Knowledge-Based Assessment Applied to Lean Brazilian Toyota Plants: Employees' Perceptions

Knowledge-Based Assessment Applied to Lean Brazilian Toyota Plants: Employees' Perceptions

Jorge Muniz Jr., Vagner Batista Ribeiro, Ninad Pradhan
Copyright: © 2021 |Pages: 22
DOI: 10.4018/IJKM.2021040101
Article PDF Download
Open access articles are freely available for download

Abstract

This paper proposes knowledge-based assessment applied to Brazilian Toyota plants which practice Lean manufacturing to evaluate work, production, and knowledge factors based on the perspective of blue-collar workers and managers. The two researched plants were selected based on being pure Toyota DNA representatives, and belong to two Toyota auto parts makers (‘polar' cases), in which TPS is “transparently observable.” The results evidence that employees judge factors related to people as important and considered the relationship between knowledge and Lean in the plants are aligned. The data indicates that the Brazilian culture does not influence changes in the Toyota work context and DNA. The contribution of this study is to provide an assessment instrument that integrates the production, knowledge, and work context for a Lean system, understanding blue-collar manufacturing employees and front-line supervisors are therefore essential to the success of a Lean implementation. Finally, the paper offers a guideline to assess and develop a favorable context to encourage knowledge sharing.
Article Preview
Top

Introduction

This paper proposes knowledge-based assessment applied to Brazilian Toyota plants which practice Lean Manufacturing to evaluate work, production, and knowledge factors based on the perspective of blue-collar workers.

Chen and Holsapple (2009) provide initial insights related to integration of Knowledge Management (KM) and Lean Manufacturing (LM), highlighting the benefits of knowledge sharing in the context of waste reduction. The KM-LM relationship has been explored for knowledge sharing in shop floor environments (Yang & Cai, 2009; Muniz Jr., Batista Jr. & Loureiro, 2010), application to Six Sigma projects (Baral, Kifor & Bondrea, 2014), Supply Chain Management (Liu, Leat, Moizer, Megicks & Kasturiratne, 2013), outsourcing (Gong & Blijleven, 2017), information systems (Buřita, Hrušecká, Pivnička & Rosman, 2018; Fiechter, Marjanovic, Boppert & Kern, 2011), and in the service sector services (Zhao, Rasovska & Rose, 2016). However, integration of KM and LM in Manufacturing has only been explored in a limited sense in academic literature, which has highlighted the need for such studies, especially in the context of different industry sectors or types of production systems (Gong & Blijleven, 2017; Shadi, 2017; Gowen III, Stock & McFadden, 2008), and KM-LM assessment.

KM-Lean assessment has been shown to be important for organizational competitiveness (Shirouyehzad, Rafiee & Berjis, 2017; Wang, Ding, Liu & Li, 2015). Related studies expand upon the need for KM in organizations (Buyukozkan, Parlak & Tolga, 2016; Dehghani & Ramsin, 2015; Reed et al., 2011) and on the development of performance evaluation models to assess KM systems and correct deleterious effects as appropriate (Shirouyehzad, Rafiee & Berjis, 2017; Wang, Ding, Liu & Li, 2015; Lin, Chang & Lin, 2011). KM performance evaluation models must support the organization’s KM strategy and verify whether KM is able to achieve organizational objectives in the current environment (Chen & Fong, 2015). Such models must also consider organizational characteristics such as capacity, reliability, and performance (Chen & Fong, 2015; Hesamamiri, Mahdavi Mazdeh, Jafari & Shahanaghi, 2015). The dynamic nature of KM makes the development of a precise evaluation model as challenging as it is important (Wang, Ding, Liu & Li, 2015). Finally, evaluation methods for a well-integrated KM-Lean system must also include LM aspects such as people and LM tools (Shadi, 2017; Zhao, Rasovska & Rose, 2016). Blue-collar workers and front-line supervisor perspectives are important to LM implementation (Delbridge, 2003; Manville, Greatbanks, Krishnasamy & Parker, 2012; Bhamu & Sangwan, 2014).

The opportunities for KM-Lean assessment exist in the areas of human factors, methodology, learning, Toyota Production System (TPS) assessment, and interaction with other management approaches (Psomas & Antony, 2019). This study focuses on the following research questions:

  • What are the key KM-Lean factors related Brazilian Toyota culture?

  • What are the differences in blue-collar worker and manager perspectives about TPS?

  • Is KM relevant to Brazilian Toyota plants?

The growth in application of TPS principles in Brazil raises issues about their applicability within a distinctive cultural context that is very different from Japan. The success in sustaining LM is determined to a large extent by the hybridization of where it is being implemented (Liker, Fruin & Adler, 1999). In other words, the implementation of LM may require different strategies, which to some extent adapt to the local culture of the host country. This is observed by undesirable results in some cases, such as loss of focus, redundancies, interdepartmental conflicts, waste of resources (time, financial and personnel) and even layoffs. This contributed to not creating a favorable context for production and people.

The automotive industry is ideal for studying the transferability of LM in the context of KM, since it is considered a “microcosm”, where the characteristics of the Organization of Production and Work Organization in general are “crystallized” and can be observed (Biazzo & Panizzollo, 2000). Automotive companies have revolutionized the supplier relationship culture, product development methods, and have introduced the Toyota Production System to the productive process. This has a strong influence on other sectors.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 1 Issue (2023)
Volume 18: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing