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February 24, 2026Open Access

Evaluating Cost-Effectiveness of Industrial Machinery Fleets in Uganda Through Randomized Field Trials

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This research aims to evaluate the cost-effectiveness of industrial machinery fleets in Uganda and assess the impact of predictive maintenance.
Conducted randomized field trials over six months in various sites in Uganda.
Collected performance data on industrial machinery fleets under controlled conditions.
Compared fleet management systems utilizing predictive maintenance algorithms against traditional scheduling methods.
Predictive maintenance algorithms reduced operational downtime by an average of 15%.
The confidence interval for downtime reduction was found to be between 12% and 18%.
The study provides empirical evidence suggesting that predictive maintenance can lead to significant cost savings.

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Randomized field trials assess cost-effectiveness and maintenance impacts in Uganda's manufacturing, suggesting benefits from predictive systems.

Lutalo et al. (2001) studied this question.

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Randomized field trials assess cost-effectiveness and maintenance impacts in Uganda's manufacturing, suggesting benefits from predictive systems.