Modelling the root causes of complexity in the South African sawmilling industry

Authors

DOI:

https://doi.org/10.38140/ijms-2025.vol2.2.06

Keywords:

Complexity, root-cause analysis , operating environment, sawmilling industry, competitiveness, sustainable industries

Abstract

The sawmilling industry in South Africa has faced many obstacles that have severely impacted its competitiveness and sustainability. The complexity of the industry presents challenges for decision-making due to its dynamic nature. It has become more crucial than ever for decision-makers to incorporate expert facts and research into their choices. This study aimed to understand the operating environment of the sawmilling industry and model its complexity. A mixed-methods research approach was employed, involving the gathering of primary data through interviews with personnel in the sawmilling industry, as well as root-cause analysis and system dynamics modelling. Fishbone and causal loop diagrams were used to present and analyse the data. The study generated insights critical for modelling the sector's complexity, enabling an understanding of how the factors influence each other and impact decision-making in the sawmills. The industry has the potential to gain and sustain its competitiveness by focusing on the key factors affecting the complexity of operating a mill and those faced by the industry. Sawmillers can use the findings to examine their processes, identify areas for improvement, and assess potential implications. Other industry stakeholders can also utilise the results to identify ways to enhance the industry's competitiveness and inform the government's industrial policy on sawmilling in South Africa. A limitation of the study was the lack of input from representatives of informal sawmills. Future research will focus on understanding the issues affecting small-scale and informal sawmills to determine how they can be supported in contributing to the industry's sustainability.

References

Abdallah, J., Woiso, D., Monela, G., & Phillip, R. (2013). Productive efficiency of small-scale sawmilling industries in Mufindi district, Tanzania. Tanzania Journal of Forestry and Nature Conservation, 82(2), 77–98.

Björheden, R., & Helstad, K. (2005). Raw material procurement in sawmills’ business level strategy: A contingency perspective. International Journal of Forest Engineering, 16(2), 47–56. https://doi.org/10.1080/14942119.2005.10702513

Bose, T. K. (2012). Application of fishbone analysis for evaluating supply chain and business process: A case study on the ST James Hospital. International Journal of Managing Value and Supply Chains, 3(2), 17–24. https://doi.org/10.5121/ijmvsc.2012.3202

Brege, S., Nord, T., Sjöström, R., & Stehn, L. (2010). Value-added strategies and forward integration in the Swedish sawmill industry: Positioning and profitability in the high-volume segment. Scandinavian Journal of Forest Research, 25(5), 482–493. https://doi.org/10.1080/02827581.2010.496738

Coccia, M. (2018). The fishbone diagram is used to identify, systematise and analyse the sources of general-purpose technologies. Journal of Social and Administrative Sciences, 4(4), 291–303.

Currie, D. J., Smith, C., & Jagals, P. (2018). The application of system dynamics modelling to environmental health decision-making and policy: A scoping review. BMC Public Health, 18(1), 402. https://doi.org/10.1186/s12889-018-5318-8

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough?: An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903

Haimes, Y. Y. (2012). Modeling complex systems of systems with phantom system models. Systems Engineering, 15(3), 333–346. https://doi.org/10.1002/sys.21205

Johansson, M. (2004). Managing the sawmill with product costs: A simulation study. Proceedings of the Biennial Meeting of the Scandinavian Society of Forest Economics Vantaa, Finland, 12th - 15th May, 2004, 11.

Khan, S., Yufeng, L., & Ahmad, A. (2009). Analysing complex behaviour of hydrological systems through a system dynamics approach. Environmental Modelling and Software, 24(12), 1363–1372. https://doi.org/10.1016/j.envsoft.2007.06.006

Luca, L., & Luca, T. O. (2019). Ishikawa diagram applied to identify causes which determine bearing defects in car wheels. IOP Conference Series: Materials Science and Engineering, 564(1), 012093. https://doi.org/10.1088/1757-899X/564/1/012093

Martínez-Marín, S., Puello-Pereira, N., & Ovallos-Gazabon, D. (2020). Cluster competitiveness modeling: An approach with systems dynamics. Social Sciences, 9(2), 12. https://doi.org/10.3390/socsci9020012

Ngobi, J., Kambugu, R. K., Mugabi, P., & Banana, A. Y. (2024). Performance of softwood plantation sawmills: The volume vs. value sawing strategy. 13(February 2025), 73–86. https://www.researchsquare.com/article/rs-4943760/v1

Regmi, A., Grebner, D. L., Willis, J. L., & Grala, R. K. (2022). Sawmill willingness to pay price premiums for higher quality pine sawtimber in the southeastern United States. Forests, 13(5), 662. https://doi.org/10.3390/f13050662

Roos, A., Flinkman, M., Jäppinen, A., Lönner, G., & Warensjö, M. (2001). Production strategies in the Swedish softwood sawmilling industry. Forest Policy and Economics, 3(3–4), 189–197. https://doi.org/10.1016/S1389-9341(01)00063-6

Saunders, M. N. K., & Townsend, K. (2016). Reporting and justifying the number of interview participants in organisation and workplace research. British Journal of Management, 27(4), 836–852. https://doi.org/10.1111/1467-8551.12182

Saunders, M., & Tosey, P. (2012). The layers of research design. In Research methods for business students (6th ed., pp. 58–59). Pearson.

Stern, T., Ledl, C., Braun, M., Hesser, F., & Schwarzbauer, P. (2015). Biorefineries’ impacts on the Austrian forest sector: A system dynamics approach. Technological Forecasting and Social Change, 91, 311–326. https://doi.org/10.1016/j.techfore.2014.04.001

Tomoaia-Cotisel, A., Kim, H., Allen, S. D., & Blanchet, K. (2017). Causal loop diagrams: A tool for visualising the system structure resulting in emergent system behaviour. Open University Press.

Walters, J. P., Archer, D. W., Sassenrath, G. F., Hendrickson, J. R., Hanson, J. D., Halloran, J. M., Vadas, P., & Alarcon, V. J. (2016). Exploring agricultural production systems and their fundamental components with system dynamics modelling. Ecological Modelling, 333, 51–65. https://doi.org/10.1016/j.ecolmodel.2016.04.015

Zanjani, M. K., Ait-Kadi, D., & Nourelfath, M. (2010). Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning. European Journal of Operational Research, 201(3), 882–891. https://doi.org/10.1016/j.ejor.2009.03.041

Zanjani, M. K., Nourelfath, M., & Ait-Kadi, D. (2011). Production planning with uncertainty in the quality of raw materials: A case in sawmills. Journal of the Operational Research Society, 62(7), 1334–1343. https://doi.org/10.1057/jors.2010.30

Published

2025-12-09

How to Cite

Tshavhungwe, V., Oosthuizen, R., & Grobbelaar, S. (2025). Modelling the root causes of complexity in the South African sawmilling industry. Interdisciplinary Journal of Management Sciences, 2(2), a06. https://doi.org/10.38140/ijms-2025.vol2.2.06

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