Automated Nominations Processing: A Midstream Case Study in 80% Manual Effort Reduction

This case study documents how a mid-size midstream operator processing 600+ daily nominations across 12 pipeline systems automated their nominations workflow, reducing manual processing effort by 80% and virtually eliminating nomination errors.

The Starting Point: Manual Chaos

Before automation, the operator's scheduling team of 8 people spent 70% of their time on manual data entry: downloading nomination files from pipeline EDI systems, parsing and validating against contracts, entering into the ETRM system, and verifying confirmations. The remaining 30% — the actual value-added work of exception management and optimization — was perpetually squeezed.

Errors averaged 3-5 per day, each requiring 30-60 minutes to investigate and correct. During peak periods, the team was routinely working 12-hour shifts just to meet NAESB cycle deadlines.

Solution Architecture

The automation solution comprised four components: an EDI ingestion layer that automatically downloads and parses nomination files from all pipeline systems, a validation engine that checks nominations against contracts, balances, and business rules, an ETRM integration layer that creates and confirms deals in RightAngle, and an exception management dashboard that surfaces only the nominations requiring human attention.

The architecture was designed to be pipeline-system agnostic, with configuration-driven parsers that could be adapted to each pipeline's format without code changes.

Implementation: Phased and Cautious

The team implemented in three phases: Phase 1 covered the four highest-volume pipeline systems (60% of nominations), running in parallel with manual processing for 30 days. Phase 2 added five more systems and enabled auto-submission for validated nominations. Phase 3 added the remaining systems and ML-based anomaly detection.

The parallel-run period was critical for building team confidence. Schedulers could see the automation's work and verify it against their manual results, building trust incrementally.

"After 90 days of full automation, the team processed 3x the nomination volume with 4 fewer full-time schedulers — who were redeployed to strategic optimization roles."

Results and Lessons Learned

Key results after 12 months: 80% reduction in manual processing time, 99.7% nomination accuracy (up from 97%), 100% on-time cycle completion (previously 92%), and $1.8M annual cost savings. The scheduling team now spends 70% of their time on strategic exception management and optimization.

Key lessons: start with high-volume, low-complexity pipelines first; invest heavily in the exception management interface; build comprehensive audit trails from day one; and maintain a parallel-run period long enough to build genuine trust.