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
Robotic Process Automation of Unstructured Data with Machine Learning