In our last blog, we shared how we assess whether measures are being used to drive the right decision-making, behaviours and outcomes. This led to conversation around how to actually select the right measures in the first place – hence this blog, drawn from our experience.

What do you want to know?

To best diagnose and define the right measures, the starting point is being clear on what you want to know. A simple approach is to understand what question you’re trying to answer. However, that can be a self-fulfilling prophecy. We’ve seen people retrofit their ‘question’ to their existing measures, which doesn’t help to challenge whether the measures are right.

We encourage clients to start by focusing on the desired result or outcome. What do you want to know to help do your work…or support your team…or manage your department’s priority areas? What people want to know typically fits into three categories:

  • How they are progressing toward a desired performance outcome
  • How they are understanding a current problem
  • How they are monitoring for a risk or fatal flaw in a process.

Spend time framing a specific, clear question and that will contribute significantly to helping you set the right measures.

What does your process tell you?

A useful cross-check to validate measures is to link that desired result to your business process. Does your process contain critical points that impact quality or safety? Are there weaknesses or single points of failure in the process? Are there critical parts of the process, perhaps where decisions are made, that have no measures at all?

Armed with this information – clarity on what you are trying to achieve, and verification against a business process – you can review your existing suite of measures to see if they are telling you what you need to know or if there are gaps.

Tonnes, conveyors and belt drift

A processing plant was delivering feed from a crushed stockpile via a conveyor. The feed conveyor was the bottleneck of the plant and there was strong attention from operations team members to ensure the conveyor operating time was as high as possible. Every time the conveyor stopped it represented tonnes of potential product lost that could not be recovered.

Working with the team, we reviewed the measures being used. These included:

  • Total feed for the shift (tonnes)
  • Availability of the conveyor for the shift
  • Conveyor stops/delays during the shift (frequency and total duration)
  • Conveyor stops due to tramp metal (number and duration)

The team wanted to know if they were achieving maximum feed tonnes into the plant for their shift and, if not, what was stopping them. They believed their measures were giving them that insight and yet, feed targets were not being consistently met.

There are two broad considerations in a context like this – equipment availability and optimal usage.

For availability, the team’s focus on conveyor stops was valid. However, the more specific focus on stops due to tramp metal meant a lot of effort and energy was devoted there – and still not resulting in the required feed targets. The team dug deeper and identified that the most frequent root cause of conveyor stops was belt drift trips, yet that measure was not being reported or tracked. Introducing belt drift trip measures brought attention and focus to solving the right problem.

For usage, the team reviewed their business process and mapped it as crushing the ROM material onto the feed stockpile; bringing the feed into the plant via the feed conveyor; screening materials etc. They quickly realised their original measures were all about equipment availability with no measures relating to how the equipment was operated. When equipment was available, was it being pushed to its maximum allowable limits? Additional measures were introduced, like tonnes per operating hour over a shift, and the percentage of a shift where rates were below target. These provided feedback to plant operators on how they were running the plant, allowing them to identify problems and modify processes to push feed rates higher. Further problem-solving by the team also showed the stockpile that fed the conveyor sometimes ran low, resulting in a lower rate. Additional measures were introduced around the feed stockpile inventory, including a trigger point to reallocate resources to increase stockpile capacity beforea low stockpile could impact feed rates.

Stepping back and looking at availability and usage, from an outcomes and process perspective, led to a new suite of measures:

  • Measure focused on output – feed tonnes into plant
  • Measures to understand equipment availability – availability, conveyor stops during shift (total and duration); conveyor stops due to belt drift and tramp metal
  • Measures to understand equipment use at maximum capacity – feed rate (t/hr); feed rate tracked over shift; % shift where feed rate <80% target; process delay – no feed; feed stockpile inventory

The changes better represented their work and provided the team with a deeper understanding of process performance. This, in turn, provided far better insight to drive continual improvement.

Measures should ultimately be simple – but simple is useless if it doesn’t direct effort in the right places. With the availability of data ever increasing, is now the right time to challenge the measures for your business?

Photo by Stephen Dawson on Unsplash