bomer 0.1.1

Bomer – deterministic BoM analysis and optimization engine

Bomer

Bomer is a small, deterministic Bill of Materials (BoM) analysis and optimization engine.

It is designed for electronics and component teams who need repeatable, automation-friendly analysis on real BoMs – not just one-off LLM chat sessions.

Bomer is currently in an early CLI-focused phase (v0.1.x) and lives inside this repository (originally named BOMnet).

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Features

Current capabilities (v0.1):

• CLI-first workflow
  bomer analyze takes a BoM CSV and supplier JSON and produces structured outputs.

• Canonical BoM normalization
  Maps common column variations (e.g. mpn, Mfr Part #) into canonical fields like PartNumber, Manufacturer, Description, Quantity.

• Deterministic optimization
  Aggregates duplicate parts, sums quantities, and produces a clean optimized_bom.csv.

• Cost analysis
  Joins the BoM with supplier pricing data and computes:

  • per-line UnitPrice and LineCost
  • total_cost
  • list of parts with missing prices

• Basic risk scoring
  Simple risk model based on:

  • single-sourced parts
  • missing prices
  • lifecycle status (e.g. Obsolete / EOL)

• Structured outputs
  Generates machine- and human-readable artifacts:

  • normalized_bom.csv
  • optimized_bom.csv
  • analysis.json
  • issues.json
  • summary.txt

LLM-assisted normalization and alternative component suggestions are planned for later versions.

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Installation

Bomer is a Python package located inside this repo.

Prerequisites:

• Python 3.9+
• pip available
• Optional: a virtual environment (recommended)

git clone https://github.com/emreyesilyurt/BOMnet.git
cd BOMnet

# Optional: create a virtualenv
python3 -m venv .venv
source .venv/bin/activate  # on macOS / Linux
# .venv\Scripts\activate   # on Windows

# Install in editable (dev) mode
pip install -e .

If installation succeeds, you should have a bomer CLI on your PATH:

which bomer     # or: where bomer on Windows

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Quick Start

1. Project layout (relevant parts)

.
├── bomer.yaml             # Optional configuration (cost/risk weights, supplier path)
├── data/
│   ├── sample_bom.csv     # Example BoM
│   └── suppliers.json     # Example supplier price data
├── src/
│   └── bomer/
│       ├── cli.py         # CLI entrypoint: `bomer analyze`
│       ├── core/          # Config, loading, schema/validation
│       ├── engines/       # Cost, risk, optimization
│       └── reporting/     # Writers for CSV/JSON/text outputs
└── output/                # Created when you run analyses (ignored by git)

2. Basic usage

bomer analyze   --bom data/sample_bom.csv   --suppliers data/suppliers.json   --output-dir output

Arguments:

• --bom: path to a BoM CSV
• --suppliers: path to suppliers JSON
• --output-dir: directory for generated artifacts (default: ./output)

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Inputs

BoM CSV

Expected columns:

• PartNumber / MPN / Mfr Part #
• Quantity / Qty

Optional:

• Manufacturer
• Description
• LifecycleStatus
• RoHS

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Outputs

Generated artifacts:

• normalized_bom.csv
• optimized_bom.csv
• analysis.json
• issues.json
• summary.txt

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Configuration (bomer.yaml)

suppliers:
  path: data/suppliers.json

cost:
  currency: USD
  default_volume: 1000

risk:
  single_source_weight: 0.4
  missing_price_weight: 0.3
  lifecycle_weight: 0.3

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Contributing

1. Clone repo
2. Create virtual env
3. Install with pip install -e .
4. Run tests with pytest

Guidelines:

• Keep functions modular and testable
• Deterministic engines should stay deterministic
• Update documentation when adding new commands or outputs

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