Implementation Framework of Advanced Planning and Scheduling System for Smart Manufacturing in SMEs

Background/Objectives: Small and medium-sized manufacturing enterprises (SMEs) are usually unable to properly respond to the rapid changes in the production environment. Methods/Statistical analysis: We propose an implementation framework of advanced planning and scheduling (APS) system specialized for SMEs. The following are considered as the main factors in implementation of APS system: 1) establishment of on-site production data collection and management system for SMEs with poor IT infrastructure, 2) establishment of production scheduling system linking experiential knowledge of field production manager, and 3) construction of various scenario analysis system using simulation technique. Findings: The proposed APS system can collect and manage the production data generated at the manufacturing site. Various production data can be stored, and realistic scheduling can be established according to actual situation of the site. Second, scheduling can be established in connection with the experiential knowledge of field managers. Using a technique for order or preference by similarity to ideal solution (TOPSIS) based approach, it is possible to select the optimal work priority assigned to the manager's empirical weight, and it is possible to make quick decision-making and rescheduling in various situations. Third, the simulation function is installed, and it is possible to analyze the forecasted scenario in terms of the productivity through the simulation result index. Improvements/Applications: It is expected to contribute to the establishment of smart production of SMEs by efficiently managing the production data at low cost and improving the system utilization.

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Publication Details

Journal
International Journal of Engineering & Technology
Published
2026-04-26
DOI
https://doi.org/10.14419/ijet.v7i3.24.22839
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
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article

Implementation Framework of Advanced Planning and Scheduling System for Smart Manufacturing in SMEs

Moonsoo Shin, Jae-yong Lee
International Journal of Engineering & Technology
Technology and Data Analysis
article

Implementation Framework of Advanced Planning and Scheduling System for Smart Manufacturing in SMEs

Moonsoo Shin, Jae-yong Lee
article en

Abstract

Background/Objectives: Small and medium-sized manufacturing enterprises (SMEs) are usually unable to properly respond to the rapid changes in the production environment. Methods/Statistical analysis: We propose an implementation framework of advanced planning and scheduling (APS) system specialized for SMEs. The following are considered as the main factors in implementation of APS system: 1) establishment of on-site production data collection and management system for SMEs with poor IT infrastructure, 2) establishment of production scheduling system linking experiential knowledge of field production manager, and 3) construction of various scenario analysis system using simulation technique. Findings: The proposed APS system can collect and manage the production data generated at the manufacturing site. Various production data can be stored, and realistic scheduling can be established according to actual situation of the site. Second, scheduling can be established in connection with the experiential knowledge of field managers. Using a technique for order or preference by similarity to ideal solution (TOPSIS) based approach, it is possible to select the optimal work priority assigned to the manager's empirical weight, and it is possible to make quick decision-making and rescheduling in various situations. Third, the simulation function is installed, and it is possible to analyze the forecasted scenario in terms of the productivity through the simulation result index. Improvements/Applications: It is expected to contribute to the establishment of smart production of SMEs by efficiently managing the production data at low cost and improving the system utilization.

International Journal of Engineering & TechnologyVol. 7(3.24)
Hanbat National University (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Technology and Data Analysis
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