Blueprinting a Manufacturing Data Lakehouse: Harmonizing BOM, Routing, and Serialization Data for Advanced Analytics

作者：Ramesh Babu Potla · 发表于：International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences · 年份：2021 · DOI：10.37082/ijirmps.v9.i1.232841 · 被引用次数：1

The manufacturing firms are becoming fond of data-driven decision-making models to streamline production, decrease scrap, improve traceability, and promote predictive abilities throughout the manufacturing systems. Nevertheless, manufacturing data sources can be too complex and heterogeneous: they may include Bill of Materials (BOM), process routing, machine telemetry, shop-floor serialization logs, and quality inspection datasets, which presents advanced analytics with significant integration challenge. The type of traditional data warehouse structures is either too basic because of the strict schema on write aspects or data lakes do not provide the governance and performance attributes required in high-value analytical loads. As a way to overcome this, there is the data lakehouse paradigm, a hybrid architecture that combines the cost and scalability of data lakes with the control and ACID transactions, and schema policies of warehouses. The paper offers a detailed framework of how one would design and deploy a Manufacturing Data Lakehouse (MDL) to standardize the data of BOM, routing and serialization to facilitate scaled analytics. The work singles out architectural elements, information pipelines, metadata layers, governance, and analytical operations required to balance structured ERP data with semi-structured and machine generated data. The integrated data representation significantly enhances the manufacturing intelligence tools including analysis of the genealogy, pro...