OrderTime Automation
OrderTime Exclusion Feed Automation
Stateful CSV automation that generates stable exclusion feeds with part normalization, duplicate removal, SQLite change tracking, historical snapshots, rotating logs, dry-run support, and audit summaries.
Exclusion feed command view
OrderTime Exclusion Feed Automation
CSV intake, part normalization, de-dupe, SQLite state, snapshots, logs, and stable publishing
Read
CSV input
Normalize
Part numbers
De-dupe
Clean feed
Compare
SQLite state
Publish
Output
Automation controls
Feed processing preview
CSV / state| Part | Action | Dup | State | Output |
|---|---|---|---|---|
| 100-4412 | Normalize | No | Changed | Publish |
| 225-0198 | Normalize | Yes | Merged | De-dupe |
| 300-7710 | Review | No | Unchanged | Skip |
| 410-2250 | Normalize | No | Changed | Snapshot |
Run evidence
Business problem
Recurring exclusion files needed stable generation and change-aware processing. Without automation, the workflow depended on repeated CSV preparation, manual part cleanup, duplicate checks, and uncertainty around whether a file had actually changed.
The process needed a controlled feed generator that could normalize parts, remove duplicates, publish stable outputs, preserve snapshots, and skip unchanged runs when appropriate.
System built
Built a stateful CSV workflow with part-number normalization, duplicate removal, stable output publishing, historical snapshots, SQLite state tracking, rotating logs, dry-run behavior, and audit summaries.
The system turns a recurring exclusion file into a repeatable feed automation with state, evidence, and cleaner downstream delivery.
Feed automation signals
Signals reviewed
The workflow reviews source readiness, normalized records, duplicate status, state changes, output paths, and run evidence before publishing the exclusion feed.
Exclusion feed flow
How it works
Read
Load recurring exclusion source files and prepare the records for normalization and validation.
The workflow starts with a controlled read layer so recurring CSV inputs are handled consistently instead of manually cleaned each run.
Normalize
Standardize part numbers, trim messy values, and prepare the feed for duplicate checks.
Part normalization protects the output from formatting drift and makes exclusion records easier to compare over time.
De-dupe
Remove duplicate exclusion records and preserve a clean feed structure before publishing.
The de-dupe layer keeps the outbound file stable and reduces repeated records that can create downstream confusion.
Compare
Check current inputs against stored state to decide whether the file changed or can be skipped.
State-aware processing prevents unnecessary output churn and makes recurring automation easier to trust.
Publish
Write stable published outputs, historical snapshots, logs, and audit summaries for review.
The final layer creates the usable feed file and the evidence needed to understand what happened during the run.
Automation layers
What the feed system coordinates
Normalization layer
Cleans part numbers and source values so repeated exclusion feeds follow a stable format.
State layer
Uses SQLite state tracking to identify changed and unchanged files across recurring runs.
Snapshot layer
Preserves historical run outputs so prior feed versions can be reviewed or compared.
Audit layer
Writes logs, dry-run evidence, and summary outputs that make the automation easier to support.
Impact signals
What the automation improved
Stable published exclusion feed outputs
Historical run snapshots for review and rollback context
Unchanged-file skip logic to reduce unnecessary processing
Part-number normalization and duplicate removal
SQLite state, rotating logs, dry-run behavior, and audit summaries
Operational value
Recurring exclusion files turned into a controlled feed process
Less recurring file prep
Turns repetitive exclusion feed creation into a repeatable automation instead of a manual CSV routine.
Cleaner exclusion records
Normalizes and de-duplicates part records so the published feed is easier to trust.
Change-aware processing
State tracking helps skip unchanged inputs and makes recurring runs more efficient.
Better supportability
Snapshots, logs, and audit summaries make it easier to answer what changed, what published, and why.
Why this project matters
Feed reliability improves when recurring CSV work has state, snapshots, and audit evidence.
This project shows how a simple recurring CSV file can become a controlled operational feed. Normalization, de-duplication, state tracking, snapshots, logs, and audit summaries help the workflow move from manual file prep to reliable automation.
The value is not just creating an exclusion file. The value is knowing what changed, what published, what was skipped, and how the feed can be reviewed later.
Confidentiality note
Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, customer records, credentials, raw exports, internal pricing, operational screenshots, or proprietary source files.