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Sensors 2016, 16(12), 2126;

Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
Research Institute of Sustainable Manufacturing System, Korea Institute of Industrial Technology, Cheonan, Chungcheongnam-do 31056, Korea
Department of Industrial and Systems Engineering, Dongguk University, 3ga, Pil-dong, Jung-gu, Seoul 04620, Korea
Author to whom correspondence should be addressed.
Academic Editor: Yike Guo
Received: 28 September 2016 / Revised: 6 December 2016 / Accepted: 12 December 2016 / Published: 14 December 2016
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase. View Full-Text
Keywords: traceability; NoSQL; IoT; smart factory; performance traceability; NoSQL; IoT; smart factory; performance

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Kang, Y.-S.; Park, I.-H.; Youm, S. Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains. Sensors 2016, 16, 2126.

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