Investigating the cost of mechanized unpaved road maintenance operations in Uganda

Abstract

Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.