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Now more than ever, small business owners rely on information to make sound business decisions.  Whether trying to determine the market potential of a new product or service, or to gain a better understanding of needs within their target market, this information is not only nice to have, but essential for small business success.  While there are many ways to obtain this valuable information, surveys remain a leading method due to their convenience and relatively low cost.  However, business owners must also be aware of the short comings when considering conducting surveys and interpreting the results.

 

Case Study

In an effort to teach students a real-world example of the scientific method and probabilities, we conducted a brief study on Facebook where we simply stated our hypothesis, that >10% of the population owns a small business. We then asked my network of over 600 individuals, “Do you currently own a small business?”

In the end, 35 responded “Yes” while 64 responded “No”.  There are plenty of interesting statistics and conclusions we can draw just on that alone.  First, nearly 35% of respondents own businesses. This appears to crush our hypothesis that a mere 10% would.  Further, we can also claim a 16% response rate (99 respondents / 607 friends).  If you’ve ever run a survey you may agree that a 16% response rate is very good!  But between the lines we’ll find the real insights of this little study that all business owners should understand!

 

Hitting the Target

Whenever we collect information for decision making purposes, we want to ensure the validity of the data to minimize the risk of making a bad decision.  The two aspects we are most interested in is accuracy and precision.  Accuracy defines how close our estimate is to hitting the target of the true value we would like to know.  Precision describes how close individual values may be surrounding our estimate.  Below is a classic depiction of accuracy and precision.  Of course, we would like to ensure we are both accurate and precise so we can hit our target and make a good business decision.

So how do we do this?

 

Representative Samples

To ensure accuracy in a survey, we want to reduce or eliminate any potential sources of bias.  Bias is defined as the distance the average estimate is from the true target value.  We want to ensure that there is nothing that will systematically cause our survey response to sway one way or another.  This is a lesson learned following the 2016 presidential election.  Polls suggested a democratic win, yet the republican candidate succeeded.  How was this possible?  The most plausible explanation was the inadvertent sampling bias among polls that favored the opinions of left-leaning, densely populated metropolitan areas but under sampling of the less densely populated rural areas that tend to be more right-leaning.

This brings up the discussion of a representative sample.  Just as in the polls, as small business owners we cannot survey every potential customer on their wants and needs so we attempt to identify a representative sample, a subset of our target market that we believe would represent all potential customers.  In our example, one of our participants commented that my social network is most likely not representative of the entire American population, and he is exactly right.  As such, any claims that we make from our data will be limited to the 607 Facebook friends that I have and cannot be extrapolated to the entire American population.  Thus, I can say that approximately 35% of all my Facebook friends are small business owners, or roughly 212 Facebook friends.  Or can I???

 

Response / Non-Response

There are two other sources of bias that can creep into our survey results – response and non-response biases. The first is response bias, which is defined as the bias that can occur if respondents do not reply truthfully.  This could be either intentional or unintentional, based on the question.  In the intentional case, respondents might feel a bit of peer pressure to respond in a certain way.  Consider if I instead asked for annual revenue figures.  Respondents may feel inclined to inflate their figures in an attempt to demonstrate their business success.  The inadvertent case may be more attributed to the survey design itself.  My simple question of business ownership may not be completely understood (at least as I intended) and respondents may answer differently due to a misunderstanding of the question asked.

Non-response bias is slightly different.  The non-response bias can be introduced if friends that tend to participate are different than those who tend not to participate.  In our current example, it is possible that small business owners may be more inclined to participate than non-business owners.  We are a different breed and take great pride in raising our hands and claiming, “I am a business owner”.  There is also the possibility that not all of my 607 Facebook friends are actively on Facebook, but the small business owners are in order to keep current and connected.  Speculation at best, but certainly another possible form of bias that could artificially inflate my 35% estimate.

 

The More the Merrier

Enough on accuracy and bias for now, what about precision?  Letting curiosity get the best of me, I was watching closely immediately after posting the survey.  As the first few responses rolled in, I felt I was on a bit of a survey rollercoaster since each response had the ability to swing the result by several percentage points.  It wasn’t until later in the week that the number seemed to stabilize. We claimed before a potential 35% estimate.  However, this single figure is what’s called a point estimate and only tells part of the story. Going back to our polling example, very often we will hear a stated poll percentage followed by a margin of error.  What is this margin of error?

While a point estimate provides a good estimate of the average response, the margin of error defines an interval that we can have a high level of confidence the true value will fall between.  In our example, the estimate of 35% includes just under a 10% margin of error (at 95% confidence).  What that means is that we can claim with 95% confidence that the proportion of my Facebook friends that are also small business owners is somewhere between 25% to 45%.  In this case, I can certainly say that my hypothesis of >10% has been demonstrated since the entire interval is well above 10% (barring the earlier biases discussed).

But that’s a pretty wide margin!  What if I wanted to make a decision where the costs incurred were relatively high and a wide margin could either make or break the budget?  In order to narrow our margin of error, we would simply seek to collect more survey respondents.  Qualitatively, the more information we have (via more respondents), the more confident we can be in the results we obtain (again, barring any bias issues).

Below is a simulation of the results we might expect to get if we were to repeat this survey 10 times. Each line represents the percentage of small business owners as calculated as the survey responses were to come in.  The red dashed line forming a bit of an envelope around the data represents the margin of error, which starts off wide with just a few responses and decreases as more responses are added to the result.  Note that a few points exceed the margin of error.  We stated earlier that we were 95% confident in our estimate.  These out of margin points represents the 5% error that we may make in our decision.

 

Conclusions

While the actual results of our case study are inconsequential, the insights we can gain through the data collection and data analysis is paramount for any small business owner. In the end, or final claim is that we are 95% confident that the percentage of my Facebook friends who own a small business is somewhere between 25% to 45% (roughly 150 to 270 friends).  If we were interested in a more precise estimate, we could increase the number of responses by allowing the survey to run longer.  However, this number may be overstated even for my group of friends due to the sampling methods involved.  Thus, if we were looking for a more accurate estimate for a population beyond my circle of friends, we would have to ensure we have a true representative sample for the population we are trying to learn about.

Small business owners who are using information and data analytics to drive key business decisions should definitely consider the accuracy and precision of their data collection methods prior to making any key decisions.  In many cases, it can prove to be crucial to understand these principles and how to apply them to help avoid making bad decisions.

 

Remember the first time you went to that new restaurant down the block and it was SO GOOD! It was so good, you just had to take your friend there, but then… it wasn’t as good. Something was different. While this might make your dinner experience less than it could be, it (usually) won’t hurt you. In some industries, however, this amount of variation is unacceptable, because it could be deadly.

Consider an airplane manufacturer. The processes they use to manufacture must be designed with the highest standards in mind and the tightest tolerances. A mistake on your plate at dinner is annoying. A mistake on your engine 32,000 feet in the air is lethal. So, we need a way to anticipate and test for issues in our processes and products to a specific, quantifiable standard.

 

Processes in Review

When discussing process design a few weeks ago, we examined the IPO Method. This is the cradle of processes, where we discover their necessary inputs and desired outputs and then find what it will take in the middle to create that. These baby steps are where you start.

But, we made the important distinction that processes are never complete. They always require improvement, and this is where the PDCA Model comes into play. Improvement is a continuous process, but it is even more than that. It is also a culture, a mindset. Everyone in the organization must be empowered to pursue perfection.

After that we addressed how to deal with problems as they arise. The DMAIC framework is your go to bug stomper in any organization. By providing a way to not only create the necessary change but also institutionalize it, DMAIC is needed in any tool box.

What we are lacking is a design-oriented approach to controlling variation in our outputs. This is the problem that Genichi Taguchi set out to solve in the 50’s at Nippon Telegraph and Telephone Corporation. The end product; the Taguchi Method.

 

Introduction to Robust Design

The Taguchi Method classifies variation into three categories; internal variation, external variation, and unit-to-unit variation.

Internal variation deals with the natural degradation of inputs. Thinking of your meal, perhaps the chef had gotten a fresh delivery the first day you went, but decided to save money by using yesterday’s ingredients the day you went back.

External variation deals with things… external to the process. Perhaps the restaurant had a sewage line bust while you were enjoying your meal, or the restaurant was cold, or something else made the product – your experience of the meal – unenjoyable.

Finally, unit-to-unit variation deals with the variation inherent in two of the same part. Every beef patty and bun is not the same, and some are just better than others.

Taguchi provides a statistical method for anticipating the amount of each type of variation in any given process and then controlling it. If your restaurant had used his findings, they might have been able to deliver the sort of experience you expect each time,

 

The Process of Design

Designing the sorts of processes that that can reliably operate and minimize this variation is challenging. Fortunately, there are tools designed for this as well. While the IPO Model is useful for creating processes in the first place, other methodologies allow us to specifically tailor our processes for their purpose.

Consider DMADV. This process is used to create new processes, so while it shares the first three steps with DMAIC (Define, Measure, Analyze), the actual focus is different. The last two steps – Design and Verify – are meant to make sure the process will produce the product or service the end user needs. There are lots of great resources out there on DMADV. If you want to learn more in detail about this methodology specifically and how it differs from DMAIC we suggest starting with an introduction here.

The Taguchi Method may be embedded within the DMADV, beginning in Analyze and continuing throughout Design and Verify. Another key tool to address variation issues include Quality Function Development, also known as the House of Quality. While it is important to pick the right tool for the job, this should never prevent you from starting the job in the first place. There are always insights to gain and ideas to further develop. The best advice is to go for it!

 

In Closing…

As we conclude our discussion on the process of process, we should remember the power of all this. According to research published by isixsigma, Six Sigma methodology saved Fortune 500 companies approximately $427 billion between 1987 and 2007. Continuous improvement sometimes gets a bad rap from people claiming that it stifles employee creativity and is too rigid in its approach to things. While that might be true of companies implementing it in a poor manner, true Six Sigma ought to be empowering to employees and customers alike.

While our series has been focused on the employee side of the issue, there is no reason customer input, insights, and feedback can’t be involved at any step of any of these processes, or all of them for that matter. While the pushback against overly bureaucratic and corporate approaches to Six Sigma is understandable, done properly it ought to be one of the greatest tools for employee and customer empowerment. Numbers and process don’t lie, and Six Sigma is all about finding out what people’s actual talents and abilities are and using them to their fullest extent to achieve a common goal. That is what process is all about.

 

Have you ever had an argument with someone close to you (of course the answer is yes!) Did you resolve the issue effectively the first time? The answer is probably no. Typically, these sorts of important, highly emotional problems are very thorny to untangle. Unless these conversations are approached in a rational, formulaic way, the results are often not good! The same is all too often true in business as well.

While most issues in business do not appear to us to be extremely emotional, there is often something more than a profit motive underlying our disagreements. A coworker does not feel respected, someone has an attachment to a project, or perhaps personal issues are crossing the work threshold. Regardless, you work with people and nothing will change that until the robots take all our jobs.

In their book Difficult Conversations, How to Discuss What Matters Most authors Bruce Patton, Douglas Stone, and Sheila Heen describe how we typically fail to address the root of the problem, and what to do about it. Their book is worth the attention of businesspeople for two reason. First, they argue that the methodologies and tips they use are universally applicable, whether talking to a spouse, business partner, or any other close relation. And not only this, but the major premise of the book is that we do not actually talk about the issue when we have these difficult conversations, but that we beat around the bush talking about symptoms of the deeper issues, not the issues themselves.

It is also remarkable how well the methods of these psychologists align with contemporary business knowledge. We’ve spent the last three weeks discussing process design and how to make things work better in your organization. Since of course every organization is going to have to have difficult conversations at some point, it should not be surprising that the psychologists confirm a very specific piece of business knowledge – DMAIC.

 

Got Problems?

Before we jump into the DMAIC for solving problems, let us first define 3 types of problems:

• Sudden Drop Off – The most commonly identified type of problem, the sudden drop off is characterized by an issue that suddenly occurs after a run of no issues.
• Gradual Decline – These are the problems that sneak up on you.  Everything seems to be going fine until you hit a low point and realize that performance has been slowly degrading.
• Performance Gap – A performance gap is when there is a measurable difference between current performance and a desired goal.  One could say this is more of an opportunity than a problem, however the methodology and tools presented will still apply.

Once the type of problem is identified, there is a natural progression to identifying the 3 components that are present in the indentation and resolving of all problems:

• Issue – The effect viewed as a pain or symptom (e.g., nonconformance or complaint)
• Cause – The factor(s) that results in the ISSUE (determined by cause and effect analysis)
• Solution – Improvement to address either the ISSUE of CAUSE

Solutions may be selected to either eliminate, reduce, or mitigate the future recurrence of the problem.  Very often however, managers address problems on the issue only.  Sometimes looking for a quick fix (a.k.a. the band aid fix), or sometime just due to a lack of a formal process.  What inevitably happens is that the problem comes back again, and always at the most inopportune time.  What is needed is a problem-solving process…

 

The DMAIC Process

The process of DMAIC is straightforward enough:

• Define – Before we address any problem, we have to define the parameters. What is in bounds and what is out of bounds?
• Measure – If you’re going to meet a goal, you have to have a metric to compare against. Measuring allows you to see if the changes you make have any effect.
• Analyze – This is the bread and butter of DMAIC. Armed with the definition of the problem and a way to quantify it, you can determine the root causes of the issue.
• Improve – Address the cause to resolve issue! At the improvement stage your team brainstorms and implements the necessary changes to counteract the problem.
• Control – Rather than quickly addressing the problem and moving on, DMAIC says that we need to make sure that our improvements are having the desired results and then do something to make them permanent, so the problems cannot recur in the future.

In order to solve any problem, you first have to find the root cause. The Define and Measure functions are aimed at enabling you to identify and learn more about the issue. The Analysis phase is meant to carry out this identification through cause and effect relationships, and the last two steps are aimed at elimination and prevention of recurrence.

 

Bus Problems Revisited

Consider our bus problems from the discussion of the IPO Model. While the IPO Model was useful for figuring out why there was a problem, we can use DMAIC to uncover and answer the root cause of the problem itself.

With buses, the definition is fairly obvious – they must get where they are going reliably and on time. It is helpful to use a SIPOC diagram to do this, looking at the Suppliers, Inputs, Process, Outputs, and Customers. Notice this is where the IPO Model meets DMAIC, with an S and a C added.

When measuring, the team would brainstorm what data to gather and how to gather it. We would probably look into the total route time, time at each stop, number of riders, etc.

When analyzing, our team will seek out cause and effect relationships by mining the data and applying business expertise. Depending on the level of statistics needed this could involve regression analyses, fishbone diagrams, or any other method of root cause analysis.

Improving is where the rubber hits the road (pun intended) and the team applies their knowledge of the cause and effect relationships to propose and implement solutions, being mindful of the metrics established earlier to track success. Perhaps the solution for our buses could involve changes to the route informed by passenger and traffic data, new policies for riders, or incentives for drivers.

If the improvements are a success, we need to institutionalize them. This is the final stage – control. The team will communicate with management in order to find a permanent solution. When handing the process over to its new owner, the DMAIC team might also provide control charts, performance software, and other means of keeping the process under control.

Only after safely securing the gains can the project be considered a success. Often follow up meetings and additional touchpoints are needed for this. And remember, a process is never done. Processes are always in need of continuous improvement.

We will conclude our process design series next week by discussing robust process design and the DMADV model. The Taguchi method works to ensure customer satisfaction by considering and reducing noise factors in engineering processes.

 

You’ve evaluated your business, you’ve identified where your major issues are, and you have created a few processes using the IPO model designed to make things more efficient. But, you know you’re not done. The work is never finished! This is an infinite game and you’ve just started.

This is where the PDCA Method comes in. Designed by W. Edwards Deming in the 1950’s at a statistical quality control seminar in Japan, the wheel you are used to seeing in fact has its roots in the Shewhart cycle. Shewhart was an American physicist and sometimes acclaimed “father of statistical quality control”. In 1939 he changed the core concept of his cycle from a linear process to one that is circular, with no end.

Deming took this already good idea and added a fourth step. To Shewart’s initial three step process of specification – production – inspection he added redesign. Therein the structure of the model was in place even if the wording had yet to fully take shape. Finally, after the conference Deming attended, Japanese manufacturing executives recast the concept in the now familiar terminology.

 

Not Just for Operations!

While PDCA is typically associated with the operations functions of a business (i.e. producing goods and services or improving processes within the business), its ubiquitous use across the entire business is noteworthy. Since the four functions are generally performed as a series of processes, the process for process improvement applies for all.

Each of the four core business functions can benefit from the PDCA cycle for improvement:

• Plan
• Do
• Check
• Act

For example, if a company wants to increase the number of leads given a fixed marketing budget, the Marketing Department should look to improve their lead generation process.  Initiating the planning phase of their effort, they may plantheir next marketing campaign based on past campaign experiences, keeping what has worked well in the past and planning to try something new that their recent studies indicate may respond well with prospective customers. Often, the use of A/B testing is used to maximize the opportunity to find the best results.

Planning should also include the measures that will be required to assess the effectivity of their efforts and to determine if an improvement in performance actually has happened.  This measure must also include a baseline measure to compare against.  One example here could be the number of leads per $100 spent since they are planning on a fixed budget. If they demonstrate they can increase the number of leads per $100 spent, they will inevitably be able to increase their overall number of leads given their fixed marketing budget.

Then they do it – they execute the marketing campaign per the plan.  When the campaign is complete, or even during the campaign, they check to see how the campaign is performing, comparing the number of leads per $100 spent to the baseline of previous efforts.  If all this work is confirmed by real world results, they act by implementing this as the new standard for the organization. PDCA illustrations typically show this as a sphere or a ball being pushed up a hill. The standard is a plateau that juts out from the hill, protecting the upward progress of the workers. Either way, the PDCA cycle begins again to drive even further improvements in their process performance.

The key insight of PDCA for process improvement is that it is circular. A process is never “perfected”. Perfection is never achieved but must be relentlessly pursued. Continuous improvement is a mindset and a culture adapted by all high performing organizations.

There are both external and internal forces that may erode an organization’s performance.  Externally, technological change prevents even the best process from becoming reliable for too long. Likewise, competition and rising consumer expectations would disallow this even if technology was static. Internally, all processes experience decay if left alone. Employees may try to shortcut things out of ease and managers may become complacent because performance is “good enough”. If businesses are not in a constant state of renewal and improvement, they will fall prey to attrition.  If a business is not moving forward, it is falling behind!

 

The Culture of Improvement

At its heart, PDCA is part of a larger way of thinking. PDCA is all about a cultural shift. It is meant to facilitate the mindset of continuous improvement.  This requires a lot more than a forensic affirmation of the need for ongoing improvements. Continuous improvement requires that everyone, from the hourly employee to the CEO and Board of Directors, be searching for ways to decrease waste and improve the company.

PDCA allows management to show this commitment and make it felt throughout the organization. It is the process of process improvement. By institutionalizing continuous improvement in the very way the organization runs and thinks, managers can show that this is not just another corporate initiative that needs to be survived until it passes, but rather that a commitment to improvement is a radical shift in the thinking of the company.

Having looked into process creation (the IPO Model) and continuous process improvement, it makes sense to examine next what to do when something inevitably goes wrong. When a process or event is beyond the simple helping hand of the PDCA Model and needs to be addressed so that it does not happen again, it needs something a little more robust. Next week, we will discuss what this something else is.

 

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Which is the more effective pitch?

Last week, we spoke about the need for companies to be intentional in their processes. Businesses must consider not only what they are doing but also why they are doing it. Starting with why, they are able to better identify critical processes and make optimal decisions. This is similar to what Simon Sinek talks about in his best-selling book, Start with Why: How Great Leaders Inspire Everyone to Take Action. While his ideas are inspirational and useful for leaders the world over, this is not exactly the same as what we mean when we suggest you start with why. The why that we speak of is more tactical, less strategic, and it assumes traditional business goals – profit. This “why” informs us of which processes to pursue, but not how to pursue them. This is where the IPO Model comes in.

The IPO Model is something that is straightforward on its face but provides quite a few insights for companies that dig into it – superficially simple, actually complex. Instead of an initial public offering (as the name might lead you to believe), the model discusses Inputs, Processes, and Outputs. It follows a resource – tangible or not – through these most basic steps and looks to see what comes out the other side.

When the IPO Model is combined with tactical thinking that starts with why, it becomes very powerful. In discussing the IPO Model, one of our interns complained of the poor quality of the buses at his apartment in Bloomington, so we decided to apply the concept to this problem and see where the issue might be.

 

What’s the Real Issue?

Many of the large apartment complexes serving students that live off campus provide a bus system for their residents to campus and back. This is a business necessity, as students would not be interested in living outside walking distance of campus unless there were reliable alternatives. Many college towns operate their own bus system, but these can be unreliable and inconvenient even when they do work.

The bus system has obvious inputs; gasoline, maintenance, labor (driver), and riders. The process is of course the bus route, leading to the output – riders on the other side of town.

When discussing the issues with the bus system at the apartment, it became obvious to everyone that the root cause of the problem with our intern’s transportation was that management had forgotten the why. The apartment complex had heavily advertised its own, independent bus system. They were so fixated on having their own buses (with their own logo on the side) they forgot the why.

Why were the buses running? To get students to campus of course! But management was obsessed with the what – their own buses, independent of purpose. When they had most of drivers quit at the same time, this thinking prevented them from exploring better, cheaper solutions that would have made their customers happy. When we fixate on the what instead of the why, we limit our use of the IPO Model, even unconsciously.

This myopic focus on the fixing the status quo rather than designing a more reliable process prevented the apartment from looking into contracting with another company, working with the city for better routes on their buses, or something else entirely.

Start by asking WHY, not what.

 

After Why, What Then How

The value of this becomes more obvious when a process grows more complicated in both steps and inputs. When writing complicated procedures start with WHY, or the purpose, then define WHAT needs to be done to deliver on that purpose by mapping out the process steps and listing out every input that you will need for that procedure with the output in mind.

We must also consider scope. The IPO Model is able to make a procedure as ambitious as the user wants, but it must maintain its scope. This is another area where remembering the why helps. Every crazy idea should be considered, but they won’t all serve the desired output of the process, so some must inevitably be rejected. Minding the why provides the justification.

There is, of course, a process to writing processes. Typically, when Business Improvement Group focuses on process improvement for clients, we brainstorm with the stakeholders. When everyone agrees on the why (no small task) we can proceed to the design. Block diagrams are useful here to show the sequence from step A to B to C, from beginning to end, helping define what needs to occur to deliver our why, or purpose of the process.

Once a process is diagrammed out, your organization will still need some teaching direction on HOW to run the process. This is where step-by-step (or “how-to”) instructions come in. Well written directions will allow any person with sufficient skill to perform the task, so that personnel can become interchangeable. Not to say that employees shouldn’t be given task ownership, but decent directions will make anyone able perform the process.

Instructions also do not have to be written. There has been a trend recently among some companies to provide video instructions, even contracting this out to others.

With all this said, it is important to remember that you are never really doneworking on a process. Next week, we will discuss continuous process improvement with the PDCA method. If an organization just creates a process and leaves it alone, it will be surpassed by others. But this is not good enough. Companies must always be improving!

 

How do you walk? The question catches many people off guard because it’s something we never think about. Which foot goes in front first? How does it get there? Does the knee bend first or the hip flex or the foot lift? All three at the same time? Very quickly, such a question becomes something only a master of human ambulation can answer. But you still know how to walk…

Ever since you were little and learned to walk, you’ve had a process for doing so. You probably don’t remember much of the learning process, but you remember the result. In many ways, most companies and organizations process development works like this. They certainly have processes for what they do. Consider the process of having IT work done. Perhaps your work computer gets a virus or has a software issue. In smaller and mid-size companies the processes tend to be much less formal, sometimes as simple as shouting down the hallway at whomever has the technical knowledge to fix the issue.

While informal and organic, this is still a process. The technician hears the request, finds the location, diagnoses the problem, and fixes it or replaces the defective component. For a small issue that involves only one or two people – like computer issues, this works well enough. But for something more complicated that spans more individuals, a more robustly defined process is needed. Just like every muscle in your legs must work in concert to move you across the room, every person in an organization must act in harmony to achieve organizational goals.

 

A Mind of Its Own

Organic processes will arise when processes are not defined. Organizations and even people develop norms and assumptions, specific ways of doing things, whether they realize it or not. The job of an effective manager is to define and control these processes to make the results as desirable as possible for the organization. If you struggle with doing this, you might not be a natural manager – so you need to hire one.

While allowing processes to develop organically might save time investment on the front end, if an organization is acting without intentionality then it will encounter problems. Thankfully, much thought has gone into process design and improvement in the last century, so organizations that are courageous enough to take charge have more tools than ever to do so.

 

Process Design – Choose Wisely

The process of process design will be the topic of our next blog series. We will first discuss the IPO Model. This has nothing to do with taking a company public (unless that is the process you are creating for a bank!) but rather with understanding how environmental inputs are turned into outputs by the organization.

After this we will discuss Continuous Process Improvement, because establishing a process is only the start. If your company creates a process then does nothing to innovate it after this, the best case scenario is that the competition surpasses you, to say nothing of the natural decay most processes experience. In order to avoid this, we will describe the PDCA method and how this can be used for continually improving the functioning of corporate processes.

This will be the first half of the series. After this, we will focus on the process of problem solving. This well-defined system for whacking problems as they come up is the difference between heading out knowing where you are going instead of aimlessly driving until you find your destination. Intentional businesses are successful businesses.

We will conclude where most companies should begin – by defining a system for creating processes. The most well-known method is DMADV – define, measure, analyze, design, verify. Taking a deep dive into this system will provide the needed background to decide if your organization could benefit from having a better process for creating processes (hint; it can!)

Just as you didn’t quit practicing walking after you took your first steps, process design and improvement is an ongoing battle. As Simon Sinek would say, it is an Infinite Game. There is no way you “win” process design, and there are no clear rules. Instead, you need to equip yourself with all the tools you can to maximize your mobility. The sooner you get walking, the sooner you’ll be running.