Abstract: Since the oil and gas sector has a complex structure with large capital requirements, demand-supply chain decision-making must be accurate and effective. This study utilizes Dynamic Capabilities Theory to investigate how big data analytics can influence supply chain decision-making in the construction sector. By adopting the Interpretive Phenomenological Analysis (IPA) approach in narratives of select studies from 2020-2024, the study emphasizes on advantages related to real-time analytics and its utility for demand forecast with enhancement of inventory management besides revealing reduced risk through big data analysis. Results showed the key benefits found when big data analytics are implemented compared to traditional practices which include advanced flexibility and operating effectiveness. However, this report notes the major challenges and hurdles to adopt HIPs include issues related with data integration, dependencies on an excessive number of resources and technological complexity. This, the conclusion emphasizes that despite its transformative potential in big data analytics aspects of implementation remain significant barriers and structured approach to overcoming these barriers are needed through strategic planning with improvement over time in this space considering new practices for managing large stores of data.

Keywords: Big Data Analytics, Supply Chain Decision-making, Dynamic Capabilities Theory, Risk Management

| DOI: 10.17148/IARJSET.2024.11122