TY - JOUR
T1 - The application of genetic algorithm and data analytics for total resource management at the firm level
AU - Jomthanachai, Suriyan
AU - Rattanamanee, Wanida
AU - Sinthavalai, Runchana
AU - Wong, Wai-Peng
N1 - Funding Information:
This work was supported by the budget revenue of Prince of Songkla University (Grant No. ENG570394S).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10
Y1 - 2020/10
N2 - Total Resource Management (TRM) in industry 3.5 relates to the evaluation and improvement of performance through the use of intelligent tools or methods in enhancing efficiency and effectiveness of operations. This paper illustrates a study of TRM in the rubberwood processing industry to prepare it towards a sustainable transition to industry 4.0. The rubberwood processing industry operates using massive production data. Such big data emerge from production using multi-processes because the various products come in different sizes and quality levels (grades). The rubberwood processing company in this study faces significant problems of inaccurate and delayed data. These problems introduce mistakes in inventory management and wage payment. The company also faces an economic issue due to confirmation which is labour-intensive. This study applied the Genetic Algorithm (GA) technique to verify accepted material or woodpiece and use data analytics to improve the efficiency of the verification system. Furthermore, a Web-Based Application (WBA) is developed for production data management. The results show a significant drop in the percentage of data inaccuracy when the GA confirmation method is applied and also a decrease in the percentage of production data discrepancy among processes. This successful sustainable transition is attributed to TRM because the achieved performance improvement enriches effectiveness in production, material, labour and service resources.
AB - Total Resource Management (TRM) in industry 3.5 relates to the evaluation and improvement of performance through the use of intelligent tools or methods in enhancing efficiency and effectiveness of operations. This paper illustrates a study of TRM in the rubberwood processing industry to prepare it towards a sustainable transition to industry 4.0. The rubberwood processing industry operates using massive production data. Such big data emerge from production using multi-processes because the various products come in different sizes and quality levels (grades). The rubberwood processing company in this study faces significant problems of inaccurate and delayed data. These problems introduce mistakes in inventory management and wage payment. The company also faces an economic issue due to confirmation which is labour-intensive. This study applied the Genetic Algorithm (GA) technique to verify accepted material or woodpiece and use data analytics to improve the efficiency of the verification system. Furthermore, a Web-Based Application (WBA) is developed for production data management. The results show a significant drop in the percentage of data inaccuracy when the GA confirmation method is applied and also a decrease in the percentage of production data discrepancy among processes. This successful sustainable transition is attributed to TRM because the achieved performance improvement enriches effectiveness in production, material, labour and service resources.
KW - Data analytics
KW - Genetic Algorithm
KW - Industry 3.5
KW - Internet of Things (IoT)
KW - Rubberwood processing
KW - Sustainable
KW - Total Resource Management
KW - Web-Based Application
UR - http://www.scopus.com/inward/record.url?scp=85086649480&partnerID=8YFLogxK
U2 - 10.1016/j.resconrec.2020.104985
DO - 10.1016/j.resconrec.2020.104985
M3 - Article
AN - SCOPUS:85086649480
SN - 0921-3449
VL - 161
JO - Resources, Conservation and Recycling
JF - Resources, Conservation and Recycling
M1 - 104985
ER -
