Statistical method using Principal Component Analysis to determine high variability parameters affecting the balancing of an assembly line

2024;
: pp. 663–673
https://doi.org/10.23939/mmc2024.03.663
Received: December 05, 2023
Revised: July 21, 2024
Accepted: July 24, 2024

Hillali Y., Zegrari M., Alfathi N., Chafik S., Tabaa M.  Statistical method using Principal Component Analysis to determine high variability parameters affecting the balancing of an assembly line.  Mathematical Modeling and Computing. Vol. 11, No. 3, pp. 663–673 (2024)

1
Laboratory of Complex Cyber Physical Systems (LCCPS), ENSAM Casablanca, University Hassan 2; Pluridisciplinary Laboratory of Research and Innovation (LPRI), EMSI Casablanca
2
Laboratory of Complex Cyber Physical Systems (LCCPS), ENSAM Casablanca, University Hassan 2
3
Laboratory Intelligent Systems and Applications (LSIA), EMSI Tanger
4
Pluridisciplinary Laboratory of Research and Innovation (LPRI), EMSI Casablanca
5
Pluridisciplinary Laboratory of Research and Innovation (LPRI), EMSI Casablanca

Modern assembly lines face numerous challenges when it comes to satisfying client expectations.  The challenges discussed include increasing customization demands, maintaining quality standards, managing lead times, addressing sustainability concerns, and effectively utilizing advanced technologies.  This challenges impact assembly lines efficiency and effectiveness in other word balancing of the line.  This research aims to identify the essential components that significantly influence the balance of assembly lines.  To achieve this objective, a novel approach is proposed using a 3D matrix interpretation and statistical method, Principal Component Analysis (PCA).  The research leverages the MATLAB tool to analyze the interactions between various parameters and identify highly changeable factors that impact assembly line balance.  By employing this methodology, the study aims to provide valuable insights into identifying the parameters of an assembly line balancing and enhancing overall operational efficiency.  The finding of this approach, reveal a significant influence of altering the piloting parameters on assembly line balancing.  This result underscores the importance of dynamically balancing the assembly line to achieve optimal performance.

  1. Ren W., Wen J., Guan Y., Hu Y.  Research on assembly module partition for flexible production in mass customization.  Procedia CIRP.   72, 744–749 (2018).
  2. Kucukkoc I., Zhang D. Z.  A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem.  Production Planning & Control: The Management of Operations.  26 (11), 874–894 (2015).
  3. Kheirabadi M., Keivanpour S., Chinniah Y. A., Frayret J.-M.  Human-robot collaboration in assembly line balancing problems: Review and research gaps.  Computers and Industrial Engineering.  186 (C),  (2023).
  4. Bortolini M., Ferrari E., Gamberi M., Pilati F., Faccio M.  Assembly system design in the Industry 4.0 era: a general framework.  IFAC-PapersOnLine.  50 (1), 5700–5705 (2017).
  5. Álvarez-Miranda E., Pereira J., Vilà M.  Analysis of the simple assembly line balancing problem complexity.  Computers & Operations Research.  159, 106323 (2023).
  6. Jaskó S., Skrop A., Holczinger T., Chován T., Abonyi J.  Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools.  Computers in Industry.  123, 103300 (2020).
  7. Battaïa O., Otto A., Sgarbossa F., Pesch E.  Future trends in management and operation of assembly systems: from customized assembly systems to cyber-physical systems.  Omega.  78, 1–4 (2018).
  8. Li Z., Janardhanan M. N., Rahman H. F.  Enhanced beam search heuristic for U-shaped assembly line balancing problems.  Engineering Optimization.  53 (4), 594–608 (2021).
  9. Pilati F., Lelli G., Faccio M., Gamberi M., Regattieri A.  Assembly line balancing for personalized production.  IFAC-PapersOnLine.  53 (2), 10261–10266 (2020).
  10. Hahs-Vaughn D. L.  Foundational methods: descriptive statistics: bivariate and multivariate data (correlations, associations).  International Encyclopedia of Education (Fourth Edition). 734–750 (2023).
  11. Jaskó S., Skrop A., Holczinger T., Chován T., Abonyi J.  Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools.  Computers in Industry.  123, 103300 (2020).
  12. Gilles M. A., Gaudez C., Savin J., Remy A., Remy O., Wild P.  Do age and work pace affect variability when performing a repetitive light assembly task?  Applied Ergonomics.  98, 103601 (2022).
  13. Romero-Silva R., Hurtado-Hernàndez M.  The effects of supply variability on the performance of assembly systems.  International Journal of Production Research.  61 (15), 4973–4990 (2023).
  14. Zamzam N., El-Kharbotly A. K.  Balancing two-sided multi-manned assembly line under time and space constraint.  Ain Shams Engineering Journal.  15 (3), 102464 (2024).
  15. Çelik M. T., Arslankaya S.  Solution of the assembly line balancing problem using the rank positional weight method and Kilbridge and Wester heuristics method: An application in the cable industry.  Journal of Engineering Research.  11 (3), 182–191 (2023).
  16. Gräßler I., Roesmann D., Cappello C., Steffen E.  Skill-based worker assignment in a manual assembly line.  Procedia CIRP.  100, 433–438 (2021).
  17. Khan S. H., Majid A., Yasir M.  Strategic renewal of SMEs: the impact of social capital, strategic agility and absorptive capacity.  Management Decision.  59 (8), 1877–1894 (2020).
  18. Álvarez-Miranda E., Pereira J., Vilà M.  Analysis of the simple assembly line balancing problem complexity.  Computers & Operations Research.  159, 106323 (2023).
  19. Schlüter M. J., Ostermeier F. F.  Dynamic line balancing in unpaced mixed-model assembly lines: A problem classification, CIRP Journal of Manufacturing Science and Technology.  37, 134–142 (2022).
  20. Lai X., Qiu T., Shui H., Ding D., Ni J.  Predicting future production system bottlenecks with a graph neural network approach.  Journal of Manufacturing Systems.  67, 201–212 (2023).
  21. Nallusamy S.  Execution of lean and industrial techniques for productivity enhancement in a manufacturing industry.  Materials Today: Proceedings.  37 (2), 568–575 (2021).
  22. Andrés-López E., González-Requena I., Sanz-Lobera A.  Lean Service: Reassessment of Lean Manufacturing for Service Activities.  Procedia Engineering.  132, 23–30 (2015).
  23. Zhang Wei, Hou L., Jiao R. J.  Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach.  Computers & Industrial Engineering.  161, 107616 (2021).
  24. Bastos N. M., Alves A. C., Castro F. X., Duarte J., Ferreira L. P., Silva F. J. G.  Reconfiguration of assembly lines using Lean Thinking in an electronics components' manufacturer for the automotive industry.  Procedia Manufacturing.  55, 383–392 (2021).