Route Optimization Strategies for Enhancing Logistics Efficiency in Agricultural Supply Chains

The article begins by explaining the strategic role of the North Bandung Cattle Breeders Cooperative, known as KPSBU, in the dairy industry of the Lembang region. Established in 1971, the cooperative supports dairy farmers through credit facilities, production inputs, and concentrated animal feed distribution. Its role is important for farmer livelihoods, milk production, and regional food security.

A central business unit of KPSBU is the distribution of concentrated animal feed, referred to as MaKo. This feed is important for maintaining milk production, especially when forage quality fluctuates. Because the cooperative serves many members, efficient logistics are necessary to maintain stable supply and protect the economic welfare of dairy farmers.

The article identifies logistics inefficiency as a major operational challenge. KPSBU’s management sets a standard distribution cost of Rp 540,000 per trip, but actual field expenditure reaches Rp 563,672 per trip. This difference indicates a significant gap between budget expectations and real operating costs.

The main cause of this gap is the cooperative’s reliance on intuitive routing. Drivers determine routes based on personal experience rather than empirical data or digital route optimization. This creates inefficiencies in travel distance, fuel consumption, travel time, and vehicle operating costs.

Distribution takes place across eleven Concentrate Distribution Points in the Lembang area, which has geographically challenging terrain and varying altitudes. The cooperative uses Toyota Dyna 110ET trucks with a 6,000 kg capacity. Without precise route planning, these vehicles face increased operating costs and reduced logistical efficiency.

The introduction reviews previous research showing that route optimization can improve agribusiness logistics. Methods such as the Saving Matrix and Capacitated Vehicle Routing Problem have been used to minimize travel distance, fuel consumption, and transportation costs. However, previous studies have often focused on static distance minimization.

The article identifies a research gap in the limited use of advanced simulation software such as SCM Globe and AnyLogic in Indonesian cooperative logistics. Existing studies have not fully integrated variables such as route distance, vehicle operating costs, vehicle lifespan, maintenance schedules, fuel volatility, and mechanical wear into a dynamic digital simulation model.

The study therefore aims to model and optimize vehicle operating costs in KPSBU’s concentrate feed distribution system. It compares existing routes with optimized scenarios using simulation software and seeks to validate digital simulation as a decision-making tool for cooperative logistics. The article positions data-driven route optimization as a practical way to reduce costs, improve asset resilience, and support sustainable cooperative operations.

This study uses a descriptive quantitative research design with a simulation-based analytical framework to evaluate and optimize feed distribution cost efficiency. The simulation approach was chosen because it allows multiple operational scenarios to be tested without disrupting real cooperative activities. The study uses SCM Globe and AnyLogic software to model a digital twin of KPSBU’s feed distribution system and to compare existing routes with optimized alternatives.

The data sources consist of secondary data from KPSBU Lembang’s internal logistics reports and historical records. Data collection included address codes, geographic coordinates of the eleven Concentrate Distribution Points, current vehicle operating cost records, route distances, travel duration, vehicle lifespan, and maintenance requirements. The unit of analysis is the feed distribution trip performed by Toyota Dyna 110ET trucks with a capacity of 6,000 kg. Google Maps was used for distance mapping, while SCM Globe and AnyLogic were used to process routing and cost variables. Validity was ensured by comparing simulation outputs for existing routes with historical cost data, while reliability was supported through careful checking of formula inputs and scenario logic. Ethical considerations were maintained by using secondary data without disclosing farmer identities or sensitive cooperative information.

The simulation using AnyLogic and SCM Globe compared the existing route with three optimization scenarios. The main performance indicators were total distance, travel duration, vehicle lifespan, vehicle operating cost per trip, and cost savings compared with the existing model.

The existing model covers 78.8 kilometers, requires 2.3 hours of travel, assumes an eight-year vehicle lifespan, and produces a vehicle operating cost of Rp 562,423 per trip. This cost exceeds the cooperative’s standard budget and confirms the operational discrepancy identified in the introduction.

Scenario 1 focuses on shortest-path optimization. It reduces total distance to 60.5 kilometers and travel duration to 1.8 hours, while maintaining an eight-year vehicle lifespan. This scenario produces a vehicle operating cost of Rp 549,146 per trip and saves Rp 13,277 compared with the existing model.

Scenario 1 is the most efficient model for immediate cost reduction. Its success shows that reducing travel distance directly lowers fuel consumption, labor-related travel time, and variable vehicle operating costs. This supports the argument that route optimization is a fundamental driver of cost efficiency in agricultural supply chains.

Scenario 2 focuses on maintenance improvement while keeping the existing 78.8-kilometer route and 2.3-hour travel duration. It extends the vehicle lifespan from eight to ten years but increases the vehicle operating cost to Rp 577,938 per trip. This creates an additional cost of Rp 15,515 compared with the existing model.

The increase in Scenario 2 reflects the financial burden of preventive maintenance. Scheduled servicing and component replacement increase daily costs, but they are intended to maintain engine performance, extend vehicle lifespan, and reduce the risk of unexpected mechanical failure.

Scenario 3 integrates shortest-path optimization with maintenance improvement. It uses the shorter 60.5-kilometer route, reduces travel duration to 1.8 hours, and extends vehicle lifespan to ten years. The resulting vehicle operating cost is Rp 560,978 per trip, which is Rp 1,445 lower than the existing model.

Scenario 3 shows that route efficiency can offset some of the additional cost created by stronger maintenance policies. Although it does not match Scenario 1’s immediate savings, it offers a balanced approach between cost reduction and long-term asset sustainability.

The findings demonstrate that distance reduction is the most significant factor in immediate cost suppression within KPSBU’s distribution network. Rearranging the sequence of visits to the eleven distribution points can help bridge the gap between actual expenditures and management’s standard budget.

The study also shows that preventive maintenance creates a managerial trade-off. Scenario 2 is more expensive in the short term, but it may protect the cooperative from costly breakdowns and distribution interruptions. This is important because delivery delays could affect feed availability and dairy cattle productivity.

The integrated model in Scenario 3 provides a moderate long-term solution. It uses savings from route optimization to absorb the cost of improved maintenance. This approach supports both operational efficiency and fleet reliability in the challenging terrain of the Lembang region.

Overall, the results confirm that data-driven logistics simulation outperforms intuition-based routing. The digital twin model allows KPSBU to test route and maintenance scenarios before implementation, forecast cost impacts, and make more accountable decisions. The study contributes a practical framework for agricultural cooperatives seeking to modernize logistics through digital supply chain simulation.

This research addresses the critical operational discrepancy between the standard logistics budget and actual field expenditures in the distribution of MaKo concentrate feed at KPSBU. By employing SCM Globe and AnyLogic simulation tools, the study evaluated several distribution scenarios against the existing route performance. The findings reveal that Scenario 1, which focuses on the shortest-path optimization (60.5 km), is the most financially viable model, achieving a significant cost reduction of Rp 13,277 per trip. While the integration of preventive maintenance in Scenarios 2 and 3 initially presented a rise in daily costs due to increased servicing allocations, the results demonstrated that these expenses could be offset by routing efficiencies. Ultimately, the study concludes that transitioning from intuitive, experience-based routing to data-driven simulation is essential for stabilizing the cooperative's profit margins and operational consistency.

This study makes a significant contribution to the field of agribusiness logistics by providing a replicable digital-twin framework for traditional cooperatives. Theoretically, it bridges the gap between static route optimization theories and dynamic vehicle operating cost (VOC) modeling in a rural Indonesian context. It provides empirical evidence that digital transformation, as advocated by modern supply chain literature, is not only applicable but vital for the economic resilience of grassroots organizations. Furthermore, the research offers practical insights into the trade-off between short-term liquidity and long-term asset sustainability, demonstrating how cooperatives can utilize simulation technology to validate strategic decisions before implementation. This work serves as a benchmark for other agricultural institutions seeking to modernize their supply chain infrastructure while facing similar geographical and budgetary constraints.

Based on the findings, it is highly recommended that KPSBU management immediately standardizes the optimized routes identified in Scenario 1 as a new Standard Operating Procedure (SOP). To maintain these efficiency gains, the cooperative should integrate real-time GPS tracking with the simulation model to monitor driver compliance and adapt to daily traffic fluctuations. Future research should expand upon this framework by incorporating multi-vehicle fleet types and dynamic variables such as real-time fuel price volatility and carbon emission metrics. Additionally, further studies could explore the impact of these logistical savings on the final price of feed provided to farmer-members, thereby assessing the broader socio-economic impact of the cooperative's digital transformation. Exploring the integration of Artificial Intelligence (AI) for predictive maintenance scheduling would also provide a deeper understanding of long-term fleet reliability.

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