Nished Singhal
Co-Founder, NeenOpal Intelligent Solutions, India

Deep learning techniques have enabled processing of complex natural language by machines. So, supply chain based analytical solutions can now be equipped with an ability to understand voice-based inputs. The importance of integrating voice aspect stems from the fact that traditionally analytical initiatives have had lower acceptance among end users and voice aspect has advantages of being hands-free and the conversational aspect which engages the users more. 

Voice-enabled analytical solutions have application in manufacturing, warehousing, logistics as well as customer-facing operations. The challenge in integrating the voice aspect stems from the fact that understanding intention from natural language requires intelligence to process unstructured data. It is difficult for machines to understand intention because of various challenges in a normal language such as synonyms, multiple ways of communicating the same point, pronunciation, loss of information in voice to text conversion, etc. It is here that Deep learning techniques have been most successful in understanding the hidden complex patterns in natural language and providing fairly accurate results. 

NeenOpal leverages machine learning techniques to build such capabilities in their supply chain solutions. Apart from building superior algorithms, NeenOpal also takes into account the business context and behavioural aspect of end-user acceptance to make sure that analytical initiatives taken by organizations are successful.