A short post to remind mine planning engineers that grade is just a proxy for value. You need to prove to yourself that it is a good proxy to use. Years ago, I used to present a value creation and #CutOffGrade seminar. I would start the seminar with the following three questions as a thinking exercise: Q1: Consider two blocks of ore (everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu. (b) 100 tonnes @ 2.5% Cu. The obvious answer would be (b), as it contains 2.5 tonnes of copper, versus (a) with 2.0 tonnes of contained copper Q2: Consider the same two blocks of ore, but now with recovery information (and again everything else being equal), which block has higher value:: (a) 100 tonnes @ 2.0% Cu - with 80% recovery. (b) 100 tonnes @ 2.5% Cu – with 85% recovery. Again, the obvious answer would be (b), as it has 2.1 tonnes of recovered copper versus (a) with 1.6 tonnes of recovered copper. Q3: Now consider the same two blocks of ore, with recovery information and throughput information (and again everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu - with 80% recovery, and SAG mill throughput of 100 tph (so soft ore, and/ore well fragmented). (b) 100 tonnes @ 2.5% Cu – with 85% recovery, and SAG mill throughput of 70 tph (so hard ore, and/or poorly fragmented). Now, the answer flips to (a), as (a) has a recovered copper per hour ‘value’ of 1.6 tonnes Cu per hour, and (b) has a ‘value’ of 1.49 tonnes Cu per hour. So, in this third situation, the lower grade, lower recovery ore provides 7% greater ‘value’ in time – and it is what we produce in time that ultimately determines the value (NPV is after-all a measure of ‘$s in time’). This leads to the ‘cash flow grade’ concept, quite well described by Dr Brett King in a paper he write back in 1999. (I loved this paper when I first read it, as I was ~90% there myself - and felt frustration that Brett had written it first!) The concept also leads to the necessary identification of system bottlenecks that need to be exploited (#TheoryOfConstraints) to increase the ‘value flow’. These concepts are effectively what Gerald Whittle has effectively based his business on: the cash flow grade concept – combined with TOC – and using software to solve complex systems that can result. So – grade is just a proxy for value. Sometimes it can be a good proxy. Other times it can be a poor proxy. I have only ever once seen a feasibility study use a cash flow grade for the mine schedule. And I will be first to acknowledge it is not an easy thing to use. It can’t be directly measured like a grade. It must be calculated from multiple factors – of which grade is just one factor. And we usually often don’t have reliable models for those other relevant factors. My advice: do as much economic value modelling of your ore value as you can, and find some approach to cut-off value that is practical and makes sense, AND captures some of that time value. References King, B. 1999, “Cash Flow Grades - Scheduling Rocks with Different Throughput Characteristics”, Proc. Conf. Optimising with Whittle, Perth 1999.

To document is to show how important is to do bench master for an optimal drill and blast activities in mining operations if it is hard rock.

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In large open-pit mining operations, a question comes up quite often: What matters more for the success of a mine — operations in the field or mine planning? At first glance the answer seems obvious. Production happens in the pit. Excavators load the material, trucks move it, and operations teams manage the equipment and crews that keep production running. But mining rarely works like a fixed industrial project. As the mine advances, geology changes. Stripping conditions vary, water can appear in unexpected places, and ore or coal quality may fluctuate. Because of that, a mine plan is never something that is prepared once and simply followed for years. It constantly evolves as new information comes in. Operations teams focus on making production happen every day. In a large open-pit with hundreds of equipment units — excavators, haul trucks, dozers and support machines — keeping everything running safely and efficiently is already a major task. Dispatching equipment, managing shifts, maintaining productivity and safety… these are all core operational responsibilities. Mine planning and technical services, on the other hand, define the framework in which those operations take place. Planning teams deal with things like pit design, block sequencing, stripping ratios, haul road layouts, production schedules and long-term reserve management. These decisions shape how efficiently the mine will run — and in many cases whether it will remain economically viable over time. Many of the biggest cost drivers in mining actually come from planning decisions. A slightly longer haul distance can increase fuel consumption significantly. Poor sequencing may lead to unnecessary waste movement. Even small design choices can translate into millions of tonnes of additional material over the life of a mine. That’s why a phrase you often hear in mining is: Operations move the material — planning largely determines the economics. In practice, these two functions represent different layers of responsibility. Operations manage the day-to-day execution, while planning teams focus more on long-term optimization of the resource and the cost structure. When these two sides work well together, the result is a much more stable and efficient operation. In many large mining companies such as BHP and Rio Tinto, technical services and mine planning groups play a central role in supporting operational decisions. Geological modeling, production scheduling and cost optimization are all part of the same system. At the end of the day, the real question is probably not which one is more important. A mine performs best when strong operations and strong planning support each other. That balance is what usually separates an average operation from a truly efficient one.

Strategic mine planning is the backbone of a mining feasibility study. It transforms a geological resource into a time‑phased, risk‑aware business plan that supports investment, design, permitting, financing, and closure decisions (Morales et al., 2019; Dowd, Xu and Coward, 2016). 1. Strategic Mine Planning: Definition and Scope Strategic mine planning is the long‑term, life‑of‑mine (LOM) planning process that determines: Which part of the resource will be mined (ultimate pit / underground extent) In what sequence and at what extraction rate material will be mined and processed With which capacities, configurations, and investments (fleets, plants, infrastructure) Under what assumptions about markets, costs, technology, environmental and social obligations For open pits, two core problems dominate (Morales et al., 2019; Dowd, Xu and Coward, 2016): Ultimate pit limit problem – define the mineable reserve within geotechnical and economic constraints Life‑of‑mine production scheduling – decide when to extract each block/panel to maximize net present value (NPV) subject to capacity, quality, and other constraints Strategic planning now extends beyond economic and technical factors to include environmental and closure costs, regulatory frameworks, and sustainability objectives (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025). 2. Role and Purpose in a Mining Feasibility Study At pre‑feasibility and feasibility level, the strategic mine plan is not just a technical deliverable; it is the central integrating element of the study. 2.1 Establishing Technical and Economic Viability A feasibility study must show that a project is technically feasible and economically worthwhile with an acceptable risk profile. Strategic planning provides (Marković et al., 2025; Morales et al., 2019; Dowd, Xu and Coward, 2016): Reserves and mine life: conversion of resources into economically mineable reserves, with pit/underground limits and life‑of‑mine horizon Production profiles: annual or period‑by‑period ore, waste, grades, and product tonnages Cash‑flow basis: time‑phased revenues, OPEX, CAPEX, sustaining and closure costs feeding NPV and IRR calculations (Marković et al., 2025; Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)Hybrid deterministic–stochastic models have demonstrated that deterministic feasibility outputs can be misleading. In a polymetallic open‑pit case, deterministic optimization yielded NPV of USD 130.8 M, while a stochastic model gave a mean NPV of USD 155.5 M with a standard deviation of USD 76.5 M, and revealed a 3 % probability of overall project unprofitability (Marković et al., 2025). This kind of analysis is central to a high‑quality feasibility study. 2.2 Sizing and Phasing Investments and Capacities Strategic mine plans drive major capital decisions: Fleet size and timing of acquisitions Plant capacity and debottlenecking (crushers, mills, concentrators) Expansion options and their triggers In a copper open‑pit complex, a multistage stochastic model identified optimal branching investment strategies (truck/shovel fleet changes and a secondary crusher) that increased expected NPV by more than US$170 M compared with a simpler two‑stage approach (Del Castillo and Dimitrakopoulos, 2019). Feasibility studies that ignore such dynamic investment options may under‑ or over‑invest. 2.3 Selecting Mining Options and Configurations For deposits amenable to both open‑pit (OP) and underground (UG) mining, feasibility studies must determine: Optimal choice among OP only, UG only, OP→UG, UG→OP, or simultaneous OP+UG Transition depth/location, crown pillar location, and extraction sequence Life of mine, strip ratio, blending strategy, and production smoothness as performance indicators (Afum and Ben-Awuah, 2021)A review of surface–underground options highlights the need for integrated (often stochastic) models at the prefeasibility stage to evaluate these configurations with indicators such as NPV, IRR, discounted cash flow, blending ratio, and mine life (Afum and Ben-Awuah, 2021). 2.4 Integrating Environmental, Social, and Closure Considerations Historically, feasibility‑level planning treated environmental and closure costs as peripheral. Recent work shows these are now core strategic variables (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025): Environmental and closure costs can materially affect NPV/IRR and even reserve definitions (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)- Premature project termination due to environmental or social issues often leads to higher closure costs than planned progressive closure (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)- Sustainable post‑mining land use must be planned strategically using tools such as SWOT and IE matrices, defining strategies for each land‑use option (Amirshenava and Osanloo, 2022)A Peruvian case study showed that integrating an advanced water quality model and closure cost tools into planning enabled ranking mine plans according to long‑term water quality impacts and associated mitigation costs, aligning short‑ and long‑term plans with closure objectives (Sanders and Fitzpatrick, 2022). 2.5 Supporting Project Finance, Permitting, and Social License Strategic mine plans are used to: Convince lenders and investors that cash‑flows are robust to key uncertainties (Marković et al., 2025; Del Castillo and Dimitrakopoulos, 2019)- Demonstrate to regulators that waste, water, and closure are planned consistently with legal requirements and policies (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)- Show communities and stakeholders a credible trajectory from construction to closure, including post‑mining land use (Amirshenava and Osanloo, 2022; Sanders and Fitzpatrick, 2022; Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)Without a rigorous strategic plan embedded in the feasibility study, these commitments lack quantitative backing. 3. Key Parameters in Strategic Mine Planning Strategic mine planning uses a large, interdependent set of parameters. At feasibility level, their accuracy, uncertainty characterization, and inter‑dependencies are critical. 3.1 Geological and Geometallurgical Parameters Block model: The core input is a 3D block model with attributes such as (Morales et al., 2019; Dowd, Xu and Coward, 2016): Tonnage (volume × density) Grades of economic and deleterious elements Lithology, alteration, rock type, structural domains Geotechnical domains (strength, RMR/Q, joint sets) Geometallurgical variables – recovery, hardness, comminution specific energy, mineralogy Traditionally, block attributes are deterministic estimates (e.g., kriging). Modern approaches: Use equiprobable simulated realizations to represent grade and geometallurgical uncertainty (Morales et al., 2019; Dowd, Xu and Coward, 2016)- Build geometallurgical models capturing spatial variability of recovery, hardness, and comminution energy, sometimes through multiple scenarios (Quelopana et al., 2023; Mata, Nader and Mazzinghy, 2022)Incorporating geometallurgy and its uncertainty can change both pit limits and schedules, with measurable financial impact (Morales et al., 2019; Dowd, Xu and Coward, 2016; Mata, Nader and Mazzinghy, 2022). 3.2 Economic and Market Parameters Key economic parameters include: Commodity price forecasts and ranges Operating costs: mining, processing, G&A, logistics Capital costs: initial, expansion, sustaining, closure Discount rate, tax and royalty regimes, exchange rates Hybrid deterministic–stochastic frameworks characterize these parameters using probability distributions (often via Monte Carlo sampling) instead of single values, explicitly quantifying uncertainty in cash‑flows and NPV (Marković et al., 2025; Sepúlveda, Álvarez and Bedoya, 2020). 3.3 Technical and Design Parameters Important design and operating constraints include: Slope design: bench and inter‑ramp angles, controlling pit shape and depth (Morales et al., 2019; Dowd, Xu and Coward, 2016)- Mining capacity: annual total material movement limits, ore mining capacity, waste stripping capacity by equipment and infrastructure (Del Castillo and Dimitrakopoulos, 2019; Morales et al., 2019; Mata, Nader and Mazzinghy, 2022)- Processing capacity: plant throughput, multiple plant streams, down‑time and maintenance patterns, metallurgical plant modes (Quelopana et al., 2023)- Cut‑off grades: fixed or variable cut‑off strategies, often central decision variables influencing reserve size, mine life, and NPV Stockpiling and blending rules: maximum stockpile capacities, reclaim rates, blending tolerances for grades and contaminants (Del Castillo and Dimitrakopoulos, 2019; Morales et al., 2019; Quelopana et al., 2023)Geomechanical constraints such as haulage ramps, bench widths, minimum mining widths, and controlled strip ratios per period must also be honored; direct block scheduling solutions that ignore these often yield operationally infeasible plans (Morales et al., 2019; Malundamene et al., 2024; Dowd, Xu and Coward, 2016). 3.4 Environmental, Closure, and Sustainability Parameters Modern strategic planning quantifies: Environmental costs: waste rock and tailings management, water management, emissions and dust control, ecosystem impacts Closure costs: backfilling, capping, recontouring, revegetation, long‑term water treatment, monitoring (Amirshenava and Osanloo, 2022; Sanders and Fitzpatrick, 2022; Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)- Regulatory and policy frameworks: environmental standards, bonding requirements, SDG‑aligned policies (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025)- Post‑mining land use: parameters defining suitability and vulnerability of land‑use options (agriculture, forestry, renewable energy, recreation, etc.) (Amirshenava and Osanloo, 2022)Environmental and closure costs are no longer exogenous; they directly influence resource/reserve reporting and project economics, and must be represented in strategic optimization models (Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025). 3.5 Energy and Renewable Integration Parameters With increasing emphasis on decarbonization, strategic planning now includes: Current and future energy mix (diesel, grid, renewables) Capital and O&M costs of renewable options (PV, wind, storage) Carbon pricing or internal shadow carbon values A SWOT‑based study shows renewable energy adoption in mining can reduce pollution, create jobs, lower operating costs, and enhance circular economy, though hindered by high initial capital and skills gaps (Pouresmaieli et al., 2023). Such parameters need to be reflected in feasibility‑level scenarios. 4. Strategies and Methods for Optimizing the Mine Plan Optimization methods have evolved from simple deterministic pit shells to comprehensive stochastic and adaptive frameworks. 4.1 Deterministic Optimization: Baseline Practice At feasibility, most projects still rely on a deterministic optimization chain: Pit optimization: Lerchs–Grossmann or equivalent network‑flow algorithms compute ultimate pit shells to maximize undiscounted or discounted profit subject to slope constraints (Anisimov, Bariatska and Cherniaiev, 2024; Morales et al., 2019). Modern software (e.g., Geovia Whittle) generates multiple nested pit shells under varying economic parameters, aiding selection of a final shell even for complex, multi‑ore‑body geometries. Phase (pushback) design: Intermediate pit shells define stages for operational practicability, controlling working widths, access, and strip ratios over time (Anisimov, Bariatska and Cherniaiev, 2024). LOM scheduling: Linear / mixed‑integer programming (MIP) or heuristic algorithms schedule blocks/panels over periods, maximizing NPV under capacity, precedence, and blending constraints (Morales et al., 2019; Dowd, Xu and Coward, 2016). Cut‑off grade and stockpiling policies: Often parametrically optimized (e.g., through nested LPs or heuristics) to maximize NPV while meeting product quality and capacity targets. NPV Pit Optimization Figure 1: NPV Pit Optimization Deterministic optimization is computationally efficient and embedded in commercial packages. However, it presumes single‑value inputs and does not quantify risk, leading to potentially misleading “optimal” plans (Marković et al., 2025; Fernanda et al., 2024; Sepúlveda, Álvarez and Bedoya, 2020). 4.2 Stochastic Optimization and Risk‑Based Strategic Planning Stochastic optimization explicitly addresses geological, technical, and market uncertainty. 4.2.1 Hybrid Deterministic–Stochastic Models with ISO 31000 A hybrid model integrates: Deterministic optimization (base case pit limit and schedule) Stochastic optimization over distributions of key parameters (prices, costs, ore grades) Risk analysis structured by ISO 31000 risk management principles (Marković et al., 2025)Monte Carlo simulation generates distributions for input parameters; these feed a stochastic optimizer that produces NPV distributions, not just a single value. In the cited case, VaR and CVaR show a 3 % probability of project unprofitability despite positive mean NPV, revealing downside risk masked by the deterministic case (Marković et al., 2025). This supports feasibility‑level decision‑making by: Quantifying downside NPV at chosen confidence levels (e.g., 95 % VaR) Identifying parameters that most drive risk Designing more robust pit limits and schedules 4.2.2 Multistage Stochastic Programming for Mining Complexes Mining complexes with multiple pits and processing streams face dynamic investment and configuration decisions: Changing fleets, adding crushers, modifying plant capacities Routing ore among multiple plants and stockpiles A multistage stochastic programming model: Uses a scenario tree with multiple recourse stages Makes sequential investment and operating decisions based on observed information in each period Embeds capital investment variables that activate capacities and costs when chosen In a copper complex, this approach generated a dynamic strategic plan with branching options for configurations; expected NPV increased by more than US$170 M relative to a two‑stage model, and the solution showed a substantial probability that the mine design should branch rather than follow a single fixed path (Del Castillo and Dimitrakopoulos, 2019). Adaptive simultaneous stochastic optimization at the Escondida complex likewise used geological simulations and branching plans to produce operationally feasible strategies that adapt to uncertainty, improving value and risk positioning (Fernanda et al., 2024). 4.2.3 Metaheuristics and Simulation‑Based Stochastic Planning Where exact MIP formulations become intractable, metaheuristic algorithms (variable neighbourhood descent, simulated annealing, evolutionary algorithms) combined with simulation are employed (Sepúlveda, Álvarez and Bedoya, 2020; Quelopana et al., 2023): These methods search large solution spaces for near‑optimal LOM schedules under multiple scenarios. Stochastic open‑pit planning models can include grade and price uncertainty and produce robust schedules that maximize expected profit and mitigate risk (Sepúlveda, Álvarez and Bedoya, 2020). Research suggests that including additional variables (environmental, social) into these stochastic frameworks would further improve realism (Sepúlveda, Álvarez and Bedoya, 2020). 4.3 Integration of Geometallurgy and Plant Operation Long‑term planning that ignores geometallurgy can under‑ or over‑estimate project value. 4.3.1 Geometallurgical Scenarios in Pit Limits and Scheduling A risk‑aware geometallurgical approach (Morales et al., 2019; Dowd, Xu and Coward, 2016): Builds multiple equiprobable scenarios of grades and geometallurgical attributes (recovery, hardness, etc.). Performs pit optimization per scenario, then defines a reliability pit that is feasible across scenarios. Uses stochastic integer programming to schedule blocks from the reliability pit, maximizing expected discounted value and minimizing deviations from production targets (Morales et al., 2019). Results show that including geometallurgical uncertainty can materially change optimal pit depth, pushbacks, and extraction sequences, thus affecting NPV and risk (Morales et al., 2019; Dowd, Xu and Coward, 2016). 4.3.2 Detailed Plant Modes within Strategic Optimization To bridge mine and plant optimization, geometallurgical detailing of plant operation has been introduced (Quelopana et al., 2023): The strategic algorithm is adapted (via Dantzig–Wolfe decomposition) to include plant operational modes (e.g., different comminution circuits) as linear sub‑problems. For each geological scenario, the model chooses not only which blocks to mine and when, but also how to process them (mode selection) (Quelopana et al., 2023). Case calculations based on the Mount Isa deposit show that a plant upgrade can significantly reduce mining equipment requirements without materially affecting NPV, demonstrating that strategic integration of plant modes can change investment and scheduling decisions (Quelopana et al., 2023). 4.3.3 Comminution Specific Energy and Global Optimization Global optimization that integrates geometallurgical variables such as comminution specific energy into pit, pushbacks, and scheduling steps can yield (Mata, Nader and Mazzinghy, 2022): ~9.7 % increase in NPV and ~5.2 % increase in ore production versus simpler strategies More stable strip ratios and better control of comminution energy over time This underscores the value of including such variables in block models and optimization objectives at feasibility stage (Mata, Nader and Mazzinghy, 2022). 4.4 Real Options and Strategic Flexibility Real options treat certain strategic decisions as options rather than fixed commitments. At project level, options include delaying development, staging expansions, scaling capacity, or abandoning (Ali and Rafique, 2024). At planning level, “planning options” include extraction sequences, cut‑off policies, slope modifications, and capacity switches (Ali and Rafique, 2024). Real options: Transform uncertainty into opportunity by allowing adaptive responses to market and geological changes (Ali and Rafique, 2024). Complement stochastic optimization: while stochastic models produce adaptive plans, real‑options valuation quantifies the value of such flexibility. The literature highlights that naïve single‑scenario optimization ignores this adaptive capability, whereas flexible designs can reconfigure in response to new information, better matching actual operating conditions (Ali and Rafique, 2024; Fernanda et al., 2024). 4.5 Multi‑Criteria and Sustainability‑Oriented Strategies Economic objectives (NPV, IRR) increasingly share space with: Resource utilization (rational depletion, recovery) Environmental impact and closure cost Social criteria (employment, community impacts) A multi‑criteria optimization applied to an underground coal mine combined geological constraints, infrastructure, and economic metrics (NPV, EBIT, FCFF). Millions of scenarios were screened digitally, revealing many better scenarios than the base case; the best scenario had NPV ~50 % higher than the base case, which ranked only 52nd of 60 (Kopacz et al., 2020). State‑of‑the‑art reviews emphasize operations research methods (LP, dynamic programming, stochastic programming, metaheuristics) as key tools to design sustainable surface mine plans aligned with SDGs (Pouresmaieli et al., 2023; Oliveros-Sepúlveda, Bascompta-Massanés and Franco-Sepúlveda, 2025). Key Parameters and Optimization Approaches in Strategic Planning Planning Focus Main Parameters / Decisions Dominant Methods Pit limits & reserves Block values, slope design, geotechnical domains Lerchs–Grossmann, network flow, global optimization in software (e.g., Geovia Whittle) LOM scheduling Block/panel sequencing, capacities, cut‑offs, stockpiles LP/MIP, stochastic integer programming, metaheuristics Mining complexes & investments Fleet, crushers, plant capacities, configuration branches Multistage stochastic programming, adaptive branching Geometallurgy & plant modes Recovery, hardness, comminution energy, plant modes Scenario‑based geomet models, Dantzig–Wolfe, global optimization Risk & uncertainty Prices, costs, grades, geomet, env. costs Hybrid deterministic–stochastic with Monte Carlo, VaR/CVaR, ISO 31000 Sustainability & closure Env./closure costs, PMLU options, energy mix Quantitative closure costing models, SWOT/IE, SDG‑aligned planning Figure 2: Core decision areas and optimization tools in strategic mine planning. 5. Risks and Potential Losses from Suboptimal Strategic Mine Plans Suboptimal or naïve strategic mine plans can cause large economic, environmental, and social losses. 5.1 Economic and Financial Risks 5.1.1 Misleading Economic Evaluation and NPV Overestimation Deterministic models that neglect uncertainty can substantially mis‑estimate project value and risk. In the hybrid risk‑based study, deterministic NPV was USD 130.8 M, but stochastic modelling showed mean NPV of 155.5 M with σ = 76.5 M and a non‑trivial (3 %) probability of project unprofitability (Marković et al., 2025). A feasibility study relying on the single deterministic number would understate downside risk. Stochastic optimization reviews emphasize that deterministic planning tools maximize profit under unrealistic assumptions and do not value risk appropriately (Sepúlveda, Álvarez and Bedoya, 2020). Investors and lenders may commit capital to projects whose downside risk is much higher than indicated by deterministic feasibility models. 5.1.2 Lost Value from Non‑Optimized Schedules The coal mine multi‑criteria optimization shows directly the cost of suboptimal plans: The “base case” schedule actually implemented ranked 52nd of 60 generated scenarios. The best scenario achieved NPV nearly 50 % higher than base case, with only small differences (

Figure 1 shows a short-term execution plan: a task-level schedule by block with a matching Gantt chart for activities like block prep, drilling, blasting, and loading/hauling across specific dates and per cent complete. This is the actionable weekly plan that converts quarterly intentions into sequenced tasks, revealing whether upstream quarterly sequencing is feasible once real equipment hours, benches, and inter block dependencies are honoured. Figure 1: Block Sequencing Why sequencing matters now • The order of drilling–blasting–hauling across blocks determines whether access to the next ore block opens on time, directly affecting short-term cash flow and NPV contributions of the quarter. If drilling slips on the lead block, blasting and ore exposure slip, starving the plant and eroding NPV. • Short-term plans must respect pit precedence, geotechnical widths, and fleet capacity; the table’s “days complete/remaining” and “per cent complete” columns are the control points to keep exposed ore ahead of the shovel, preventing production delays. Sustainable ore access in weekly plans • Use the execution plan to enforce a minimum “exposed ore” buffer: ensure each ore block’s loading window begins only after preceding waste and prep tasks clear access, and maintain a rolling stock/exposure KPI for the next 1–2 weeks. This operationally enforces the “minimum exposed ore" concept. • Sequence pushbacks so that waste removal tasks in the Gantt precede high-grade ore starts by several days, keeping pit geometry regular and avoiding re handles; this short-term discipline is what sustains long-term access. Leveraging market conditions • When prices are high, front-load high-grade blocks in the near-term chart by accelerating drilling and blasting earlier and allocating more shovel hours, while shifting some low-grade or waste to night shifts or subsequent weeks; when prices weaken, delay lower-margin ore or feed from stockpiles. This dynamic cutoff and rate adjustment enhance quarter NPV. • Scenario test the next four weeks against price bands: re-sequence blocks and update the Gantt to schedule capital-intensive moves (e.g., additional shovel or contractor drill) only in favourable windows. Adaptive plans can outperform static ones significantly. How the figure should be used • Validate quarterly plan: if the Gantt shows overlapping haul or drill windows causing resource conflicts, it is an early signal that the quarterly sequencing was unrealistic; fix at the execution plan first because this is where actions occur. • Maintain leading indicators: track “per cent complete” on critical path blocks and a daily “days of exposed ore ahead” metric; if exposure drops below target, reassign drills or add a blast to protect mill feed continuity. Where to invest effort • Prioritise execution scheduling quality: tighten activity durations, resource calendars, and inter-block links so the plan is feasible and resilient; poor execution plans translate directly to downtime and lost margin. • Build a rolling 4–6 week look ahead tied to quarterly milestones: the weekly Gantt is updated daily, but always anchored to opening the next pushback’s ore blocks on time to preserve sustainable access and market timing benefits

The Importance of Sticking to Mine Planning Compliance in Achieving Copper Production Targets In the fast-paced, high-stakes world of open-pit mining, success isn’t just about moving tonnes—it’s about moving the right tonnes, in the right sequence, at the right time. Mine planning compliance sits at the core of this precision. It’s the heartbeat of production control, the difference between meeting copper targets and falling short despite burning through fuel, hours, and manpower. Every blast, every bucket, and every truckload has a purpose defined by the mine plan, and when operations stray from it, even slightly, the ripple effects can distort the entire production curve—from ore delivery to metal output. At its core, mine planning compliance ensures that mining activities align with the strategic intent of the operation. The mine plan is not merely a document; it is a carefully modeled roadmap that balances ore exposure, haul distances, safety considerations, and economic priorities. In the context of a copper operation, deviations from the plan can directly influence the grade and tonnage of ore delivered to the mill, affecting daily and monthly copper targets. When mining shifts away from the designed cutbacks, bench elevations, or haul routes, the result is often dilution, ore loss, or unnecessary movement of waste. Each misaligned blast or wrongly positioned ramp can mean the difference between meeting metallurgical recovery expectations and missing key production milestones. A common challenge in maintaining compliance arises during ramp construction—whether temporary or permanent. Ramps are vital arteries of the mine, enabling access to deeper benches, new ore zones, and waste dumping points. However, the construction of these ramps must adhere strictly to the design parameters set in the mine plan. When ramps deviate from design—say, by being steeper than planned, misaligned, or constructed in a way that encroaches into ore blocks—the consequences can be severe. Poorly placed ramps may sterilize ore, restrict future pushbacks, or create unsafe working conditions. They can also elongate haul distances, increasing cycle times and lowering productivity. Moreover, temporary ramp alignments are often used to accelerate access to priority areas. These short-term adjustments are useful, but they require careful execution and coordination with the planning team. If operators, supervisors, or contractors construct ramps without proper survey guidance or geotechnical validation, they risk cutting through high-grade zones or leaving behind “pockets” of stranded ore. This not only affects copper recovery but also distorts reconciliation between planned and actual performance. Interestingly, there are moments when operations make controlled deviations from plan—not out of negligence, but out of necessity. For example, to speed up ramp development, especially when the goal is to access priority ore or remove waste material obstructing ore, teams may decide to mine the ramp design only halfway down, then use a dozer to push material to the toe. This tactical approach allows for faster material movement and ensures that the operation remains ahead in exposing ore sources critical for mill feed continuity. While this method is practical, it still requires coordination with mine planners and geotechnical engineers to ensure that safety and long-term design integrity are not compromised. The essence of this practice lies in operational agility—adapting methods to meet immediate production targets without completely abandoning the plan. However, it must be done with discipline. If pushed too far, such shortcuts can lead to excessive ramp gradients, insufficient width for double-lane traffic, or instability near the ramp toe. Over time, this can slow down truck traffic, increase maintenance on haul roads, and even necessitate rework—ironically wasting more time than was saved. Furthermore, non-compliance in mine planning doesn’t just affect access and safety—it affects the copper balance sheet. The mine plan dictates not only where to mine but also when certain grades are scheduled to feed the mill. Mining out of sequence can mean sending lower-grade material too early or delaying access to high-value ore zones, which reduces the average head grade. In a business where copper price fluctuations can make or break quarterly performance, the consistency of ore quality is critical. Poor planning compliance can thus translate to reduced revenue, missed sales contracts, and credibility issues with stakeholders. Another overlooked impact of non-compliance is on waste management. If waste is not mined or dumped according to the design sequence, it can block access to ore zones, increase rehandling costs, and limit space for future dumps. For instance, failing to clear waste on schedule can delay ore exposure by weeks or months, forcing production teams to chase tonnage from less favorable areas. The result is often increased haulage distances, reduced equipment efficiency, and rising operational costs—all of which erode profitability. Mine planning compliance is also integral to safety and sustainability. The plan is designed with geotechnical stability in mind—berm spacing, bench heights, and ramp gradients are calculated to prevent slope failures and equipment accidents. Deviating from this design introduces hazards that might not be immediately visible but can accumulate over time. For example, oversteepened ramps created during rushed development can become dangerous during the rainy season, when water ingress weakens the slope face. Thus, adhering to plan is not only about copper targets—it’s about keeping people and assets safe. In modern mining operations, compliance is measured not only by adherence to design coordinates but also by reconciliation data. High mine-to-plan compliance indicates that the operation is in control, predictable, and efficient. It also reflects strong communication between planning, geology, and production teams. On the other hand, poor compliance suggests disconnects between design and execution, which can trigger a chain reaction of inefficiencies, from grade control errors to logistical bottlenecks at the plant. Ultimately, the discipline of sticking to the mine plan is what separates successful mines from struggling ones. It builds a culture of accountability—where every operator understands that their work, down to the last truckload, contributes to a bigger, structured goal. It’s about respecting the effort behind each design line drawn by the planning team, knowing that every ramp curve, pit limit, and berm width was engineered for purpose. In conclusion, mine planning compliance is more than a metric—it’s a philosophy of precision, coordination, and intent. In copper mining, where every tonne and every grade fraction counts, drifting away from the plan can quietly erode both output and profitability. Temporary measures, like partial ramp mining with dozer assistance, have their place but must always be executed with alignment to planning principles. The mine plan is the language of the operation—deviate from it, and communication breaks down. Stay true to it, and the operation thrives—safe, efficient, and consistently hitting copper targets, one bench at a time.

A deep dive into the world of short term mine planning. Every mine could have a different classification of short term planning. Some mines could have it at 3 months and others at 1 month and less. But the general idea is as highlighted in the text below. From the start of an entire mineral exploration to the end of it we have many holes drilled which will be logged with all sorts of properties and material types. These data sets are then put in place together and are processed through software’s which create what we call a block model. This block model includes lots of information such as material strength. Economic materials present as well as the percentages at each position in a 3D space (x,y,z) This 3D model is what we use to create the entire pit outline. (This is the most economical extraction method and sequence of the mineral deposit in order to maintain our profitability at the mining operation) The long term planning team will generally give a projection or forecast for an entire 2 years to maybe about 5 years. The short term planning team has to strategize and navigate the constraints to meet these numbers on a monthly basis. Weekly basis and daily basis. Tracking the ore feed and waste movement closely. Making plans with very clear instructions in order for the team to execute the operations without ambiguity. Softwares on the market operate differently, the one I’m familiar with is Deswik. This software is designed such that you merge the pit design together with the current topography and include the block model inside of the pit design. You then start selecting machines and utilize them to mine out blasted material as well as material you intend to blast. Mining is done with a single unit throughout the end of the month. As you mine with each unit there will be a report that will keep being generated which will include ore and waste numbers. You continue with this process and sometimes you may have to change the strategy or mining direction in order to meet the budgeted numbers. When all other strategies aren’t really giving you the desired copper numbers you are seeking. You may have to explain your reasons to senior management and let them know of the major constraints you are facing. Through experience they could give you better guidance on how to schedule. Sometimes we have to bite the bullet and ensure we fast track other projects when we’re struggling with ore. Some of the projects we fast track is things such as ramp developments that can potentially lead us into better ore blocks with better grades. Ultimately short term mine planning is an integrated function among the process plant, mining operations and mining technical team. Making real time decisions to ensure constant flow of material and optimization of mining to achieve the best possible result on a month to month basis. The block model = the mine WE MINE FOR PROFIT TO SUSTAIN DEVELOPMENT

Mine planning is the heart of any successful mining operation — it transforms geological resources into valuable, mineable reserves, ensuring technical, economic, and operational feasibility. 📊 Here's a simplified look at the Mine Planning Cycle — from resource modeling to operations: 🔹 Geological Model: The foundation — understanding the ore body. 🔹 Optimization Inputs: Mining method, geotechnical data, costs, and constraints. 🔹 life of Mine (LoM) Considerations: Business goals, community impact, and operational limits. 🔹 Optimization & Its Results: Selecting the best pit shell for maximum value. 🔹 Mine Design: Creating the final pit, haul roads, and infrastructure layout. 🔹 Production Schedule: From LoM to weekly plans, aligned with NPV optimization. 🔹 Financial Modeling: Forecasting cost, revenue, and profitability. 🔹 Reconciliation: Comparing plan vs. actual — adjusting for real-world conditions. 🔹 Operational Plan: Turning plans into safe and efficient daily mining activities. 💡 This cycle is not linear — it’s dynamic and requires constant adjustment based on field feedback, economics, and safety. As a mining engineer, mastering this cycle means improving both productivity and sustainability.