We don’t have control, we have choices. The best we can do is improve our method of making choices and hope for good results.
One of the problems with Lean applications, Six Sigma, Kaizen, 5-S, etc., is that they get applied without an adequate understanding of the target business. The result is a failure of the tool to “take”, and any improvements gained are short lived. Within a few days, things start sliding back to what the “normal” used to be.
The missing step is a readiness assessment. A thorough understanding of the business and its culture must be coupled with a thorough understanding of the Lean tool being used, in order to provide the best chance of success. This readiness assessment takes time to develop, requires good listening skills, and business acumen.
Waste can take many forms. There is waste of time, material, human resources, etc., all of which result in a waste of money for the business and its customers. Time and material is easy to understand, even if not always easy to see. The waste of human resources is more insidious.
Everything is interconnected and waste is usually found to be both the result of other waste and the cause of other waste. The ability to see both the big picture and the little picture at the same time is important. Fixing waste in one area that creates waste somewhere else is called sub-optimization and is counterproductive. Solid leadership and a shared vision will save the day in any waste reduction initiative.
Value stream analysis is an examination of the sequence of activities required to design, produce and deliver a product or service. It involves an analysis of way pieces of the value stream interact with each other. Some of these pieces are:
– The people who perform the tasks and their knowledge and skills
– Tools and technology used to perform and support the value stream tasks
– Physical facilities and environment in which the value stream resides
– Organization and culture of the enterprises which owns the value stream
I have been working on a value stream analysis case study. I believe that you will find it useful and thought provoking. We used the VSA to define weaknesses in a process’s work flow, made changes, and documented the improvement that resulted. Cycle time was the metric we were pursuing.
Unfortunately, due to a death in the family, I will not get it finished this week. I apologize, but family does come first. We’ll take more next week.
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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.
This post deals specifically with the form, fit and function method of reverse engineering. This is a general methodology and a good starting point. A more specific methodology may be needed for specific types of projects. Reverse engineering is an important process in Lean Six Sigma. We may not call it reverse engineering, but that is what it is. Please bear in mind that this post is a general, not a detailed, description of this methodology.
One of the fundamental flaws with process improvement programs is the assumption that all aspects of a business environment are determinant and predictable to a high degree of precision. Certainly some business systems and functions fall into this highly predictable category and fit well into the various quality programs we have seen.
What happens, though, when you try to apply Six Sigma tools to a process or function that is indeterminate? The answer is that incorrect conclusions can be drawn. To be clear, predictions that have a higher precision than the evaluated process or function is capable of, need to be viewed with suspicion. Examples of indeterminate systems are the weather and search engine impressions that a keyword receives on a periodic basis.
The internet, like the weather is an indeterminate system. With indeterminate systems, macro (low precision) predictions can be made reliability (hot in summer, cold in winter) because at the macro level indeterminate systems demonstrate repeatable cyclic behavior. At the micro level, though, this repeatable cyclic behavior becomes less consistent and less reliable. For more on this read the work of Edward Lorenz regarding chaos and weather prediction.
Getting back to the internet, economic systems are indeterminate. This does not mean that Six Sigma tools cannot be applied to indeterminate systems like internet search engine key word impressions. It is instead a matter of using the right tool for the job. In indeterminate systems, since you cannot control or adequately predict all of the variables in the system being worked on, a Six Sigma project team will focus on less precise factors (macro). This means statistical inferences that have much higher standard deviation parameters and may even defy statistical evaluation altogether.
With indeterminate systems, the Six Sigma team will be trying to reduce uncertainties surrounding the system and determine the boundaries associated with these uncertainties. We have to realize that we cannot increase the precision of an indeterminate system beyond the system’s natural state. We can, though, control the precision of how we react to the system’s behavior.
With internet impressions, you may not be able to predict search engine behavior very far into the future, but you can calibrate how you will act to take advantage of what you see. For example, you can build a website that is robust enough to deal with the uncertainty of web searches on the internet. You can also take more frequent measurements of key word impressions and use pay per click tools to react to the impression “terrain”.
Basically, what I am saying is that with determinate systems, Six Sigma teams can work directly on the process to reduce variation and improve performance. With indeterminate systems, the team must work with the uncertainty that exists outside the process to improve performance.