Open AccessA Simulation Model to Improve Productivity in the Pipe Manufacturing Industry
Author(s) -
Rabia Almamlook,
Harith M. Ali,
Arz Qwa Alden,
Anad Afhaima,
Faieza Saad Bodowara,
Sahapol Hoon Knew
Publication year - 2020
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.55.2.5
Subject(s) - productivity , manufacturing , manufacturing engineering , interview , process (computing) , scheduling (production processes) , advanced manufacturing , competition (biology) , multifactor productivity , workstation , computer science , industrial engineering , industrial organization , engineering , operations management , business , marketing , mechanical engineering , economics , ecology , total factor productivity , biology , political science , macroeconomics , law , operating system
Improving productivity in the pipe manufacturing industry is a major challenge that manufacturing companies in contemporary competitive markets face. The purpose of this study was to improve productivity in the pipe manufacturing industry by applying manufacturing principles that employ simulation modeling. An approach to improve productivity which focuses on the process of workstations and workforces was proposed . The proposed approach’s target was to boost the productivity of providing clients’ prerequisites and leaving a few products in the store for other clients. A simulation model based on the data collected from the steel pipe company, Bansal Ispat Tubes Private Limited’s in India, was used to improve its operational performance. The research methodology included a pro-simulation model, suitable distribution and investigating data. The simulation model was created by simulating each work station and assessing all relevant processes depending on the collected data. The real job-shop data was collected from the machinery production line and supervision workers with observations made during the manufacturing process. The techniques used include videotaping of the operation, interviewing liber by a video camera. The best continuous distributions were choose to achieve a suitable statistical model. The outcomes maybe contribute to improving the productivity of the manufacturing industry. Moreover, the results might help solve scheduling problems in modeling and simulating pipe manufacturing, revealing effective strategies to increase productivity in pipe manufacturing. Thus, the findings could encourage healthy competition between businesses and industries.