Novel Approach for Robotic Process Automation with Increasing Productivity and Improving Product Quality using Machine Learning

Robotic Process Automation (RPA) is one of the smartest technology evolutions in recent years. It is, a software installed on a system. RPA can be implemented in a well-defined environment with defined procedures and clarity with reference to decision making. RPA’s limitation is that it cannot be automated if it involves decision making supported by knowledgebased application. Highly invasive and intertwined supply chains are now confronted by producers, which reduce manufacturing life cycles and raise product sophistication. You therefore sense the need, at all stages of value formation, to change and adjust more rapidly. The theory of self-optimization is a positive method to coping with uncertainty and unexpected delays within supply chains, devices and processes. It would also boost manufacturing industries' stability and productivity. This paper explores the idea of development processes that are self-optimized. Following a quick historical analysis and understanding the particular needs, specifications and self-optimizing criteria of the various stages of value generation from supply chain planning and management to manufacture and assembly. Examples at both stages are used to demonstrate the self-optimization principle and to explain its simplicity and efficiency ability.. We proposed Novel approach for Robotic Process Automation with increasing productivity and improving product quality using machine learning.