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The term reengineering came into vogue during the 1990s when the first wave of computerization hit full on. Databases, PCs and automated systems overnight made work easier but complicated at the same time. Hordes of professionals now get engaged in noting historical trends so that processes could be revised. Managers would develop these business rules which the engineers would code. Now the present movement may be termed as machine-reengineering as instead of individuals having to keep track, it is machines that are tracking real time business intelligence. The algorithms developed by humans no longer need to remain static as machines are constantly improvising over them. The original reengineering movement stressed on changing too many things far too soon, but the machine based process is more of one step at a time. Machine reengineering processes can broadly be divided into different kinds of tasks- natural language, predicative analytics, visual sensing and anomaly detection. Studies have confirmed that these fields have helped manage five kinds of processes- customer service, enterprise risk or compliance, financial resources, business capabilities and marketing. This process of involving machine learning in improving business processes is called “Scientific Revenue Machine” or SRM. A consumer food company has implemented the same to substantially reduce accidents and delays. Its intelligent visions sensor warns of road problems long before actually encountering the same. Contrary to conventional thinking, machine learning actually creates new workflows and early adopters have proven how useful the concept can be.

Source:https://hbr.org/2016/02/companies-are-reimagining-business-processes-with-algorithms

 

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