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The Fourth Industrial Revolution started in the year 2011 with the aim to improve productivity similar with that of the previous three industry revolutions that occurred 200 years ago. The revolutions were promoted and implemented to improve the efficiency and speed of productivity. However, Industry 4.0 is likely a supplementation of the existing approach, with the purpose to centralize the processes and factories together to be controlled by a central console. The goal is to procure productivity and manufacturing data to enable data analysis so that the industry’s   performance affected by big data variables, namely, velocity, veracity, variety, value, and visibility are monitored and rectified for continued productivity. This research initiates the development of a theory of Industry 4.0 ROI Relativity from the economic theory of firms, incorporating the pick-and-place test equipment and Industry 4.0 variables. A case study through the experimental research approach (ERA) was conducted by measuring the effects of velocity and veracity accuracy on the good-unit per hour (UPH), profit, and return on investment (ROI) of the pick-and-place test equipment and Industry 4.0. Then, the data were analyzed with Pearson correlation coefficient to determine inter-correlations among the velocity and veracity accuracy with the UPH, profit, and ROI. This research concluded that a significant average and negative correlation exists among velocity, UPH, profit, and ROI. Furthermore, the inter-correlation analysis results show a significant average and positive correlation among veracity accuracy percentage, UPH, profit, and ROI.

A Case Study of Return On Investment for Multi-sites Test Handler in The Semiconductor Industry Through Theory of Industry 4.0 ROI Relativity