Browsing by Author "Axumawit Shmuye"

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    Optimizing Distribution Network Design in the Tomato Supply Chain to Reduce Post-Harvest Waste: A Case Study in Southern Tigray
    (Mekelle University, 2025-08-25) Axumawit Shmuye
    Tomato farmers in Tigray, Ethiopia lose about 45% of their harvest after harvesting because of poor transport, storage, and weak connections between sellers. Most studies focus only on describing the amount of postharvest waste and identifying the causes, but there is lack of research how to improve the distribution network. Owing to the crop's economic and food security value, the study implies a geographical distribution model aimed at minimizing wastes. A mixed-method approach was utilized that included GIS-based spatial analysis, simulation. Analysis tools used spatial mapping and blended simulation to determine why delivery was delayed, routing was inefficient, and storage was insufficient. Only 55% of tomatoes picked are delivered to markets in good condition, as 17–20% are lost in storage and 15–20% through logistical inefficiencies as well as the absence of central aggregation centers from the total amount of 36,167,000 quintals annually. Simulation outcomes demonstrate how integrating routing optimization with storage can reduce postharvest loss. The evidence confirms that systemic and spatial inefficiencies are the key causes of postharvest loss. Methodologically, this research advances by integrating GIS diagnostics and simulation modeling to develop a region-specific optimization supply chain by developing and designing the distribution network. It offers policymakers, farmer producer organizations, and development stakeholders’ hands-on guidance on how to make the food system more resilient, and the research reduces the overall waste. However, the study is geographically limited only in the Tigray region districts and fixed only for tomato distribution. These limitations also pose possibilities of future research to encompass how to expand to other regions of Ethiopia and assess the scalability of the model to other perishables and locations.