With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with smart and connected infrastructure that empower workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. For many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities, this presents a challenge of capital cost and complexity. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through deployment of consumer grade electronics and novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM), and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).