DETERMINATION OF PROCESS VARIABILITY BY USING TRIANGULAR FUZZY NUMBER TO MINIMIZE PRODUCTION LEAD TIME IN A DYNAMIC VALUE STREAM MAPPING

Authors

  • M. Thulasi Department of Mechanical & Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
  • A.A Faieza Department of Mechanical & Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
  • A.S Azfanizam Department of Mechanical & Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Z. Leman Department of Mechanical & Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.

DOI:

https://doi.org/10.11113/aej.v13.18574

Keywords:

Value Stream Mapping, Triangular Fuzzy Number, Variability, Dynamic Production, Production Lead Time

Abstract

This paper shows the incorporation of Value Stream Mapping (VSM) with triangular fuzzy numbers to determine variability and uncertainty in a conveyor manufacturing company. VSM is a pen and paper tool which is used to indicate wastes and bottleneck processes graphically and develop an action plan to enhance the production line. However, some weaknesses are identified in the conventional VSM where it fails to consider variability in a dynamic manufacturing environment. As such, this paper fills up the research gap by using Triangular Fuzzy Number (TFN) to illustrate time intervals, inventories and other variables of VSM operation. The purpose of this paper is to minimize total production lead time (TPLT) and total value-added time (TVAT) in the current value stream of the conveyor chain. More accurate details of variability in the dynamic manufacturing environment can be illustrated by a Triangular Fuzzy Number (TFN) of VSM.  As a result, the future value stream map shows 50% and 22% reduction in TPLT and TVAT respectively compared to the current value stream. In conclusion, this paper also recommends that in order to optimize the accuracy of VSM analysis further, a discrete event simulation can be used to examine the fuzzy VSM. 

References

Azizi, A., and Manoharan, T. 2015. Designing a Future Value Stream Mapping to Reduce Lead Time Using SMED-A Case Study. Procedia Manufacturing. 2(1): 153–158. DOI : https://doi.org/10.1016/j.promfg.2015.07.027

Mudgal, D., Pagone, E., and Salonitis, K. 2020. Approach to Value Stream Mapping for Make-To-Order Manufacturing. Procedia CIRP. 93: 826–831. DOI : https://doi.org/10.1016/j.procir.2020.04.084

Valencia, E.T., Lamouri, S., Pellerin, R., Dubois, P., and Moeuf, A. 2019. Production planning in the fourth industrial revolution: A literature review. IFAC-PapersOnLine. 52(13): 2158–2163. DOI : https://doi.org/10.1016/j.ifacol.2019.11.525

Pacchini, A.P.T., Lucato, W.C., Facchini, F., and Mummolo, G. 2019. The degree of readiness for the implementation of Industry 4.0. Comput. Ind. 113. DOI : https://doi.org/10.1016/j.compind.2019.103125

Rafique, M.Z., Ab Rahman, M.N., Saibani, N., and Arsad N. 2019. A systematic review of lean implementation approaches: a proposed technology combined lean implementation framework. Total Quality Management and Business Excellence. 30 (3-4): 386–421. DOI : https://doi.org/10.1080/14783363.2017.1308818

Kumar, S., Dhingra, A.K., and Singh, B. 2018. Kaizen selection for continuous improvement through VSM-Fuzzy-TOPSIS in Small-Scale enterprises: An Indian case study. Advance Fuzzy System. 2018. DOI : https://doi.org/10.1155/2018/2723768

Roh, P., Kunz, A., and Wegener, K. 2019. Information stream mapping: Mapping, analysing and improving the efficiency of information streams in manufacturing value streams. CIRP Journal Manufacturing Science Technology. 25: 1–13. DOI : https://doi.org/10.1016/j.cirpj.2019.04.004

Romero L.F., and Arce, A. 2017. Applying Value Stream Mapping in Manufacturing: A Systematic Literature Review. IFAC-PapersOnLine. 50 (1):, 1075–1086. DOI : https://doi.org/10.1016/j.ifacol.2017.08.385

Kumar, S., Singh, B., Qadri, M.A., Kumar, Y.V.S., and Haleem A. 2013. A framework for comparative evaluation of lean performance of firms using fuzzy TOPSIS. International Journal of Production Quality. Management. 11(4): 371–392. DOI : https://doi.org/10.1504/IJPQM.2013.054267

Deshkar, A., Kamle, S., Giri, J., and Korde, V. 2018. Design and evaluation of a Lean Manufacturing framework using Value Stream Mapping (VSM) for a plastic bag manufacturing unit. In Materials Today: Proceedings. 5(2): 7668–7677. DOI : https://doi.org/10.1016/j.matpr.2017.11.442

Braglia, M., Frosolini, M., & Zammori, F. 2009. Uncertainty in value stream mapping analysis. International Journal of Logistics. 12(6): 435–453. DOI : https://doi.org/10.1080/13675560802601559

Busert, T., and Fay, A. 2019. Extended value stream mapping method: Harmonizing information flows for the control of production processes. IFAC-PapersOnLine. 52(13): 54–59. DOI : https://doi.org/10.1016/j.ifacol.2019.11.129

Tasdemir, C., and Hiziroglu, S. 2019. Achieving cost efficiency through increased inventory leanness: Evidences from oriented strand board (OSB) industry. 2019. International Journal Production Economy. 208: 412–433. DOI : https://doi.org/10.1016/j.ijpe.2018.12.017

Lugert, A., Batz, A., and Winkler, H. 2018. Empirical assessment of the future adequacy of value stream mapping in manufacturing industries. Journal of Manufacturing Technology Management. 29(5): 886–906. DOI : https://doi.org/10.1108/JMTM-11-2017-0236

Kowang, T. O., Ying, Y. C., Yew, L. K., Hee, O. C., Fei, G. C., Long, C. S., & Saadon, M. S. I. bin. 2019. Industry 4.0 Competencies for Production Equipment Manufacturers in Malaysia. International Journal of Academic Research in Business and Social Sciences, 9(2): 300–311. DOI :https://doi.org/10.6007/IJARBSS/v9-i2/5545

Woehrle, S. L., Shady, L.A. 2010. Using Dynamic Value Stream Mapping And Lean Accounting Box Scores To Support Lean Implementation. American Journal of Business Education. 2(8): 1-2. DOI: https://doi.org/10.19030/ajbe.v3i8.472

Abideen, A., and Mohamad, F.B. 2020. Improving the performance of a Malaysian pharmaceutical warehouse supply chain by integrating value stream mapping and discrete event simulation. Journal of Modelling in Management. 14(1): 70-77. DOI :https://doi.org/10.1108/JM2-07-2019-0159

Abdul-Hamid, A.Q., Ali, M.H., Tseng, M.L., Lan, S., and Kumar, M. 2020. Impeding challenges on industry 4.0 in circular economy: Palm oil industry in Malaysia. Computer and Operations Research 123: 1050-1052. DOI :https://doi.org/10.1016/j.cor.2020.105052

Gungor, Z.E., and Evans, S. 2018. Understanding the hidden cost and identifying the root causes of changeover impacts. Journal of Clean Production. 167: 1138–1147. DOI : https://doi.org/10.1016/j.jclepro.2017.08.055

Karim, A,N.M., Jaafar, A.A.B., Abdullah, M.A.I., Haque, M., Ali, M.Y., and Azline, S.A. 2012. Applying Value Stream Mapping for Productivity Improvement of a Metal Stamping Industry. Advanced Material. 576: 727–730. DOI : : https://doi.org/10.4028/www.scientific.net/AMR.576.727

Liu Q., and Yang, H. 2020. Incorporating Variability in Lean Manufacturing: A Fuzzy Value Stream Mapping Approach. Mathematical Problems in Engineering. 2020: 1-17. DOI :https://doi.org/10.1155/2020/1347054

Stadnicka, D., and Litwin, P. 2019. Value stream mapping and system dynamics integration for manufacturing line modelling and analysis. International Journal of Production Economics 208: 400–411. DOI : https://doi.org/10.1016/j.ijpe.2018.12.011

Downloads

Published

2023-02-28

Issue

Vol. 13 No. 1: March 2023

Section

Articles

How to Cite

DETERMINATION OF PROCESS VARIABILITY BY USING TRIANGULAR FUZZY NUMBER TO MINIMIZE PRODUCTION LEAD TIME IN A DYNAMIC VALUE STREAM MAPPING . (2023). ASEAN Engineering Journal, 13(1), 163-168. https://doi.org/10.11113/aej.v13.18574