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The purpose of this paper is to develop a decision-making protocol to meet the new requirements in an atypical panorama, such as the economic instability, in the textile industry.

The methodology consists of analyzing technical criteria, costing parameters and efficiency scores of knitted fabrics using the data envelopment analysis (DEA) and classification and regression (C&R) trees models, together with statistical techniques. From these tools, it is possible to guide the portfolio management of these products in a textile company, identifying those that are inefficient and require immediate management measures. The results are expected to be replicated in other companies because the DEA and C&R trees analytical procedures are applicable to different portfolios, whether in the same industry or not.

The results allowed identifying inefficient textile products regarding the input-output relationship and the main technical reasons related to the most significant inefficiencies, such as fiber composition and knitted fabrics rapports used in manufacturing.

DEA and C&R trees, in combination with the study of textile technical parameters, can be fundamental to investigating the efficiency and profitability of industries in periods of economic instability or other adverse situations. In addition, it is noteworthy that there are practically no studies in the literature on DEA applied in the textile industry, indicating excellent development potential.

The purpose of this paper is to determine leadership styles at each hierarchic level that best support the LM implementation process in a given company.

To achieve that, the authors propose a method that combines information from two sources in three major steps. First, using opinions from experts on lean implementation from an activity sector of interest the authors determine leadership styles that best suit each phase of the LM roadmap for that sector. Next, the authors analyze a specific company within the activity sector to determine: at which lean roadmap phase the company is at the moment; and the style of its current leaderships at each hierarchical level. Finally, the authors combine information from previous steps to diagnose the suitability of company’s leaderships to its lean implementation needs.

The method points at improvement alternatives that may be developed simultaneously at different leadership hierarchical levels in companies. Further, using the matrix of ideal leadership styles, companies may be able to identify implementation phases in the lean process that are poorly served by current leadership styles, anticipating problems and developing HRM practices to mitigate them. That is quite relevant, since changes in leadership behaviors and expectations may take longer time to be implemented; therefore, it is important to understand these opportunities and have a clear vision of current gaps within the company.

The identification of leaderships’ attributes and behaviors in companies at different phases of the lean implementation roadmap contributes to the existing body of knowledge on lean manufacturing. The method is intended as a supporting tool for lean implementation, as it enables the assessment of gaps in leadership behaviors in the organization, and directs to improvements according to the phase of lean implementation. The goal is to complement existing lean roadmaps by driving improvements in leadership-related aspects of the implementation process.