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Performance Beyond Results

25 June 2017
 

Last week I wrote about how conventional management approaches routinely set results related priorities. We hear this in their language. There are cost reduction plans, service improvement plans, risk management plans and so on. When the focus isn’t on results based plans it’s often on results themselves. Services have objectives, budgets, targets and standards to meet. Individuals, teams and departments are to be held to account.

However all results are symptoms; a result of something, namely the underlying health of the ways of working that create them.

With our physical health, we know that treating symptoms may help us feel better in the short term but that this does nothing to improve our underlying health. Moreover, unless we are conscious that we are only treating symptoms, we risk masking deteriorations in our health by providing false reassurance. We can feel better when we are getting worse.

Tending to symptoms can therefore allow underlying problems to grow unchecked, only to reveal themselves in crisis, when remedying them requires urgent, sometimes Herculean effort, often at greater cost and often when there is only the possibility of poorer outcomes than if we had acted sooner.

And sometimes, of course, reality only reveals itself when it is already too late...

So conventional management approaches, with their ardour for results can end up being a wet flannel on the brow of a sick organisation, not the regime of diet and exercise that is necessary to creating a healthy one, something last week's example illustrated.

How then do we help our organisations to become and stay healthy?

Left Shifting

One useful answer to this question is "Left Shifting" - a process of getting from a preoccupation with results to a deep understanding of the shared system that creates them.

Left Shifting involves 2 steps, from outcomes to inputs and from inputs to patterns.

Here's an example of how it works...

Processing Life Insurance Applications

Working with a team that processes life insurance applications I heard that the sorts of OUTCOMES that mattered to the company were things like market share, profitability and risk exposure (not wanting to underwrite customer risks at a price that left the company at risk). There were also outcomes that mattered to the people involved, like having a job design that offered them purposeful work and less frustration than the status quo.

Clearly, outcomes like these are reasonable enough but they were not providing operational clarity to those involved in the processes of work; that is, they were not helping people to visualise or describe what a healthy system of work would look like, only what it would achieve.

To shift from a focus on outcomes to a better understanding of the INPUTS that would create them, we needed to consider what good would look like at a process level and how that would shape what people spent their time doing.

We did this in two steps. Firstly, the team established that there were 3 high level inputs:

  • Right and relevant information should be gathered.
  • The risk should be rated and a quote offered.
  • If the quote was accepted the policy should be sent live & documentation issued.

Next, for each input the team wrote descriptions of what perfect would look like. For example, the team surmised that perfect for the first input would mean:

  • Information gathered is necessary and sufficient to accurately understand a customer's needs and rate their risk.
  • It is gathered 'one stop' at the front of the process (i.e. no need to go back for more information later because something was missed).
  • Where 'one stop' is not possible, all additional information requirements are identified at the same time, as early in the process as possible and gathered using the fastest means possible.

Together these descriptions enabled the team to describe what a healthy system would look like in practice - they described the perfect INPUTS that they believed would create the desired OUTCOMES.

This provided a hypothesis to test - did improving these inputs create better outcomes? - but also a lens with which to look at the status quo in order to identify PATTERNS. That is, what things were predictably preventing perfect inputs from being achieved every time? Understanding these patterns would be key to improving the health of the system of work.

The team took an application form and asked, "Do we have the necessary and sufficient information?". There was discomfort. Some of the underwriters felt confident that they could accurately rate the risk while others noted that normal practice would be to get more information.

They took more applications, reviewing them together in order to explore this issue until a clear pattern emerged - a bias towards seeking additional information for fear of what might happen if a risk was wrongly rated.

The team worked through what the basis of this fear was, what the real world consequences were of making a mistake but also what evidence they had that gathering more information in fact reduced the risks that they were trying to avoid. From these discussions a simple but powerful idea for change emerged.

Returning to their definitions of perfect inputs, they rehearsed once more what 'necessary and sufficient' really meant; namely, that it would be impossible to consistently rate risks accurately without additional information... but would it? For example, how consistently could they predict the right rating using only the information that was routinely submitted? No one knew.

The team devised a test. They would take a random selection of cases. Some of them would be given the application forms that had been received, prior to any additional information being sought. They would be asked to rate the risk based on only this information.

Alongside their rating they would state their confidence that they had rated the risk accurately - very confident, confident or not confident. For cases where they were not confident, they would provide a list of the additional information that they felt would be necessary and sufficient to change their view to "confident".

Once the test was run, others on the team would analyse how their colleagues' risk ratings compared to those that had emerged from the normal process, where all of the usual additional information had been sought.

We waited with baited breath...

  • There was almost no variation at all - and no material variation - between the test ratings and the actual ratings for cases where people had stated they were confident or very confident.

Acting on this would improve the team’s ability to quote on demand from 46% of cases to 87% - a phenomenal uplift in service and, at a stroke, knocking cost, hassle and frustration out for everyone.

In addition:

  • For cases where the team were not confident in their prediction, there was no variation between the information that they felt would be material and the information that had eventually shaped the actual rating.
  • The additional information being identified as material was typically much less than was being sought by the normal process.
  • In most instances it was possible that this narrower information requirement could be satisfied over the phone, rather than through lengthy (and costly) back-and-forth written comms.
  • Only around 4% of all cases would require the same highly detailed information as the normal process sought.

It was an opportunity for the team to move from underwriting on the basis of fear to underwriting on the basis of knowledge, enabling them to quote on demand (or following a short phone call) for around 96% of all cases (up from 46%) whilst significantly reducing the associated frustration and administrative costs and burden.

In revenue terms this meant €2m of additional new business that previously would have been quoted for but lost because of processing delays and this was only the starting benefit. Factoring in their now improved, more responsive service, the team had the healthy system with which to become the preferred provider, opening up the potential to grow market share further and faster than their competitors; something they were now able to cope with through the reduced admin overhead of their new design.

The Lessons

  • Results come from means; getting better performance means shifting focus from outcomes to the inputs that create the outcomes.
  • This means creating clarity about what good looks like in operational terms, otherwise focus is continually dragged back onto managing symptoms at the expense of improving the underlying health of an organisation's ways of working.
  • Improving inputs means paying attention to the patterns that explain and enable or disable their successful delivery.
  • Such patterns are often rooted in entrenched ways of behaving, usually reinforced by ways in which work has been designed or is being managed. This puts a premium on ensuring that agency for change sits with those whose work is changing. Without this, the norms of the status quo - and the associated performance - will persist.
  • Very often patterns reveal points of leverage - issues that, if acted upon, have a disproportionate effect on overall performance, switching off a range of issues and creating a step change in outcomes. Acting on these points of leverage can unlock performance and build capacity for further change.
  • Paying close attention to the relationship between patterns, inputs and outcomes can be a successful way to build agency and enthusiasm - to create an engine for ongoing improvement (see more here).

It seems unlikely that there is one silver bullet solution to improve all issues in all organisations but, to me at least, it seems just as unlikely that any broadly applicable, sustainable solution won't understand and leverage these lessons. If that's true then they are and will remain fundamental to anyone who is interested in understanding and improving the health of their organisations... and why wouldn't we all be interested in that?


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