adoption of lean thinking practices at brazilian...

International Journal of Lean Thinking Volume 3, Issue 2 (December 2012)

Lean Thinkingjournal homepage: www.thinkinglean.com/ijlt

ADOPTION OF LEAN THINKING PRACTICES AT BRAZILIAN AUTO PART COMPANIES

Ana Beatriz Lopes de Sousa JABBOUR Assistant Professor, The Sao Paulo State University (Unesp),

Brazil E-mail: [email protected]

Charbel José Chiappetta JABBOUR* Associate Professor, The Sao Paulo State University (Unesp), Brazil

E-mail: [email protected]

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

Lean Thinking, Brazil,

Auto Parts Sector

Received 05 May 2012

Accepted 13 July 2012

Available online 01 October 2012

The objective of this study is to analyze the adoption of lean

thinking practices at Brazilian auto part companies. For such, a

survey was conducted at 75 sector companies. The results reveal

that among all practices, the “systematic search for continuous

improvement" obtained the highest implementation average. In

terms of correlation, interdependence was observed among all

Lean Manufacturing variables, most especially between LM5

(Kanban) and LM6 (Just-in-Time). This correlation can be explained

by the importance of Kanban systems when implementing Just-in-

Time.

________________________________

* Corresponding Author

1. Introduction and literature survey

In the 1990s, after publication of the book “The Machine that Changed the World”

(Womack and Jones, 2004), the term “lean production” (or lean manufacturing) became

synonymous with Toyota’s pioneer practices (Toyota Production System) (Schonberger, 2007)

and began to become an important production management paradigm (Eswaramoorthi, Prasad

and Mohanram, 2010). The principles of lean manufacturing are to increase productivity, that

is, make more with fewer resources, and eliminate sources of waste throughout the value chain

(Shah and Ward, 2003).

As a consequence of lean manufacturing, it is possible to obtain better operational

performance through cost reductions (Ohno, 1988), manufacturing of zero defect products in

compliance with customer needs (Womack et al., 1990) and to focus on the customer (Dennis,

2008). These benefits are the reason for adopting several lean manufacturing practices, such as

continuous improvement, Just-in-Time, Kanban, vendor development/collaboration, 5S, total

production maintenance, lot reduction, multifunctional employees and Kaizen circles (Biazzo

and Panizzolo, 2000; Shah and Ward, 2003; Bahasin and Burcher, 2006; Pettersen, 2009).

However, little is known about the adoption of these lean manufacturing practices at Brazilian

auto part companies. For such, this paper presents the results of a survey with the objective of

better understanding this aspect.

Wesley Ricardo de

Souza FREITAS Assistant Professor, The Federal

University of Mato Grosso do Sul, Brazil E-mail:

[email protected]

Adriano Alves TEIXEIRA Associate Professor, The Sao Paulo State University

(Unesp), Brazil E-mail: [email protected]

JABBOUR, JABBOUR, TEIXEIRA, FREITAS / International Journal of Lean Thinking Volume 3, Issue 2(December 2012)

48

2. Conceptual Background

The Toyota Production System emerged as a managerial practice after World War II. According to

Bahasin and Burcher (2006), it arrived as a counter-intuitive alternative to mass production, the system in

effect at the time.

After World War II, Japan found itself in an unfavorable situation. The country’s economic context had

an impact on Toyota, such as capital restrictions for investments, devastated and low volume consumer

market (demand for different models of cars) and stagnant although high levels of finished product stock

(Holweg, 2007; Dennis, 2008). In order to overcome these challenges, Toyota laid off a number of

employees, leading to a series of discussions with the union. An important agreement was reached with

the remaining employees from these discussions. They had to be flexible and able to perform any task and

they had to be involved in the process in such a manner that they did not permit any failures. If they

complied, Toyota guaranteed them lifetime employment and payments tied to productivity and seniority

(Dennis, 2008).

These circumstances were determining factors for Toyota to seek a different way to administer

production, without an emphasis on the production capacity of equipment (Shimokawa and Fujimoto,

2011). As the result of capital restrictions, the equipment purchased had less production capacity, forcing

the production of smaller lots and giving origin to production flexibility, which made it possible to

accommodate the different needs of the consumer market. As a result, it was possible to cut costs by

eliminating waste, the excessive use of storage space and the eventual generation of large scale failures

(Holweg, 2007).

In order to sustain the small lot and production flexibility ideas, two pillars of the Toyota Production

System emerged: autonomation (Jidoka) and Just in Time (JIT) (Ohno, 1988; Holweg, 2007). The

principle of autonomation stems from the operation of weaving mill looms where the equipment had a

device that could stop operations if something was being done incorrectly. This principle evolved and

incorporated operator intervention in the process, whether automatically (ex. poka yoke), or manually.

Thus, if there is any failure in the operation, the equipment should be stopped to find out why. And this is

only possible when there is full involvement of employees in the process. In turn, Just in Time states that

one should produce only what is needed, when it is needed and in the quantity needed (Ohno, 1988).

In the 1990s, after publication of the book “The Machine that Changed the World” (Womack et al.,

1990), the term “lean production” became synonymous with Toyota’s pioneer practices (Toyota

Production System) (Schonberger, 2007) and began to become an important competitive production

management technique.

Although the Toyota Production System has been discussed since the 1950s, due to its evolution after

Western incorporations and adaptations, the term lean production does not have a clear definition and

measurement (Shah and Ward, 2007; Pettersen, 2009). For example, Womack et al. (1990), in a

summarized manner, state that lean production means making more with less. Shah and Ward (2007), in

turn, define lean production as an integrated social-technical system aimed at eliminating waste by the

concomitant reduction or minimization of internal variability of vendors and clients. For Scherrer-Rathje et

al. (2009) lean production is a philosophy focused on identifying and eliminating waste throughout the

value chain, and not only within the organization. As a consequence of lean production’s


49

multidimensional approach, there is great variety in the management practices and goals that constitute it

(Shah and Ward, 2003).

In this sense, lean production’s main objectives and goals include cost reductions (Ohno, 1988),

manufacturing products with zero defects in conformity with client needs (Womack et al., 1990) and

focusing on the client (Dennis, 2008). Thus, in order to achieve the objectives reported, various practices

may be established. Based on the studies by Biazzo and Panizzolo (2000), Shah and Ward (2003), Bahasin

and Burcher (2006) and Pettersen (2009), Table 1 tries to systematize these main practices and

characteristics associated with lean manufacturing.

Practices and

Characteristics

Description

LM1 – Multifunctional

employee/ involvement in the

process

Development of employee skills and incentives for autonomy to avoid

failures throughout the process.

LM2 - Continuous

improvement

Seeks incremental continuous improvement in quality, cost, delivery and

design.

LM3 – 5S A form of visual management for reducing disorder and inefficiency in

the production and administrative environment.

LM 4 - Total productive

maintenance

The objective is to improve machine reliability and capacity through

periodic maintenance regimes.

LM5 - Kanban Card system for creating a pulled flow.

LM6 - Just in Time Seeks continuous production flow.

LM7 – Lot reduction/ stock

reduction

Formation of small production lots for reducing stock in process and

increasing variety.

LM8 – Improvement circles/

Kaizen circles

Promotion of systematic discussions between operators and managers to

promote incremental continuous improvement.

LM9 – Vendor

development/collaboration

Activities geared towards developing relationships with the vendor to

obtain their collaboration.

Table 1. Practices and characteristics associated with lean production.

3. Research materials and methods

The Brazilian automotive sector is the target for this study, specifically, the auto parts sector. Brazil’s

automotive sector began in the 1950s. Sector evolution presents the following data in Brazil: there are 26

car manufacturers with 53 factories supplied by more than five hundred auto part companies, with an

installed production capacity of 4.3 million vehicles and 109 thousand farm machines per year. This

positions Brazil as one of the six biggest producers of vehicles in the world (Anfavea, 2011).

At present, the sector employs approximately 1.5 million people, earns more than US$ 107.6 billion

annually (including auto parts), has a direct participation in Gross Domestic Product (GDP) of 5.2%, and

can reach 22.5% of GDP if all indirect effects are considered (Anfavea, 2011).

A data collection instrument was planned for gathering data based on a structured questionnaire

about the practices/concepts previously reviewed in Section 2, elaborated according to the

recommendations contained in Synodinos (2003).

Besides information about the characterization of responding companies, the questionnaire has nine

questions about the adoption of “Lean Manufacturing” practices. The first version of the questionnaire was

submitted for content validation through an analysis of five researchers in the area, as well as the


50

adjustment to conceptual presuppositions. In its final version, the questionnaire was hosted in a virtual

environment especially elaborated for this research.

A 5-point Likert scale was adopted, where 1 represents "totally disagree" and 5 "totally agree".

The research data were collected between October 2010 and March 2011. First, email addresses and

telephone information were collected at the National Automotive Vehicle Component Industry Union –

SINDIPEÇAS, for 654 automotive sector companies (auto part segment) located in Brazil. Emails were sent

to these companies containing a brief explanation about the study and an invitation to participate directed

to the production manager. The email contained a link to guide the target respondent directly to the

questionnaire hosted in the study’s virtual environment. Phone calls were also made to increase the return

of valid questionnaires, where an attempt was made to contact the employees responsible for the

company's production area. Thus, 72 questionnaires were collected through the survey site and 4

questionnaires were collected from alternative means, as requested by the respective respondents. Thus,

76 questionnaires were obtained, 1 of which was discarded because it was incomplete. This was a total

return rate of 11.11% (75 valid questionnaires), a number considered adequate compared to the

percentages indicated by Synodinos (2003). Each filled out questionnaire automatically fed a data

spreadsheet for subsequent statistical processing.

The data analysis process involved the use of statistical procedures with the support of data

spreadsheets, from IBM’s Statistical Package for Social Sciences (Version 19.0).

4. Results

The survey’s results were obtained through three steps: (1) analysis of averages for lean

manufacturing practices; (2) factorial exploratory analysis; and (3) analysis of correlations between lean

manufacturing practices.

The LM2 variable – Systematic Search for Continuous Improvement – was perceived to be the one

with the highest average among the LM practices (Table 2).

Table 2. Average and standard deviation for the LM Construct variables

Variables Average Standard

Deviation

LM1 3.69 1.12

LM2 3.86 1.05

LM3 3.78 1.18

LM4 3.20 1.27

LM5 2.90 1.41

LM6 3.04 1.42

LM7 3.52 1.01

LM8 3.20 1.37

LM9 3.17 1.18


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Figure 1 shows the averages obtained in graph format.

Figure 1. Average of lean thinking practices.

In order to refine the results, the Factorial Exploratory Analysis began to reveal only loads for

variables greater than 0.6, factors with Eingenvalues greater than 1 and coefficients for the Anti-Image

matrix diagonal greater than 0.6. The communality of the variables was seen to be equal to or greater than

0.5 for each variable (Hair Jr et al., 2005). In relation to the Lean Manufacturing Construct (LM), only one

factor was formed, explaining an accumulated variance of approximately 64.27%, with an Eigenvalue of

5.78 and values adjusted to the main Anti-Image Matrix diagonal (0.917; 0.904; 0.927; 0.903; 0.867; 0.841;

0.891; 0.943; 0.908). The KMO test, which checks sample fitness, was 0.900, considered fit, as was the

value obtained from the Bartlett Sphericity test (460.202) and Cronbach’s Alpha (0.927). All LM Construct

variables presented satisfactory values (Table 3).

Table 3. Result of the Factorial Exploratory Analysis for LM.

Variables Load Communalities

LM1 0.79 0.63

LM2 0.84 0.71

LM3 0.81 0.65

LM4 0.81 0.66

LM5 0.74 0.54

LM6 0.81 0.65

LM7 0.82 0.67

LM8 0.82 0.68

LM9 0.75 0.57

The Pearson coefficient of correlation test was also run, revealing that all LM1-LM9 variables have

significant correlations, underscoring the relation between LM5 (Kanban) and LM6 (Just-in-Time) (Table

4).


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Table 4. Pearson correlation for the LM Construct variables

LM1 LM2 LM3 LM4 LM5 LM6 LM7 LM8 LM9

LM1 1

LM2 0.737* 1

LM3 0.637* 0.708* 1

LM4 0.627* 0.653* 0.671* 1

LM5 0.499* 0.479* 0.518* 0.535* 1

LM6 0.512* 0.568* 0.546* 0.657* 0.771* 1

LM7 0.600* 0.643* 0.528* 0.534* 0.615* 0.701* 1

LM8 0.598* 0.688* 0.638* 0.579* 0.530* 0.581* 0.677* 1

LM9 0.524* 0.567* 0.590* 0.592 0.419* 0.497* 0.627* 0.647 1

* p value < 0.01

5. Conclusions

The Lean Manufacturing construct is perceived to have all variables validated. Among all the practices,

the “systematic search for continuous improvement” obtained the highest implementation average and

was also the most important variable for the Lean Manufacturing construct. In terms of correlation,

interdependence was observed among all Lean Manufacturing variables, most especially between LM5

(Kanban) and LM6 (Just-in-Time). This correlation can be explained by the importance of Kanban systems

when implementing Just-in-Time.

In order to continue this line of research, a multiple case study should be conducted in order to

understand the results of this survey in qualitative terms. The main limitations of this research are sample

size, which, despite all the effort in data collection only included 75 participating companies, and the

restriction of analyzing a single industrial sector.

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