Managing quality control systems in intelligence production and manufacturing in contemporary time

Abstract

In contemporary time, the production arena has become an interesting scene with introduction of innovations that is changing the climate of production technology. The innovation comes in the form of smart technique of production for an enhanced productivity. Which is termed intelligence manufacturing or production. Intelligence manufacturing has led to enhance productivity in manufacturing sector in recent time on account of 4^th industrial revolution. Intelligent manufacturing (IM) involves the use of sophisticated and advanced analytics, sensor application in robotics and internet based connections to things popularly known as IoT. This study was centered on application of intelligent manufacturing in industrial productivity and cost/time wastage. Purposive sampling method was adopted in this study, random sampling survey methods was used to pick samples for collation of responses from production managers of manufacturing companies at the study area (Lagos state, Nigeria). Population frame of 100 product manufacturing companies was adopted, out of which 73 respondents that constitute production managers and supervisors were selected using random sampling technique. The study censored the opinion and view of professionals such as managers (production), production supervisors on calibrated. The study highlighted emerging areas of application of quality monitoring system in intelligent manufacturing to include advanced analytical tools and censored based applications such as robotics applications used in design and product calibration, virtual and augmented reality application that simulates real situation using virtual approach, machine learning, expert systems (AI), block chain technology, drones for real time supervision of production process.

Keywords:

• Internet of things
• calibration
• intelligent system
• merging
• dynamics

Acknowledgments

Effort of Center for Innovation Research and Development of Covenant University was appreciated in funding the APC of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).