Capacity Constraint Analysis Using Object Detection for Smart Manufacturing

TL;DR

This paper presents a CNN-based object detection and tracking system for smart manufacturing, enabling capacity constraint analysis by monitoring object presence and activity over time to optimize operations.

Contribution

It introduces a novel framework combining object detection, tracking, and data analysis for capacity constraint assessment in manufacturing environments.

Findings

Station C is only 70.6% productive over 6 months

Time spent at each station is recorded and aggregated

Data supports annual audits and resource management

Abstract

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.