Supply Chain Risk Management and Organizational Performance in England

Purpose: The aim of the study was to assess the supply chain risk management and organizational performance in England. Methodology: 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 demonstrated that effective SCRM practices can significantly mitigate disruptions and uncertainties in the supply chain, leading to improved operational efficiency and resilience. By proactively identifying, assessing, and addressing potential risks, organizations can maintain continuity and ensure the smooth flow of goods and services. This risk mitigation not only minimizes financial losses and operational downtime but also enhances customer satisfaction and trust. Furthermore, integrating SCRM with strategic planning allows organizations to better align their supply chain objectives with overall business goals, fostering a more agile and competitive market position. Consequently, companies that invest in robust SCRM frameworks tend to experience enhanced performance metrics, such as increased profitability, market share, and long-term sustainability. Implications to Theory, Practice and Policy: Resource-based view, contingency theory and transaction cost economics may be used to anchor future studies on assessing the supply chain risk management and organizational performance in England. Organizations should invest in advanced technologies like blockchain, artificial intelligence, Internet of Things (IoT), and data analytics to enhance SCRM capabilities. Policymakers should establish regulatory frameworks and standards related to SCRM practices, data security, supply chain transparency, and ethical sourcing