Evaluation of Agricultural Machinery Management System and Cost Optimization: A Case of Arjo Didessa Sugar Factory

Abstract

Agricultural Machinery Management Techniques In A Sugar Factory Are Essential For Optimizing Productivity, Minimizing Costs, Ensuring Product Quality, And Maintaining A Safe Working Environment. However, Mishandling Farm Machinery And Implements Led To Needlessly High Production Costs, Which Had A Detrimental Effect On The Factory's Financial Situation. The Objective Of The Study Was To Evaluate The Current State Of The Agricultural Machinery Management System And Optimize The Operation Costs Of Machinery At The Arjo Dedessa Sugar Factory. To Achieve This Objective Of The Study, Data Was Collected Through Semi-Structured Questionnaires, Interviews, And Observation Surveys During The Growth Seasons Of 2016?�?17 Up To 2021?�?22. Tractors, Both Wheel And Crawlers And Implements Were Among The Farm Machinery Available In The Study Area. For Cost Analysis Purposes, Current Functional Or Active Tractors And Implements Were Considered. In This Study, The Relationship Between Variables And Parameters Was Analyzed Using Simple Microsoft Excel. The Linear Programming Model Was Analyzed Using Linear Interactive And Discrete Optimizer (Lindo) Software To Optimize Farm Machinery Operational Costs. Approximately 49.09% Of The Remaining Non-Operating Machinery Needs Minor To Major Maintenance. About 13.97% Of The Machinery Must Be Disposed Of. The Calculated Rate Of Work Was Found To Be 35.33% Higher Than The Actual Ones, With Uprooting Showing The Least Variation (5.73%) And Inter Row Cultivation Activities Showing The Most Variation (67.21%). According To This Finding, New H.Tm 7020, And Yto 130 Tractors Have Lower Depreciation Costs Than Yto180 Tractors, Which Have The Highest. This Study Showed That All Of The Study Area's Machinery Had Low Initial Repair And Maintenance Costs, Which Increased As The Machinery Grew Older. The Optimization Model's Impact On The Components Of Reducing Agricultural Machinery Operating Costs Indicated That The Greatest Cost Savings (10.60%) Were Achieved In 2018?�?19, The Lowest Cost Savings (0.66%) In 2016?�?17, And 0.85% In 2020?�?21. Model Verification Was Made By Comparing The Total Operation Costs Of Arjo Didessa Sugar Factory For Seasons 2016?�?17?�?2021/22 With Those Estimated By The Model. The Model Succeeded In Reducing The Operation's Total Costs By 10.60%. Effective Estimation Of Machinery Work Rates And Performance Analysis Are Essential For Optimizing Operational Efficiency And Maximizing Productivity. An Effective Recording System For Specific Machinery Data And Efficient Cost Analysis Methods Are Required.