Managing Technology in Operations Management
Introduction In last decade or so technology has changed the way organization conduct their business. Advent of technology in operation management has increased productivity of the organization. Technology and Operations Management The scope of Technology and operation management has evolved over a period of time and has moved from development of products into design, management […]
Recording the Inputs (Xs) at Actionable Level
The 5 why method or the Root Cause analysis method that has been described in the Tools section plays an important role in determining that the X’s are recorded at actionable level. In this implementation of the 5 why tool, there is a slight variation from the standard methodology and hence it has been explained […]
Disadvantages of the BPO Phenomenon
In previous articles, we looked at the humungous benefits that the BPO phenomenon brings to the economies of the host and the target nations. Mainly, we focused on how cost savings for the companies outsourcing their back office work and increased revenues for the companies doing the outsourced work have been actualized leading to a […]
Why Donald Trump Will be able to Stop Outsourcing ?
Donald Trump has positioned himself to be a populist leader. Although in reality, he is an opportunist leader. He has promised his voters that he will stop outsourcing and protect their jobs from cheap foreign labor. Most political pundits did not believe Trump will be able to achieve anything of that sort. After all, even […]
ERP – System-Auditing, Device, Text and Job Management
IN addition to functions covered by technical modules such as user’s management, License management, installation (as discussed in earlier papers), ERP technical module provides several other important functionalities. Some of these functionalities, as given below, will be very briefly discussed in this paper: Audit management Device management Text management Job management Audit management: Large organizations […]
Correlation analysis is a vital tool in the hands of any Six Sigma team. As the Six Sigma team enters the analyze phase they have access to data from various variables. They now need to synthesize this data and ensure that they are able to find a conclusive relationship.
One can best understand correlation analysis with the help of an example. Let us suppose that the management of a factory has come up with data which says that the as the shift time of the workers is increased, their productivity decreases.
However, as of now there is raw data and this is only an observation that some Six Sigma team member may have come up with after having a first look at the data. But Six Sigma methodology does not lie on opinions of the people involved but rather on objective fact. Correlation analysis will help statistically confirm the fact that this is indeed the case.
In order to perform the correlation analysis, there must be sufficient data for the variables under question. Once there is sufficient data, this data was plugged into a formula developed by Karl Pearson. This formula was famously called Karl Pearson’s co-efficient of correlation. This involved complex calculation and mandated the presence of a statistician in the Six Sigma team.
However, fortunately nowadays most of the calculations are performed by a software tool. The humans involved must merely know how to add data to the tool and how to interpret the results.
Correlation analysis typically gives us a number result that lies between +1 and -1. The +ve or ve sign denotes the direction of the correlation. The positive sign denotes direct correlation whereas the negative sign denotes inverse correlation.
Zero signifies no correlation. And the closer the number moves towards 1, the stronger the correlation is. Usually for the correlation to be considered significant, the correlation must be 0.5 or above in either direction.
Correlation analysis just confirms the fact that some given data moves in tandem. A dangerous implication that mangers make is of causality. Based on the correlation analysis it is impossible to say which variable is the cause and which is the effect? It is also likely that both the variables move in tandem because they are affected by some third common variable. However, these are just cases and the fact remains there are other analyses available to find out the causal relationship. However, in most cases the fact the variables have a correlation is enough to take relevant action.
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