Integrating Business Intelligence Dashboards for Real-Time Supply Chain Visibility and Operational Decision Support

Abstract

Contemporary supply chains generate operational data, yet many organizations experience fragmented information, delayed visibility, unreliable dashboards, and slow responses because business intelligence systems are not integrated across functions and partners. This study aimed to develop and evaluate an integrated business intelligence dashboard framework for improving real-time supply-chain visibility and operational decision support. A quantitative, cross-sectional, descriptive, explanatory, and case-based research design was applied across enterprise cases in manufacturing, logistics, distribution, retail, e-commerce, warehousing, transportation, procurement, pharmaceuticals, food-related operations, and supply-chain environments. From 350 distributed questionnaires, 300 valid responses were retained, producing an effective response rate of 85.71 percent. The study examined Supply-Chain Data Integration and Quality, Real-Time Analytics and Dashboard Responsiveness, Dashboard Information Quality and Visualization Usability, System Interoperability and Technological Integration, User Analytical Competency and Organizational Readiness, Real-Time Supply-Chain Visibility, and Operational Decision Support. IBM SPSS was used for data screening, descriptive statistics, reliability testing, Pearson correlation, multiple regression, ANOVA, and diagnostic assessment. Cronbach’s alpha values ranged from .823 to .917, the KMO value reached .902, and Bartlett’s test was significant, χ² (861) = 5,482.76, p < .001. Supply-Chain Data Integration and Quality recorded the highest predictor mean, M = 4.13, SD = .54, while Real-Time Supply-Chain Visibility and Operational Decision Support achieved M = 4.06, SD = .56 and M = 4.09, SD = .55, respectively. The visibility model explained 68.1 percent of variance, R² = .681, adjusted R² = .676, F (5, 294) = 125.42, p < .001, with data integration strongest, β = .29. The decision-support model explained 70.4 percent, R² = .704, adjusted R² = .698, F (6, 293) = 116.15, p < .001, while visibility remained strongest, β = .37. The findings imply that integrated data, responsive analytics, usable visualization, interoperable systems, and organizational readiness can strengthen monitoring, coordination, exception detection, decision speed, accuracy, and disruption response.