearer picture of their manufacturing process. 

Using Braincube’s IIoT platform, Digital Twins, and Business Intelligence Applications, ofi’s teams quickly uncovered key findings that resulted in a wide array of wins ranging from a perfect quality score to improved customer satisfaction. 

In this IIoT case study, you’ll:

  • see ofi’s results for their leading KPIs, including quality and yield
  • discover the key technologies and tools that empowered ofi’s teams to optimize their production processes
  • learn more about ofi’s change management initiatives—a valuable aspect of their digital transformation strategy

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Three tips to improve reliability in food manufacturing

Measuring, tracking, and interpreting your reliability score doesn’t need to be time-consuming or complex.

Here are three simple ways that the food manufacturing industry can use IIoT to improve reliability. 

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Why product traceability matters for food manufacturing

Food manufacturing relies heavily on interdependencies between multiple systems and stakeholders. These complexities put pressure on your internal supply chains and the teams working at each site. 

Product traceability can help: here’s how.