One-Line Idea#

Turn fragmented engineering work-order revenue, labor, billing, and cost records into a single explainable profit-and-loss monitoring layer—without using confidential production data in the public repository.

Why This Project Exists#

Engineering delivery teams often manage work orders through multiple operational and financial signals. Revenue may be represented through work units or sales-order lines. Labor effort may be recorded through activity or daily reports. Billing may arrive later through invoice or progress-billing events. Travel, permit, subcontractor, and exception costs may sit in separate sources.

This creates a visibility gap. A team may know that a work order is active, but not immediately know:

• how much revenue is currently forecast;
• how much delivery-center and field-operations labor has accumulated;
• whether billing is lagging behind operational progress;
• whether travel, rework, permitting, or other costs are consuming margin;
• which work orders are below target margin;
• which projects should be reviewed before they become losses.

This project organizes those signals into a synthetic, reproducible analytics platform that can be demonstrated publicly and later mapped to governed enterprise data sources.

The goal is not to create an audited accounting system. The goal is to create a practical operational analytics layer that helps engineering, operations, finance, and delivery leaders understand work-order profitability earlier in the lifecycle.

Project at a Glance#

What Makes the Platform Different#

The project treats work-order profit and loss as both a financial analytics problem and a delivery lifecycle problem.

Important: The repository is intentionally generic. It does not contain confidential customer, employee, operational, financial, or production data. All records, identifiers, names, rates, dates, and values are synthetic.

Reader Walkthrough#

A reader can understand the platform in nine steps:

1. Synthetic work-order master records define account, program, region, job category, status, intake date, and margin target.
2. Work-unit revenue lines provide estimated revenue using quantity and unit-rate logic.
3. Activity/labor records capture milestone, activity type, delivery organization, date, and hours.
4. Billing events represent invoice or progress-billing activity.
5. Other-cost records capture travel, permit, subcontractor, or miscellaneous expenses.
6. Labor-rate assumptions convert hours into delivery-center and field-operations labor cost.
7. The metric engine produces revenue, cost, margin, billed amount, variance, travel, rework, and risk reasons.
8. The Streamlit dashboard exposes portfolio KPIs, drill-downs, lifecycle charts, rate scenarios, and quality checks.
9. Snowflake SQL scripts show how the same logical model can be implemented over governed warehouse tables or views.

Platform Architecture#

The platform is organized around two complementary implementation paths.

Data Model#

The project uses a compact star-style analytical model.

The central analytical output is a one-row-per-work-order summary used by the dashboard.

Read the Data Model Wiki page

Synthetic Dataset#

The bundled dataset is deterministic and can be regenerated with the default seed.

The dataset spans January 2025 through May 2026 and is designed only for demonstration. It should not be interpreted as an industry benchmark.

Read the Synthetic Data Wiki page

Financial Calculation Engine#

The core calculations are intentionally transparent.

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Estimated Revenue = Σ(Work Unit Quantity × Item Rate)
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Labor Cost = Σ(Activity Hours × Applicable Hourly Rate)
3
Actual Cost = Labor Cost + Other Cost
4
Forecast Revenue = MAX(Estimated Revenue, Billed Revenue)
5
Forecast Margin = Forecast Revenue − Actual Cost
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Forecast Margin % = Forecast Margin ÷ Forecast Revenue
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Margin Variance % = Forecast Margin % − Target Margin %

Risk bands are rule-based and interpretable.

The platform exposes risk reasons instead of hiding them inside an opaque score. This makes the dashboard easier to review with operations and finance stakeholders.

Read the Financial Calculations Wiki page

Dashboard Experience#

The Streamlit dashboard is organized around practical questions.

Snowflake-Ready Integration#

The implementation includes Snowflake-oriented SQL inside the application folder.

A production implementation should not upload confidential data into a public repository. Instead, it should map approved enterprise source fields into curated warehouse tables or secure views and connect the dashboard to those governed objects.

Read the Snowflake Integration Wiki page

Technology Stack#

Repository Structure#

1
Engineering-Work-Order-Profit-and-Loss-Analytics/
2
├── assets/
3
│   └── diagrams/
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│       └── workorder_profit_loss_architecture.svg
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├── insite_pl_mvp/
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│   ├── data/
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│   │   ├── dim_labor_rate.csv
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│   │   ├── dim_milestone.csv
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│   │   ├── dim_work_order.csv
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│   │   ├── fact_billing.csv
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│   │   ├── fact_dwr_labor.csv
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│   │   ├── fact_other_cost.csv
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│   │   └── fact_work_unit_revenue.csv
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│   ├── scripts/
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│   │   └── generate_synthetic_data.py
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│   ├── sql/
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│   │   ├── mvp_views.sql
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│   │   └── snowflake_schema.sql
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│   ├── src/
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│   │   ├── __init__.py
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│   │   ├── data_loader.py
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│   │   └── metrics.py
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│   ├── tests/
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│   │   └── test_metrics.py
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│   ├── DATA_DICTIONARY.md
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│   ├── README.md
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│   ├── app.py
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│   ├── requirements.txt
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│   ├── setup_and_run.bat
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│   └── setup_and_run.ps1
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├── A-Z_FILE_GUIDE.md
32
├── ARCHITECTURE.md
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├── CHANGELOG.md
34
├── CODE_OF_CONDUCT.md
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├── CONTRIBUTING.md
36
├── DATA_DICTIONARY.md
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├── GITHUB_REPOSITORY_SETUP.md
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├── PROJECT_STRUCTURE.md
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├── README.md
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├── ROADMAP.md
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├── SECURITY.md
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├── configuration.md
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├── data-model.md
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├── deployment.md
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├── developer-guide.md
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├── faq.md
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├── financial-calculations.md
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├── installation.md
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├── security-privacy.md
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├── snowflake-integration.md
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├── synthetic-data.md
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├── testing.md
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├── troubleshooting.md
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└── user-guide.md

Read the Project Structure guide

Local Run#

Clone the repository and enter the application folder:

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git clone https://github.com/dranubhaparashar/Engineering-Work-Order-Profit-and-Loss-Analytics.git
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cd Engineering-Work-Order-Profit-and-Loss-Analytics\insite_pl_mvp

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.\setup_and_run.ps1

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setup_and_run.bat

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py -m venv .venv
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.\.venv\Scripts\Activate.ps1
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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streamlit run app.py

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git clone https://github.com/dranubhaparashar/Engineering-Work-Order-Profit-and-Loss-Analytics.git
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cd Engineering-Work-Order-Profit-and-Loss-Analytics/insite_pl_mvp
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python3 -m venv .venv
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source .venv/bin/activate
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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streamlit run app.py

1
http://localhost:8501

Current Implementation Status#

Known Limitations#

The project is designed as a public, synthetic demonstration. Important limitations include:

1. The financial logic is not an accounting standard and should not be treated as audited finance logic.
2. Revenue recognition, overhead allocation, tax, cost capitalization, and labor-burden rules vary by organization.
3. The risk model is rules-based rather than predictive or statistically validated.
4. CSV loading is appropriate for an MVP; production usage should rely on governed warehouse views.
5. The bundled dataset is synthetic and should not be interpreted as an industry performance benchmark.
6. Authentication, authorization, row-level security, audit logging, and secrets management must be added for real deployments.
7. Any mapping to enterprise data must be approved by the data owner and reviewed for privacy, pricing, contract, employee, and customer sensitivity.

These limitations are explicitly documented so the portfolio post does not overclaim the maturity or authority of the platform.

Roadmap#

Security, Privacy, and Claim Boundary#

Work-order financial analytics can involve sensitive information, including customer names, employee identifiers, labor rates, contract terms, billing values, site locations, operational notes, and internal margin targets.

The public repository therefore follows a strict boundary:

• no confidential company data;
• no customer or employee records;
• no production work-order numbers;
• no real billing or pricing extracts;
• no proprietary internal source files;
• no credentials or secrets;
• no claim of audited financial correctness.

Before connecting the project to real systems, teams should obtain written approval, use role-based access control, mask sensitive fields, keep secrets outside source control, validate all formulas, and review repository history before publication.

Read Security and Privacy

Testing and Validation Strategy#

The repository includes automated tests for data quality and financial logic. A stronger production test plan should add:

• source-to-target reconciliation tests;
• revenue aggregation tests;
• labor-rate effective-date tests;
• billing status tests;
• margin-threshold boundary tests;
• duplicate and orphan record tests;
• large-data performance tests;
• dashboard regression tests;
• Snowflake view validation;
• security and access-control tests;
• finance stakeholder sign-off.

Read Testing and Quality

Complete Wiki Documentation#

Practical Use Cases#

Key Strengths#

• Uses fully synthetic public data.
• Demonstrates a complete work-order analytics workflow.
• Combines revenue, labor, billing, other costs, and risk into one model.
• Preserves drill-down detail while also producing portfolio KPIs.
• Provides clear formulas instead of opaque financial logic.
• Separates delivery-center and field-operations labor assumptions.
• Includes travel and rework risk indicators.
• Includes data-quality checks inside the application.
• Provides Snowflake-ready schema and analytical views.
• Includes README, architecture documentation, data dictionary, implementation guides, and GitHub Wiki pages.
• Avoids company-specific, customer-specific, or source-system-specific public claims.

Key Innovation#

The strongest contribution is the translation of scattered work-order operational and financial events into a transparent, explainable, and privacy-safe profit-and-loss monitoring layer.

The project shows how a team can move from late-stage financial visibility to lifecycle-based margin awareness. Instead of waiting until final billing, stakeholders can review estimated revenue, accumulated labor, non-labor costs, billing progress, target-margin variance, and risk reasons while the work order is still active.

Project Links#

• GitHub Repository
• GitHub Wiki
• README
• Application Folder
• Architecture
• Data Dictionary
• Financial Calculations
• Snowflake Integration
• Streamlit App
• Metric Engine
• Synthetic Data Generator
• Snowflake Schema
• Analytical Views
• Tests
• Testing and Quality
• Security and Privacy
• Roadmap

Conclusion#

Engineering Work Order Profit and Loss Analytics demonstrates how synthetic data, transparent calculations, and a lightweight dashboard can turn fragmented delivery and financial events into actionable work-order intelligence.

The platform estimates revenue from work-unit lines, prices labor through configurable rate assumptions, adds operational costs, monitors billing events, calculates forecast margin, and surfaces risk reasons that stakeholders can understand. Its value is strongest as a reproducible public MVP and reference architecture for teams that want to build safer, governed work-order financial analytics without exposing confidential data.

The next step for a real organization would be controlled source mapping, finance-approved formulas, warehouse-level governance, access control, audit logging, and validation against approved historical records.

Final Thought#

Estimate early. Track continuously. Explain margin risk. Protect sensitive data.

Work-order revenue · labor cost · billing visibility · profit and loss · margin risk · synthetic data · Streamlit · Snowflake-ready analytics · complete GitHub Wiki