Detection and Correction of Buffer Occupancy Data Error in Two-Machine Bernoulli Serial Lines

As the manufacturing industry transforms its practice to adapt to the Industry 4.0 era, various new technologies are deployed on the factory floor. With these technologies, more production data are being generated and collected. On the other hand, development of analytics tools to digest these factory floor data and extract useful information for production decision-making and control has been lagging behind. Moreover, due to technological limitations of the sensors and the noisy environment in manufacturing facilities, it is quite common that the raw data collected from the factory floor contain errors and often must go through a time-consuming, labor-intensive data cleaning process before they are ready to be fed to any analytics tools. This is one of the main challenges and concerns about effective use of factory floor production data in practice. In this paper, we focus on the buffer occupancy time series data of a production system (i.e., the number of parts in each buffer at every time point). Specifically, we study the buffer occupancy data in two-machine Bernoulli serial lines that are subject to noise and develop an effective algorithm to automatically detect and correct errors in these data. Numerical experiments show that the proposed method is capable of restoring data integrity and significantly improving system performance estimation accuracy using the corrected data.