If you are running a business, what would you rather have, satisfied, happy customers or loyal customers? I suspect many might say that the idea of a satisfied customer seems like the right visual in their minds eye. Some one who is pleased with the experience they have just been through, leaving the store with a smile on their face and contentment with their purchase. Trouble with that is that your in business to make money. I know, I know, the standard mantra is that satisfied customers will come back again and again. That may be true, or maybe its not.
Loyalty and satisfaction differ in one significant respect: action. Satisfaction is defined as a feeling about a past interaction or set of interactions. Satisfaction is fleeting and does not translate to actions on the part of the customer. This is where loyalty differs. Loyalty is focused on the buying behavior of the customer. Research by the Corporate Executive Board (an industry benchmarking research company, www.executiveboard.com) discovered that loyal customers were more likely to behave differently in three ways:
Loyal Customers
1. Recommend the supplier to others more often
2. Increase purchases
3. Partner with the supplier on new opportunities
To contrast, Satisfied Customers
1. Purchase based on price
2. Purchases remain steady or decline
3. Will readily jump to competitor based on price, availability, or negative experience
4. No partnership on new development
So which of those sounds like the customers you want? I don't know about you but give me loyal customers any day.
So whats the difference? Is there a difference? Turns out there is a difference. A satisfied customer is merely a spectator in the operation of your business. What I mean by this is that their involvement is more superficial, more circumstantial, and more subject to change at a moments notice than the loyal customer.
Ever had a conversation with a die hard car brand enthusiast? There is no swaying them from their brand, and if you press hard enough strong emotions and maybe some fists will fly. That's loyalty. They know all of the gory little details that make their brand the best. They are in it up to their eyeballs. Satsified customers really do not know much about the brand they have purchased, and as such, do not have a strong attraction to it.
I'll relate a personal experience. My wife owns a General Motors car. The now defunct brand has been problematic from the beginning. I have owned various General Motors brand vehicles over the years. Some have been pretty good, but none have been great, none have wowed me in the areas that matter most to me. I have been a satisfied customer, but not a loyal customer. Now back to my wife's car. Like I said, problematic from the first, but thats not the end of the story. We have owned problematic cars before from other brands (not GM) and I would consider owning one again if I did my homework and found their quality and engineering to be good. I will never allow another GM product into my garage again though. To say that I am a dissatified customer of GM is a vast understatement on par with saying that World War 2 was a minor disagreement between friends. Never, I say, NEVER will I own another GM. Why such strong emotions on this brand? It was not the problems alone that caused it, it was the problems and the fact that they could not be solved in 6 (count them 6!) tows to the dealer, it was the problems and the tows and the lack of fixes, and the moronic engineering that makes it so I can not change a burnt out headlamp, and the poor structure that required suspension work normally reserved for cars older than decade.
In short, my several ownership experiences with this car have led me to conclude that as a company GM is not competent at what it does...build and sell cars. The entire experience has been awful, thoroughly and completely awful. Interestingly enough though, I may never have arrived at this conclusion were I not a die hard car brand enthusiast for another brand. About 8 years ago, I bought my first Japanese car, a Subaru. Now let me say that I didnt get the whole "Subaru Love" thing for the first couple of years, but after we bought my wife's car, I started to notice all of the things that I did NOT have to do to my car. In 8 years, nothing but regularly scheduled preventive maintenance. If the headlamp goes out (which it has) I can replace it myself in about 10 minutes for about $20. No ball joints needing replacement after 60,000 miles, no intermittent electrical outages. No problems at all. In my entire car buying adult life, I have never owned a car this well put together, this well thought out. In short, I am a very loyal Subaru customer.
So this little story brings us to the essential element of loyalty versus satsifaction. I'll give you one guess which brand my next car will be, and one guess which brands it will not be. Subaru will be rewarded with my money in short because I see that they are competent at what they do and I am loyal to them for it.
So, which kind of customers do you want?
Sunday, November 28, 2010
Thursday, October 28, 2010
What takes more time? and costs more?
I am having a debate. For those who know me, that won't come as a surprise. I love to debate about things that I'm passionate about. The current debate that I'm having is about product design. The specific topic is about how to approach determining part tolerances for a new design.
The debate is prompted by problems we are having getting a supplier to achieve the tolerances we specified for the parts they make for us. We have been through three rounds of back and forth with this supplier trying to get good parts only to finally decide to do an experiment and learn that we could have given more tolerance to the supplier to begin with.
Of course, my suggestion to rectify this problem is well designed and planned Design of Experiments (DOE) for the purpose of determining the edges of the performance window. The counterpoint is that DOE takes too long and we don't have time or the samples to do it. This post is not a how to on DOE, I can cover that another time. This post is about what is the best business decision to make.
So, lets analyse the sides of this debate. On one side the arguement is that DOE takes too long, we don't have the samples and we can't test everything. I would agree with one of those viewpoints, we can not test everything. We should only test the important things. How to determine the important things, for that you need a QFD (Quality Function Deployment) tool. We'll save that for another time also.
The other side of this debate is that NOT doing DOE takes too long, costs more money and reduces the chances of success of the design.
What are the facts? DOE can take a while to complete IF too much is thrown into the mix to test. DOE matrices can grow to hundreds of replicates and samples if not contained. There are strategies to whittle down the many factors to the critical few, among the best I have used are Taguchi Designs. Taguchi design matrices allow for testing many factors at two or three levels for the purpose of screening factors for importance. I have completed simple two factor two level designs in less than a week from start to finish, as recently as this summer. Can DOE take a long time, yes, if we dont plan it well. So what is the cost in time and money associated with NOT doing Designed Experiments? Those costs are real and painful. In the real life debate I am having, we have been working with a supplier since April of 2010 to try to get good parts, now, in October, we still don't have good parts. This has delayed the intended release of the design several times and forced us to decide between another delay and accepting a 40% scrap rate for these parts. This is only half of the story though. By not doing experiments to determine the real needed specification, we have asked the supplier to make a part that costs more to produce because of the extra care required to obtain the tight tolerances we ask for. Additionally, the tight tolerances require that we measure the parts on a sofisticated, highly precise measurement machine that takes quite a few minutes to make the measurement for one part, and finally because we can not get good parts, we must measure every part, further adding to cost and slowing the process. The final cost associated with this decision is in the manufacture of the design. Due to all of the issues, Assemblers will probably have to rework these products in order to make sure they function properly before leaving the factory, taking more time that could be spent doing other things.
To me, the decision is easy. Determine the proper specification upfront by building samples to test the boundaries of performance on critical dimensions, incorporate those learnings into the tolerance for the design, give the suppliers as much room as possible to make good parts, and measure it as simply as possible with acceptable precision. Seems worth the investment of a few weeks effort to me.
The debate is prompted by problems we are having getting a supplier to achieve the tolerances we specified for the parts they make for us. We have been through three rounds of back and forth with this supplier trying to get good parts only to finally decide to do an experiment and learn that we could have given more tolerance to the supplier to begin with.
Of course, my suggestion to rectify this problem is well designed and planned Design of Experiments (DOE) for the purpose of determining the edges of the performance window. The counterpoint is that DOE takes too long and we don't have time or the samples to do it. This post is not a how to on DOE, I can cover that another time. This post is about what is the best business decision to make.
So, lets analyse the sides of this debate. On one side the arguement is that DOE takes too long, we don't have the samples and we can't test everything. I would agree with one of those viewpoints, we can not test everything. We should only test the important things. How to determine the important things, for that you need a QFD (Quality Function Deployment) tool. We'll save that for another time also.
The other side of this debate is that NOT doing DOE takes too long, costs more money and reduces the chances of success of the design.
What are the facts? DOE can take a while to complete IF too much is thrown into the mix to test. DOE matrices can grow to hundreds of replicates and samples if not contained. There are strategies to whittle down the many factors to the critical few, among the best I have used are Taguchi Designs. Taguchi design matrices allow for testing many factors at two or three levels for the purpose of screening factors for importance. I have completed simple two factor two level designs in less than a week from start to finish, as recently as this summer. Can DOE take a long time, yes, if we dont plan it well. So what is the cost in time and money associated with NOT doing Designed Experiments? Those costs are real and painful. In the real life debate I am having, we have been working with a supplier since April of 2010 to try to get good parts, now, in October, we still don't have good parts. This has delayed the intended release of the design several times and forced us to decide between another delay and accepting a 40% scrap rate for these parts. This is only half of the story though. By not doing experiments to determine the real needed specification, we have asked the supplier to make a part that costs more to produce because of the extra care required to obtain the tight tolerances we ask for. Additionally, the tight tolerances require that we measure the parts on a sofisticated, highly precise measurement machine that takes quite a few minutes to make the measurement for one part, and finally because we can not get good parts, we must measure every part, further adding to cost and slowing the process. The final cost associated with this decision is in the manufacture of the design. Due to all of the issues, Assemblers will probably have to rework these products in order to make sure they function properly before leaving the factory, taking more time that could be spent doing other things.
To me, the decision is easy. Determine the proper specification upfront by building samples to test the boundaries of performance on critical dimensions, incorporate those learnings into the tolerance for the design, give the suppliers as much room as possible to make good parts, and measure it as simply as possible with acceptable precision. Seems worth the investment of a few weeks effort to me.
Monday, May 17, 2010
Whats the difference between flavors of six sigma?
Six Sigma for Manufacturing; Six Sigma for Healthcare; Six Sigma for Public Sector; Six Sigma for Education.
There's a lot of marketing materials out there hyping the specialty classes and programs for Six Sigma or Lean for a particular industry sector or application. So, what is really different between these different approaches? In short-Nothing.
Lets start with what Six Sigma is and what it is not. Six Sigma is a universally applicable logic based improvement process, it is not specific to manufacturing environments only. Six Sigma is flexible in the use of specific tools for a given problem, it is not so flexible that the logical problem solving process can be avoided. Six Sigma is a scientific method of discovery, it is not a substitute for knowledge.
One thing that may not be universally recognized is that the specific tools that are endorsed and needed to make the six sigma method work effectively are not unique to six sigma. The early practioners of Six Sigma did not invent Gage R&R, Capability Analysis, Process Flow Diagrams, Brainstorming, Regression or Designed Experiments. What they did do was recognize how those tools could add value and strengthen the six sigma method. Why is that important to understand? because understanding that the "inventors" of six sigma borrowed good tools from other areas where they were already in use informs us that we also can adapt tools and apply them in the six sigma method without violating the 'spirit" of the method. For instance; the generally accepted statistic used to express capability in a six sigma project is the Z score, however if you have a transactional of office process that you are working on and continuous data is not available to derive a Z score, thats OK. Just count occurences of your defect regardless of what it is (time, errors, missed deliverables, rework) and report a percentage of failure against the requirement. That's the same thing in "spirit" as a Z score. Some dogmatic practitioners might insist that the only acceptable metric is a Z score. I assure you this is not correct, what is required is that we measure our baseline capability against the requirement of the process (specification) and express that in some appropriate way.
The other area of flexibility in the six sigma method is in the area of specific process steps that apply. I have led and mentored many project teams and in some of those cases we have had to abandon efforts to complete certain steps because the process we were working on did not lend itself to effectively completing that particular step. In other cases there have been steps that were not needed. One of the typical steps that some teams struggle to complete is the Gage R&R requirement. In some cases it is not practical or necessary to conduct a proper Gage R&R but it is always necessary to ensure that the data is reliable. See this post for a further discussion fo Gage R&R. The step that most often gets skipped is the Designed Experiment step in Analyze. many times this step is skipped intentionally and quite properly because earlier analysis of historical data via regression has produced a conclusive list of reliable root causes and no experimentation is needed to confirm or screen these root causes. If you know from historical data what needs to be fixed, why waste time and money on a confirmation experiement. Conversely, recognizing that your suspected root causes are weakly correlated to the problem should drive the design of an experiment to determine conclusively what the causes are. Of course in an office or transactional project, a designed experiment may not be possible.
Six Sigma is a flexible, universally applicable process improvement method that follows the logical thought pattern of; Define the problem, Baseline performance, Discover root causes, Implement improvements, and Standardize the new process. These concepts apply to any process, anywhere in any professional setting. What is required to use the method successfully in any setting is an understanding of the improvement process, the reasons why each step is important, how all of the steps fit together, and a concerted effort to apply each step. Finally, if you are doing a six sigma project and have used a particular tool effectively in the past, there is no limitation to the use of new tools within the six sigma process. if it works for you and is effective at helping the team move forward, use it.
There's a lot of marketing materials out there hyping the specialty classes and programs for Six Sigma or Lean for a particular industry sector or application. So, what is really different between these different approaches? In short-Nothing.
Lets start with what Six Sigma is and what it is not. Six Sigma is a universally applicable logic based improvement process, it is not specific to manufacturing environments only. Six Sigma is flexible in the use of specific tools for a given problem, it is not so flexible that the logical problem solving process can be avoided. Six Sigma is a scientific method of discovery, it is not a substitute for knowledge.
One thing that may not be universally recognized is that the specific tools that are endorsed and needed to make the six sigma method work effectively are not unique to six sigma. The early practioners of Six Sigma did not invent Gage R&R, Capability Analysis, Process Flow Diagrams, Brainstorming, Regression or Designed Experiments. What they did do was recognize how those tools could add value and strengthen the six sigma method. Why is that important to understand? because understanding that the "inventors" of six sigma borrowed good tools from other areas where they were already in use informs us that we also can adapt tools and apply them in the six sigma method without violating the 'spirit" of the method. For instance; the generally accepted statistic used to express capability in a six sigma project is the Z score, however if you have a transactional of office process that you are working on and continuous data is not available to derive a Z score, thats OK. Just count occurences of your defect regardless of what it is (time, errors, missed deliverables, rework) and report a percentage of failure against the requirement. That's the same thing in "spirit" as a Z score. Some dogmatic practitioners might insist that the only acceptable metric is a Z score. I assure you this is not correct, what is required is that we measure our baseline capability against the requirement of the process (specification) and express that in some appropriate way.
The other area of flexibility in the six sigma method is in the area of specific process steps that apply. I have led and mentored many project teams and in some of those cases we have had to abandon efforts to complete certain steps because the process we were working on did not lend itself to effectively completing that particular step. In other cases there have been steps that were not needed. One of the typical steps that some teams struggle to complete is the Gage R&R requirement. In some cases it is not practical or necessary to conduct a proper Gage R&R but it is always necessary to ensure that the data is reliable. See this post for a further discussion fo Gage R&R. The step that most often gets skipped is the Designed Experiment step in Analyze. many times this step is skipped intentionally and quite properly because earlier analysis of historical data via regression has produced a conclusive list of reliable root causes and no experimentation is needed to confirm or screen these root causes. If you know from historical data what needs to be fixed, why waste time and money on a confirmation experiement. Conversely, recognizing that your suspected root causes are weakly correlated to the problem should drive the design of an experiment to determine conclusively what the causes are. Of course in an office or transactional project, a designed experiment may not be possible.
Six Sigma is a flexible, universally applicable process improvement method that follows the logical thought pattern of; Define the problem, Baseline performance, Discover root causes, Implement improvements, and Standardize the new process. These concepts apply to any process, anywhere in any professional setting. What is required to use the method successfully in any setting is an understanding of the improvement process, the reasons why each step is important, how all of the steps fit together, and a concerted effort to apply each step. Finally, if you are doing a six sigma project and have used a particular tool effectively in the past, there is no limitation to the use of new tools within the six sigma process. if it works for you and is effective at helping the team move forward, use it.
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