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In many situations, the monitoring of process variability is associated to a failure of the characteristics of interest to preserve the quality of the process and that are characterized by the covariance matrix of the data. The last decades, much more than the control charts for changes in the covariance matrix. Thus, the article sought the process of producing graphs by applying control graphs to the variables (x ̅ and R) generated by the Minitab® software with the reduced number of screws on the screw. The methodology used is the mapping of the data, in which an estimated covariance matrix is obtained, which is used to construct the purpose control graphs. The tool used to generate graphics was the Minitab18® software. The desire came to be the process of managing levers of statistical control and quality control. How information is favored for your research through other quality tools that can aid in decision making.

Small companies find it difficult to implement production management systems. This causes problems of overproduction, high inventory and high rates of defective products. Lean Manufacturing acts as a methodology to achieve waste elimination. It is important that companies identify their current level of Lean to improve their operations. This work aims to present a model using fuzzy logic, applied in a small metallurgical industry. A Fuzzy inference model was specified and modeled on the software fuzzy toolbox MATLAB R2013a®. The simulation performed on the model took into account the current reality of the company, noting that there are no Lean metrics in its processes. A great advantage of the model is the possibility of adjustments for any type of organization since the input and output variables can receive other linguistic values.

Current market conditions require organizations to understand the business environment in order to achieve strategic planning and decision-making processes. An organization's competitive advantage is associated with an understanding of how to determine the potential of these companies when examining internal and external conditions (insertions) and the effort to meet customer needs. Among the many tools that contribute to this understanding, the SWOT analysis stands out, which can assist organizations to better understand the internal and external environment and formulate strategic plans in a collaborative way. This work aimed to implement an evaluation model for SWOT analysis using fuzzy inference methods. The adopted methodology started from a survey on the internal and external characteristics of the organization, definition of the linguistic criteria of the SWOT matrix, correlation between the variables found and elaboration of the fuzzy inference system for crossing the inputs. The approach proposed by the Fuzzy Inference model for the SWOT matrix proved to be simplified and efficient for a better collection of information that allows the prediction of the future environment, enabling reasoned strategies resulting from the model presented.

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