Data-Driven Integration of Smart Performance Dashboards and Organizational Agility to Improve Operational Efficiency in Iran’s Sugarcane Agro-Industrial Sector

Abstract

Improving operational efficiency in agro-industrial systems requires a coordinated integration of data-driven decision tools and organizational agility mechanisms. In Iran’s sugarcane sector, which operates under conditions of fluctuating environmental inputs, resource constraints, and increasing market volatility, traditional performance management systems have shown limited capacity to support timely and evidence-based decision-making. Recent advancements in smart performance dashboards and agile management capabilities offer a new pathway for strengthening responsiveness, operational visibility, and strategic alignment across production, logistics, and processing units. Building on contemporary literature in analytics capability, agility, and industrial decision systems [1–15], this study develops an integrated model that links real-time dashboard intelligence with organizational agility constructs to enhance operational performance in sugarcane agro-industrial enterprises. The research uses a data-driven approach based on actual operational indicators obtained from sugarcane production and processing units in Iran, including harvesting efficiency, mill throughput, transportation cycle time, energy utilization, and maintenance-related downtime. The model conceptualizes how smart dashboards consolidate key performance indicators (KPIs) and transform them into actionable insights, while agility dimensions enable organizations to respond flexibly to operational disruptions, demand fluctuations, and process uncertainties. The proposed framework is tested through multivariate analysis, evaluating the interplay between dashboard accuracy, analytics adoption, agility metrics, and efficiency outcomes. Findings demonstrate that real-time dashboards significantly strengthen operational transparency and improve decision speed, while higher levels of organizational agility amplify the performance effects generated by data-driven insights. Synergistic integration of both mechanisms results in measurable gains across multiple efficiency indicators, particularly in reducing process variability and optimizing resource flows. This integrative approach supports the development of a scalable decision architecture suitable for agro-industrial contexts that rely heavily on synchronized, high-volume operations. The study contributes to both managerial practice and academic discourse by presenting a validated, evidence-based framework tailored specifically to the operational realities of Iran’s sugarcane industry. It further outlines pathways for future enhancements using predictive analytics, enabling organizations to transition toward more intelligent and adaptive management systems.