Abstract: In the dynamic landscape of retail manufacturing, effective demand forecasting hinges on sophisticated data engineering practices that streamline and optimize data flows. This paper explores the intricate processes involved in developing an end-to-end data engineering solution tailored for demand forecasting within retail manufacturing ecosystems. By integrating heterogeneous data sources, constructing robust data pipelines, and implementing advanced analytics, businesses can transform raw data into actionable insights that drive strategic decision-making. Leveraging cutting-edge technologies such as cloud computing, machine learning algorithms, and real-time data processing, this approach addresses the inherently volatile nature of consumer demand.
The framework delineated in this work emphasizes scalability and flexibility, essential for adapting to the ever-evolving market conditions. Key components include data ingestion, cleansing, enrichment, storage, and the deployment of predictive models, each of which plays a pivotal role in refining data utility and forecasting accuracy. Particular attention is given to data governance and security, ensuring compliance with regulatory standards and fortifying data integrity. By adopting this comprehensive methodology, organizations can enhance their agility, mitigating risks associated with demand surges and supply chain disruptions.
This study contributes to the discourse on integrating technological advancements in data engineering with practical demand forecasting applications. It demonstrates how leveraging data-driven strategies can optimize inventory management, reduce waste, and improve customer satisfaction. As retail manufacturing ecosystems become increasingly complex, the insights presented provide a blueprint for harnessing the full potential of data engineering, fostering innovation and competitiveness in the industry.
Keywords: End-to-end data pipelines, Retail demand forecasting, Data engineering for manufacturing, Predictive analytics retail supply chain, ETL for demand forecasting, Time series forecasting pipelines, Big data in retail manufacturing, Data lakes for supply chain analytics, Machine learning demand prediction, Real-time data integration, Forecast accuracy optimization, Feature engineering for demand models, Scalable data infrastructure, Cloud-native data platforms, Demand sensing with AI.
|
DOI:
10.17148/IJARCCE.2020.91222
