Electric motors are the drivers of an economy, as they allow interoperability of machine functions through their working. Consequently, an induction motor is an integral component of electric motor driven systems (EMDS), found downstream of the electrical energy grid. Failure of an electric motor is usually an unanticipated bottleneck and servicing is the preferred replacement mode, conducted through an order-based repair schedule. Rewinding of induction motor stators is an intricate traditional reverse engineering (RE) practice of remanufacturing through distributed recycling. The aim of this paper is to develop a concept and a method that will be used in the development of a rewinding software based system. With the prevalent precedence of lean and agile manufacturing into the industrious economic environment, the rewinding process is mainly fixated on speedily resuscitating faulty induction motors rather than the quality of repair. This paper outlines the experimental set-up for developing a software based remote embedded automatic low voltage induction motor stator remanufacturing machine. Through methods of engineering focusing on work efficiency and mapping system to meet future trends, an intelligent remanufacturing machine is conceptualized.  Intelligent assets will enable use of an adaptive neural fuzzy inference system (ANFIS) for real-time dynamic system control and database management. Tunneling through cost, whole systems thinking results towards multiple benefits by optimizing the induction motor rewinding process and yields system optimization through energy and resource efficiency.