KNIC Undertakes Autonomous Factory Project Integrating ERP, MES, and Digital Twin Technologies
Establishing an integrated management system for production, quality, inventory, and equipment data
Implementing digital twin-based production monitoring and AI-powered equipment and process analysis
KNIC, a company specializing in ERP and MES implementation and maintenance, announced that it is carrying out a two-year autonomous factory implementation project for a textile manufacturer selected for the second round of the 2025 Autonomous Factory Implementation Support Program initiated by the Ministry of SMEs and Startups (MSS) of South Korea.
The project integrates digital twin and artificial intelligence (AI) technologies to establish an autonomous manufacturing environment.
The project aims to develop an integrated system for achieving quality objectives through process routing optimization, enabling predictive maintenance through equipment anomaly detection, and managing equipment information.
Data generated on the production floor is collected in real time and utilized for digital twin-based monitoring, AI-powered equipment analysis, and production process analysis.
Integrating Production Information Through ERP and MES
KNIC has integrated production plans, item information, material data, and process master data managed in the ERP system with work orders and process management functions in the MES.
The system’s data architecture connects production results, quality inspection records, inventory movements, and equipment information collected on the shop floor, allowing them to be utilized for production operations and enterprise management.
The project has completed the business analysis, system design, and equipment data collection phases and is currently in the implementation and development stage.
The system is scheduled to undergo unit testing and integration testing in 2026 before going live.
Building a Digital Twin-Based Production Monitoring System
The digital twin-based monitoring system is designed to represent the operating conditions of physical production equipment and manufacturing processes within a virtual environment.
The monitoring interface provides visibility into equipment operating status, production progress, process-level work results, and abnormal operating conditions.
Managers can access information collected from ERP, MES, and shop-floor equipment through an integrated dashboard, enabling them to review actual production results against planned targets, equipment operating conditions, and the progress of individual manufacturing processes.
Applying Equipment Data Collection and AI-Based Anomaly Detection
The AI-based predictive maintenance function analyzes operational data collected from production equipment, including operating hours, temperature, vibration, and electrical current.
Using data collected from thermal processing equipment, KNIC has established normal operating ranges and criteria for detecting potential anomalies for each machine.
When collected data exceeds the defined operating ranges or deviates from established operating patterns, the system is designed to identify and display the relevant information.
Equipment operating histories and anomaly records are also stored for use in equipment inspections, maintenance activities, and maintenance history management.
Analyzing Process Routing Using Production Plans and Operational Data
AI-based process routing analysis utilizes production plans, work priorities, equipment operating conditions, process capacities, and delivery schedules.
Based on this information, the system analyzes work requirements and process operations, allowing users to review work sequences and process routes under different production conditions.
Process-level work progress, waiting status, and equipment assignments are recorded in the MES and linked to production plans and delivery schedules.
KNIC has applied this functionality to the processing operations covered by the project.
Providing End-to-End Services from On-Site Analysis to User Training and System Stabilization
As part of the project, KNIC is responsible for analyzing the customer’s production methods and equipment environment, designing business processes, identifying data collection requirements, developing the system, integrating ERP, MES, and equipment data, conducting integration testing, providing user training, and supporting system stabilization.
Most of the technical design and development work has been completed.
Following the system launch, KNIC plans to monitor user activities, data collection status, and interface operations during the stabilization period.
A KNIC representative stated:
“This project focuses on connecting production plans managed in ERP, shop-floor information collected through MES, and data generated by production equipment within a unified management framework. During the remaining project period, we will conduct integration testing and user validation to ensure stable system operation.”
Following the system launch, KNIC will evaluate data collection rates, system integration status, and the time required to record and aggregate production results based on operational performance indicators agreed upon with the customer.
Founded in 2003, KNIC serves as the southern regional branch of Younglimwon Soft-Lab, providing K-System ERP implementation and consulting services, smart factory solutions, customized system development, and maintenance services.
The company has carried out system implementation and maintenance projects for domestic enterprises, public institutions, and overseas subsidiaries across various manufacturing industries, including automotive parts, machinery, shipbuilding and marine equipment, defense, electronics, textiles and apparel, and secondary batteries.