Optimization of Transportation Costs in a Multi-Plant Distribution Network: A Linear Programming Approach for the Nigerian Bottling Company

Silas U. Etiti; Kennedy C. Owuama; Swift O.N.K. Onyegirim; Ifeanyichukwu U. Onyenanu

doi:10.61424/ijans.v4i2.918

Authors

Silas U. Etiti Department of Mechanical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria
Kennedy C. Owuama Department of Mechanical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria
Swift O.N.K. Onyegirim Department of Mechanical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria
Ifeanyichukwu U. Onyenanu Department of Mechanical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria

DOI:

https://doi.org/10.61424/ijans.v4i2.918

Keywords:

Linear Programming, Transportation cost optimization, Supply chain management, Distribution network, Cost efficiency, Nigerian Bottling Company, Logistics optimization

Abstract

Transportation is one of the largest controllable expenses in the distribution of fast-moving consumer goods. This research aims to minimize transportation costs in the Southeast and South-South region distribution network of the Nigerian Bottling Company (NBC), based on real data from 2014 to 2019. The four-plant, five-depot network has a total capacity of 5,760 truckloads to meet a demand of 5,328 truckloads. An analytical nonlinear programming model for route efficiencies and cost drivers, as well as plant-depot allocations under supply/demand constraints, was developed within a structured optimization framework. The findings indicate that the six-year baseline transportation cost of ₦284,089.68 was minimized to ₦9,633.64 after optimization, leading to a 96.6% reduction in cost and a profit of ₦274, 456. Maintenance and fuel consumption costs constituted 39.20% and 20.50% of the total baseline cost, respectively. Sensitivity analysis (±20% fuel) validates robustness (95.9 - 97.3% of savings retained) and cost-distance correlation (r = 0.8411) emphasizes the impact of infrastructure. The study leads to the conclusion that integrating transportation cost optimization with supply chain cost reduction can be required considerably.

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