How do you help your managers make better decisions?

Data and actionable information are not the same thing. The approach we have taken with Eltirus Enable is borne out of experience hard won in the field and can be summed up in two examples.

Example 1 – know what good looks like

During a consulting assignment looking at haul truck efficiency, Steve Franklin was interested in understanding the average payload of the trucks in use. In talking to the site Operations Manager, it was found that he received a monthly report on payload and noted an average of 52t. To Steve, this seemed low, and he asked the manager what his opinion was of the figure. The reply he got was that “52t sounds about right as they are 50t trucks”. Actually, their rated payload was more like 62t and in the configuration the site was running (body liners etc.) the average payload should have been more like 59t.

The reason that this example is pertinent is that it highlighted that a piece of data on its own (average payload) is not valuable unless you know what the correct figure (and an acceptable range) should be, something that we call “experience”.

But what if you don’t have experience of that particular piece of data? The reality is that it then tends to get ignored and sub-optimal performance results.

Example 2 – know what you are going for

Working as a quarry manager on a large site with a history of poor labour relations, Steve was tasked with getting the production up. What he identified was that the team were never given a target to achieve – they were just “doing their best” each week.

As an experiment, a graph was posted at the sign-on area each morning before shift that showed tonnes produced vs. running target. Production progressively went up as the team understood what the target was and whether they were ahead or behind. From this the lesson learned was, that as for a team on a sporting field, the crew need to know if they are winning or losing and what they are aiming for.

Summary

Eltirus Enable was designed from day one to address these key issues:

• Make sure the data is available
• Ensure that it is presented against an acceptable range of operation
• Keep score against the target
• Enable management by exception, not data overload

This approach is practical, workable and gets results.