Capacity Constraint Analysis Using Object Detection for Smart Manufacturing
This addresses capacity optimization for traditional industries post-COVID-19, but it is incremental as it applies existing object detection methods to a new domain-specific problem.
The study tackled capacity constraints in smart manufacturing by using a CNN-based object detection model to identify chairs and individuals on the production floor, tracking them to analyze productivity, and found that Station C was only 70.6% productive over 6 months.
The increasing popularity of Deep Learning (DL) based Object Detection (OD) methods and their real-world applications have opened new venues in smart manufacturing. Traditional industries struck by capacity constraints after Coronavirus Disease (COVID-19) require non-invasive methods for in-depth operations' analysis to optimize and increase their revenue. In this study, we have initially developed a Convolutional Neural Network (CNN) based OD model to tackle this issue. This model is trained to accurately identify the presence of chairs and individuals on the production floor. The identified objects are then passed to the CNN based tracker, which tracks them throughout their life cycle in the workstation. The extracted meta-data is further processed through a novel framework for the capacity constraint analysis. We identified that the Station C is only 70.6% productive through 6 months. Additionally, the time spent at each station is recorded and aggregated for each object. This data proves helpful in conducting annual audits and effectively managing labor and material over time.