Status Quo Begets Status Quo

I have been listening to the book Brave New Work by Aaron Dignon. Have you read it? I find it very interesting. 

When talk we (I) think about Process Behavior Charts (PBCs), it is usually in order to filter out the “noise” and highlight any “signals" in our metrics; to determine if we are still within the Natural Process Limits (NPLs). A PBC that stays within the NPLs indicates the system which the metric is monitoring is working as it was designed. Any data outside of the NPLs is one (of three) of the signals that indicates something has changed in the system, necessitating the need for closer scrutiny.  A PBC is referred to as the “Voice of the System” (VOS).

But the corollary to this is: 

If you are not happy with where the metric is charting, then in order to improve the chart, it means changing (improving) the system; the thinking and approach. 

As Dignon points out, if we want a 10-fold increase in production, we pretty much intuit that that will require major changes in our thinking and approach; the system. But, if we are only looking for a 10% improvement, somehow, we think that that can be achieved by "status quo " thinking. He calls this the "status quo bias." Any change in the metric requires a change in the system; the thinking and approach.

Even the modest 10% change will require different thinking and approach. Status quo thinking will only lead to status quo results. So, when the Practice Manager or area director (or even the C-suite) issues a new goal or benchmark for the new year or next operating period, then our response should be, "Great!! What are you all going to change within your thinking and approach (the system) to result in any chance of hitting that new goal?” Because, without that change first, the new goal 'just ain't going to happen!' Commanding it to happen or incentivizing it to happen or threatening if it doesn't happen will not make so.

Status quo begets status quo!

Remember, operational systems are created by and are the responsibility of Management. Workers are at the mercy of systems! 

Come join Mark Graban and I at the 2020 AVMA Veterinary Leadership Conference in Chicago as we facilitate the audience participation workshop of W. Edward’s Deming’s “Red Bead Experiment” which was designed to illuminate these concepts and more. 

Lean Veterinary Scoreboards

In my last post, I discussed the difference between Management By Means (MBM) versus Management by Results (MBR). MBR means only focusing on the end result of the metric or KPI (and hoping the means of getting there are efficient and value-adding processes) or focusing on the processes that lead to that result, understanding that if all of the processes are behaving as designed and under control, the end result is a reasonable assumption. Too often with MBR, dollars is the only metric.

So, I wondered, could it be possible (theoretically speaking only!) to successfully manage a practice without actually measuring any money related metrics? And, what would that look like?

In other words, without tracking gross income, average client invoice, payroll expense as a percent of gross; any money metric?

What non-financial Key Performance Indicators (KPIs) could be used to assure the monetary goals are met? The trick is to be able to identify all (or many) of the processes that are components of the money goals and to assign a metric to adequately monitor those processes.

For example, Average Client Transaction is composed of gross income divided by the number of client visits. However, these two components are the result of other systems, such as fees, reminder efficacy, ease of appointments, number of incoming calls that result in a busy signal, hours of operation, client satisfaction, medical record audits (% of services performed that get invoiced) , etc.

Here is a list of some of the non-monetary metrics I came up with in order to manage a veterinary practice:

Quality 

• Door to Doc time 
• The time from the moment the client enters the practice to the time they see the doctor; a measure of flow and, therefore, value to the client.
• Number of unscheduled follow-ups
• The number patients needed to be seen a second ( or more) time in order to get resolution of the pet's condition. Notice this is not scheduled follow-ups for additional treatment, tests or monitoring.
• % visits scheduled for recall
• % recalls made
• Response to 1st reminder
• Response to 2nd reminder
• Response to 3rd reminder
• % pets current on RV (rabies vacs)
• # client surveys returned
• # of client referrals
• % staff completely cross trained
• % dogs current on HW (heartworm) prevention
• % pets current on flea prevention
• % blood sample hemolysed
• # of blood samples requiring redrawing
• % of medical rounds performed on time

Morale

• # idea (kaizen) cards submitted
• # idea (kaizen) cards/ staff
• # idea (kaizen) cards completed
• % idea (kaizen) cards completed

Safety

• # staff injuries / time period
• # pet injuries / time period
• # of adverse anesthesia events

Costs

• # of drugs or supplies found to be out of stock
• % blood sample hemolysed
• # of blood samples requiring redrawing

Some of these probably have sub-systems or sub-processes that might require monitoring and, thus, metrics of their own.

I am not proposing that no financial KPIs be watched. Veterinary practice is a business, after all. Some financial metrics are important and necessary. (However, they shouldn't all be financially focused, either.) Maybe we have not thought about the role that systems, processes, quality, waste, and flow plays in the money numbers; the Lean mindset. Maybe we should not worry as much about the end results and concentrate more on how we get there.

What metrics you decide to monitor is up to you and your staff. Remember, Lean is about making your practice the best "your" practice it can be, not a carbon copy of the practice down the road or on the other side of the country or, even, what a management guru says it should be in order to conform to some cookie cutter model. Your True North and your team (and some experimentation) will decide what metrics are important.

There would be high level, focus area metrics for leadership to monitor, sub-system metrics that managers would monitor and, still another layer (sub-sub-system) that frontline staff would create. These metrics are all aligned and make up your management scoreboards. Incidentally, this is an example of visual management.

Scoreboards

Note that these layers generally flow down from our True North statement (they're top down, but there's input from lower levels along the way). Leadership metrics arise from our practice's focus areas. They check the destiny or course of the ship. Are we heading in the right direction?

The middle and lower level metrics, created by the people responsible for that work (with some input from their leaders), check the systems and processes that make up the focus area (leadership) metrics. Are we getting to our destination with quality, safety, effectiveness and without waste?

This flow is, in essence, what Lean strategy deployment (hoshin kanri) is all about; getting the ideals of the practice down to the floor (gemba) and aligned throughout the practice. It is everyone pulling the same rope, from the same end, in the same direction and at the same time.

The routine of management and frontline staff huddling together in a safe, honest and trusting environment every morning to discuss the scoreboards is Lean Daily Management; a topic of later posting.

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Thanks for reading. Comments always welcome. Please, let your friends and colleagues know about the blog.

Process Behavior Charts: A Better Way to Evaluate Your KPIs

Have you ever learned something new and thought, ”Gosh! If I had had only known this years ago, my life would have been so much easier!”? We all have, I suspect. What I'm writing about in this post falls into that category.

I remember owning my practice. I dutifully kept stats on everything I could think of:

• Gross income, 
• number of new clients, 
• average invoice total, 
• number of dentals or spays or neuters, etc. 

I would even plot them on charts and tape them to wall of my office like a war room, constantly watching the numbers and bouncing emotionally between feelings of “we made it through another period in good shape” and “oh sh*t, we’re down, and this must be the beginning of the end.”

The same sort of thing happened when I worked for a corporate practice. Every week, the practice manager (PM) and I were on a conference call with our area managers to discuss “the numbers” -- our KPIs (key performance indicators) -- whether we were achieving our benchmarks, by comparing them to last month, last quarter or last year, and why or, more importantly, why not. Same emotional rollercoaster.

Now, for the stuff I wish I’d known back then. I recently read a book called Understanding Variation: The Key to Managing Chaos 2nd Ed., by Donald J. Wheeler, at the recommendation of my co-blogger Mark Graban. It is a fun little book about some statistics (there is that ‘S’ word) and creating Process Behavior Charts (PBC).

You know that in any process or system there is going to be some amount of variation from period to period. When this is plotted on a chart, it shows up as “ups” and “downs.” Most of this is normal, it is just “noise.” But, sometimes it can mean something significant -- a “signal.” So, how do you tell the difference? By turning your data into PBCs. This way of plotting your data will “filter” out the noise and highlight any “signals.”

I encourage you to read the book as there is more than I can briefly blog about, but, having said that, let me share some points before we get into the charts.

1. Tables with lists of numbers are difficult to understand. There is no context, and the data is difficult to visualize in this format.
2. Line graphs, over a longer period of time, are easier to understand and put the data in some form of continuity and context with prior periods.
3. Comparing two data points, such as the current period data with the same data last month or last year, doesn't offer any context. Who says the data from last period was normal? Maybe, it was a really bad period due to extraneous influences, e.g. inflation or a natural disaster that occurred at that time.
4. Averages tend to be pretty much in the middle of a range of data. Comparing to averages tends to create “binary output.” You are either above average (“good”), or you are below average (“bad”).
5. The setting of arbitrary goals, such as a 10% increase over last period, becomes more objective and rational. It is well and good to set the goal, but if the system cannot produce to that degree, it is simply a futile “wish.” No manner of cajoling, incentivizing or threatening employees is going to help. If the goal is outside the limits, then the system is going to have to be changed from what it is right now, and employees have no control over the systems under which they operate. That is management’s domain.
6. PBCs are the voice of the system. They show how the system is functioning, and the extents to which the system can function, as it is now designed and operating. One can also assume that, without any change, the system will continue into the future as it is currently; it is predictable. If it is not where it should be, then the system has to be changed somehow. It also shows when a data is outside the limits of the system and, therefore, is a signal that something unusual has happened, and it needs to be investigated.

The following data represents the number of new clients seen per month over the last 18 months.

18, 16, 14, 19, 15, 17, 16, 18, 15, 14, 19, 18, 15, 18, 18, 17, 19, 11

Total = 297  Average = 16.5

When just looking at a list of numbers, it is difficult to really appreciate what is going on with the data. In this form, one might easily miss the value of the last data point.

Converting the raw data into a graph is more visually helpful.

You can see that this running graph (or X-chart) is easier to understand and gives better context to the table of numbers.

This appears to be a rather stable system (or process) until, possibly we get to the 18th and last data point. Is this part of the normal “noise” or do we need to investigatte? It is lower than any prior period we have recorded. I can tell you that, for me, this would have been good for at least a week of sleepless nights and two stupid, stress related arguments with my wife!

Continuing with the chart methodology, we next determine the Moving Range (mR), between each two successive data points. This distance is always a positive number, regardless of whether the first number is larger or smaller than the first. For example, the distance between -3 and 2 is 5, or the distance between 8 and 4 (or 4 and 8) is 4.

By comparing the first data to the second, the second data to the third, the third data to fourth, etc., we get the following table:

2, 2, 5, 4, 2, 1, 2, 3,1, 5, 1, 3, 3, 0, 1, 2, 8

Total = 45  Average MR = 2.53

Graphically:

To see if the variation in the X chart is all routine or if there's something exceptional going on in that last data, we then complete the X-chart by calculating and drawing the average, an Upper Control Limit (UCL) and a Lower Control Limit(LCL).  These are calculated as follows:

          Avgx= Totalx/#x

          UCL= Avgx+(2.66×AvgmR)  = 16.5+(2.66×2.53) = 23.54

        LCL= Avgx-(2.66×AvgmR)  = 16.5-(2.66×2.53) = 9.46

Note: The 2.66 is a conversion factor that approximates three standard deviations (but we don't calculate a standard deviation in this methodology).

Updating the X-chart:

To complete the mR chart we need to calculate the Upper Range Limit, as follows:

         Upper Range Limit = AvgmR×3.27  = 2.53×3.27 = 8.66

  
Note: The Lower Range Limit is always zero, since the variances can never be a negative number.

Updating the mR chart:

Together, these two graphs make up an XmR chart or Process Behavior Chart.

Looking at the XmR charts, we can see that the last data point is still within our calculated limits. This indicates that the data is just “noise.” It is just part of the normal variation for this system or process, as it is currently designed.

That said, if we're uphappy with the average level of performance, we could try to improve the system in a systematic way. There's nothing worth investigating in terms of a reactive question like "what went wrong that month?"

The first hint of a "signal" would be any single data point above the upper limit or below the lower limit.

Wheeler’s book gives much more information about the meaning of the charts and some other types of “signals” to be aware of such as: 3 out of 3 or 3 out of 4 data points being closer to one of the limits,

or

a run of eight or more consecutive data points being on one side or the other of the central line are interpreted as being a “signal.”

Mark Graban is currently writing a book on this material. It should be completed by June, but you can buy the first three chapters now through his use of the "Lean Publishing" approach. All of Mark’s books are “Top Class.”

Heads up! I will be the guest on a webinar hosted by Mark Graban and KaiNexus on May 7th at 12:00 noon Central time. We will be discussing the new emergence of Lean management in veterinary medicine.  Use this link. Hope you will join us!

Update 4/4/18: Watch Mark Graban talk about this material here.

Thanks for reading. Tell your friends and colleagues and, as always, comments welcomed.