Watching success spiral into failure! Why six sigma doesn’t yield long term results…

Recently in class we have been discussing various types of ways to improve processes and quality, within management operations systems, these include the Baldridge, Six Sigma, and ISO 9000 models. In class we have looked at the different ways that these models can improve quality and productivity, however the article I found talks about some of the downsides of these models, in this case, Six Sigma. The article from the Wall Street Journal specifically talks about an aerospace company that implemented Six sigma, but then what also happens after the new processes were put into place.

The comparison that was used to describe the Six Sigma process was compared to a spring, initially when the process is implemented employees stretch out to accommodate the new work processes and work load that has been implemented, this phase appropriately named the “stretching phrase” which is when data is collected on how best the process will be implemented and which departments issues are most critical to address. However the problem is that when you become so focused on the process improvement initiatives you often begin to relegate some of the normal responsibilities in each department.

The next phase described in the article is the “yielding phase” where the “spring” is still being stretched, and as described will become permanently deformed. Meaning that now management and improvement experts believe the issue has been resolved they more onto more pressing issues, the problem though is that these newly renovated departments now struggle to hold onto the gains in improvements they’ve made without any further direction from Six Sigma advisors. Without having the leadership that guided them on the initial improvements many departments begin going back to their old familiar ways resulting in a process that once again is not meeting its full potential.

The last stage described in the failure of Six Sigma is appropriately named the “failing stage” where essentially the “spring breaks apart” meaning without further direction the departments lose motivation to keep pursuing their earlier successes. A main reason for this is the success or failure of these is that the employees personal reviews have little or nothing to do with these projects so it really holds little for them to gain or lose, in turn causing no one to step forward as a leader to continue the improvements they had made early on.

With these three stages identified the articles also points out four key points that can be learned from the failure of a Six Sigma initiative. The first being that if success is to be permanent, a long term SixSigma advisor should be appointed. Second employee performance appraisals need to be tied to success or failure of initiative put into place. Third have small focused groups for initiatives so those involved knowing exactly what their goals are. Last Management should be directly involved in all aspects to know exactly what is taking place.

What else do you think should be done to improve upon these processes other than the four stated “lessons learned”? Can you think of different ways to improve the processes that were described in the article?

The Future Is Now, Predict It Before It Happens

Recently in our management classes we have been discussing the advantages and disadvantages of forecasting for operations management, and the implications these forecasts have on company operations. As useful as these forecasting tools are for conventional corporations, I had come across an article in the wall street journal that talks about forecasting in a whole other type of perspective: personal investing and creating wealth What I found interesting about this article is that it essentially discusses the same topics we have in class about business operations, but it relates it to investors both, big and small, and relates to many different investing opportunities in todays markets.

What I found interesting about the short term forecast analysis was all of the factors that are taken into account before an investor should make a decision about what investments they should consider.  In class we also discussed all the different factors that should be considered before a business makes a decision to go forward, with business operation decisions such as production level, or actively seeking new employees to meet production demand. In the short term this article discusses many different points that investors should be wary of before they invest, including factors that will impact the markets in the future such as volatility, uncertainty and the European debt crisis. This however does not vary too much with factors that business operations face such as short term factors such as immediate demand and materials available. However forecasting is a very useful tool when deciding what type of business decision’s should be made, and it can make all the difference between having a profitable quarter, or suffering substantial losses from lack of preparation.

Counter to the short term analysis for investors, it seems the long term analysis goes more in depth because there is more historical data that can be used to forecast the trends that could appear in the markets. The long term forecast of the markets however does not seem to have a good outlook, and it looks as if it could be risky for investors looking to get substantial returns without having significant risk. The article goes on to explain the different options that are available to investors, much like forecasting for management operations gives managers different options for how they should proceed forward, as well as what different strategies they have at their disposal. The question I would like to propose to you is made up of two parts, first do you think that historical data is relative for forecasting when recently the markets, and the economy in general has been so volatile? Do you feel like traditional methods of forecasting markets are relative to forecasting business operations? Can careful data analysis help management make sound decisions even if past data collections have not been consistent in the past few years?