Manufacturing Technology 2022, 22(2):124-138 | DOI: 10.21062/mft.2022.028

Application of Lean Six Sigma for Improve Productivity at The Mechanical Plant. A Case Study

Minh Ly Duc ORCID..., Minh Nguyen Thu ORCID...
Faculty of Commerce, Van Lang University, 69/68 Dang Thuy Tram, Ward 13, Binh Thanh District, Ho Chi Minh City, Vietnam

This study focuses on performing the analysis of manipulator operations on the machining line of precision mechanical products using the Human-Machine correlation analysis tool through images collected from the camera, wasteful operations incurred in machining according to Lean Six Sigma (LSS) standards to control fluctuations in the machining line, improving the overall productivity of the line (OEE). Specifically, contributing to improving productivity, quality, and competitiveness of the company in the market, create a good product image for consumers. This paper proposes a 7-step quality control (QC) cycle improvement model, called 10 step QC cycle. In step 5, use Man - Machine correlation analysis tool from video images to identify wasteful activities. In step 6, we propose a Direct Numerical Control (DNC) model to call the machining program for MC machines using a barcode system and a computer vision model for human identification at each processing line according to a controlled fixed layout. The right people have been trained enough to operate the line; the specific result is eliminating the occurrence of accidents in processing from 7 cases to none. A model of a product dimensioning system implemented for fully automated product quality control combined with redesigned machining jig with a vapor sensor system eliminating the reliance on human manipulation Specifically, the result from this improvement activity is the increase in productivity from 115 products per 8 hours to 155 products per 8 hours and the handling time has decreased from 1.3 hours per day to 0.36 hours per day (reduce 0.94 hours per day). The Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the results of the survey of employees' opinions about the usefulness, convenience, and technical factors after the operation. The results from improvement activities show that user loyalty is highly appreciated in terms of usefulness and convenience. However, in terms of technical factors, it is still necessary to improve the quality of the information network system, the barcode scanning system and the quality of barcodes in the oil environment

Keywords: Productivity improvement, QC Story; DMAIC; PLS-SEM; Continuous improvement

Received: January 24, 2022; Revised: April 29, 2022; Accepted: April 30, 2022; Prepublished online: April 30, 2022; Published: May 15, 2022  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Ly Duc M, Thu MN. Application of Lean Six Sigma for Improve Productivity at The Mechanical Plant. A Case Study. Manufacturing Technology. 2022;22(2):124-138. doi: 10.21062/mft.2022.028.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. LIZARELLI, F. L.; TOLEDO, J. C. D. (2016). Practices for continuous improvement of the Product Development Process: a comparative analysis of multiple cases. Gestão & Produção, Vol. 3, pp. 535-555. Go to original source...
    2. DE MAGALHAES CASTRO, C.V.V.; DE CAMARGO JUNIOR, J. B. (2020). The benefits and challenges of a continuous improvement area in a manufacturing plant. Quaestum, Vol. 1, pp. 1-6. Go to original source...
    3. LAHIDJI, B.; TUCKER, W. (2016). Continuous quality improvement as a central tenet of TQM: History and current status. Quality Innovation Prosperity, Vol. 20, No. 2, pp. 157-168. Go to original source...
    4. SOLIMAN, M. (2017). Why continuous improvement programs fail in the egyptian manufacturing organization? A research study of the evidence. American Journal of Industrial and Business Management, Vol. 7, pp. 202-222. Go to original source...
    5. LI, J.; PAPADOPOULOS, C. T.; ZHANG, L. (2016). Continuous improvement in manufacturing and service systems. International Journal of Production Research, Vol. 54, No. 1, pp. 6281-6284. Go to original source...
    6. NGUYEN, M. H.; PHAN, A. C.; MATSUI, Y. (2018). Contribution of quality management practices to sustainability performance of Vietnamese firms. Sustainability, Vol. 10, No. 2, pp. 375. Go to original source...
    7. JEVGENI, S.; EDUARD, S.; ROMAN, Z. (2014). Framework for continuous improvement of production processes and product throughput. 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, Indian Institute of Technology Roorkee, India, 26-29 November 2014.
    8. NAVARRO, P.; ALEMÁN, I.; SANDOVAL, C.; MATAMALA, C.; CORSINI, G. (2020). Statistical Testing Methods for Data Analysis in Dental Morphology. International Journal of Morphology, Vol. 38, No. 5. Go to original source...
    9. CHEN, K. S.; CHEN, S. C.; HSU, C. H.; CHEN, W. Z. (2021). Statistical Hypothesis Testing for Asymmetric Tolerance Index. Applied Sciences, Vol. 11, No. 14, pp. 6249. Go to original source...
    10. EMMERT-STREIB, F.; DEHMER, M. (2019). Understanding statistical hypothesis testing: The logic of statistical inference. Machine Learning and Knowledge Extraction, Vol. 1, No. 3, pp. 945-961. Go to original source...
    11. DELGADO-GÓMEZ, D.; GONZÁLEZ-LANDERO, F.; MONTES-BOTELLA, C.; SUJAR, A., BAYONA, S.; & MARTINO, L. (2020). Improving the Teaching of Hypothesis Testing Using a Divide-and-Conquer Strategy and Content Exposure Control in a Gamified Environment. Mathematics, Vol. 8, No. 12, pp. 2244. Go to original source...
    12. DANIYAN, I.; TLHABADIRA, I.; ADEODU, A.; PHOKOBYE, S.; MPOFU, K. (2020). Process design and modelling for milling operation of titanium alloy (Ti6Al4V) Using the Taguchi method. Procedia CIRP, Vol. 91, pp. 348-355. Go to original source...
    13. HAN, J.; PARK, D.; FORBES, H.; SCHAEFER, D. (2020). A computational approach for using social networking platforms to support creative idea generation. Procedia CIRP, Vol. 91, pp. 382-387. Go to original source...
    14. KAN, K.; MATIVENGA, P.; MARNEWICK, A. (2020). Understanding energy use in the South African manufacturing industry. Procedia CIRP, Vol. 91, pp. 445-451. Go to original source...
    15. TENG, Y. C.; CHEN, K. S. (2016). Analysis, Design, and Control of a Novel Elastomeric Bearing Positioning Stage. Inventions, Vol. 1, No. 3, pp. 17. Go to original source...
    16. FORTUNATO, A.; ASCARI, A. (2013). The virtual design of machining centers for HSM: Towards new integrated tools. Mechatronics, Vol. 23, No. 3, pp. 264-278. Go to original source...
    17. MENGHI, R.; ROSSI, M.; PAPETTI, A.; GERMANI, M. (2020). A methodology for energy efficiency rede-sign of smart production systems. Procedia CIRP, Vol. 91, pp. 319-324. Go to original source...
    18. SAMARGHANDY, H.; LI, Y. (2013). Detecting re-design area for increasing manufacturability of drilling and three-axis pocketing operations. The International Journal of Advanced Manufacturing Technology, Vol. 69, No. 14, pp. 337-349. Go to original source...
    19. WANG, J.; NIU, W.; MA, Y.; XUE, L.; CUN, H.; NIE, Y.; ZHANG, D. (2017). E A CAD/CAE-integrated structural design framework for machine tools. The International Journal of Advanced Manufacturing Technology, Vol. 91, No. 1, pp. 545-568. Go to original source...
    20. VIJAYAN, K. K.; MORK, O. J.; GISKE, L. (2019). Integration of a case study into learning factory for future research. Procedia manufacturing, Vol. 31, pp. 258-263. Go to original source...
    21. GOUDSWAARD, M.; NASSEHI, A.; HICKS, B. (2019). Towards the democratisation of design: The implementation of metaheuristic search strategies to enable the auto-assignment of manufacturing parameters for FDM. Procedia Manufacturing, Vol. 38, pp. 383-390. Go to original source...
    22. FAVI, C.; MARCONI, M.; GERMANI, M.; MANDOLINI, M. (2019). A design for disassembly tool orient-ed to mechatronic product de-manufacturing and recycling. Advanced Engineering Informatics, Vol. 39, pp. 62-79. Go to original source...
    23. GRUBE, D.; MALIK, A. A.; BILBERG, A. (2019). SMEs can touch Industry 4.0 in the smart learning factory. Procedia Manufacturing, Vol. 31, pp. 219-224. Go to original source...
    24. VALENTE, A.; CATALDO, A.; CARPANZANO, E. A dispatching algorithm and software tool for managing the part flow of Reconfigurable Transportation System. Procedia CIRP 2.
    25. MÄKIÖ-MARUSIK, E.; COLOMBO, A. W.; MÄKIÖ, J.; PECHMANN, A. (2019). Concept and case study for teaching and learning industrial digitalization. 9th Conference on Learning Factories, Technische Universität Braunschweig, Germany, 26-28 March 2019. Go to original source...
    26. ISTARDI, D.; SYAIFUL, K. (2016). Design of soft contact lens indexer inspection semi-automatic. 3rd Inter-national Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, 19-20 October 2016. Go to original source...
    27. KASHKOUSH, M.; ELMARAGHY, H. (2017). An integer programming model for discovering associations between manufacturing system capabilities and product features. Journal of Intelligent Manufacturing, Vol. 28, No. 4, pp. 1031-1044. Go to original source...
    28. PAPETTI, A.; GREGORI, F.; PANDOLFI, M.; PERUZZINI, M.; GERMANI, M. (2020). A method to improve workers' well-being toward human-centered connected factories. Journal of Computational Design and Engi-neering, Vol. 7, No. 5, pp. 630-643. Go to original source...
    29. SHIH, W. H.; YU, K. M.; LEE, C. L. (2017). An overhead contact line monitoring system based on wireless sensor network technology. Sixth International Conference on Future Generation Communication Technologies (FGCT), Dublin, Ireland, 21-23 August 2017. Go to original source...
    30. BOEHME, D.; GAMBLIN, T.; BECKINGSALE, D.; BREMER, P. T.; GIMENEZ, A.; LEGENDRE, M.; SCHULZ, M. (2018). Caliper: performance introspection for HPC software stacks. SC'16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, Utah, 13-18 November 2018.
    31. LEE, K. Y.; HUANG, C. F.; HUANG, S. S.; HUANG, K. N.; YOUNG, M. S. (2012). A high-resolution ultrasonic distance measurement system using vernier caliper phase meter. IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, pp. 2924-2931. Go to original source...
    32. MICHAL, B.; VLADIMIRA, S.; IVANA, S. (2020). Application of lean manufacturing methods to streamline the welding line. Manufacturing technology, Vol. 20, No. 2, pp. 143-151. Go to original source...
    33. TAMER, H.; BASHEER, W.; ISTVAN, N. (2021). Improving overall equipment effectiveness (OEE) of extrusion machine using lean manufacturing approach. Manufacturing technology, Vol. 21, No. 1, pp. 56-64. Go to original source...
    34. KRZYSZTOF, K. (2021). The use of quality tools to reduce surface defects of paint steel structures. Manu-facturing technology, Vol. 21, No. 6, pp. 805-817. Go to original source...

    This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content