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Lean production myths: An exploratory studySaurin, Jose T; Tortorella, G; Soliman, M; Garza-Reyes, Jose Arturo; Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Universidade Federal de Santa Catarina, Florianopolis, Brazil; The University of Melbourne; Universidade Federal de Santa Maria, Santa Maria, Brazil; University of Derby (Emerald, 2020-11-25)Tthis paper presents an exploratory investigation of myths on lean production (LP), by identifying, dispelling, and assessing their pervasiveness. A list of myths was proposed mostly based on seminal LP texts and our rich experience from researching, teaching, and consulting in lean journeys. Complexity thinking was adopted as a lens for dispelling the myths, as it challenged generalizations implied in myths. An investigation of the pervasiveness of the myths was conducted, based on a survey with 120 academics and practitioners. Ten myths were identified and dispelled. Survey’s results indicated that belief in lean myths was more common among less experienced practitioners (< 10 years), while experience was not a relevant factor for academics. The lean myths partly reflect the experience of the authors. Furthermore, a larger sample size is necessary for a full analysis of pervasiveness. The lean myths might be underlying barriers to LP implementation (e.g., lack of knowledge of managers and workers), and they might be proactively accounted for in lean training and education programs. This is the first work to explicitly frame a set of lean myths.
Proposition of a method for stochastic analysis of value streamsLuz, G., Tortorella, G.L., Bouzon, M., Garza-Reyes, J.A., Gaiardelli, P.; Universidade Federal de Santa Catarina, Brazil; The University of Melbourne; University of Derby; Università degli Studi di Milano (Taylor & Francis, 2020-10-19)This article aims at proposing a method to stochastically analyze values streams taking into consideration the effect of critical uncertainty sources on lead time. The proposed method combines value stream mapping (VSM) and Monte Carlo simulation to identify improvement opportunities. To illustrate this approach, we carried out a case study in the special nutrition value stream of a Brazilian public hospital. Results show that the proposed method allows the identification of improvement opportunities that would not be considered in the classical deterministic VSM approach. Further, the integration of the stochastic analysis enables the determination of a more realistic lead time, which supports a more assertive planning and scheduling of the value stream. The proposed method addresses a fundamental gap in traditional VSM without adding much complexity to the analysis procedure, which is a common practical issue in previous works that integrated other stochastic methods into VSM.