Opportunity area


In any context, large scale production projects are extremely complex. Success depends on predictability and efficiency. This is particularly true of construction projects in mining environments. Big Data and automation have important parts to play in understanding the nature of error and unpredictable change, and in developing more efficient and effective ways of working.


Estimating the costs of construction delays

Based on historical data, this tool estimates the cost of construction delays as well as modelling their impact on production parameters. The simulation software not only identifies where the bottlenecks are but removes them, delivering an implementation, monitoring and support roadmap for management. The software can also generate statistical information, test possible scenarios, conduct risk analysis and create dynamic views for optimisation and experimentation, all without interrupting ongoing operations.


Increased production due to debottlenecking initiatives


Income at risk due to construction delays already identified

Spares Management for Mining via Highly Efficient 3D Metal Printing

This technology is the perfect choice for mining companies in need of 3D printers for the prototyping and manufacture of metal components. This technology supports different types of metal such as inconel, titanium, stainless steel, and spherical stainless steel, among others. With the innovative technology of laser metal deposition (LMD), you will be able to cut the initial investment cost by half and improve your overall performance and productivity. The technology is unique in the Metal 3D Printing world due to its wide range of applications and the capability of printing 100% dense metal parts with practically zero waste.


dense metal parts


lower hardware costs


wastage material

Using Big Data to reduce capex deviation

This solution uses Big Data based on historical data to reduce capex deviation and make costs changes more predictable. It incorporates an early warning system that predicts unexpected change cost based on the rework intensity of technical designs. In addition, its planning granularity indicator pinpoints the optimal degree of detail that a project plan should contain. Based on an ongoing data project that merges three disconnected data pools, the system can analyse high volumes of historical data from over 2, 000 projects and 700, 000 line items to make accurate predictions and significantly reduce change costs.


Reduction in change costs

Our team has decades of collective experience, spanning all areas of industrial operations.

Dr Nick Mayhew

Chief Commercial Officer

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