Goal-oriented clustering algorithm to monitor the efficiency of logistic processes through real-time locating systems

Modern internal logistic systems face several challenges, from supply chain disruption to mass customization of marketed products. In such a highly dynamic scenario, Internet of Things technologies provide a reliable path to digitizing low-standardized systems and quantitatively monitoring their functioning. In addition, acquired measurements are often combined with machine learning methods to achieve improved data analytics. For this purpose, this work presents a digital architecture to detect logistic activities during order management. While an ultrawide band-based real-time locating system acquires the positioning information of forklifts, a goal-oriented clustering algorithm called Industrial DB scan classifies process-driven operations during the shift. These insights represent valuable information for constantly evaluating the operational efficiency of logistic systems. The robustness and validity of the industrial DB scan are tested from different perspectives. On the one hand, a quantitative benchmark with traditional clustering methods is performed. The proposed algorithm results in the most effective approach to detect uptime forklift operations. On the other hand, a warehousing system proves the operational functioning of the algorithm. In this regard, a Tracking Management System interface is developed to achieve user-friendly process monitoring, where plant supervisors can analyze several internal logistic key performance indicators.