CCC / PartsTrader
PartsTrader Quote Flattening & Customer Parts Automation
Automation system that transforms messy PartsTrader item and summary exports into clean master files, customer-specific reports, manufacturer splits, and downstream-ready reporting artifacts.
Quote flattening command view
PartsTrader Automation
Pair exports, repair columns, enrich quote context, split outputs, and publish files
Pair
Item + summary
Flatten
Rows
Repair
CSV columns
Enrich
Metadata
Deliver
Files
Automation controls
Flattened output preview
CSV / XLSX| Quote | Customer | Make | Lines | Output |
|---|---|---|---|---|
| PT-10492 | Shop A | Toyota | 18 | Master |
| PT-10518 | Shop B | Ford | 11 | Customer |
| PT-10544 | Shop C | Honda | 24 | MFG Split |
| PT-10591 | Shop D | Nissan | 9 | XLSX |
Output delivery
Business problem
PartsTrader quote exports required repetitive cleanup, file pairing, shifted-column repair, and customer-specific formatting before they could be used for reporting or follow-up.
The process needed an automation layer that could connect related exports, normalize inconsistent file shapes, extract useful quote context, and produce clean outputs without rebuilding the same reporting files manually.
System built
Built a Python and Pandas automation pipeline that pairs item and summary files, repairs shifted CSV columns, extracts quote and customer metadata, normalizes records, and generates master, customer-specific, and manufacturer-level reporting outputs.
The system turns raw PartsTrader exports into a controlled reporting workflow with cleaner files, richer context, and better downstream usability.
File and quote signals
Signals reviewed
The automation evaluates file structure, quote context, customer details, vehicle metadata, and output readiness before producing reporting files.
Flattening flow
How it works
Pair
Match messy PartsTrader item exports with related summary files so quote records can be processed together.
The pipeline starts by connecting separate source files that belong to the same reporting workflow.
Flatten
Normalize item-level and summary-level records into a clean, row-based reporting structure.
This converts scattered export formats into a usable master dataset that is easier to filter, analyze, and share.
Repair
Detect shifted CSV columns, inconsistent headers, and field alignment problems before outputs are created.
The cleanup layer protects the final files from common export issues that would otherwise require manual correction.
Enrich
Attach quote metadata such as customer details, VIN, make, model, body, delivery information, and manufacturer context.
The enrichment stage turns raw quote lines into records that make sense for customer review and operational follow-up.
Deliver
Produce master CSV/XLSX files, customer-specific outputs, manufacturer splits, and reporting-ready artifacts.
The final delivery layer gives teams clean files that can be reviewed, distributed, loaded, or used in downstream reporting.
Automation layers
What the pipeline coordinates
File pairing
Connects item files and summary files so records from the same PartsTrader workflow can be processed together.
Flattening logic
Turns quote rows, customer context, vehicle metadata, and line details into a consistent reporting structure.
Column repair
Handles shifted CSV columns, inconsistent export shapes, and header normalization before downstream use.
Output splitter
Creates master reporting files, customer-specific files, and manufacturer-level split outputs.
Impact signals
What the automation improved
Master CSV/XLSX generation from messy PartsTrader exports
Customer-specific reporting files for review and distribution
Manufacturer-level splits for sourcing and operational analysis
Quote metadata enrichment using VIN, make, model, body, and customer context
Cleaner pipeline for downstream inventory matching and reporting
Operational value
Messy exports turned into controlled reporting files
Less manual cleanup
Reduces repetitive file preparation, copy/paste cleanup, shifted-column fixes, and customer-specific formatting work.
Cleaner reporting structure
Turns messy quote exports into a normalized master dataset that is easier to filter, review, and compare.
Better customer follow-up
Customer-specific outputs make it easier to package the right quote and parts information for review.
Downstream-ready files
Creates outputs that can support inventory matching, manufacturer analysis, and later reporting pipelines.
Why this project matters
A messy export process converted into a repeatable reporting workflow.
This project shows how file automation can turn inconsistent vendor or portal exports into clean reporting assets. By pairing files, repairing CSV issues, enriching quote context, and splitting outputs, the system reduces manual prep and improves reporting readiness.
The value is not just file cleanup. The value is creating a repeatable bridge from raw quote exports to customer, manufacturer, and master reporting outputs.
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.