Three statistics techniques you can implement for improving your logistics

Three statistics techniques you can implement for improving your logistics

During the last years, the results offered by the implementation of statistical techniques in the logistic field have become essential in the big organizations. Mainly, what a proper statistical management in your company’s logistics and supply chain can do is to develop a total control data, and the monitoring and evaluation of the results obtained during certain periods of time (but the advantages of statistics, of course, are not limited to that.) The quantitative and qualitative approaches to statistics make it possible to collect, organize, summarize, and analyze data that, in the first place, can lead to crucial conclusions and, secondly, help you to make reasonable decisions to improve the performance of your organization.

Let’s look at some of the best and commonly implemented methods for performing statistical analysis in companies worldwide.

Recommended: The ultimate list of marketing statistics

Correlation analysis

This statistical data analysis technique serves to determine whether there is a relationship between two different quantitative variables, and it measures how strong that relationship between both variables actually is. It is usually used when two variables are suspected to follow (or have) a similar evolution. In general, the study of the relationship between variables, whatever they may be, must be accompanied by descriptive graphs, exhaustive or not in the apprehension of the data at your disposal in order to avoid using the old calculations some people still use.

Nevertheless, as soon as it is necessary to look at links between many variables, the graphic representations may no longer be possible or be at best illegible. Calculations are a tremendous help to simplify the interpretations that you may have, which may be derived from links between your variables, and this is its main interesting element. It will then be necessary to verify that the main hypotheses is essential for a correct reading, and thus validated before any further interpretation.

Image courtesy of ibmphoto24 at

Data mining

This is a data analysis process designed to work with large volumes of information. It is now better known as Big Data and is used to detect patterns, relationships or relevant information that can improve the performance of customer-related operations, as well as the Internet of things. Data mining is able to report on important events (unknown so far,) allowing reliable forecasts, which make it possible to take action in minimum risk conditions. The technique used to carry out these feats is called modeling. Data mining models originate, either as a set of examples or as a mathematical relationship, based on the data of situations in which the answer is already known.

Modeling techniques are not new, but they have been available for decades, although it has only been possible to achieve the data storage and communication capacity required to collect and store large informational volumes during the last three decades. The analysis here makes it indispensable to add the calculation power which is necessary to automate the modeling techniques, which allows working directly on the data. The creation of models of data mining applied to businesses and logistics allows to directly extract knowledge of the data produced by every movement, to express all the value that this knowledge contains, and to perfect the business strategy.

An example of this would be a model that defines a specific group of customers. Who is the best, who is the one who consumes the most and more frequently, etc. In order to build this data mining model, it would be necessary to know the clients better, and for that, it is necessary to consult a database and go deeper into the information that available about each one of them.

Read also: Why data mining is the best statistics method to improve your business, by David Kiger

Regression Analysis

This is another technique of statistical data analysis to investigate the relationship between different variables, and, above all, to predict the behavior of one of them due to the behavior of the other. It is used when you suspect that one of the variables, the independent one, may be affecting the behavior of the other, the dependent one, or even others. This technique is used because many times decisions are based on the relationship between two or more variables, and around one of them, there is always some margin of uncertainty. It is useful when a variable is very difficult to measure but depends substantially on the behavior of one that is not, for example, the application of fertilizer doses in a cotton crop will affect the subsequent yield of the crop. In a regression model, the two essential ingredients of any statistical relationship can be evidenced; a trend of the dependent variable, which varies along with the variation of the independent in a systematic way.

In another post, I’ll talk about how you can make accurate (and, of course, useful) predictions by using statistical methods that will allow you to make better logistical decisions in your organization. I will talk again about data mining and big data because this great trend is certainly inescapable when it comes to information control and management.

* Featured Image courtesy of Oscar de Lama at

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