The purpose of this study is to introduce a framework aimed at helping manufacturing companies in developing countries start their digital transformation journey, focusing on small and medium manufacturers.
This study proposes a CAP-Do cycle framework to facilitate digital transformation at the shop floor level. This study emphasises effective data collection using affordable Internet of Things tools and sensor statuses to differentiate between machine and human losses in semi-automated production lines. This paper also compares unit- and time-based overall equipment effectiveness indicators for measurement and offers a step-by-step analysis guide.
Sensor data’s active and inactive statuses help identify whether losses stem from machine or human issues, crucial for semi-automated production lines in developing countries. Accurate loss data guide improvements in machinery and operator activities. Additionally, time-based calculations of performance and quality rates provide a detailed loss breakdown, unlike unit-based methods that overlook process time variations.
The framework’s implementation is tested on a prototype testbed, suggesting its real-world application may require adjustments to address the diverse factors of loss encountered in actual production environments.
This study outlines methods for data collection and detailed analysis using external sensors to classify machine and human issues. This study advocates for applying time-based overall equipment effectiveness calculations within the CAP-Do cycle for continuous improvement.
