TRANSFORMER PRODUCTION IMPROVEMENT BY LEAN AND MTM-2 TECHNIQUE
DOI:
https://doi.org/10.11113/aej.v12.16712Keywords:
Lean Manufacturing, Value Steam Mapping, Flow process chart, MTM-2 (Method Time Measurement version 2), Cause and Effect Diagram, Labor Productivity, Lean Manufacturing, Value Steam Mapping (VSM), ECRS technique, Method Time Measurement version 2 (MTM-2), Labor ProductivityAbstract
The situation of the covid-19 epidemic is a driving force of the global market’s demand increase of electronic devices and parts. Entire electronic component manufacturers, especially the transformer manufacturing industry, which is a device that supplies power to many electronic devices, encounters problems in producing products that are unable to keep up with the quickly increasing demand. This research aims to increase the productivity of small transformers by lean approach. The paper depicts processes relevant to improving production processes, reducing waste, and finding unnecessary processes. The method begins with two actions. First, study the current situation in transformer manufacturing of a case study. Second, study the customer order to delivery process using the Value Stream Mapping (VSM) and analyze entire processes of transformer manufacturing to identify standard time by unit work. The main technique is for measuring working time by timing the forward motion with the time measurement method version 2 (MTM-2). The Cause and Effect diagram was displayed with improving guidelines on two operations. First the concept of lean manufacturing was used in principal role, second the ECRS technique (Eliminate, Combine, Rearrange and Simplify) was applied to reduce "waste" as well as to optimize and reduce the manufacturing process of the transformer. The results lead to an increase in the final product per hour from 45 pieces per hour to 75 pieces per hour which increases up to 30% per hour. In addition, the productivity improvements increased the productivity of 3.46 workers per hour to 6.82 per hour (increase of 97.11%) and production time was reduced from 1,109 seconds to 229 seconds (73.04% of productivity).
References
Min, C., and Jianwen, L. 2020. Influence of COVID-19 on Manufacturing Industry and Corresponding Countermeasures from Supply Chain Perspective. Journal Shanghai Jiao Tong Univ. (Sci.), 25(4): 409 - 416. DOI: https://doi.org/10.1007/s12204-020-2206-z
Thi, P., and Gi-Tae, Y. 2018. A Comparative Analysis Selecting the Transport Routes of Electronics Components from China to Vietnam. Journal Sustainability. 10(1): 18. DOI: https://doi.org/10.3390/su10072444
Benjamin, S. 2019. Poverty Chains and Global Capitalism. Journal Competition & Change. 23(1): 71 - 97. DOI: https://doi.org/10.1177/1024529418809067
Sukwon, K. 2017. Time and Motion Study Methods for Manufacturing a Pump. International Journal of Innovative Research in Computer Science & Technology. 5(6): 390 - 392. DOI: doi:10.21276/ijircst.2017.5.6.2
Valentin, M., and Anca, Ș. Lean Manufacturing in SMEs in Romania. 2018. Procedia – Social and Behavioral Sciences. 238: 492 - 500. DOI: doi: 10.1016/j.sbspro.2018.04.028
Murugesan, M., Rajenthirakumar, D., and Chandrasekar, M. 2016. Manufacturing Process Improvement Using Lean Tools. International Journal of Engineering. 2: 151 - 154.
Huay, L., and Stephen, W. 2017. Digitalization of Learning Resources in a HEI - A Lean Management Perspective. International Journal of Productivity and Performance Management. 66(5): 680 – 694. DOI: doi: 10.1108/IJPPM-09-2016-0193
Sunil, K., Ashwani, K., and Bhim, S. 2018. Process Improvement Through Lean-Kaizen Using Value Stream Map: A Case Study in India. The International Journal of Advanced Manufacturing Technology. 96: 2687 – 2698. DOI: doi.org/10.1007/s00170-018-1684-8
Chi On, C., Huay Ling, T. 2017. Combining Lean Tools Application in Kaizen: A Field Study on The Printing Industry. International Journal of Productivity and Performance Management. 67(1): 45 – 65. DOI: doi: 10.1108/IJPPM-09-2016-0197
Aditya, P., Mahesh, P., and Chandrakant, S. 2021. Application of Value Stream Mapping to Enhance Productivity by Reducing Manufacturing Lead Time in A Manufacturing Company: A Case Study. The International Journal of Advanced Manufacturing Technology. 19(1): 11 – 22. DOI: doi.org/10.1007/s00170-018-1684-8
Genett, J., Gilberto, S., Jose, C., Sandy, R., Jose, P., Ana, P. and Hogo, H. 2019. Improvement of Productivity and Quality in the Value Chain Through Lean Manufacturing - A Case Study. Procedia Manufacturing. 41: 882 – 889. DOI: doi.org/10.1016/j.promfg.2019.10.011
Philip, R., Andreas, K., and Konrad, W. 2019. Information Stream Mapping: Mapping, Analyzing and Improving the Efficiency of Information Streams in Manufacturing Value Streams. CIRP Journal of Manufacturing Science and Technology.25: 1-13. DOI : doi.org/10.1016/j.cirpj.2019.04.004
Juthamas, C., Monsiri, O., Phrompong, S. 2015. Improving the Productivity of Sheet Metal Stamping Subassembly Area Using the Application of Lean Manufacturing Principles. Procedia Manufacturing. 2: 102- 107. DOI: https://doi.org/10.1016/j.promfg.2015.07.090
Zsolt, V., Bernadett, B. 2020. Comprehensive Comparison of MTM and Basic MOST, As the Most Widely Applied PMTS Analysis 17th IMEKO TC 10 and EUROLAB. Virtual Conference “Global Trends in Testing, Diagnostics & Inspection for 2030.
Bakhtiar, Erliana, C. and Dermawan, W.2019. Work Time Measurement Analysis with Indirectly Working Measurement Method on Cement Bagging Station. International Conference on Industrial and Manufacturing Engineering. 505. DOI: doi:10.1088/1757-899X/505/1/012141
Halimatussa, d., Ali, P., and Muchamad, S. 2018. Productivity Improvement in The Production Line with Lean Manufacturing Approach: Case Study PT. XYZ. MATEC Web of Conferences. 154. DOI: doi.org/10.1051/matecconf/201815401093
Leksic, l., Stefanic, N., Veza, I. 2020. The impact of using different lean manufacturing tools on waste reduction. Advances in Production Engineering & Management. 15(1): 81-92. DOI: doi.org/10.14743/apem2020.1.351
Rafaela, H., Andre, L., Maria, C., Mario, S.and Renan, M. 2019. Analysis of the Influence of Standard Time Variability on the Reliability of the Simulation of Assembly Operations in Manufacturing Systems. The Journal of The Human Factors and Ergonomics Society. 61(4): 627- 641. DOI: https://doi.org/10.1177/0018720819829596
Turk, M., Pipan, M, Šimic, M., and Herakovič, M. 2020. Simulation‐Based Time Evaluation of Basic Manual Assembly Tasks. Advances in Production Engineering & Management. 15(3): 331 - 344. DOI: doi.org/10.14743/apem2020.3.369
Snježana, K., and Anica, S. 2020. Determination of Working Methods and Normal Times of Technological Sewing Operation using MTM System.Tekstilec.63(3): 203-215. DOI:10.14502/Tekstilec2020.63.203-215
Marco, F., Philipp, P., Benedikt, L., Alexander, M., Christopher, B., Thomas, F., Jörg, H., Peter, K. and Verena, N. 2019. Empirical Validation of the Time Accuracy of the Novel Process Language Human Work Design (MTM-HWD). Production & Manufacturing Research. 7(1): 350 - 363. DOI: doi.org/10.1080/21693277.2019.1621785
Giusepp, G., Carmine, M., Antonio, L., Adelaide, M., and Gianluca, R. 2012. Improving MTM-UAS to Predetermine Automotive Maintenance Times. International Journal on Interactive Design and Manufacturing. 6: 265- 273. DOI: 10.1007/s12008-012-0158-8