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Related Experiment Video

Updated: Mar 10, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

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

Yong-Shin Kang¹, Il-Ha Park², Sekyoung Youm³

¹Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea. yskang7867@skku.edu.

Sensors (Basel, Switzerland)

|December 17, 2016

Summary

This study introduces a performance assessment method to evaluate traceability systems in smart factories. It verifies the scalability of MongoDB-based systems, aiding hardware capacity planning for complex manufacturing and logistics.

Keywords:

IoT NoSQL performance smart factory traceability

Related Experiment Videos

Last Updated: Mar 10, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

Industrial Engineering
Computer Science
Data Management

Background:

Smart factory environments necessitate advanced traceability systems due to increasing process complexity.
Scalability and performance assessment are critical for implementing effective traceability solutions using technologies like the Internet of Things (IoT) and BigData.

Purpose of the Study:

To develop and validate a performance assessment method for traceability systems in smart factories.
To verify the scalability of a MongoDB-based traceability system for manufacturing and logistics.

Main Methods:

Analyzed traceability requirements and designed an event schema for MongoDB.
Developed a query-level performance model based on traceability query algorithms.
Utilized linear regression to model response times and conducted a case analysis on virtual automobile parts logistics.

Main Results:

Verified the scalability of a MongoDB-based traceability system.
Successfully predicted optimal data node server expansion points for a virtual logistics case.
Demonstrated the effectiveness of the proposed performance assessment method.

Conclusions:

The developed performance assessment method serves as a valuable decision-making tool for hardware capacity planning in traceability systems.
The research confirms the suitability of MongoDB for scalable traceability solutions in smart factory contexts.
This method supports both the initial construction and operational phases of traceability system implementation.