• Controlling the A/C ratio in the digester is critical to the alumina refining process and a major production driver
• However, the client was relying on an outdated predictive model, resulting in high variability throughout the process

The team used SparkBeyond to improve model accuracy through:

• Analyzed internal sensor data from the existing DBO model
• An iterative process to identify optimal time windows and interactions between sensor readings
• Built several models, from linear regression to advanced boosted trees, to maximize impact

• SparkBeyond identified several ways to improve consistency in the A/C ratio, with one improvement alone potentially driving over $3 million in added production value
• The client implemented changes valued at $1.8 million annually

• Identify and reduce production inefficiencies.
• Improve productivity in high-precision semiconductor environment.
• Address recurring issues with long idle times and CPU overload.
• Predict equipment failures to prevent downtime.

• Analyzed 13 months of I/O data and 20+ parameters.
• Used RCPS to identify 1,000+ statistical patterns and top 15 root causes.
• Applied predictive maintenance using vibration sensor data.
• Built failure prediction models with 90% event coverage.

• 40% reduction in yield detractors in target workcenter.
• 5 root causes identified with clear mitigation actions.
• Predicted failures 4+ weeks in advance with ~10% false positives.
• Boosted maintenance capacity and line stability.

• A leading global beverage manufacturer wanted to improve Overall Equipment Effectiveness (OEE) for its filling and packaging lines
• Despite having Lean, Six Sigma, and excellence programs in place, they sought to use data to better understand root causes of downtime and to foresee breakdowns proactively

• Collected performance data on OEE shifts, outages, process orders, and machine status
• Identified “built-back” events as the biggest negative driver of OEE
• Applied predictive analytics to identify root causes and recommended targeted actions

• Sparkbeyond autonomously pinpointed key root causes: volume output spikes, older product batches and failures at upstream machines
• Estimated 13% potential OEE improvement from addressing these causes

• Client needed to anticipate and manage ESP (Electrical Submersible Pump) failures to minimize production loss
• Built a model to predict the probability of ESP failure within the next 100 days‍
• Data Sets Used: Past ESP failures, sensor readings, well trajectories, coordinates, completions

• Reframed task as remaining uptime prediction due to dataset imbalance
• Identified if an ESP is likely to fail within the next 100 days
• Used Discovery Platform with 8 datasets to uncover failure drivers
• Delivered insights as both code and natural language
• Provided daily SHAP-based predictions and explanations
• Outputs shared with maintenance teams to support preventive action

• $2M impact per early failure alert
• Enabled proactive maintenance, minimizing downtime and production loss

Automatically identify the root causes of non-conformance amongst a sample of annealed metal parts, augmenting engineering intuition of operators

• Combined visual inspection data with furnace sensor in historian, power consumption, nitrogen, oxygen levels and environmental factors (room temp, humidity)
• Generated hypothesis and identified root causes

Identified nitrogen pressure and oxygen content levels during heating and cooling respectively, which when fixed, help reduce non-conformance rates from 29% to 4%