Over the last five years, most mining companies have moved towards collecting more operational performance data from an increasing number of sources including fixed and mobile equipment, people and entire supply chains. But are these ‘data-rich’ organisations really leveraging their data to support front-line continuous improvement? To succeed at this they must provide user-friendly tools that turn what is often an overwhelming amount of data into actionable insights to enable front-line supervisors to ‘own’ the performance of their teams and empower them to drive continuous improvement through short-interval control.

Partners in Performance has identified four key steps that mining companies need to work through in order to empower front-line supervisors and managers with real-time decision-support tools.

Step 1: Quantify key operational value drivers

It is critical to assess all data-gathering and analysis in the context of bottom-line value creation and how specific metrics can help front-line supervisors to ‘own’ the performance of their teams.

A value driver tree assessment of a mining operation quickly uncovers which drivers, including people and equipment-related ones, can have the biggest impact on operational improvement through the front line. 

Once a value driver tree has been completed for each key area in a mining operation, each area can identify the 2-4 highest-value operational ‘input’ levers based on their impact on the output key performance indicator (KPI) on the left-hand side of the value driver tree.

For example, in the case of a mining-truck fleet, a high-value operational input KPI may be the number of maintenance tasks completed per day, which directly impacts fleet availability. If the tonnes hauled are  below target because of poor fleet availability,  the company may need to focus hard on resolving its maintenance bottleneck. In summary, it is important to retain an operational, value-based approach to determining which data points are useful to collect and store, not an IT-centric view (although IT is a critical enabler).

Step 2: Collect and store operational performance data

Value driver tree analysis of key operational areas will yield several questions about front-line performance data that need to be considered, including:

• Which data points are required 

  (safety, mobile equipment, fixed plant, supply chain, people, exter- nal variables such as climate data for pollution control, financial, etc.)?

• Which data-capture capabilities and level of data accuracy exist currently, or are required?

• What is the optimum frequency of data collection?

• How many hours/days/weeks of data should be available for visualisation?

• Do the current processes and IT systems support the ability to collect and store operational data, including history, in real time?

• If current databases are not suitable for collecting real-time data, what cost-effective and secure cloud-based database options can be considered for data storage?
• Can current gaps in data points be addressed short-term through manual data collection using elec- tronic forms on tablets or smart- phones?

The end goal of Step 2 is to identify one or more existing databases or historians that collect and can be accessed to expose clean, real-time operational data to support operations 24/7. This step requires close collaboration between senior operations, maintenance and IT managers.

Step 3: Visualise real-time KPIs on mobile devices

Once the key value drivers have been identified in Step 1 and operations’ data capture and storage considerations have been addressed in Step 2, the team can progress to Step 3, which is focused on quickly creating a live, simple, real-time visual KPI dashboard that can be viewed on a smartphone or  tablet.

There are a few important principles that need to be considered when designing a useful KPI dashboard:

• Keep the dashboard as simple as possible with only two, four or six KPIs, so that it is easy to see which high-value assets or activities are performing above or below target.
• Work out a practical KPI cascade where a supervisor can drill down into a red KPI to understand which person, piece of equipment or process is underperforming.
• Make sure that each supervisor has a KPI dashboard that is tailored to their area so that they ‘own’ and control each of the metrics e.g. drill and blast; pre-strip; load and haul; etc.
• Set realistic operating targets for the red/green cut-off point. Ideally, targets should have 3-5% stretch built into them to get the team focused on continuous improvement.
• Work out simple rules for setting SMS or email alerts so that supervisors are not swamped with alerts, but also do not miss out on alerts associated with costly underperformance.

In practice, once the relevant KPIs and corresponding data sources have been identified, it only takes two to three hours to configure and connect pre-built data connectors from the visual KPI app to the relevant real-time data, which can be stored on one or multiple databases. As the app operates in an HTML5 browser, the user interface automatically scales to the right size on every operating system and device.

Step 4: Implement ‘wiring’ to embed new front-line behaviours

To ensure that mines get real value out of deploying new tools such as visual KPI on smartphones, every implementation should be supported with ‘wiring’ and on-the- ground front-line coaching.

Wiring is all about ensuring there are clear accountabilities, processes and systems in each core mining area to ensure that people and equipment can perform at optimal productivity levels. A front-line operator or maintainer can achieve optimum productivity by doing the right thing at the right time and by proactively adjusting the frequency or duration of activities (their behaviour) to take account of changing operational needs.

Clear accountabilities mean defining who is accountable for each key operational (or business) activity and which other team members need to provide support, be consulted or be informed. Some people who are accountable for certain activities (e.g. the truck-fleet supervisor may be accountable for making sure that 12 dump trucks are operating efficiently for 10.5 hours out of every 12-hour shift) may delegate certain accountabilities to other team members; truck drivers can be made responsible for driving the  trucks.

Once each team member’s role and operating process has been clearly defined, KPIs with targets can be set for each person. In practice, team member roles and associated KPIs should be set up as a cascade.

Mining Magazine October 2015