548 Manufacturing Execution Systems (MES). The current state-of-the-art involves deploying advanced technologies, including automation and data analytics, across various stages of the mine-to-port value chain to reduce waste and improve local efficiencies. However, despite these technological advancements and the abundance of data, organizations frequently struggle to transition from the mere intent to innovate to a strategic and measurable execution. As William Thomson (Lord Kelvin) accurately stated, "What is not defined, cannot be measured. What is not measured, cannot be improved". In contemporary mining operations, the limitation is rarely the lack of data or technology, but rather the absence of a holistic framework to prioritize these investments. Driven by traditional efficiency metrics, departments often operate in vertical silos, optimizing local processes that do not necessarily increase the overall throughput of the entire operation. This leads to the well-known trap of sub-optimization, where capital and human efforts are heavily invested in non-constraint areas, yielding no real financial or systemic benefit. 1.3. Improvement: What is lacking What is critically lacking in standard continuous improvement and digital mining programs is a precise, constraint-focused prioritization method. The industry urgently needs a bridge between raw operational data and strategic innovation funnels. There is a critical need for a systemic approach that can accurately pinpoint the true bottlenecks within the macro-level value stream, define exactly where to innovate, and quantitatively measure the systemic impact of those technological investments. 2. IMPROVEMENT AND CONTRIBUTION The primary contribution of this research is a replicable methodology and a data analytics digital tool, implemented at a major mining company in Peru, that digitally applies the foundational principles of the Theory of Constraints (Goldratt & Cox, 2004) and Value Stream Mapping (Martin & Osterling, 2013). Instead of deploying technology indiscriminately or jumping into micro-level tactical improvements before the entire macro picture is fully understood, this approach acts as a dynamic, macro-level framework that links actual operational constraints with innovation funnels. By visualizing the interconnectedness of various departments and processes across the value chain, the platform prevents the common and costly trap of suboptimization. Ultimately, by prioritizing the primary bottlenecks, the proposed tool ensures that continuous improvement efforts and capital investments are strictly focused on initiatives with the greatest potential for systemic impact. 3. METHOD Before implementing complex solutions, this methodology returns to the basics of operational performance by rigorously analyzing constraints across the value chain—from exploration and mineral extraction to crushing, milling, concentration, and the port. The approach consists of a structured framework: 1. Understand the constraints across the entire value chain. 2. Check the root causes and perform OEE analysis to identify gaps versus the budget and benchmarks. 3. Validate insights on the field (going to the gemba) with operational experts. 4. Apply targeted innovation (involving technology, processes, and people) to close the gaps. 5. Measure performance and ensure continuity. The cornerstone of the method is the first step: identifying systemic bottlenecks. According to the Theory of Constraints, a system with local optimums is highly inefficient, and any hour gained at a nonbottleneck is a mirage (Goldratt & Cox, 2004). To operationalize this principle, the data architecture integrates automatic and manual inputs from sources such as PI System, Dispatch, and MES into a centralized SQL database, which feeds visualization dashboards. The digital platform acts as a dynamic Value Stream Map, allowing leadership to visualize the workflow horizontally, overcome traditional vertical silos, and accurately measure where the true constraints lie.
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