Key Insights: Analyzed production plant data to identify bottlenecks and inefficiencies, with recommendations for predictive models to address issues that can cause 20–30% annual revenue loss.

About the Client

A major energy company with global operations, focused on optimizing manufacturing processes to improve efficiency, reduce costs, and ensure timely delivery.

The Challenge

Like many manufacturing-based businesses, the client needed to improve production and scheduling to:

• Reduce labor, inventory, and operating costs.
  
  
• Optimize equipment utilization and increase capacity.
  
  
• Improve on-time product delivery.
  

Bottlenecks in the production process were leading to inefficiencies and potential revenue loss, and the client wanted greater visibility into these issues.

Marvik’s Approach

Using production plant data, we:

• Identified bottlenecks and inefficiencies in planning and operations.
  
  
• Performed data cleaning and feature engineering to prepare the dataset, which included ~1,500 examples and 44 planning/manufacturing tracking variables.
  
  
• Conducted preliminary experiments to explore patterns and opportunities.
  
  
• Proposed recommendations for future predictive models to:
  
  
  
  • Improve forecasting of production yield and inventory based on historical trends.
    
    
  • Optimize allocation of equipment and operator resources.
    
    

The solution was built with Python, Jupyter, Scikit-learn, TensorFlow, Keras, and PyTorch.

The Results & Impact

• Delivered actionable insights on process inefficiencies.
  
  
• Outlined a clear roadmap for implementing predictive models to reduce bottlenecks and improve resource planning.
  
  
• Addressed a challenge that, according to IDC, causes 20–30% annual revenue loss for most companies.
  

Why This Matters

By leveraging plant data and laying the groundwork for predictive modeling, the client can move from reactive problem-solving to proactive efficiency, reducing costs, increasing capacity, and protecting revenue.