From Data to Decisions: Hyper Automation and Real-Time Analytics in Building Resilient Operations and Supply Chain Management

Abstract

This research explores how hyper automation and real-time analytics can be used to create resilient operations and supply chain management in the manufacturing and logistic industries. Although hyper automation will be adopted faster, and by 2024, 79% of global supply chain organizations have deployed a hyper automation platform, most are finding it challenging to turn frenzied data streams into robust decision-making systems. This study used a concurrent mixed-methods study design and examined data collected on 167 operations and supply chain leaders in 92 multinational companies. With the use of the recently confirmed Hyper automation Resilience Index (HARI), the results indicate that organizations that attained the Resilience Maturity Level 4 had 63% fewer disruption recovery periods and 52% higher supply chain visibility than the baseline. On the other hand, immature hyper automation deployments were associated with 41% decision paralysis plus 37% operating organization trust. It resulted in five key design principles; real-time sensemaking architecture, autonomous decision protocols, resilience feedback loops, socio-technical governance, and dynamic capability orchestration. The article offers a tested diagnostic tool and roadmap of implementation to design hyper automation systems that transform data into resilient decisions. The future studies should consider longitudinal effects on the competitive advantage and industry-specific disruption patterns.