Application of the Digital Twin Concept in Assessing the Readiness of Production Systems

Anna Borucka, Patrycja Guzanek
European Research Studies Journal, Volume XXV, Issue 2B, 45-58, 2022
DOI: 10.35808/ersj/2935

Abstract:

Purpose: This paper is devoted to evaluating the efficiency of production systems using classification methods. These methods are not popular in application to manufacturing companies, therefore the possibility of their use in the process of system improvement and enhancement of business models is presented. Design/methodology/approach: The article uses classification methods, emphasizing their practical significance in assessing the performance of production processes. They allow the construction of a model for classifying new objects based on the relationships found in the collected empirical observations. Such data mining methods may find application primarily in non-computerized systems with limited information processing capabilities. Findings: The result of the publication is the presentation of a comprehensive method for assessing the efficiency of the production system. As a result, this solution will allow for more effective planning of processes and tasks, their ongoing correction, adequate to the available human, material and equipment resources, and reducing the risk of the system not being ready to perform the activities for which it is intended. Practical implications: The presented method is primarily used to assess the impact of selected factors on the efficiency of production processes as well as to support decisions in the area of production planning. Originality value: The presented model is built on the basis of archival data, but allows the transfer of the solution to cloud computing and obtaining readings in real time (online), which will allow for ongoing assessment and support of the operation of the investigated system in terms of monitoring and ongoing analysis of the implemented processes in real time, but also through the creation of simulation scenarios, considering decision-making options.


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