Lean manufacturing implementation in regions with scarce resources

Abstract

Purpose The purpose of this paper is to explore the adoption of lean practices by manufacturing companies in regions of low economic and technological development and to compare findings with previous studies from more developed regions highlighting important contextual differences. The paper uses the contingency theory to explore how contextual variables and scarce resources influence the adoption of lean practices. Design/methodology/approach A survey of 233 manufacturing firms was conducted in the State of Pará in the Amazon Region of Brazil. Findings The results demonstrate that six internal lean practices (single minute exchange of dies, human resource management, continuous flow, total productive maintenance, pull and statistical process control) and two external lean practices (supplier feedback and customer involvement) are implemented. However, the two external lean practices of just-in-time delivery by suppliers and supplier development were not implemented. Furthermore, from the 36 operating items comprised in eight lean practices that are being used, 13 were not implemented. As such, compared to developed regions, there is evidence for a more fragmented implementation in less developed regions. The results reveal empirical evidence explained by the contingency perspective, such as national, geographical, strategic context and culture. Originality/value There is broad evidence on lean implementation in developed and developing countries in the literature. However, little is known about lean implementation in poorer regions of developing counties. This is one of the first studies mapping lean implementation in a region with low economic and technological development. This has important implications for research and practice, especially to cross-country/cultural research on operation management.

An exploratory study of the relationships between Lean practices and team performance in higher education

“…This corresponds to a response rate of 16.85% of the total population (this response rate here is the number of responses received divided by total population). Such response rate is similar to those ones observed in the studies of Handfield et al (2015), Salimian et al (2017), Gutierrez-Gutierrez et al (2018), Negrão et al (2020), Tortorella et al (2021) and Klein et al (2023a). With this number of responses, we also met the criteria of 15–20 responses for each item in the questionnaire, as suggested by Hair et al (2014), to perform more sophisticated data analysis procedures.…”

A relationship between bias, lean tools, and waste

“…For example, Singh and Rathi (2019) and Gleeson et al (2019) linked pre-attentive bias (knowledge of items' location) to Lean Six Sigma methods while this study had Lean as its scope. Another reason could be that this research focused on Lean related biases other than research-methodological errors related biases such as nonresponse bias, (Tortorella et al, 2020, Sahoo, 2019, Negrão Léony Luis et al, 2019, review and response biases (Ibrahim et al, 2020).…”

“…Shi et al (2019)). Many authors considered or verified nonresponse bias, (Tortorella et al, 2020, Sahoo, 2019, Negrão Léony Luis et al, 2019, review bias and response bias (Ibrahim et al, 2020) in their case studies. The influences of perception's bias, self-serving bias, actorobserver bias (Antomarioni et al, 2020), operational bias (Cruz et al, 2019), observational bias (Gleeson et al, 2019), subjective bias (Valente et al, 2019) and technocratic bias (Claudia and Geraldo, 2020) were linked to Lean projects.…”

“…Linkage of Lean modelling to biases related to research included Shi et al (2019), who considered selectivity bias, selection bias and self-selection (self-serving/self-centred) bias in productivity modelling. Many authors considered or verified nonresponse bias, (Tortorella et al, 2020, Sahoo, 2019, Negrão Léony Luis et al, 2019, review and response biases (Ibrahim et al, 2020) in lean related research to correct research errors. Antomarioni et al (2020) observed perception bias, selfserving and actor-observer biases to Lean practices while Cruz et al (2019) linked operational bias to Lean performances.…”

Laying foundation for successful implementation of lean manufacturing through the elimination of non-value adding activities in conventional engineering products manufacturing companies

“…One such paradigm is lean manufacturing. [1][2][3][4][5][6] The origin of lean manufacturing can be traced to the implementation of Just in Time (JIT) manufacturing at Toyota during the 1980s. 1,7,8 In 1990, Womack et al adopted the terminology, 'lean manufacturing' in their book titled 'The machine that changed the World' to bring out the expanded and refined form of JIT.…”