Utilising database management systems and statistical process control to elevate quality assurance for agricultural perishables

Abstract

Inefficiencies within agricultural packhouse operations undermine both quality assurance and profitability. However, to address the inefficiencies, the following industrial engineering tools are developed in this study: a decision support system (DSS) that leverages the power of database management systems (DBMS) and statistical process control (SPC). This industry-wide solution transcends the limitations of isolated data and reactive quality control. The DSS captures real-time operational data by seamlessly integrating a robust DBMS with Excel and Power BI, identifying previously hidden insights. Advanced SPC algorithms analyse this data, providing insight into dimensions of defect patterns and process variability. This study contributes towards literature and the entire agricultural industry by following a data-driven revolution in quality assurance. The proposed framework offers a blueprint for potential integration across diverse operations, fostering a paradigm shift towards proactive, data driven quality control.