Impact of automated picking systems on operational efficiency in South Africa’s manufacturing warehouses
Tshepo Mabotja ORCID ↗
International Journal of Research in Business and Social Science · 2025 · Vol. 14 / Issue 3 · 18-29
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Abstract
The study seeks to examine the determinants affecting warehouse performance, emphasising the impact of warehouse management systems on improving the worldwide competitiveness of South African steel manufacturing firms. It aims to underscore the difficulties in existing procedures and the strategic significance of proficient warehouse management. The study utilised a qualitative methodology to examine warehouse management methods in South African steel manufacturing firms. Data was gathered via structured interviews and questionnaires to ascertain current management system frameworks, picking methodologies, and the incorporation of automated solutions. The results indicate that the majority of South African steel manufacturers employ non-automated and non-integrated warehouse management systems. A notable deficiency in formal picking systems and zone picking was seen, leading to inefficient operations, excessive moves, and a failure to attain a competitive advantage in the worldwide market. The report emphasises a widespread undervaluation of warehouse management's strategic importance by senior management in enhancing manufacturing competitiveness. This report emphasises the necessity for South African steel manufacturing firms to implement integrated and automated warehouse management systems to optimise processes and improve global competitiveness. The enhancement of warehouse operations, particularly through the adoption of formal picking methods, could markedly diminish operational inefficiencies and elevate consumer satisfaction. This study offers significant insights into the constraints of existing warehouse management procedures in South Africa's steel manufacturing industry. By pinpointing particular obstacles and opportunities for enhancement, it provides actionable recommendations for utilising warehouse management as a competitive edge in the global marketplace.
Keywords: {'name': 'Zone picking'}, {'name': 'Warehousing'}, {'name': 'Manufacturing'}, {'name': 'Picking systems'}, {'name': 'Supply chain management'}
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Tshepo Mabotja
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DOI: 10.1186/s44147-023-00175-y
DOI: 10.1002/net.21995
Automated Warehouse Consulting. (2024). The impact of automated picking systems on supply chain management. Retrieved from https://automatedwarehouse.net
DOI: 10.1080/21693277.2023.2191115
DOI: 10.1080/00207543.2024.2401901
Exotec. (2024). Overview of warehouse robotics. Retrieved from https://www.exotec.com
DOI: 10.1080/00207543.2023.2169383
DOI: 10.1080/00207543.2020.1788734
DOI: 10.33093/jiwe.2024.3.3.5
Li, K., Wang, J., Wu, X., Peng, X., Chang, R., Deng, X., ... & Hong, B. (2024). Optimizing automated picking systems in warehouse robots using machine learning. arXiv preprint arXiv:2408.16633.
Magoro, N. M. (2019). Increasing warehouse performance by effective order picking system: Case study of South African steel manufacturing sector. University of Johannesburg (South Africa).
Mobile Industrial Robots. (2024). Optimize warehouse efficiency with advanced picking systems. Retrieved from https://www.mobile-industrial-robots.com
DOI: 10.1109/ACCESS.2024.3357689
DOI: 10.1080/00207543.2023.2183343
DOI: 10.1016/j.ejor.2017.09.002
DOI: 10.1016/j.cie.2022.107981
DOI: 10.1109/JIOT.2024.3352658