This study develops a data-driven performance measurement framework for the poultry meat supply chain to support operational decisions and farmer sustainability. A descriptive quantitative case study was conducted at Pawon Ayu, Kartasura, Indonesia, using observations, interviews, documentation, focus group discussions, and Analytical Hierarchy Process (AHP). Seven collaborative data groups were identified: demand, stock, production, distribution, sales, financial, and logistics. Demand emerged as the top priority (0.4087), followed by stock (0.2206) and production (0.1266). Key indicators include poultry consumption, meat availability, and daily production volume. A traffic light system translates inventory KPIs into actionable signals for decision-making. Validation through multi-stakeholder discussions confirms alignment between prioritized indicators and real operational practices. The framework enhances data transparency, supports collaborative dashboards, and enables faster, more informed decisions across the supply chain.

Food loss and waste (FLW) in the fruit and vegetable (F&V) supply chain has become a global issue due to its significant economic and environmental impacts. The perishable nature of F&V products and seasonal production patterns contribute to high levels of FLW throughout the supply chain. This study develops a system dynamics model to analyze the dynamic behavior of FLW generation within an F&V supply chain. The model captures the supply chain holistically in an end-to-end manner, from production and harvesting to retailing, while also incorporating FLW generation, and is represented through causal loop and stock and flow diagrams. Several scenarios, including different demand patterns and supply disruptions, were simulated. The results demonstrate that weather conditions, demand fluctuations, and supply disruptions significantly influence demand satisfaction and FLW generation over time. The proposed model also provides insights for improving supply chain sustainability and supporting decision-making for FLW reduction.

The escalating water crisis, intensified by climate change and inefficient irrigation practices, highlights the urgent need for innovative solutions in agriculture. This paper presents a design thinking-driven approach to developing an IoT-based smart irrigation system for sustainable precision agriculture. The system integrates real-time data collection from humidity and temperature sensors deployed via a mobile robotic platform, combined with weather forecast APIs to enable data-driven irrigation scheduling tailored to plant-specific requirements. The proposed architecture consists of four interconnected layers: Sensor, Edge, Cloud, and Application. Sensor data is transmitted via MQTT protocols to a Firebase-powered cloud infrastructure, while a ReactJS dashboard provides farm managers with real-time monitoring, automated alerts, and irrigation control. The paper documents the design thinking process, multidisciplinary collaboration challenges, and lessons learned from early field testing. K

Universities are increasingly expected to encourage sustainable practices not only through academic programs, but also through day-to-day operations like campus meals. This study employs conjoint analysis to analyze how students evaluate sustainable food alternatives in cafeterias at various universities. The food profiles evaluated by 219 respondents varied in terms of vendor transparency, sustainability commitment, packaging type, and source. The findings indicate that sustainability commitment and packaging type had the greatest impact on overall preference. Recyclable and biodegradable packaging received the highest ratings, while high sustainability commitment and plant-based sourcing were rated lower. According to these findings, students are more open to sustainability programs that appear familiar and practical than those that are excessively difficult. This study gives guidance to campus food service providers intent on hitting a balance between environmental objectives and customer acceptance, as well as highlighting how sustainability signals influence food-related decisions in institutional contexts.

Food supply chains (FSC) in the United States face growing disruptions from production delays, transportation bottlenecks, regulatory changes, labor shortages, and market uncertainty. These challenges increase the need for reliable methods to assess and strengthen supply chain resilience. This study aims to predict FSC resilience and identify the factors that most strongly influence resilient performance. To address this objective, six key predictors are examined, including supplier reliability, production flexibility, digitalization index, financial stability, transportation risk, and geopolitical risk. Seven machine learning (ML) models, including Logistic Regression (LR), K Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, XGBoost, and Artificial Neural Networks (ANN), are trained and compared using hyperparameter tuning and cross-validation. Results show that ensemble models perform best, with XGBoost achieving the strongest overall predictive performance. The findings support contingency planning, resource allocation, resilience improvement, and food security decision-making across the supply chain.