Synthetic Aviation Spare Parts Demand, Inventory, and Stockout Dataset for Supply Chain Analytics and Digital Twin Research (2018–2026)

This dataset is a synthetic aviation supply chain dataset developed to support research in demand forecasting, inventory optimization, predictive maintenance, digital twin development, and aviation operations management. The dataset contains 12,960 records representing monthly spare-parts demand and inventory activities across multiple maintenance locations between 2018 and 2026. Variables include spare part identifiers, part classifications, ATA chapters, aircraft types, demand quantities, inventory levels, stockout indicators, flight cycles, and supplier lead times. The data were generated using realistic operational assumptions and industry-inspired patterns to simulate the behavior of aviation maintenance, repair, and overhaul (MRO) supply chains. As a fully synthetic dataset, it does not contain any real airline, airport, or proprietary operational data, ensuring confidentiality while preserving the statistical characteristics necessary for analytical and methodological research. The dataset is suitable for applications in machine learning, time-series forecasting, supply chain resilience analysis, inventory management, and digital twin research within the aviation sector. Researchers, students, and practitioners may use this dataset to develop, validate, and compare analytical models for aviation operations and supply chain decision-making.