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The paper aims to present a clear methodological path for assessing the competitiveness of a specific industrial sector with the use of the Industrial Excellence Award (IEA) model. The paper introduces the concepts evaluated by the IEA model and addresses the ways with which varied management data may be analyzed in order to provide useful insights for improvement in industrial processes such as new product and process development, supply chain management, strategy formulation and deployment.

Sixty European textile companies provided information concerning their business processes over the course of three years in accordance with the Industrial Excellence Award (IEA) model developed by INSEAD Business Schools. Subsequently, the textile industry companies were compared with 73 excellence‐driven European manufacturers which either won or distinguished themselves in the award competition during the same time period. The management information from both datasets was treated with the proper statistical tools (according to their nature) in order to attain secure and minimally biased conclusions.

The benchmarking process revealed the areas in which the textile sector was lagging behind the excellence‐driven manufacturers. Furthermore, it detected their differences in specific measures of industrial management and business mentality. On a theoretical level, the analysis verified the general reliability of the IEA model's scales, aiming at assessing abstract management constructs while fine tuning them.

The thorough inspection of the textile companies' performance attributes and characteristics has identified many of the sector's shortcomings that merit further investigation.

The results of the analysis served as valuable feedback to textile managers aiming at bettering their industrial processes in many ways, such as benchmarking their performance against their sector or other sectors, and observing trends that managers from other sectors are putting effort into in order to improve their performance.

The paper provides a clear‐cut methodology for the understanding and statistical analysis of multifaceted industrial management data included in excellence models.

Lean Six Sigma (LSS) is a continuous improvement methodology used to increase the efficiency and effectiveness of processes. Although there are several articles published, only two have analyzed the literature from a bibliometrics perspective. The purpose of this paper is to analyze the LSS literature by bibliometrics, identifying its state of the art, scientific gaps and research trends.

Articles published up to 2016 in the database Scopus were investigated to identify the most significant articles, authors, journals, institutions and countries based on citation counting as well as the most frequent keywords and subject areas on LSS. Articles published in 2014, 2015 and 2016 were analyzed to point out scientific gaps and to identify eight main research trends on LSS.

The research trends are: “LSS implementation”, “Healthcare”, “LSS tools”, “Human factors”, “Expansion of results”, “SME”, “LSS combined with other methodologies” and “Education”. The research outcomes also point out the most significant articles, authors, journals, institutions and countries in LSS literature.

This research contributes to develop the state of the art of LSS and helps professionals as well as researchers to identify which issues new studies should address.

The performance of the literature is measured based on the number of citations and not on the number of published papers, and the bibliometric analysis covers the highest number of articles so far (319 articles). Besides, the identification of the main research trends on LSS is exclusively based on the most recent studies.