QUALITY AND STANDARDS IN CUSTOM FURNITURE PRODUCTION: A PRACTICAL SMALL-SCALE PRODUCTION APPROACH
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Abstract
Purpose. The purpose of this study is to develop a practical quality management framework for custom furniture production that enables small-scale manufacturers to ensure product quality while maintaining the flexibility required for highly customized, low-volume production. Methodology. The research is based on an analytical review of recent developments in wood defect detection, Total Quality Management (TQM), Lean methodologies and intelligent manufacturing systems. Approaches such as Artificial Intelligence (AI)-based inspection, Multi-Criteria Decision-Making (MCDM) methods and sustainability-driven design were adapted to small-scale conditions, resulting in a hybrid quality management model that integrates simplified Lean Six Sigma tools, defect detection technologies and process optimization strategies. Results. Consistent quality in custom furniture production depends primarily on structured process control, effective knowledge transfer and flexible production planning rather than on extensive automation; small manufacturers can achieve high product quality with a limited number of appropriate digital technologies alongside continuous process improvement. Practical implications. The framework offers a scalable solution for small and medium-sized custom furniture enterprises, improving process consistency, reducing defects and aligning quality assurance with customer-specific requirements without compromising operational flexibility. Value/originality. Unlike quality systems developed for mass manufacturing, the model combines Lean Six Sigma principles, modern defect detection technologies and process optimization techniques in a framework adapted to artisanal and highly customized production, bridging the gap between industrial quality standards and small-scale furniture production.
How to Cite
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custom furniture production, quality management, small-scale manufacturing, process optimization, defect detection, sustainable production
Djedidi, M., Hassen, M. B., Mrad, H., & Koubaa, A. (2025). Real time contaminants detection in wood panel manufacturing process using YOLO algorithms. Procedia Computer Science, 253: 1226–1235. https://doi.org/10.1016/j.procs.2025.01.184
Haonan, J., Huadong, X., Lihai, W., Jinsheng, Z., Xiaohui, C., & Xu, T. (2023). Quantitative identification of surface defects in wood paneling based on improved YOLOv5. Journal of Beijing Forestry University, 45(4): 147–155. https://dx.doi.org/10.12171/j.1000-1522.20220419
Hernadewita, H., Setiawan, I., & Hendra, H. (2022). Enhance quality improvement through lean six sigma in division Side Board Clavinova piano's. International Journal of Production Management and Engineering, 10(2): 173–181. https://doi.org/10.4995/ijpme.2022.16140
Kılıç, K., Kılıç, K., Doğru, İ. A., & Özcan, U. (2025). WD detector: Deep learning-based hybrid sensor design for wood defect detection. European Journal of Wood and Wood Products, 83: 50. https://doi.org/10.1007/s00107-025-02211-5
Klarić, K., Barčić, A. P., Greger, K., Vukman, K., Perić, I., & Klarić, M. (2024). Striving for excellence: deconstruction of Total Quality Management measuring model for Croatian furniture industry. Sustainability, 16(24): 11236. https://doi.org/10.3390/su162411236
Klarić, K., Perić, I., Vukman, K., Papić, F., Klarić, M., & Grošelj, P. (2024). Hybrid MCDM-FMEA model for process optimization: a case study in furniture manufacturing. Systems, 13(1): 14. https://doi.org/10.3390/systems13010014
Lee, H.-H., & Hsu, C.-M. (2025). Enhancing sustainable innovations in intelligent wood pellets through smart customized furniture and total quality management. Sustainability, 17(14): 6604. https://doi.org/10.3390/su17146604
Li, H., Yi, Z., Wang, Z., Wang, Y., Ge, L., Cao, W., & Sun, Q. (2024). FDADNet: detection of surface defects in wood-based panels based on frequency domain transformation and adaptive dynamic downsampling. ш, 12(10): 2134. https://doi.org/10.3390/pr12102134
Lu, W. (2025). Enhancing efficiency in custom furniture production with intelligent scheduling systems. Processes, 13(9), 2721. https://doi.org/10.3390/pr13092721
Marcineková, K., & Janáková Sujová, A. (2024). Multi-objective optimization of manufacturing process using artificial neural networks. Systems, 12(12): 569. https://doi.org/10.3390/systems12120569
Muminović, A. J., Gierz, Ł., Rebihić, H., Smajić, J., Pervan, N., Hadžiabdić, V., & Sydor, M. (2024). Enhancing furniture manufacturing with 3d scanning. Applied Sciences, 14(10): 4112. https://doi.org/10.3390/app14104112
Sabbir, M. M. (2025). Product feature and lifespan as a quality indicator for inducing eco-friendly furniture purchase. Heliyon, 11(3). https://doi.org/10.1016/j.heliyon.2025.e42422
Sepúlveda-Rojas, J. P., Aravena, S., & Carrasco, R. (2024). Increasing efficiency in furniture remanufacturing with AHP and the SECI model. Sustainability, 16(23): 10339. https://doi.org/10.3390/su162310339
Skorupińska, E., Hitka, M., & Sydor, M. (2024). Surveying quality management methodologies in wooden furniture production. Systems, 12(2): 51. https://doi.org/10.3390/systems12020051
Suandi, M.M.E., Amlus, M. H., Hemdi, A. R., Abd Rahim, S. Z., Ghazali, M. F., & Rahim, N. L. (2022). A review on sustainability characteristics development for wooden furniture design. Sustainability, 14(14): 8748. https://doi.org/10.3390/su14148748
Ukey, K., Chinta, L., Majumder, H., Patil, D. S., Mitkari, S., Sahu, A. R., & Dhutekar, P. K. (2025). Implementation of the Theory of Constraints (TOC) for a Furniture Manufacturing-Based Organization. Engineering Proceedings, 114(1): 17. https://doi.org/10.3390/engproc2025114017
Yang Fan, Yang Bokai, & Li Rongrong. (2024). Surface defect detection of engineered wood panels based on image segmentation and deep learning. Journal of Zhejiang A&F University, 41 (1): 176–182. https://doi.org/10.11833/j.issn.2095-0756.20230280
Zhang, Z., Zhu, J., & Qi, Q. (2023). Research on the recyclable design of wooden furniture based on recyclability evaluation. Sustainability, 15(24): 16758. https://doi.org/10.3390/su152416758
Zhao, Z., Ge, Z., Jia, M., Yang, X., Ding, R., & Zhou, Y. (2022). A particleboard surface defect detection method research based on the deep learning algorithm. Sensors, 22(20): 7733. https://doi.org/10.3390/s22207733
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