The literature and viewed items on this subject show no statistical data examination regarding the relationship or approach between critical factors (CF) and their association or significance for project evaluation and corroborate for successful realization. Executives, project managers, and staff members have no statistical data on the matter and importance regarding CF, the influence for the possible outcomes, and scenarios; these factors and criteria yield neither a basic structural model on project management nor achievement rationale to conduct their time, process standards, efforts, and resources. For this work, we analyzed the basic formats of scientific articles under definite and established methods to develop a scientific study and strategies to determine an evaluated structural model, with the goal analysis methodology for the management on industrial projects, identifying sequence patterns, articles, classification factors, calculations frequency factors, and statistical relationships between them, within the proposed path diagram structural model, simulated and exanimated regarding success.

Purpose This paper aims to describe the application of several Six Sigma tools to explain the improvement changes needed in a company that manufactures concrete blocks. The paper explains the methodology and the tools of the Six Sigma system, their use in the project, the application of the DMAIC (Define, Measure, Analyse, Improve and Control) process for the identification and definition of the problems, the related performance variables and the results obtained. Design/methodology/approach The paper reports the research made to improve the production of concrete blocks, specifically, the application of the DMAIC process, which is part of the Six Sigma methodologies; DMAIC stands for Definition of the problem, Measurement of the performance, Analysis using specific statistical methods and tools, Improvement the factors that cause the problem and Control the processes to ensure that the problem will not occur again. Each of those steps is explained in detail in the paper, which also presents the application of other improvement techniques. Findings The results show the adaptability and relevance of Six Sigma for the improvement of production operations. It is clearly demonstrated that it leads to benefits such as the elimination of machine downtime, reduction of scrap from 18 to 2 per cent and the improvements made in plant layout and production facilities to increase the productivity. Research limitations/implications In improvement projects, the differential between the initial and final conditions varies, depending on the magnitude of the problems or potential opportunities. Although this paper describes only the application of Six Sigma, the methodology has a wide potential application in most manufacturing industries. Practical implications With the Six Sigma and DMAIC tools’ application and the improvement process, the agility obtained is driving a more mechanized perspective of production operations. The customer service level was increased, through fast deliveries of complete orders. This project shows that the application of the Six Sigma methodology is feasible and produces attractive financial and operational results in this segment of the construction industry. Originality/value The companies dedicated to the production of concrete blocks commonly reproduce the systems and standards of the industry, which are commonly designed around civil engineering and technical issues. Thus, the application of improvement tools is exceptional in manufacturing environments. Although this paper is just one application of the methodology, it explains in detail the DMAIC use for companies that are committed to the development of new competencies to increase their competitiveness.

The application of the response surface methodology in the optimization of industrial processes has had a great boom in recent decades, however, with a significant limitation, the null inclusion of qualitative factors in the noise variables. Since the methodology assumes the behavior of the noise factors as a continuous behavioral variable that follows a normal distribution. But what happens if this is not the case? How to treat a qualitative noise factor? What probability distribution would best fit the qualitative noise factor? What would be the correct inclusion of this type of noise factor in the methodology? This article summarizes the four-year research work from the mathematical approach to the new equations, case simulations using mathematical software and 2 real cases in maquiladora plants that manufacture plastic parts.