Development of a Decision Support System in a Canning Industry

Decision Support System frameworks have great importance in the context of Industry 4.0 to prevent production bottlenecks, machine malfunction and to increase the reliability of the industrial processes environment. With the development of digitalization, Decision Support Systems (DSS) alongside Cyber Physical solutions, Internet of Things (IoT) devices and Big Data approaches constitute the main core of an industrially oriented smart manufacturing application. However, a considerable amount of industries lack the technological infrastructure in order to effectively utilize the vast amount of data, collected from various sensors and heterogenous sources scattered at various points of the production process on a daily basis. The scope of this paper is to present a conceptual framework of a DSS in a Canning Industry in order to utilize high-volume data collected from embedded sensors in the production process in order to detect and eliminate bottlenecks. The first part of the solution is dedicated to the integration of Programmable Logical Controllers (PLC) and the KEP Open Platform Communications (OPC) Server for the data acquisition and communication with a MySQL relational database, while the second part is on the data manipulation and the presentation of data analysis strategy for the decision making. Three machine learning models namely Random Forest, Naïve Bayes and SVM were tested for the prediction of total production losses with Random Forest outperforming the rest with Accuracy of 90%. As a final step, a dashboard with real time descriptive statistics and an alarm-based system for upcoming potential failures are presented. This approach will improve the existing production process and act as a guideline for upcoming research in Decision Support Systems in a Canning Industry with a significant replication potential in other types of industries.