Track: Statistical Process Control
Product quality is critical to manufacturing sectors because defective parts and production scraps affect business profitability and sustainability. In alignment with the Lean Six Sigma manufacturing approach, reducing process variability using statistical process control (SPC) is the primary strategy for controlling product quality. Motivated by the increased prevalence of mobile devices and their influences on the smart manufacturing environment in the industry 4.0 era, this study presents the development of the ProcMon app, a mobile-cloud application designed
for factory SPC monitoring. The system consists of mobile devices, internet connectivity, and cloud services. The ProcMon app adopts a three-layer client-server architecture comprising the presentation, business, and data layers. The factory SPC monitoring is realized via automated control chart plotting, process capability Indices, and Nelson rules. A short message service alert is sent if out-of-control processes are detected. The ProcMon app is developed using two open-source software platforms: MIT App Inventor 2 and Google Workspace. This research demonstrated that MIT App Inventor 2 and Google Workspace offer a low-cost and less complex development approach for mobile cloud applications in manufacturing, benefiting nonprofessional programmers and stakeholders with a minimal budget. After completing app development, a system test was conducted using simulated data from a machining part fabrication case study for the app functional validation, and the acceptance test conducted by a focus group concluded that the ProcMon app meets the requirements for a low-cost, flexible, available 24/7, efficient, and easy-to-use application for SPC.
Cloud services, smart manufacturing, statistical process control (SPC), mobile applications development, MIT App Inventor 2.