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P2EP AI Coal Planning

A machine learning and optimization platform for forecasting demand, modeling coal supply constraints, and recommending allocation plans across PLN EPI operations.

Problems we had to solve

  • Coal planning depended on analyst-maintained spreadsheets that were difficult to audit, version, and rerun under changing assumptions.
  • Planning decisions had to balance demand, coal quality, plant constraints, supplier commitments, delivery lead time, and cost.
  • Scenario comparison was slow because each revision required manual spreadsheet changes and repeated reconciliation.

What we implemented

  • Built a planning pipeline that reads governed warehouse tables, prepares planning features, and produces demand and supply recommendations.
  • Implemented optimization logic to evaluate allocation options against operational constraints and objective trade-offs.
  • Created scenario outputs that planners can compare, inspect, and rerun with updated assumptions instead of rebuilding spreadsheets.

Tools used

Stack selected for production data work, not a demo.

AWS
AWS ECS/Fargate
Terraform
Git
Python
scikit-learn
PyCaret
Stable-Baselines3 (PPO/DQN)
Snowflake
FastAPI
Svelte

Architecture

How the project is structured technically.

01

Feature preparation

Historical plant demand, supplier realization, coal quality, shipment lead time, and contract values are pulled from governed warehouse models.

02

Forecasting layer

Demand and operational input forecasts are generated as planning features, then written back for review and downstream optimization.

03

Optimization layer

A constrained solver evaluates allocation candidates and produces recommendations based on cost, availability, quality, and fulfillment requirements.

Work showcase

Sanitized project screens

AI orchestration overview

AI orchestration overview

Purpose-built design, engineering, and infrastructure architecture — crafted for clarity, scale, and production reliability.

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