Natural Gas Scheduling System (GSS) Application

Redesigned a critical revenue-generating application for DCP Midstream, a Fortune 500 natural gas processing company. 

The Gas Scheduling System (GSS) serves Gas Optimizers and Marketing Traders who schedule and sell natural gas daily.

The project transformed an outdated, manual system into a standardized, automated platform that reduced errors and improved accuracy in gas trading operations  

through user research, workflow mapping, and interface redesigns.

• Manual data entry:  Reduced using RPA bots and auto-filled fields

• Time on task: 30% faster for Optimizers

• Error rate:  Reduced error rates across operations

• Accuracy improvement: More reliable "Available for Sale" calculations

• Business impact: More gas trades, boosting profitability

• Launch: On-time and successful

Gas Optimizers and Marketing Traders relied on outdated software requiring extensive manual data entry, manual calculations, and reliance on subjective industry knowledge - creating a high error risk.

Project obstacles included: slow progress, inconsistent design from previous iterations, the departure of the Product Owner (and loss of critical contextual knowledge), and communication gaps between designers and developers with deep but siloed domain expertise.

Role: Senior User Experience Product Designer - Energy Lab

Collaboration:

• Partnered with Optimizers and Traders through listening sessions and usability tests
   
• Consulted stakeholders and SMEs for insights
   
• Collaborated with development and UX teams using Agile and Lean UX
   
• Coordinated onsite with plant managers and technicians

• Mapped business logic and task flows to identify issues and improvement opportunities

• Audited content and UI patterns for consistency with design standards

• Designed wireframes and high-fidelity interactive prototypes

• Conducted iterative usability testing

• Applied Agile Scrum and Lean UX for continuous improvement

The new GSS application launched on schedule with comprehensive training and documentation. 

RPA bots eliminated manual research from third-party plant websites, while auto-populated fields and auto-calculated imbalance volumes streamlined operations. 

Both Optimizers and Marketing Traders gained increased accuracy and efficiency, producing reliable "Available for Sale" numbers that drove more successful gas trades and positively impacted company revenue.