Self-Service Network Optimization at Scale: Data Engineering Framework for Democratizing Supply Chain Planning
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P124Keywords:
Data Engineering, Self Service Platform, Democratization, Simulations, Network Design Evaluation, What-If Analysis, Supply Chain Analytics, Network Optimization, Real-Time Processing, Artificial Intelligence, Big Data, Automated Systems, Distributed Computing, Supply Chain Optimization, Data Pipeline Architecture, Etl, Scalable Systems, Data Infrastructure, Block ChainAbstract
Running a modern supply chain means dealing with billions of transactions spread across fulfillment systems that never stop moving. This paper lays out a practical framework for building real-time data engineering solutions that let organizations evaluate and optimize their network designs at serious scale. The core problem is straightforward but hard: how do you crunch large datasets fast enough to actually be useful, while also making the results accessible to people who aren't data scientists? Equally important is the question of democratization. Sophisticated optimization models have traditionally lived inside small specialist teams, but the real value comes when business users can run these models themselves without needing deep technical expertise. Making that happen through a self-service platform is as much a design challenge as a technical one, requiring careful attention to usability, guided workflows, and computational resource management. The approach described here tackles unified change evaluation, automation that hits 90%+ efficiency, self-service accessibility for non-technical users, and integration patterns for multi-agent decision support. The framework is applicable across manufacturing, retail, healthcare, and logistics, achieving 75% faster processing, 65% better end-to-end efficiency, and 35% gains in resource utilization.
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