Mine to Market integrated mining plan from mine to market

Mine to Market integrated mining plan


Key Features

  • An integrated approach to mine planning
  • Model supply chains with processing, stockpiling, transport, and sales, from Mine to Market
  • Directly integrate short term schedules and long term plans.
  • Align sales and production planning.


  • Coal
  • Iron Ore
  • Graphite
  • Bauxite
  • Gold
  • Mineral Sands


BlendOpt.Integrate can generate integrated mine plans which can simultaneously consider decisions across a mining supply chain and across time.

Designed specifically for complex operations, BlendOpt.Integrate is uniquely able to simultaneously optimise processing, logistics and marketing decisions that impact production, revenue, and profit.

When incorporated into a mine planning study, BlendOpt.Integrate creates a complete end to end solution for integrated Mine to Market plan improvement.
Product model and price functions

Predictive analytics that maximises value from processing,
logistics, and market strategies.

To remain competitive in today’s challenging market, miners need to develop an integrated strategy for value creation as well as improve the coordination of operations planning across the resource supply chain.

Plans for mining, processing, logistics, and marketing are difficult to align with enterprise objectives if each planning activity is developed separately instead of holistically. In practice, supply chain optimisation is only achievable through integrated planning. By integrated planning, we mean coordinated planning of decisions between mining, processing, logistics, and sales. While the importance of integrated supply chain planning is recognised, its execution is difficult without the right tools to support this process.

BlendOpt is the first mathematical optimisation software tool that supports complete integrated planning of mining resource sales and supply chain strategies. Starting with a mine schedule, BlendOpt discovers strategies for how ore should be processed, blended, and allocated to different products or sales contracts with the objective of maximising profit. Mathematically optimised plans are created using predictive analytics algorithms that test millions of possible plans in search of the highest profit from the data provided.

Previous mining supply chain optimisation software has historically been unable to accurately model the problem and did not contain algorithmic techniques that could solve the integrated mine planning problem in a timely fashion. In addition, any results generated were difficult to analyse, thereby limiting what insights could be gained to inform improvements to a mine’s business strategy. These themes provide the lens through which BlendOpt’s technical capabilities are reviewed below.

Defining the Right Problem

Solving the right planning problem can influence whether operational decisions will align across a demand chain. To highlight this point, we review case studies where changes to the fidelity of sale price modelling and cost modelling significantly altered the financial optimality of ore processing and commodity marketing decisions.

Integrated Planning from Mine to Market:
A New Priority for the Coal Industry

Market conditions will no longer allow for operations that are not competitive in costs and cash flow. While asset quality is paramount, competitiveness is also influenced by operational decisions made across a resource supply chain.

The effectiveness of operational decisions is limited by their coordination and subsequent alignment

In other words, what is a good decision for each separate business unit depends on those decisions made within other units. Silo business drivers for mining (ROM tonnes), processing (utilisation), and marketing (revenue) only align with enterprise objectives (cash flow, NPV) when operational performance is low. Under high performance operating conditions, decisions guided by silo KPIs will misalign with the interests of the group and erode value.

This is particularly true of coal processing decisions and their interdependency with decisions in mine planning, logistics, and market planning. Decisions in coal processing impact coal quality, product specifications that can be achieved and thus sold, production yields and operating costs. While maximising plant utilisation and “washing to an ash spec” instead of blending to an ash spec have known limitations, tighter margins are revealing more subtle and deeply held assumptions about what product a coal ‘should be’ that become invalidated as revenue approaches costs.

Coordinate planning of decisions between
mining, processing, logistics, and sales.

In practice, value chain alignment requires the coordinated planning of decisions between mining, processing, logistics, and sales. While the importance of integrated planning is recognised, its execution can be challenging. Understated difficulties arise in data integration and technical challenges arise in the creation and optimisation of an integrated plan. Also important are the organisational challenges to realising effective coordination at every stage of the planning process from agreement on scope, procedures for plan development, plan refinement and execution. While such challenges were relatively less important in a high margin operating environment, today’s mining industry faces a convergence of challenging conditions that increase the importance of integrated planning. In particular, integrated planning becomes more significant as easy access high-quality deposits are replaced by more complex variable grade operations and also as price differentials between product grades create new questions about how a producer should process, blend and market their resources.

Review Integrated Mine Planning Modelling Requirements

When undertaking an integrated planning exercise, it’s important to model only those constraints and problem features that significantly influence the calculated performance of a plan. When relevant problem features are ignored, the performance of decision options will not be accurately evaluated and can become misaligned with the actual financial metrics of an organisation. For instance, ignoring the difference in operating costs between washing coal and bypassing coal can bias mathematical optimisation in favour of higher quality products.

Product Price Modelling within BlendOpt

BlendOpt allows for the configuration of any product target the planner desires. For example, in Coal production, Ash, Calorific Value, CSN, Volatile Matter, Total Sulphur and other quality attributes can be important constraints in an integrated planning exercise. Defining these quality requirements as a range as well as a target value is sometimes helpful to ensure the optimisation of product quality is constrained within acceptable bounds.

In the short-term production plans sometimes must deliver contracted volumes by specified dates in addition to maximising profits from non-contracted sales. For some operations, defining a maximum product volume can be used to reflect sales expectations for each product and thereby introduce practical constraints into a mathematically optimised planning process.

In practice, a product’s sale price can change due to several factors including market and currency volatility. For high volume producers, the total tonnage sold can also influence sale price due to customer requirements for volume based discounts or due to the impact of volumes on regional or global supply. The delivered product quality can also alter the final sale price as specified within contractual agreements. Below, is an example price function that can be configured within the BlendOpt tool which details how contract price is modified by energy and ash content.

Example price function from BlendOpt where the sale price is influence by coal quality.

Some quality attributes from the Coal industry, such as Hargrove Grindability Index, Vitrinite reflectance, and Crucible Swelling Number display non-linear mixing relationships such that it is not possible to accurately predict blended coal quality using a weighted average of the individual components. Without an accurate model to predict blended qualities, it may be necessary to introduce additional blending constraints. For instance, constraints may be needed to prevent materials from being added to a product based on pre-blended quality characteristics, constraints may require explicit rules that include/exclude particular seams or plys from a product, or constraints might require products to consist of at least or at most a designated percentage of a seam by weight. The relationship between coal properties and coking properties also exhibits a non-linear relationship that may similarly require additional blending constraints. BlendOpt can optimise non-linear models, unlike most offerings which require naive linear modelling assumptions which are disconnected from operational realities.

Supply Chain and Constraint Modelling

If ore is sourced from different geographic locations, it may be necessary for capacity constraints to be defined for the haulage of ROM from different locations. Similarly, there can be constraints on what ore can be processed or stockpiled together.

BlendOpt supports any number of user definable time-variant constraints in order so that the generated production plan is realistic and useful.

For example, time-variant rail and port material flow constraints may also be considered if these have the potential to act as bottlenecks within the supply chain. Sales to domestic buyers are unlikely to have the same downstream costs as exported commodities. These cost model differences may need to be considered as it can influence product ratios and blended product quality decisions. Conversely, some operations purchase domestic 3rd party product that is blended with internal production prior to sale in the export market. Third party purchases will have their own contractual obligations, costs and price structures that may need to be accounted for to obtain accurate financial comparisons of planning alternatives. With BlendOpt’s supply chain modelling constraints, end users can have confidence in their practicality and realism of their plans.

Optimise across multiple planning-horizons with BlendOpt
strategic “look ahead” optimisation

One of the most significant challenges in integrated mine planning optimisation arises from the need for optimisation across a planning horizon. For instance, plans that must achieve future contractual requirements may require an ability to measure how decisions in one time period will affect the decision options available in a later time period.

BlendOpt’s artificial intelligence and optimisation engine can optimise each time-period simultaneously, whereas most mining software that implement mathematical optimisation techniques have been designed to solve each time period in isolation of all others. This may cause challenges in satisfying contracted sales if the optimal plan requires strategic stockpiling to meet future contract requirements or more generally to deliver the maximum profits from an operation. Similar difficulties can arise when planning processing campaigns. For instance, there is sometimes a need to process or not process certain stocks to prevent a future plant bottleneck.

The importance of strategic optimisation that “looks ahead” influences which optimisation algorithms are suitable for solving this class of mining value chain problems. Other problem aspects that influence algorithm suitability include stockpile modelling requirements, non-additive/non-linear blending relationships, and product price modelling.

Time resolution is also an underappreciated complexity within the integrated mine planning problem. Although there is uncertainty in the timing of future mining activities, it is important for plans to be defined at short time intervals (eg weekly) to impose a conservative constraint on the materials that will be available to blend together at a single point in time.

For this and other reasons, the size of integrated value chain planning problems can be very large. BlendOpt has been applied to problems with dozens of unique quality resources actively mined at a single time from multiple working sections along with multiple processing, stockpiling, and product decision options.

Considering such a problem at a weekly time granularity over life of mine introduces a discrete combinatorial problem space where the number of unique solution possibilities is on the order of 1030. It is therefore not an exaggeration to state that for some algorithm classes, the corresponding run time would on the order of geological time.

Many of the problem features just discussed cannot be suitably addressed by Excel solver, and at least some of these issues cannot be addressed in linear programming (LP) software that uses 3rd party mathematical optimisation libraries from CPLEX or Gurobi.

Paradyn’s experiences in industrial mathematics and commercial applications have led us to design specific mathematical optimisation and artificial intelligence algorithms in order to efficiently, effectively, and robustly optimise this particular class of problems.