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

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.