An Integrated Modelling Framework for Lean Mining: The ASDI-LM Approach to Improving Mining Supply Chain Performance

The mining sector faces increasing pressure to enhance efficiency and sustainability while navigating complex, interdependent constraints. Lean Mining (LM), an adaptation of Lean Management principles, remains inconsistently implemented and lacks a structured, decision-oriented methodology. Existing work rarely connects LM to decision support systems (DSS) that enable ex-ante scenario evaluation, especially given the sector’s spatio-temporal variability and the need for decisional reconciliation across interdependent subsystems. This paper addresses this gap by proposing a comprehensive, operational and transferable framework to integrate LM into mining supply chains (MSC). Drawing on a literature review and action-research, this paper proposes ASDI-LM (Analysis, Specification, Design, Implementation), a meta-methodological framework that integrates Lean principles into DSS through a model-driven architecture combining simulation and optimization. ASDI-LM enables the ex-ante evaluation of organisational scenarios in highly variable mining environments. The framework guides the systematic construction of knowledge models and action models, facilitating decisional reconciliation across operational levels. Application to a real-world MSC demonstrated significant improvements in effectiveness (extracted volume, ore quality, diversity) and efficiency (equipment utilization and residual stocks), thanks to integration of mine planning, the introduction of dynamic organizational methods, and the ex-ante analysis of scenarios within a DSS. This paper offers a structured framework for embedding LM in DSS for complex, variable contexts. The results validate its capacity to support data-driven decision-making while operationalizing Lean principles in constraint-driven contexts. This research contributes to the DSS field by bridging conceptual Lean frameworks with quantitative decision tools, offering a transferable methodology for complex process industries.