The Impact of Demand Forecasting Accuracy on Customer Satisfaction

Abstract

Purpose: The aim of the study was to analyze the impact of demand forecasting accuracy on customer satisfaction 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 field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Accurate demand forecasting significantly enhances customer satisfaction by reducing stockouts, improving delivery reliability, and aligning inventory with consumer needs. Advanced predictive models, such as AI and machine learning, enable businesses to adapt to market dynamics, fostering trust and loyalty. Studies show improved repeat purchase rates and customer retention, highlighting forecasting precision as crucial for competitive advantage. Unique Contribution to Theory, Practice and Policy: Resource-Based View (RBV) Theory, Expectancy-Disconfirmation Theory (EDT) and Systems Theory in supply chain and management maybe used to anchor future studies on the impact of demand forecasting accuracy on customer satisfaction. Research should focus on developing integrative frameworks that combine demand forecasting accuracy with customer satisfaction metrics, emphasizing the role of advanced technologies like AI and machine learning. Companies should also invest in training programs to equip supply chain professionals with the skills to utilize advanced forecasting tools effectively. Governments and regulatory bodies should establish guidelines to encourage secure data sharing between supply chain stakeholders, enhancing forecasting accuracy