With the advancement of various advanced technologies towards information access system using pervasive and ubiquitous computing, the field of Supply Chain Management (SCM) is yet not ready to adopt the latest technological system. The commercial usage of SCM is still limited to adoption of conventional Radio Frequency Tags and simple sensors with internal communication performed on internet system. However, the entry of cloud-computing has brought a significant revolution in the area of automation, which is expected to make the complete SCM process a ubiquitous one. There is a less focus on SCM process improvement in order to keep pace with faster changing dynamics of technologies, and this hypothesis is explored in proposed review work. This paper discusses recent studies being carried out in SCM, which proved that existing SCM system has many open-end limitations as well as significant research gap that is yet to be bridged in order to give SCM a shape of industry 4.0 standard
An assembly line is a place where materials are put together and processed to check if the end product meets the quality standards. One of the biggest challeng-es faced by the manufacturing sector is quality management. Any newly built product is prone to have surface defects like scratches or dents or paint errors which might happened during the process of manufacturing and transportation. Currently such quality checks are being done manually using Human Vision. Now with the advent of IoT and Deep Learning techniques, we could build an anomaly detection system that recognize defects on the surface of the system by capturing photographs of the Automobile in the assembly line and sends it to an image processor system for validation. We were able to detect defects with 90% and above accuracy with the existing detection algorithms.
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