If you truly want to achieve maximum results from your improvement effort, it’s essential to have an effective implementation and sustainability plan.
Consider that, even when people excel at identifying major opportunities for improvement, if they don’t execute, they don’t make gains. In our work with hundreds of organizations, we have observed that the most successful are outstanding at execution.
These execution plans involve the use CI tools for measurement, applying accepted best business practices such as the “4 Disciplines of Execution” (see related post), as well as a number of key focus areas, including:
Get senior leaders to become actively involved
Identify clear project plans for delivering results, including measures and milestones
Engage team members and stakeholders
Set expectations and consequences — both positive and negative
Develop an organized structure and an activity / accomplishment reporting plan – communication matters!
Several previous posts focused on identifying waste or opportunities for improvement. Once this step is completed, and a specific problem is identified as the “best” opportunity, the next step often involves finding the root cause of the problem.
This is a critically-important step and, if we’re not careful, we can find ourselves working on the “wrong assumptions.” In fact, we’ve consistently found that few things are more dangerous than common knowledge – when it is wrong.
Root causes are tricky and elusive things. Brainstorming and the “Five Whys” can be effective tools, but neither approach guarantees the “right” result or conclusion. In fact, when the “wrong” root cause is selected, the most common culprit is an untested conclusion.
The best course of action is to think quite broadly when brainstorming and to consider carefully every possible way that the people, technology, information, materials, environment, or methods might be contributing to the problem.
In addition, when the brainstorming of possibilities is over, we should put on our skeptical hat and test each one – before going to the next “why” to find the root cause. Otherwise, we risk arriving at the wrong conclusion.
Here are five key questions you might consider to test a possible cause is to see if it is consistent with the data you already have.:
Did the proposed cause precede the effect? If not, it is probably not the real cause. If poor call response rate is being blamed on the new answering system, was the call response rate better before the system was installed? If not, the new system cannot be the culprit.
Does the data indicate the problem is trending or cyclical? If so, you can probably rule out ideas about causes that would produce steady effects. For example, to test the possibility that shipping errors are on the rise due to poor technology, ask whether the technology has changed. If there have been no changes in the technology, any changes in the results must be caused by something else.
What other effects would you see if the proposed cause were true? Are you seeing them? If not, look elsewhere for the cause. For example, to test whether ‘poor morale’ is causing a high number of defects, ask where else would signs of poor morale show up. Are you seeing them there?
If the proposed cause were not true, could the effect have happened? Could the product weight be dropping if a blockage had not developed in the dispensing line? If the answer is ‘no’, you know you must find the blockage.
If the cause had been X, would it always produce this effect? If the answer is ‘yes’, then in order to test this, you simply need to check whether the supposed cause actually occurred. For example, if my car will not start, a possible cause is that I left the lights on. (I drive one of those old fashioned cars that require operator involvement to turn off the lights.) If I check and find the lights are in the ‘on’ position, I can confirm my theory. Otherwise, I must keep looking for the cause.
Our previous few posts have focused on identifying waste.
After an area of waste or an opportunity for improvement is identified, the next step is to define the specific problem. Few decisions have a greater impact on the likelihood of success of an improvement project than the definition of the problem.
For example, Stephen Covey says that the way we see the problem is the problem. Albert Einstein warns that we cannot solve problems at the same level of thinking with which we created them. The way we define and communicate the problem the team is expected to solve will greatly influence the speed and efficiency with which a team will complete its work, the degree of satisfaction between the team and the project sponsor, and the efficacy with which an organization prioritizes and sequences the problems to devote resources to.
Consider these different approaches to defining the same problematic situation:
Order fulfillment is too slow and is costing us a lot of business.
Our lost sale rate has increased from an average of 125 per month over the previous six quarters to 190 per month this quarter.
Our Order-to-Delivery timeline has increased to 60 days due to a bottleneck in packaging.
Profits are down.
Sales has missed their target for the past three months.
Packaging is too slow due to old equipment.
Order-to-Delivery time from the Mid-western plant in Q3 increased by 15 days over the same quarter prior year, and was cited as the cause of 42 lost sales in Q3 impacting revenue by $270,000 in the quarter.
Some of these are statements of fact, while others are judgments. Some are very broad and others are very specific.
They may ALL be valid observations about the same situation, yet the problem solving efforts they would guide would differ greatly in urgency, efficiency, and efficacy. Developing a good problem statement at the start will help you define and lead an improvement project that most efficiently arrives at better results.
In our next post we’ll share four best practices for defining problems.
Continuing with our theme of Continuous Improvement (CI) tools, people often need to increase the throughput or output of a process, but don’t have access additional resources to do so.
In these situations a Process Flow Chart is an excellent tool to start with.
A Process Flow Chart or Process Evaluation Chart (the latter is populated with measurement data) can be created by bringing together the participants in the process and mapping it out together. Some organizations believe that mapping the processes with the frontline associates always results in lightbulbs going on and the associates voicing concerns and ideas once their process is on the wall.
There are always surprises, as people find ‘black holes’ or dead ends, and see the ‘wastes’ that are often invisible during the typical business day. Unnecessary waiting and handovers suddenly become visible; details about the things other teams or team members do suddenly become clear as people realize how their work impacts others and vice versa. Action logs based on the concerns/ideas are almost always a result at well, and they serve as the basis for the improvement project to increase output simply due to the heightened awareness stemming from the flow chart.
Here is a sample of a simple flow chart:
When creating a flowchart, process steps are shown as shapes of various kinds, and their order by connecting the shapes with arrows or lines. Different shapes are used to indicate actions, decision points, recycle loops, work and wait times.
Here is a summary of the most commonly-used shapes:
Every organization has more processes with opportunity to improve than they have organizational capacity and management attention units to execute. That’s why it is so important to identify the best opportunities and to work on the right things.
Over the years we’ve compiled a list of eleven factors that can predict, with a fair degree of accuracy, if a project will be successful. A successful project certainly does not need to score 10’s in all of these, and some of these eleven are more important than others and carry more weight in the prediction:
The potential benefit of the project to the organization is clear, substantial and quantifiable. (10 = very clear, quantifiable, substantial)
The problem to be solved is clearly defined and quantifiable, and the project scope is focused and well-defined. (10 = very clear, focused, and well-defined)
The project has top management’s commitment and support (resources, sponsorship and follow-up); no influential person is actively opposed to the project. (10 = very strong support)
The sponsor and team leader are clear about each one’s role and partner effectively to ensure the success of the project. (10 = very clear)
The team leader and key resources are devoting enough of their time to the project to complete it very quickly. (10= full time)
The team is staffed and led by the right people for the job, and they are determined and capable to quickly achieve results. (10 = very determined and capable)
Meaningful and accurate facts and data about the process are available. (10 = very available)
H. The process to be improved is repeated frequently enough to efficiently study variation in the current process and to and test and measure improvements. Hourly? Monthly? Annually? (10 = very frequently).
The processes to be improved are within the team’s span of control. (10 = under control).
J. The expected time frame for completion of the project or for achieving concrete and measurable milestones. (10 = 4-8 weeks to completion or measurable milestone)
The processes are stable, that is not undergoing very recent or imminent major change (10 = very stable).
Careful consideration of each of these eleven factors will help you focus your capacity on those improvements with the best chance of long term success, moving your organization further faster down that never-ending road of Continuous Improvement.
Continuing the theme of “waste walks,” there are several fundamental guidelines that should be followed in order to optimize the value and outcomes.
Here are some best practices for implementing Waste Walks (or “going to gemba“) that have proved successful in organizations and that have brought-about break-through results:
Communicate before starting. Begin by breaking the ice with the people in the work area so they know what is happening and why; make it clear that this is not a fault-finding mission, that there is amnesty, and that the Waste Walk is an effort to “help, not to shoot the wounded.”
Communicate with the gemba team. Establish ground rules, making sure to describe the theme or the forms of waste the team will be targeting, along with any other expectations relative to objectives people issues, desired outcomes, and so on.
Describe the start and end points of what you want to observe and study.
Conduct the Waste Walk and maintain communication protocols throughout; remind the team that as they interact with and pose questions to those doing the work, they must listen carefully to the answers.
Reconvene in a meeting room afterward to record ideas, consider what the team has learned, set priorities, and move into action! Sometimes it gets harder as the team disperses, so be sure to maintain communication and measure progress after-the-fact.
Make Waste Walks a regular part of people’s work; they should not happen once in a blue moon
Finally, if there is an over-arching theme or mantra associated with an effective waste walk, it is NOT “Don’t just stand there; do something!”
Conversely, the best over-arching mantra is, “Don’t just do something; stand there and learn!”
Our previous few posts have focused on leveraging high-performance teams and agility in order to more effectively execute strategic improvement plans. An additional tool that can significantly boost the execution step is a “quick win.”
According to John Kotter, author of Leading Change and The Heart of Change, creating “quick wins” build momentum, defuse cynics, enlighten pessimists, and energize people.
The key elements of a “quick win” are right there in those two words: it’s got to be quick and it’s got to be successful.
A “quick win” must be completed in 4 to 6 weeks at most, but many are implemented much faster such as in a kaizen blitz where a small group focuses full time on an improvement for a day or two, or half-time for a week.
For a solution to become a “quick win” it is almost always an improvement that can be completed with the people closest to the work and with the resources close at hand. Sometimes a “quick win” is a high value improvement executed with speed. But even an improvement with small dollar impact can have a great ROI — because the time and expense invested is so low and the organization begins reaping the benefits so quickly.
Because of the speed imperative, if a solution requires a significant capital investment, it is not going to be a “quick win.” If it requires a large team or cross-functional buy-in, chances are it will be a slow win if it succeeds at all. Many “quick wins” do not require a formal team; often a natural work team can identify the problem and implement a quick solution.
We should also note that there are some potential risks associated “quick wins,” which we’ll discuss in our next post, after which we’ll share keys to successful “quick wins.”
If you’d like to assess your personal engagement level, or see how your organization or client organization compares to others, the Enterprise Engagement Alliance (EEA) offers several no-cost benchmark tools.
Accessible through their website, the EEA provides three free and confidential tools to help individuals and organizations benchmark various aspects of engagement. There are currently three different ways in which you can benchmark engagement levels:
Gauge your personal engagement – how your personal level of engagement compares with others
Gauge your company’s or client’s general level of engagement – how it compares in terms of the same criteria used to create the Engaged Company Stock Index
Benchmark your company’s or client’s engagement practices – how they compare with best practices and other survey respondents in terms of employee, customer, distributor, and vendor engagement practices
Run Charts are simple line graphs of data plotted over time. They are used to better-understand the performance of a process, as they help people distinguish between random variation and special causes, or to track information and predict trends or patterns.
A run chart can also reveal whether a process is stable by looking for a consistent central tendency, variation and randomness of pattern.
One of the most common CI tools, a run chart is easy to interpret and does not require tedious calculations or special software to produce.
How to create a run chart:
Identify the question that the run chart will answer and obtain data that will answer the question over a specified period of time. For example, if you were looking at how long it takes to complete a task, you will make note of the time taken (in minutes) to complete it over a specified period of time.
Gather data, generally collect at least 10 data points to detect meaningful patterns.
Create a graph with vertical line (y axis) and a horizontal line (x axis).
On the vertical line (y axis), draw the scale related to the variable you are measuring. In our example, this would include the complete range of observations measuring time-to-completion
On the horizontal line (x axis), draw the time or sequence scale.
Plot the data, calculate the median and include into the graph.
Interpret the chart. Four simple rules can be used to distinguish between random and non-random variations:
Shift – 6 consecutive points above or below the median
Trend – 5+ consecutive points going up or down
Too many/too few runs – too few or too many crossings of the median line
Astronomical data point – a data point that is clearly different from all others (often a judgement call)
First introduced by Karl Pearson, an English mathematician and bio-statistician credited with establishing the discipline of mathematical statistics, a histogram is a graph figure which is used to display past data. It differs from the more well-recognized bar graph because a bar graph relates two variables, but a histogram relates only one (i.e., “earnings per month” in our example.
More specifically, histograms represent the distribution of numerical data, providing an estimate of the probability distribution of the continuous variable. Data within a histogram is displayed in “bins” and each bin has the same width. The example above uses $25 as its bin width and shows how many people earned between $800 and $825 per month, $825 and $850 per month, and so on. In other words, the “frequency” of each.
Histograms often provide new insights into the dynamics of process performance by indicating the number of times (frequency) each outcome occurred. Note that the mode of this frequency distribution is between $900 and $925, which occurs some 150 times.
To make a histogram, follow the following simple steps:\
On the vertical axis, place frequencies. Label this axis “Frequency” covering the total span of gathered data points. In the example above, the span ranges from 0 to 200.
On the horizontal axis, place the lower value of each interval measured. In the example above the first “bin” represents earnings between $800 and $825 per month, followed by a bin representing earnings between $825 and $850 per month, and so on.
Draw a bar extending from the lower value of each interval to the lower value of the next interval on the horizontal axis, and reaching up to the associated frequency measurement.
Challenges and best practices associated with continuous improvement