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

Authors: Ramesh Babu Potla

DOI: https://doi.org/10.37082/IJIRMPS.v9.i1.232841

Short DOI: https://doi.org/hbds2t

Country: United States

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Abstract: 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, process capability, prediction of the cycle time, component traceability, and root-cause analysis of the quality. Our proposal includes five pillars (1) multidomain data ingestion pipelines in structured and unstructured manufacturing systems, (2) universal metadata modeling merging BOM hierarchy, routing schedules, and serialized product lineage, (3) layered lakehouse storage paradigm (raw data into bronze data into silver data into gold data), (4) streamlined semantic model based on surrogate keys and star schemas to support analytics and (5) governance and security models enabling tracking lineage, ACID transactions, and auditability. We use mapping formulas to align BOM and routing, and initiate a probabilistic method to evaluate genealogy completeness. The paper illustrates how this architecture addresses the traditional pain points involving data duplications, inconsistent tracking of the product lineages, ERP- MESE disconnects, and the absence of standard identifiers in the legacy shop-floor systems. Experiments conducted using simulated datasets that modeled a discrete manufacturing scenario confirmed performance enhancement in query performance, schema consistency, and maximum depth of traceability. It has been observed that reduction of data redundancy by up to 54 percent, reduction of time to search the genealogy by up to 38 percent, and an increase in the accuracy of the component-level traceability queries by 62 percent have been achieved. The blueprint is expected to assist the manufacturing engineers, digital transformation architects, and analytics teams with reference base on the design of scalable, interoperable analytical ecosystems. In general, MDL approach offers a solid future-proof approach to Industry 4.0 applications and intelligent analytics, including machine learning, predictive maintenance, and optimization in real-time.

Keywords: Manufacturing Data Lakehouse, Bill of Materials, Routing Data, Serialization, Digital Thread, Advanced Analytics, Industry 4.0, Data Engineering, Traceability, Data Modeling.

Paper Id: 232841

Published On: 2021-01-07

Published In: Volume 9, Issue 1, January-February 2021

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