MCS Basic Production Solution: How a cost-effective monitoring solution transformed underground mining performance

AT A GLANCE

A lean, data-driven approach to underground production monitoring delivered measurable safe production gains within six months.

THE CHALLENGE

A major underground mining operation was running on a manual, Excel-based reporting system, out of step with three sister operations already using the MCS Premium Suite monitoring platform. The disparity came to a head at a senior management intervention that MCS had been invited to attend.

The validity of the numbers was questioned by the COO and it became clear that some form of commonality with the other operations was required. The major issue was that the operation was not performing at the required safe production levels and, as a contractor-operated site in a cost-conscious industry, it was difficult to justify the expenditure to install monitoring to the same level as the other operations.

MCS needed to find a smarter path.

THE SOLUTION

Applying the 80/20 principle, that 80% of the output can be achieved from 20% of the input, MCS designed a lean, cost-effective monitoring solution. Rather than a full flagship deployment, the approach leveraged two existing MCS assets:

A short-term planning system with shift-level capability, running on a SQL platform
An Android APK designed for intrinsically safe tablets, covering production, breakdowns, labour and checklists

Rather than connecting the tablet to an underground Wi-Fi node, a surface-based connection was used. Targets were set for safety, production uptimes and downtimes, and in-section labour. Shift data was captured underground, uploaded to a surface SQL database, and a shift report generated, providing management with a comparable view to the other operations. Once achievable, the data could then be repackaged to create a similar management operating system (MOS) as used across the other operations.

ACTIONS TAKEN

Delivering the solution required a coordinated programme of technical and operational effort across five workstreams, spanning proprietary software development, hardware configuration, infrastructure establishment, network integration and hands-on underground data collection. Each workstream was carefully scoped to work within the constraints of a contractor-operated site, and the entire programme was completed within the three-week pilot window, demonstrating both the agility of the MCS team and the robustness of the underlying platform.

IMPLEMENTATION AND REFLECTIONS

The solution was tested on the MCS test facility before deployment, identifying most potential issues in advance. Two friction points emerged during the pilot:

The contractor network facilitator differed from the MCS standard environment, causing initial connectivity delays to the server
The limited-capacity server resulted in slower upload, processing and report generation than a flagship environment

Both issues were resolved quickly, aided by strong engagement from the mining house senior management, a clear demonstration of the need and value of effective change management.

RESULTS AND OUTCOMES

Comparing the MCS-generated output to the existing Excel reports revealed significant flaws in the legacy data, most critically inaccurate capture of time-based factors and an inability to identify non-reported shift time. The method of calculating time and production rates was also fundamentally different to the other operations, which explained the inconsistencies flagged at the COO review.

The pilot was declared a success. A full roll-out project was initiated across the operation. Within six months, measurable safe production improvements were achieved, results that validated the case for accurate, real-time data as an enabler for continuous improvement.

On the strength of these outcomes, the mining house is now evaluating the next level of MCS solution.

Whilst monitoring is only ever an enabler for continuous improvement, the results achieved in the first six months after roll-out speak volumes for the need for accurate data and information.”
— Gary Smith, MCS