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The purpose of this paper is to test and develop a method for generation and selection of six sigma projects. This is done by testing the use of the generation and selection method for six sigma projects (GSM_SSP) in a Brazilian manufacturing industry with the participation of managers, aiming to gather the user’s perspective and improvement opportunities for the approach itself.

The work adopts the action research (AR) approach once the researchers were busily involved in the training, implementation and use of the GSM_SSP. The intervention was performed in on a series of 15 workshops, with a group of managers, during six months.

The application of the eight steps of the GSM_SSP approach assisted the company’s management team to generate nine project candidates and also to select three six sigma projects. This study also finds and discusses barriers and lessons learned used to improve the GSM_SSP.

This study presents an example of how six sigma project generation and selection has been applied to a manufacturing industry by adapting AR to the process using the eight steps of GSM_SSP, demonstrating how the management team was involved. This study should be replicated in different companies because AR is limited in its generalization.

To the best of the authors’ knowledge, this study represents the first use of AR methodology in six sigma project selection. This study contributes a method that can generate and select six sigma projects. In doing so, the research offers a simple approach that can be used by managers. In addition, the steps of the approach before selection were explored.

This paper aims to analyze the degree of contribution of the entrepreneurial orientation (EO) of organizations in the use of agile methods (AM) in project management.

A quantitative approach with the application of a survey with project professionals resulted in 206 valid answers. The data were analyzed using structural equation modeling and the method of partial least squares (PLS).

The results present empirical evidence of the significant contribution of EO in the use of AM in project management, confirming the main hypothesis of this study. This effect was not influenced by the control variables tested.

This study contributes to the development of research on AM and minimizes the literature gap on the connection between entrepreneurship and AM. The results are limited to the sample. Studies with broader samples and different segments are suggested, as well as the contribution of each dimension of the EO to AM.

Innovativeness, risk-taking, proactiveness, autonomy and competitive aggressiveness (EO dimensions) can contribute to the use of AM and guide actions to develop these behaviors, pursuing better adherence to agile values and the use of AM in project management.

The originality of this study regarding the connection between EO and AM lies on the presentation of a theoretical model of this relationship and reduces the gap in this research field. Given the degree of EO contribution in AM (19.7%), there are other factors that affect the use of AM in project management that should be investigated.

This study aims to broaden the understanding of the additive manufacturing (AM) body of knowledge, presenting a model better suited to the current level of technological development that supports the decision to implement AM in industries, based on the experience of companies in the industry of orthopedic medical implants.

Based on the design-science research, the model for the decision to adopt the AM was designed and submitted to experts from the industry of orthopedic implants in Brazil for refinement. For the empirical test of the final model, interviews were used in a company that was considering implementing AM and in another that was not, to evaluate the model.

The model considers seven dimensions for decision analysis of AM implementation: legal constraints, financial, technological, operational, organizational, supply chain and external factors, being subdivided into 42 criteria that play a relevant role in the implementation decision. The analysis factor of each dimension and criteria are also presented.

The model seeks to be as complete as possible and can be used by various industrial productive sectors, incorporating the analysis of the requirements of health regulatory agencies, suitable for the analysis of the decision to implement AM for the manufacturing of medical implants, not found in other models.