Customer Story

Industrial Biomass Energy Producer

Learn how this biofuel company used dataPARC to reduce production costs by enabling data-driven decision-making.

Company’s Story

This company was formed in May 2004 when investors came together to build a 50 million gallon per year dry mill ethanol plant. To keep up with the growing industry and generate additional revenue for the company, they’ve expanded to provide other co-products including dried distillers grains, carbon dioxide, and corn oil. They strive to be an efficient and low-cost producer of ethanol to be a long-term player for the future of clean, renewable, and domestically produced fuels.

dataPARC’s real-time analytics eliminated dependency on static Excel documents.

Shared Data

All individual sites can now access and view process data from each other site all at once.

Root Cause Analysis

The company can now perform superior root cause analysis, tracking data to the event.

Real-Time Data Access

Operators, engineers, & managers now access process data in seconds instead of hours or days.

Company’s Challenges

The company struggled with an inefficient Excel-based data analysis process that made process optimization difficult. Data silos meant plant information was accessible only to a few power users, and the team had difficulty discovering the root cause of process deviations and how to prevent them.

The dataPARC Solution

With dataPARC, this company was able to integrate all of their data sources into a centralized dashboard. This provided a comprehensive view of the entire plant at a glance, and democratized access which empowed more team members to harness the data for informed decision-making. Advanced diagnostic tools and analytics were implemented within the system, enabling prompt identification of process deviations’ root causes and providing actionable insights to rectify and prevent future discrepancies, enhancing overall process reliability. Additionally, by eliminating dependency on Excel, users gained access to real-time analysis and swift trend identification, leading to rapid process optimization.