Extendable Framework for Data Collection and Analysis in Production Systems

Abstract

Production systems constitute the backbone of any organization aiming to maximize its profits and cut down its cost. The data accumulated from numerous processes is critical to optimize the chain of operations within the company. However, the major hindrance for the successful operation of production systems is the way organizations manage their data. Normally, data is collected from diverge sources across the industry and therefore poses integration challenges for achieving interoperability. The poor quality of data collected from legacy systems is reported as being the major reason for the frequent failures of the modern systems. For successful interoperability of heterogeneous systems, the production systems should not only accommodate the legacy systems, but also need to build a system flexible enough to support integration for the contemporary systems. This research work aims to implement a flexible data collection and analysis framework, allowing the user to collect the data, clean it, and convert it in to the specified format and thereafter, perform desired analysis on it. The implemented work has been accomplished on a general level rather than examining a specified organization. The research is primarily divided in to theoretical and practical parts. The former part describes scientific literature regarding the entitled research topic and then shapes the ground for the practical implementation. The later part demonstrates the implementation of the uses cases for collecting, converting and analyzing the data. The implementation has been performed on the legacy system hub developed as part of C2NET project. The function block approach has been extensively used while implementing the framework in the provided platform. The framework presents a unified platform capable enough to provide data collection, its transformation and analysis, thereby, solving the integration and interoperability issues faced by the organizations

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This paper was published in TUT DPub.

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