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Purpose In a view to its comprehensive and diverse applicability, the methodology Lean Six Sigma (LSS) has the potential to the generation of impacts on various aspects of an organization. This paper aims to carry out a systematic study of LSS impacts on organizations, analyzing their relation to organizational sustainability through the triple bottom line perspective. Design/methodology/approach A systematic review of the existent literature on LSS was performed and the identified impacts were later classified as correspondence with three dimensions of organizational sustainability: financial, social and environmental. Findings There were identified 25 main impacts of the LSS through a systematic research of the literature. A greater correspondence of the LSS have been identified over a range of economic results in organizations; however, it was also possible to notice that the methodology has been directing its efforts gradually to meet further perspectives of sustainability, particularly by looking for a better relation with employees and customers. Practical implications The organizations that use the LSS as methodology to continuous improvement will be able to identify which impacts may need to be achieved primarily by the projects undertaken to enhance the achievement of a more sustainable management. Because of its comprehensive character, this paper can help organizations belonging to various industries and sectors of society. Originality/value Despite the growing number of published articles on the methodology LSS, it has not been identified articles that look for the study of the application of the methodology as a mechanism for increasing sustainability in organizations. Within the scenario presented, this study sought to assist the filling of this gap.

Purpose The purpose of this paper is to identify how multicriteria decision aid (MCDA) can assist the investment portfolios formation, increasing the reliability of decision-making. Design/methodology/approach To develop this paper, a simulation-based approach is used. Information about the assets traded on the spot market of the São Paulo Stock Exchange - BM&FBOVESPA was selected. They had 100 per cent participation in the 246 trading sessions carried out in 2015 and had an average number of business/day greater or equal to 1,000. The stratification resulted in the selection of 111 assets. Aiming assets evaluation, data are collected from 21 financial indicators. Subsequently, the principal component analysis (PCA) is used to reduce the mass of collected data without the loss of essential information. PROMETHEE II method is used for assets ranking; it belongs to the group of methods for MCDA. At the end of these stages, four groups of investment portfolios are created for simulation. Findings After the construction of portfolios, a simulation was performed with real data of the assets from January 03, 2011 to November 14, 2016. It resulted in a comparison in which it was observed that 100 per cent of portfolios showed positive returns on the investment. The result of portfolios’ group composed of assets based on the 21 financial indicators was higher than the other one formed from PCA criteria. Both of them were higher than Ibovespa result in the same period. Research limitations/implications As a contribution to new research, the model presents an opportunity for improvement through linear programming methodologies with the objective of optimizing the results, as the results obtained with the model were not optimized. Practical implications This research presents an alternative logic to the traditional one, as it seeks the reduction of investment risk based on the results of the management of the companies, reflected through their indicators. The model implies a change in how companies, financial institutions and small and medium investors choose their assets to form investment portfolios. The authors believe that the model has the potential to attract investors looking for long-term gains, such as public servants, retirees, professionals and others who seek to build heritage to overcome the adversities of the uncertain future. The model offers these investors the opportunity to choose which companies to invest in, based on established indicators in the literature, whose information is available in the market. The model systematizes the information and builds a ranking of the best companies so that the investor can make a conscious decision, thus avoiding what experts call a “herd effect”, which makes the majority of investors decide according to the oscillation of the market, thus ignoring the financial fundamentals of companies. Originality/value This study presents a proprietary methodology by merging the PCA tool with MCDA to build efficient investment portfolios.

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