Capacity analysis is critical for manufacturing and industrial operations, yet many teams struggle with fragmented approaches. This article presents a comprehensive methodology using DolphinDB, a high-performance time-series database, to streamline the entire process from capacity planning to bottleneck identification and optimization. The framework begins with defining capacity metrics and collecting relevant production data, then moves through simulation and what-if analysis to identify constraints. Key techniques include using historical data to model future capacity needs and applying statistical methods to pinpoint bottlenecks. The article emphasizes the importance of continuous monitoring and iterative improvement. For data engineers and analytics professionals, this methodology offers a structured way to deliver actionable insights that directly impact operational efficiency. The principles are adaptable to various industries, making it a valuable reference for teams building analytics solutions.
A complete methodology for capacity planning and bottleneck optimization using DolphinDB, applicable to industrial analytics.