Lean and Mean Process Improvement is now available on CD as a PDF along with an assortment of Six Sigma Tools. Email me at walt.m@att.net for details on how to purchase this CD.
Work started last week on converting Lean and Mean Process Improvement to an audio book. This work is in progress. I will post notification about availability as soon as it is ready. If you email me at walt.m@att.net, I will notify you when it is ready for distribution.
After putting in several years as a manager in a sales organization, I have found that the key to training up a quality sales person is developing their understanding of what I would call the essence of selling. The misunderstandings that exist regarding selling and the sales process are many and profound. Sometimes these misunderstandings are right there in the hearts and minds of the trainers who are teaching your staff to sell.
At its heart, selling is an interpersonal experience that is focused on an exchange of value. Too often the focus of sales training is on one or the other, relationship or value exchange, when both are required. This mistaken approach comes from a failure to see that both parties are part buyer and part seller. Both sides pitch their perspective on the value of their side of the potential transaction. A successful sale happens when an agreement is reached about the perceived value of each side of the exchange.
Each side of the value equation (buyer and seller) sees the potential exchange differently. For example, a car sales person sees a particular tipping point between the value of their merchandise and the value of the proposed dollar amount being offered by the prospect. The person considering the purchase of the car sees a different tipping point. The tipping point for each side is where the perceived value exchange favors their position. In other words, there will be no deal until both sides see their value proposition being satisfied.
If a sales person listens for the potential buyer’s value proposition, and then frames the value exchange around that information, they will be well on the way to closing the deal. For example, if a prospect wants a sporty vehicle that has room for their family and luggage, the sales person will not be successful in trying to sell them a standard cab pick-up at a low price. The perception of value for the prospect is not just about price in this case. On the other hand, if the sales person tries to sell a more expensive sport sedan with a large trunk, they will be more successful.
Selling is about listening to the other person and applying what you hear to the value proposition. An educated buyer will do the same thing. The best listener will nearly always win.
I recently visited several contract manufacturers (CM) to discuss a project I am working on. The purpose of the visits was to evaluate their ability to produce an electronic device we are developing for the automotive industry. One of the production control metrics I asked from each project manager was an estimate of the typical roll throughput yield on their production lines.
Only one of the project managers knew the rolled throughput yield (RTY) on their lines. All the others gave me a first time yield (FTY) instead. When I pressed each of these about how they manage quality on their production lines, they gave me their version of how failed units are repaired or disposed of before shipping, so that our customers are protected. This approach makes the yield look better than it really is and increases the CM’s cost of production. Make no mistake, increased cost for the CM means increase cost to you, the customer.
The one CM who knew his production line’s rolled throughput yield, also gave me dollar amounts of lost value through wasted components and rework. This CM also addresses the yield issues at each step in their production process with improvement teams.
A significant difference in the quotes received for the CM’s we visited was their circuit board testing schedule. Rather than test every circuit board in the production stream, as the first time yield CM’s did, the CM using roll throughput yield was able to reduce this to 10 percent of every production run. This is a direct result of having good control of their production process. The result was the roll throughput yield CM giving us the lowest quoted cost of production.
This experience led me to write this piece on the various ways to calculate the yield from a process. If you are a CM, I encourage you to use roll throughput yield and make yourself a hero of cost reduction in your business. If you are evaluating CM’s for a project, make sure you look hard at the way they calculate yield on their production lines and how they use the results.
First Time Yield (FTY): The probability of a defect free output from a process is called the First Time Yield. This metric considers only the criteria at the end of the process. The first time yield is unit sensitive and is calculated by dividing the outputs from a process by its inputs.
The First Time Yield will not detect the effect of hidden factories. Consequently, it will typically indicate that a process is performing better than it really is. Even so, this is the most common way to calculate process yield in business today. This is due, in part, to the way businesses report their performance to financial analysts. It is useful to the business in this way, but First Time Yield will not help the business find and correct problems in their processes.
Rolled Throughput Yield (RTY): Rolled Throughput Yield is the probability of passing all “in-process” criteria for each step in a process, as well as all end process criteria. Rolled Throughput Yield is defect sensitive. Mathematically, Rolled Throughput Yield is the result of multiplying the First Time Yield’s from each process step together.
When a process step produces defects, the yield for that step will be less than 100%. Even if the defective outputs are corrected (a separate process step), the yield for this step is unchanged. The drawing below shows the relationship between First Time Yield and Rolled Throughput Yield.
Yield
In the example above, the First Time Yield indicates a good process with no defects getting to the customers. There are 100 inputs and 100 outputs. The First Time Yield does not capture the effect of the 5 % defect rate from each of the process steps. Ten percent of the outputs are being reworked to keep customers from getting defects. The process has to do enough work to make 110 outputs to produce the resulting 100, defect free, outputs. The two hidden factories exist because of defect generation and the process owner’s desire for the customer to receive defect free outputs. The rework (repair or replacement of the 10 defective outputs) will show up as a component of the process’s Cost of Poor Quality.
The rolled throughput yield in the diagram indicates a marginal process because it captures the work done by the two hidden factories. Instead of a process in 100% compliance, as described by the first time yield, rolled throughput yield describes a process that wastes 10 % of its resources.
These calculations demonstrate the difference between an “As we think it is” process and an “As is” process. As a result, they point the way to where improvement efforts are needed.
Collecting data, voice of the customer or otherwise, requires a sample collection plan. It is important to know what you want to know, how to get the information, where to get the information, who to get the information from, and other details. You begin this process by knowing what you are trying to learn from the data.
Business receive reactive data after the customer has experienced the product or service. Many times businesses get reactive data whether they want it or not through complaints, returns, and credits. This data is normally easy to obtain and can help to define what the defects are and how frequently they are occurring.
Sources of reactive data are customer complaints, technical support calls, product returns, repair service hits, customer service calls, sales figures, warranty claims, web site hits, surveys, and the like. Most businesses make it a point to track this information and make it available to process improvement teams.
Reactive data can be used to find out what aspects of the product or service the customers are having issues with, what needs are not being met, and what the customers may be expecting from the business in the future (new services, products, and features). The danger with reactive data is that some customers will tell the business about the defect by not buying from that business again. This insidious problem can sneak up on an unsuspecting organization. A business should never assume that they have all pertinent reactive data.
Data that is collected before the customer experiences their first, or next, encounter with the business’ product or service is proactive data. An example of this type of data would be the information collected in a market research effort regarding potential new products or services.
Sources of proactive data are interviews with potential customers, focus groups, surveys, market research, and benchmarking. This type of data can be difficult to obtain. Customer surveys and focus groups can miss customer segments or ask the wrong questions. Market research may be expensive, hard to obtain, or be unreliable for the business’ customer base. Proactive data collection requires careful planning.
A business can use reactive data to point the way to where proactive data collection will do the most good. This helps to focus data collection activities on important customer issues. Without this focus, the business will be shooting in the dark. Consider, for example, asking customers what color of widget they prefer when the sharpness of the widget is their real concern. Not only will dull widgets turn away customers (regardless of color), asking the wrong questions will indicate that the business is out of touch with its customers. The customer may feel that a business is not focusing on their needs (and they would be right in this case) and buy from a competitor instead.
Proactive data helps focus the business on the important issues of the future. The future could be anything from the next customer visit, to consideration of where the business is investing their research and development dollars. Where reactive data helps a business to define defects in the customer’s language, proactive data helps to prevent defects before they affect the customer. Both data types are important and depend on each other for the synergy to improve the customer’s satisfaction level.
I want to address two aspect of death in this post. The death of a significant other in your life and your own death. What we say and what we do have meaning to others and ourselves and should not be taken lightly
By following a consistent format the Six Sigma team and its customers can better understand and explain hypothesis test results and conclusions. Reviewers know exactly where to look for information, which will increase their confidence in the results. This is an example format to use.
Practical Problem
This is a statement that describes the practical question to be answered by the test. It is written in process owner or customer language and states what is being asked. It is phrased as a question.
Statistical Problem
This is a statement that describes the specific hypothesis test that will be used along with a definition of the “null” and “alternate” hypotheses for the test. The statement is written in the specific statistical terms required by the hypothesis test being used.
Statistical Solution
This is a statement that describes the solution to the statistical problem. It too is written in the specific statistical terms required by the hypothesis test used.
Practical Definition of the Statistical Solution
This is a statement that describes the statistical solution in practical terms. It is written as a statement and answers the practical problem question in step one. Process owner or customer language is used. No elaboration is allowed. Just the specific answer to the specific question posed in step one.
Example:
Practical Problem:
The vender promised service in an average of 5 minutes. Is this a true statement?
Statistical Problem:
Single population t-Test with H0: m = 5.
Ha: Service time does not average 5 minutes. Confidence interval equals 95%
Statistical Solution:
P = 0.0000, H0 is rejected because P < 0.05.
Practical Definition of Statistical Solution:
The service time does not average five minutes.
Hypothesis testing does not establish the why or how. Other process knowledge will help answer these questions. Note that the way the test is set up, it does not indicate whether the actual average service time is greater than or less than 5 minutes. The test can be restructured to look at one side of the data’s distribution, or other process information can be used to determine the direction from 5 minutes the distribution’s actual mean really is.
The analysis of data is now, and always has been, problematic. We are not machines. Our thinking is affected by intuition and experience, which are not empirical in nature. In business, Six Sigma or not, the ability to see information from both an empirical perspective and from the perspective of human stake holders (not empirical), is critical to quality decision making.
Let me give you an example of a non-empirical, intuitive/experiential perspective. If you have ever returned to a playground that you knew when you were young, you may have remembered the sliding board being really high, but now it seems small. It did not shrink. Your perspective changed. This is a fundamental rule of human thought. We learn through experiment (experience). These learnings change the way we view the world. In other words, the context of our information changes.
This is both good and bad. We learn not to put our hand on a hot stove, to look both ways before crossing the street and to not insert keys into electrical wall sockets, because of severe negative consequences. This is the result of the power of observation.
This works well on simple systems where results are not ambiguous, and are easy to understand and predict. On systems where there is complexity and results are not easy to predict, you must “peel the onion” with your observations. When evaluating a complex system, intuition must be used very carefully.
Dr. Daryl Bren does a magic trick with his students on their last day of class with him. The magic trick is really a lesson. He attempts to demonstrate his ability to read a student’s mind by way of giving information about their personal history that he could not otherwise know. He is always successful and his students grapple with what to think about this intriguing skill. Their intuition, based on trust in their professor, compels them to want to believe. Once he has given them enough time, he tells them how he accomplished the feat. Basically he knew who he was going to” read” and collaborated with their family ahead of time, without the student knowing. It is a trick, not magic. He then delivers the punch line, never substitute your intuition for real data.
Another story. Several years ago I had an employee that told me that he had started a rumor about the possibility of a major management shakeup. Two weeks later he came to my office, excited, saying that he had it on good source that there was going to be a major management shakeup. He even had details (facts?), which his intuition bought into.. I had to remind him that he, in fact, started that rumor two weeks earlier and fell victim to it. Intuition, in the absence of fact, will nearly always lead to incorrect conclusions.
This is not to say that intuition is not important. Intuition is a critical evaluation tool, and just like any other tool, must be used properly. Intuition can indicate that either your perspective or the data is skewed in some way. Maybe both are skewed. Intuition will point to what needs a reality check or more information.
This is just another case where balance and perspective play important roles in our lives. In reality, what I am talking about is finding the “why” behind a set of data or “facts”. Successful Six Sigma Projects and quality business decisions depend on it.
In the 1950’s, if you wanted to bring the world’s best minds together to solve a problem, it involved weeks or months of effort, and the exercise limited the number of participants. Today, with the internet, a million minds can be brought to bear on a problem in minutes. If you hold to the idea that within our corporate human hearts and minds we have the answers to our most pressing concerns, than you must also believe that we are on the cusp of great change. What can hold back a million great minds communicating at the speed of the internet?
This is both the opportunity and the threat. We need leaders focused on the truth, not just random facts, that can bring people of diverse backgrounds together and attack our common problems. We have shrunk our world with technology. This means that global opportunities are within our grasp, not just local or regional optimizations. There are few excuses for leaving anyone behind.
It can also be said that with technology moving as quickly as it is, there is a threat of sub-optimization. That is technology used by one people group to subvert another people group. This refocuses on leadership. The 21st century will be defined by leaders who are able to leverage diverse opinions toward the common problems we face, providing opportunity for participation to anyone who has the desire to make a positive difference. The 21st century leader knows that we are stronger when we work together than when we work against each other.
From a business point of view, the same perspectives apply. The status quo will be replaced by paradigm shifts, and thinking big with bold ideas will lead to financial rewards. Here are a few talking points for global business leadership in the 21st century.