Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Abstract

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.