TY - GEN
T1 - Predictive Service Allocation for Informal Automotive Mechanics Using Machine Learning
T2 - 6th International Conference of Accounting and Business, iCAB 2025
AU - Thaba, Sebonkile
AU - Nkadimeng, Edward
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - The informal automotive sector in South Africa is crucial for its contribution to economic growth and job creation. Due to it being informal, it is largely excluded from the mainstream of formal businesses, resulting in less benefit from what larger formal businesses benefit from. In trying to understand how service delivery might be improved in South Africa’s informal automotive sector, this project looked at the possible role of digital technologies, particularly machine learning. The main idea was to see whether a simple prediction model could help manage common delays in service response, especially in areas where informal mechanics operate without structured systems. For this purpose, we designed a basic model and tested it on the Big Five platform. Since actual service data were not available yet, we relied on a simulated dataset to train the model. The development process used PyTorch, which allowed for flexibility in adjusting the architecture during early testing. The dataset itself was designed to reflect real-world conditions as closely as possible, incorporating factors like the type of mechanical problem, where the request came from, whether urban, township, highway, or rural, what kind of vehicle was involved, and what sort of service was being requested. Although based on synthetic data, the model achieved promising initial results. It reached a mean squared error of 3.8 min2, a mean absolute error of 1.6 min, and an R2 value of 0.82, indicating reasonably strong predictive performance under test conditions. But beyond the numbers, this study sheds light on how predictive tools can play a key role in promoting digital inclusion, especially in informal markets that have long been excluded from structured service and supply chains. The model demonstrates how Artificial Intelligence can help small-scale mechanics move towards more formalized operations, improving both customer satisfaction and operational performance.
AB - The informal automotive sector in South Africa is crucial for its contribution to economic growth and job creation. Due to it being informal, it is largely excluded from the mainstream of formal businesses, resulting in less benefit from what larger formal businesses benefit from. In trying to understand how service delivery might be improved in South Africa’s informal automotive sector, this project looked at the possible role of digital technologies, particularly machine learning. The main idea was to see whether a simple prediction model could help manage common delays in service response, especially in areas where informal mechanics operate without structured systems. For this purpose, we designed a basic model and tested it on the Big Five platform. Since actual service data were not available yet, we relied on a simulated dataset to train the model. The development process used PyTorch, which allowed for flexibility in adjusting the architecture during early testing. The dataset itself was designed to reflect real-world conditions as closely as possible, incorporating factors like the type of mechanical problem, where the request came from, whether urban, township, highway, or rural, what kind of vehicle was involved, and what sort of service was being requested. Although based on synthetic data, the model achieved promising initial results. It reached a mean squared error of 3.8 min2, a mean absolute error of 1.6 min, and an R2 value of 0.82, indicating reasonably strong predictive performance under test conditions. But beyond the numbers, this study sheds light on how predictive tools can play a key role in promoting digital inclusion, especially in informal markets that have long been excluded from structured service and supply chains. The model demonstrates how Artificial Intelligence can help small-scale mechanics move towards more formalized operations, improving both customer satisfaction and operational performance.
KW - Digital inclusion
KW - Informal economy
KW - Machine learning
KW - Predictive analytics
UR - https://www.scopus.com/pages/publications/105036713238
U2 - 10.1007/978-3-032-13388-5_21
DO - 10.1007/978-3-032-13388-5_21
M3 - Conference contribution
AN - SCOPUS:105036713238
SN - 9783032133878
T3 - Springer Proceedings in Business and Economics
SP - 305
EP - 320
BT - Embracing Technological Agility in Accounting and Business – Vol. 3 - Proceedings of the 6th International Conference of Accounting and Business iCAB, Cape Town 2025
A2 - Moloi, Tankiso
PB - Springer Nature
Y2 - 19 June 2025 through 20 June 2025
ER -