Background and research gap - Operational risk management remains underexplored in medium-scale food distribution enterprises, particularly those handling perishable products whose business continuity depends on transportation, product quality, human resources, and information systems. Purpose – This study examines how an Enterprise Risk Management (ERM) framework can support operational risk prioritization and mitigation at UD Anugrah, an ice cream distribution enterprise in Surabaya, Indonesia, while interpreting its implications for responsible business practices relevant to Islamic business and economics. Design/methodology/approach – Using a descriptive qualitative case study design, data were collected through semi-structured interviews, direct observation, and document analysis. Seven operational risks were identified across four categories: human resources, technology, processes, and the environment. A five-point likelihood-impact matrix was used to assess and prioritize these risks.. Key Findings – Seven operational risks were identified across four categories: human resources, technology, processes, and the environment. A five-point likelihood-impact matrix was used to assess and prioritize these risks. Transportation damage and product melting emerged as high-priority risks, whereas ineffective employee development and computer system disruption with potential data loss were classified as medium-priority risks. Proposed mitigation measures included regular vehicle maintenance, backup power provision, employee training, and systematic data backup and computer security. The findings illustrate how ERM can translate risk identification into context-specific control recommendations aimed at protecting product quality, Research limitations/implications – The study’s originality lies in demonstrating the practical application of ERM in a medium-scale perishable-food distribution enterprise and articulating its potential relevance to responsible business conduct. Originality/value – its findings provide context-specific analytical insights rather than statistically generalizable or causal conclusions.
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