Capacity Constraint Analysis Using Object Detection for Smart
  Manufacturing
Hafiz Mughees Ahmad Afshin Rahimi Khizer Hayat
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.