WIRE gathers and stores operational data from various sources containing information about material movements and processes the information through first principle metallurgical formulas and balances.

Using spreadsheets to manage plant data commonly has the following issues: 

  • Work becomes nested between workbooks, worksheets and more workbooks with unintended consequences cropping up repeatedly as data and calculations are changed incorrectly

  • Segregation of duties is difficult. The knowledge of a workflow is tied to specific individuals (as they are the only ones who understand their macros) or detailed thinking with limited transparency 

  • Limited or no input validation on data is able to be performed, increasing the chance of human error which can have large knock on impact

  • Limited automation is available therefore mundane tasks need to be repeated and teams become demotivated doing repetitive, mundane work 

  • Teams need to be enabled to do their jobs and technology is a way to support that shift. Sifting through mounds of paperwork in search of what they need to do their jobs is not effective. Teams need to be able to work collaboratively and transfer knowledge between shafts and teams easily

The challenges that evolved from the use of these primitive systems over the years has led to huge amounts of dead time and frustration, but also to financial losses due to wrongly accounted inventories and poor decision making.

These limitations and pitfalls have informed some of core features of WIRE where we solve for:

  • Lack of auditability and transparency

  • Limited and complex data integration which leads to lost data, errors associated with manual transfers and time wasted

  • Limited security and control over user access/rights 

  • Version control issues leading to corrupted files, contradicting reports and multiple versions claiming to have the truth

  • Limited insights - error detection, fault finding and analysis on meta-data


Data Input validation and bias detection

WIRE enables input validation controls covering limit checks, consistency checks and data completeness checks to improve the quality of information available at all levels of the organisation. Bias can be detected through nodal discrepancy and T-Tests and CUSUM charts especially between adjusted and measured data.

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