Decision Support Framework to Improve Mining Productivity: Prioritization of Operational Factors in Medium-Scale Mining

Abstract

The mining industry faces increasing challenges in enhancing productivity within complex operational environments characterized by the interaction of technical, organizational, human, and external factors. Nevertheless, existing studies frequently examine these determinants independently, thereby limiting a comprehensive understanding of their relative importance and combined influence on operational performance. This study develops a decision framework to identify and prioritize the factors affecting productivity in medium-scale mining in the Coquimbo Region, Chile, through the application of the Analytic Hierarchy Process (AHP) based on expert judgment obtained from active mining operations. The AHP model was structured using two criteria, seven subcriteria, and twenty-three decision factors, whose consistency and reliability were assessed through the consistency ratio (CR) and Cronbach’s alpha coefficient. The results revealed a marked predominance of Internal factors (75.0%) over External factors (25.0%), indicating that productivity is influenced primarily by variables that can be managed at the organizational level. Among the evaluated subcriteria, Work planning achieved the highest priority (32.0%), whereas Scheduling and control (10.2%), Human factors (6.4%), and Working conditions (5.2%) emerged as the most influential decision factors. Furthermore, sensitivity analysis confirmed the robustness and stability of the model. Beyond establishing a prioritization of productivity determinants, this study provides a decision-support framework that can assist mining companies in strengthening productivity management and improving operational performance in medium-scale mining.