# Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada

Didérot Déraillet Tadja, Fatma Lehyani, Samuel-Jean Bassetto, Alaeddine Zouari, Michel Tollenaere, Tony Wong

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Didérot Déraillet Tadja, Fatma Lehyani, Samuel-Jean Bassetto, Alaeddine Zouari, Michel Tollenaere, et al.. Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada. Cogent Engineering, 2025, 12 (1), 10.1080/23311916.2025.2535692. hal-05405723

HAL Id: hal-05405723

https://hal.science/hal-05405723v1

Submitted on 9 Dec 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada

Didérot Déraillet Tadja, Fatma Lehyani, Samuel-Jean Bassetto, Alaeddine Zouari, Michel Tollenaere & Tony Wong

**To cite this article:** Didérot Déraillet Tadja, Fatma Lehyani, Samuel-Jean Bassetto, Alaeddine Zouari, Michel Tollenaere & Tony Wong (2025) Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada, Cogent Engineering, 12:1, 2535692, DOI: 10.1080/23311916.2025.2535692

**To link to this article:** https://doi.org/10.1080/23311916.2025.2535692

<figure><img src="image_3.png" /></figure>

© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

<figure><img src="image_4.png" /></figure>

View supplementary material 🌐

<figure><img src="image_5.png" /></figure>

Published online: 29 Jul 2025.

<figure><img src="image_6.png" /></figure>

Submit your article to this journal 🌐

<figure><img src="image_7.png" /></figure>

Article views: 841

<figure><img src="image_8.png" /></figure>

View related articles 🌐

<figure><img src="image_9.png" /></figure>

View Crossmark data 🌐

CrossMark
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

# Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada

Didérot Déraillet Tadja<sup>a,b</sup>, Fatma Lehyani<sup>c</sup>, Samuel-Jean Bassetto<sup>a</sup>, Alaeddine Zouari<sup>c</sup>, Michel Tollenaere<sup>b</sup> and Tony Wong<sup>d</sup>

<sup>a</sup>Département de mathématiques et de génie industriel, Polytechnique Montréal, Montréal, Canada; <sup>b</sup>School of Industrial Engineering, Grenoble Institute of Technology, Grenoble, France; <sup>c</sup>OLID Laboratory LR19ES21, Higher Institute of Industrial Management of Sfax, University of Sfax, Sfax, Tunisia; <sup>d</sup>Département de génie des systèmes, École de technologie supérieure, Montréal, Canada

## ABSTRACT

Manufacturing companies operate in aggressive environments and require high productivity for survival. This study examined the implementation status of lean manufacturing practices in a sample of Greater Montreal's industries by assessing maturity levels and their relationship with the company profile. A survey questionnaire encompassing technical and non-technical practices was developed and distributed to a population of manufacturing companies in the primary and secondary sectors in Greater Montreal. The unit of analysis was the manufacturing system. Valid data were collected from 35 companies using the random sampling technique. Maturity indices were evaluated with the factor-weighting method, and maturity levels were subsequently assigned to each company. Based on ascending hierarchical classification and Fisher's exact tests, the findings revealed a mitigated maturity level in technical practices (average index of 51.554%), influenced by location (p-value = 0.018) and size (p-value = 0.08). However, non-technical practices exhibited a high maturity level (average index of 74%) linked to company size (p-value = 0.005).

## ARTICLE HISTORY

Received 27 May 2024
Revised 20 January 2025
Accepted 8 May 2025

## KEYWORDS

Lean manufacturing
behavior; lean
manufacturing; lean
manufacturing tool;
maturity assessment;
manufacturing company

## SUBJECTS

Industrial Engineering &
Manufacturing; Engineering
Management;
Manufacturing Engineering

## 1. Introduction

Since the beginning of the industrial era, the quality of life and economic development of nations have depended strongly on the value-added activities of manufacturing companies. Smith's radical insight was that a nation's wealth is the stream of goods and services that it creates (Adam Smith Institute, 2023). Today, wealth-generating manufacturing companies operate in a globalized environment characterized by unfair competition. Moreover, the economic climate after COVID-19 has been impacted by inflation, which is often followed by a recession. Manufacturing companies typically operate in aggressive environments in which they must be productive to survive. This requires the capability to realize substantial gains with few resources. Lean manufacturing (LM), which is a powerful and widely recognized managerial approach for improving operational performance (Bortolotti et al., 2015; Liker, 2004; Shah & Ward, 2003), is a solution to this situation (Sahoo, 2020).

Despite the undisputed relevance of LM for business survival, many companies are struggling to operationalize LM practices (Pearce et al., 2018). Lean implementation projects often fail because of a superficial approach (Liker & Rother, 2011), underestimation of lean concepts by managers (Zhu & Lin, 2017), or inappropriate organizational culture (Bhasin, 2011; Bhasin & Burcher, 2006). The manufacturing industry in Quebec, Canada, largely comprises small and medium-sized businesses (Ministère de l'Économie, 2021).

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<footnote>**CONTACT** Didérot Déraillet Tadja
Montréal, Canada, H3T 1J4</footnote>

<footnote>diderot-deraillet.tadja@polymtl.ca</footnote>

<footnote>Polytechnique Montréal, 2500 Chemin de Polytechnique,</footnote>

<footnote>Supplemental data for this article can be accessed online at https://doi.org/10.1080/23311916.2025.2535692.</footnote>
<figure><img src="image_15.png" /></figure>

Relative to the Canadian average, productivity in this industry is lower (STIQ, 2022, 2023) and ranks at the bottom of 30 Western economies (Deslauriers et al., 2020). As LM is a recognized solution for enhancing productivity, it is legitimate to question if growth in the productivity of Quebec's manufacturing industry could also be driven by LM practices. It is, thus, necessary to determine the status of LM implementation in Quebec. Assessing the maturity levels of LM practices undoubtedly represents the best method to do so.

For management, periodic assessments of the status of implementing lean practices are of great importance. At the very least, corrective action plans for operations management can be drawn from the results of such assessments and lead to better productivity. The only survey of this nature conducted in Quebec was published by Beaulieu-Paré (2011). However, thirteen years later, these results appear to be outdated. Moreover, the maturity levels of LM practices are not presented in Beaulieu-Paré's study, calling for a reassessment in Greater Montreal, where many of the manufacturing companies in the province of Quebec are located.

Most LM surveys in the lean literature just focus on technical tools. Some researchers, such as Tortorella et al. (2017), have proposed methods for assessing LM practices that include both socio-technical and ergonomic aspects. Bortolotti et al. (2015) also presented a rare assessment that addressed technical aspects, organizational culture, and certain soft practices. As LM is a people-centered system (Liker, 2004), an assessment of its practices should explicitly cover technical and non-technical dimensions in order to be inclusive. This study, thus, established two research objectives:

RO1: To determine the maturity levels of the technical and non-technical LM practices of a sample set of manufacturing companies in Greater Montreal.

RO2: To explore the relationships between the maturity levels of technical and non-technical LM practices and the company profiles.

The remainder of this paper is organized as follows. Section 2 reviews the main concepts of LM. Section 3 outlines the general methodology used to address the research objectives. Section 4 presents and discusses the results. Finally, Section 5 presents the study's conclusions and recommendations for the future.

# 2. Review of lean manufacturing concepts and background

## 2.1. What is lean manufacturing?

Since its formal advent in 1990 with the publication of *The Machine that Changed the World* by Womack et al. (1990), LM has captured the attention of several researchers and practitioners who have proposed various interpretations, orientations, and definitions. In simple terms, LM is a system of structured practices that aims to improve the operational performance of manufacturing systems, with a particular focus on the reduction or elimination of non-value-added activities known as waste. Consequently, a manufacturing system that adopts management approaches based on LM operates without constraints that prevent the full realization of its operational performance. This leads to a higher quality-price ratio, resulting in better customer satisfaction. This approach also benefits companies because production costs are reduced. It is, therefore, a win-win management system as all the main stakeholders (suppliers and customers) are satisfied (Tadja et al., 2021).

For a manufacturing company, value addition results from the operational transformation of a product based on one or more initial values acquired from the stock market. Over a given period, this is equal to the value of production, with less intermediate consumption (INSEE, 2020). As such, the creation or increase in value essentially originates from a reduction in the intermediate consumption associated with operations without added value. This is referred to as value-added production and pertains specifically to the business. However, when customers evaluate a product, they evaluate its perceived value in relation to the asking price (Almquist et al., 2016); therefore, from the customer's perspective, value addition is the only perception that creates a feeling of satisfaction and persuades customers to
<figure><img src="image_16.png" /></figure>

pay the acquisition price. In this context, LM extends value-added production as a mutually beneficial management system that creates value for the company, its customers, and, by extension, the nation.

## 2.2. Lean manufacturing practices

In English, 'lean' means skinny, and 'degrease' means to remove excess fat. In economic terms, 'grease' refers to operations that do not add value to the customer or company (Zajkowska, 2013). Researchers and practitioners have demonstrated objectively effective practices for creating a 'lean or skinny company'. Since their advent, LM practices have evolved according to the cultural realities and daily experiences of manufacturing companies. LM implementation is possible through a system of practices that includes both technical and non-technical facets. This section explores the existing literature to identify relevant LM practices.

### 2.2.1. Identification of groups of practices

The problem of identifying groups of LM practices has existed since its beginnings, becoming particularly apparent when managers analyzed management practices to consider the implementation of total quality management (TQM) (Flynn et al., 1995). TQM was, thus, implicitly considered an LM practice group.

According to Shah and Ward (2003), the primary LM practices include just-in-time (JIT), total productive maintenance (TPM), TQM, and human resource management (HRM). Their conclusion was based on a principal component analysis of 22 US manufacturing industry practices. As a result of a consensus between practices within manufacturing companies and scientific publications, Lyonnet (2010) identified six possible groups of LM practices: waste hunting (WH), JIT, TQM, continuous improvement (CI), visual management (VM), and HRM. Belekoukias et al. (2014) consolidated several previous academic publications and established that JIT, TPM, value stream mapping (VSM), CI, and autonization (AUT) are the five essential groups of LM practices. In addition, Sahoo (2019) applied structural equation modeling to establish a relationship between LM practices and the operational performance of manufacturing companies in India, identifying TQM, TPM, JIT, production scheduling and systematization (PSS), and HRM as groups of LM practices.

The findings of the studies described above indicate that more than 30 years after the first publication on LM, there is still no consensus in the literature on the groups of practices in LM. This is often the case for scientific concepts that are still in their infancy (Chalmers, 2013). Table 1 summarizes groups of LM practices reported by various studies in the literature. In the table, a cell filled with '1' indicates that the authors of the study cited in that row consider the group in that column to be a group of LM practices. Otherwise, the cell is filled with '0'. The empirical frequency of each group, as shown in Table 2 was evaluated using Equation (1).

$$E(x) = \frac{1}{N} \sum_{i=1}^{N} x_i \quad (1)$$

Here, *E(x)* represents the empirical frequency, *x*<sub>*i*</sub> represents the practice occurrence, and *N* represents the total number of authors.

Groups of practices with an empirical frequency of >50% can be considered as sufficiently cited in the literature to be included in the corpus of LM practices. These included JIT, TPM, TQM, CI, and HRM.

<table><caption>**Table 1.** LM practices categorized in the literature.</caption><thead><tr><th>Studies</th><th>JIT</th><th>TPM</th><th>TQM</th><th>CI</th><th>VSM</th><th>WH</th><th>VM</th><th>PSS</th><th>AUT</th><th>HRM</th></tr></thead><tbody><tr><td>Flynn et al., 1995</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Shah & Ward, 2003</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Lyonnet, 2010</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Belekoukias et al., 2014</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Sahoo, 2019</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td></tr></tbody></table>

Note: 1/0 indicates that the practice is/is not considered part of the LM group of practices according to the author(s) of the corresponding study.

Source: Adapted from Tadja et al. (2021).
<figure><img src="image_17.png" /><figcaption>**Table 2.** Empirical frequency of groups of LM practices.</figcaption></figure>

<table><thead><tr><th></th><th>JIT</th><th>TPM</th><th>TQM</th><th>CI</th><th>VSM</th><th>WH</th><th>VM</th><th>PSS</th><th>AUT</th><th>HRM</th></tr></thead><tbody><tr><th>Frequency (%)</th><td>80</td><td>60</td><td>80</td><td>60</td><td>20</td><td>20</td><td>20</td><td>20</td><td>20</td><td>60</td></tr></tbody></table>

Source: Adapted from Tadja et al. (2021).

<figure><img src="image_18.png" /><figcaption>**Figure 1.** LM practice groups.</figcaption></figure>

Notes: JIT: Just-in-Time; CI: Continuous Improvement; TPM: Total Productive Maintenance; TQM: Total Quality Management; HRM: Human Resource Management; WTB: Work Team Behavior.

However, LM is a management system that integrates adequate technical and non-technical dimensions (Pakdil & Leonard, 2015; Subramanian et al., 2023). It should, thus, also focus on people engaged in problem-solving (Liker, 2004). In manufacturing companies, these individuals are often organized into work teams. Operational performance depends on the behavioral dynamics of these teams (Rousseau et al., 2006). Human behavior is necessary for the implementation of LM (Emiliani, 1998). Consequently, in addition to the groups identified in Tables 1 and 2, careful attention to work team behavior (WTB) is required for successful LM implementation. WTB is fundamental to improving performance because people in a work team can decide on the need for relevant actions. A group of practices specific to WTB should, therefore, be included among the LM practice groups, as presented in Figure 1, which offers a pictorial representation of these groups.

### 2.2.2. Technical practices of lean manufacturing

Technical LM practices refer to all methods and tools directly oriented toward production and processes. These are the most popular and have visible and measurable effects on the productivity of manufacturing companies. Most LM practices are technical, namely, JIT, TPM, TQM, and CI. Each of these groups has specific tools or practices for operationalization. Table 3 presents a select list of LM technical practices associated with each group, the ID assigned to each practice, and citations to the literature that supports the categorization of these practices into each group or provides more details.

### 2.2.3. Non-technical practices of lean manufacturing

Non-technical practices in LM refer to the behavior and management of people engaged in value-added activities in manufacturing companies. Companies do not consider these as important as technical practices; however, they are crucial for achieving high levels of performance (Hines et al., 2004). HRM and WTB are two groups of non-technical LM practices. Table 4 presents a select list of LM non-technical practices associated with HRM and WTB, the ID number assigned to each practice, and citations to the literature that supports the categorization of these practices into each group or provides more details.
<figure><img src="image_19.png" /><figcaption>Table 3. Groups of LM technical practices, associated practices and tools, and supporting literature.</figcaption></figure>

<table><thead><tr><th>Group of practices</th><th>Practice ID</th><th>Practice/technical tool</th><th>Referenced and/or presented by</th></tr></thead><tbody><tr><td rowspan="8">JIT</td><td>PT1</td><td>Control of cycle time</td><td>(Belekoukias et al., 2014; Marodin et al., 2015; Yash, 2016)</td></tr><tr><td>PT2</td><td>Control of setup time</td><td>(Pakdil & Leonard, 2015; Pettersen, 2009)</td></tr><tr><td>PT3</td><td>Application of Single Minute Exchange to Die (SMED)</td><td>(Hu et al., 2015; Mayr et al., 2018; Panwar et al., 2015; Sanders et al., 2017)</td></tr><tr><td>PT4</td><td>Leveling of production rate</td><td>(Bai et al., 2019; Belekoukias et al., 2014; Garza-Reyes et al., 2018; Jasti & Kodali, 2015; Nawanir et al., 2013)</td></tr><tr><td>PT5</td><td>Application of pull production</td><td>(Belekoukias et al., 2014; Negrão et al., 2016; Pakdil & Leonard, 2015; Siegel et al., 2019)</td></tr><tr><td>PT6</td><td>Production based on objectives</td><td>(Bortolotti et al., 2015; Maasouman & Demirli, 2015; Thanki & Thakkar, 2014)</td></tr><tr><td>PT7</td><td>Computer-Aided Production Management or Enterprise Resource Planning</td><td>(Halgeri et al., 2008; Jituri et al., 2018; Lyonnet, 2010)</td></tr><tr><td>PT8</td><td>Stock management on demand (Kanban)</td><td>(Fullerton et al., 2014; Tortorella & Fettermann, 2018; Yash, 2016)</td></tr><tr><td rowspan="8">TQM</td><td>PT9</td><td>Visual Control System</td><td>(Bai et al., 2019; Garza-Reyes et al., 2018; Velarde et al., 2011)</td></tr><tr><td>PT10</td><td>Poka-Yoke or Error prevention system</td><td>(Alefari et al., 2017; Belhadi et al., 2018; Mostafa et al., 2015; Negrão et al., 2016)</td></tr><tr><td>PT11</td><td>Work standards</td><td>(Dombrowski et al., 2017; Fullerton et al., 2014; Siegel et al., 2019)</td></tr><tr><td>PT12</td><td>Statistical Process Control</td><td>(Belhadi et al., 2018; Costa et al., 2021; Panwar et al., 2015)</td></tr><tr><td>PT13</td><td>Policies, manuals, and quality control plan</td><td>(Baillargeon, 2016; Thanki & Thakkar, 2014)</td></tr><tr><td>PT14</td><td>Quality circle</td><td>(Woong Jin & Doolen, 2014; Yash, 2016)</td></tr><tr><td>PT15</td><td>Checklists</td><td>(Baillargeon, 2016; Battistella et al., 2023; Tomic et al., 2017)</td></tr><tr><td>PT16</td><td>Process capability measurement</td><td>(Thanki & Thakkar, 2014; Tomic et al., 2017)</td></tr><tr><td rowspan="4">TPM</td><td>PT17</td><td>5S</td><td>(Narasimhan & Das, 2001; Randhawa & Ahuja, 2017; Velarde et al., 2011)</td></tr><tr><td>PT18</td><td>Maintenance procedure</td><td>(Maasouman & Demirli, 2015; Pakdil & Leonard, 2015; Singha Mahapatra & Shenoy, 2022)</td></tr><tr><td>PT19</td><td>Overall Equipment Effectiveness (OEE) evaluation</td><td>(Belekoukias et al., 2014; Garza-Reyes et al., 2018; Jain et al., 2015; Siegel et al., 2019)</td></tr><tr><td>PT20</td><td>Computerized Maintenance Management System (CMMS)</td><td>(Balouei Jamkhaneh et al., 2018; Jasti & Kodali, 2015; Mostafa et al., 2015)</td></tr><tr><td rowspan="2">CI</td><td>PT21</td><td>Periodic inspections of machines</td><td>(Khan & Haddara, 2004; Wang, 2011)</td></tr><tr><td>PT22</td><td>Autonomous maintenance</td><td>(Alsyouf, 2009; Singh & Awoke, 2023; Tortorella et al., 2021; Yang & Yang, 2024)</td></tr><tr><td rowspan="5">CI</td><td>PT23</td><td>Identification or hunting of 7 types of waste</td><td>(Alefari et al., 2017; Pettersen, 2009; Velarde et al., 2011)</td></tr><tr><td>PT24</td><td>Kaizen projects</td><td>(Chan & Tay, 2018; Farris et al., 2009; Woong Jin & Doolen, 2014)</td></tr><tr><td>PT25</td><td>Value Stream Mapping (VSM)</td><td>(Hussain & Figueiredo, 2023; Patel et al., 2022; Sangwa & Sangwan, 2023)</td></tr><tr><td>PT26</td><td>Ishikawa method (5M)</td><td>(Ben Romdhane et al., 2017; Veres, 2020)</td></tr><tr><td>PT27</td><td>Gemba walk</td><td>(Gesinger, 2016; Micieta et al., 2021)</td></tr></tbody></table>

## 2.3. Maturity level assessment models

The concept of assessing maturity levels began in 1986 when researchers focused on building a culture of excellence in software engineering and management. The aim was to study conceptual frameworks for assessing the maturity levels of software development processes (Vivares et al., 2018). This resulted in the creation of the capability maturity model (Paulk, 2002), followed by the integrated capability maturity model, a more comprehensive model that covers different fields of organizational activities (Santos Bento & Tontini, 2018).

Studies have indicated a positive relationship between LM implementation and a company's operational performance (Chavez et al., 2013; Rasi et al., 2015). Although LM may have only a
<figure><img src="image_20.png" /><figcaption>**Table 4.** Groups of LM non-technical practices, associated practices and management approaches, and supporting literature.</figcaption></figure>

<table><thead><tr><th>Group of practices</th><th>Practice ID</th><th>Approach/behavior</th><th>Referenced and/or presented by</th></tr></thead><tbody><tr><td rowspan="2">HRM</td><td>PNT1</td><td>Preservation of health and safety of employees</td><td>(Tortorella et al., 2017)</td></tr><tr><td>PNT2</td><td>Professional development of employees</td><td>(Megha & Srikantha Dath, 2024; Tortorella & Fogliatto, 2014)</td></tr><tr><td rowspan="8">WTB</td><td>PNT3</td><td>Equity (non-discrimination)</td><td>(Charest, 2014; Kumar et al., 2023)</td></tr><tr><td>PNT4</td><td>Employee empowerment and development</td><td>(Liker, 2004; Secchi & Camuffo, 2019)</td></tr><tr><td>PNT5</td><td>Benevolence</td><td>(Cappelletti et al., 2010; Chatelain, 2018; Emiliani, 1998)</td></tr><tr><td>PNT6</td><td>Compassion</td><td>(Emiliani, 1998; Lilius et al., 2008; Seppälä, 2015)</td></tr><tr><td>PNT7</td><td>Calmness</td><td>(Emiliani, 1998; Peart, 2019)</td></tr><tr><td>PNT8</td><td>Honesty</td><td>(Aij & Teunissen, 2017; Emiliani, 1998)</td></tr><tr><td>PNT9</td><td>Humility</td><td>(Baldoni, 2009; Emiliani, 1998; Van Dun & Wilderom, 2016)</td></tr><tr><td>PNT10</td><td>Coherence</td><td>(Emiliani, 1998; Feldt et al., 2004; Huet & Lensen-Gillette, 2012)</td></tr><tr><td>PNT11</td><td>Generosity</td><td>(Baker, 2021; Emiliani, 1998; Glickman, 2011; Sezer et al., 2021)</td></tr><tr><td>PNT12</td><td>Patience</td><td>(Comer & Sekerka, 2014; Emiliani, 1998)</td></tr><tr><td>PNT13</td><td>Humor</td><td>(Emiliani, 1998; Rosenberg et al., 2021; Wang et al., 2024)</td></tr><tr><td>PNT14</td><td>Respect</td><td>(Cardona & Bribiescas, 2015; Emiliani, 1998; Radziwill, 2015)</td></tr><tr><td>PNT15</td><td>Trust</td><td>(Emiliani, 1998; Henderson Brower et al., 2017; Salari et al., 2014; Zenger & Folkman, 2019)</td></tr><tr><td>PNT16</td><td>Discipline</td><td>(Emiliani, 1998; Klein, 2005)</td></tr><tr><td>PNT17</td><td>Cooperation</td><td>(Rousseau et al., 2006; Salari et al., 2014)</td></tr><tr><td>PNT18</td><td>Communication</td><td>(Pentland, 2012; Rousseau et al., 2006; Tortorella & Fogliatto, 2014)</td></tr><tr><td>PNT19</td><td>Psychological support</td><td>(Rousseau et al., 2006)</td></tr><tr><td>PNT20</td><td>Sincerity</td><td>(Emiliani, 1998; Hanson, 2018)</td></tr></tbody></table>

minimal influence on operational performance in certain cases, it remains true that a manufacturing company with a low level of LM maturity does not systematically apply lean methods and tools and does not have an appropriate culture to sustain the results (Santos Bento & Tontini, 2018). Conversely, companies with a high level of LM maturity likely have a well-established LM culture and a broader application of associated methods, tools, and managerial approaches. An assessment of LM maturity levels can provide managers with a clear understanding of the status of LM implementation. This can allow them to guide step-by-step strategies for improvement that are specific to their organization in order to sustain LM practices and improve productivity (Deschamps, 2018; Laroche, 2018).

A system for measuring maturity levels must be quantifiable and aligned with the company's strategic objectives and customer values (Hallam, 2003). This system must also have the following characteristics:

* It allows the monitoring and evaluation of performance

* It can clarify the current status and identify opportunities for improvement.

* It is realistic and up-to-date

From this perspective, the Lean Aerospace Initiative (LAI) at MIT developed a lean maturity assessment model known as the Lean Enterprise Self-Assessment Tool (LESAT) (Nightingale & Mize, 2002). Other models for assessing LM maturity levels reported in the literature are presented in Table 5. Maturity levels range from 1 to 5, and the table shows the meanings ascribed to each level.

# 3. Research methodology

## 3.1. Hypothesis formulation

The aim of RO1 was to generate an overview of the LM practices used within a sample of manufacturing companies in Greater Montreal. A hypothesis was not formulated for RO1 because of its exploratory nature. The hypothesis model illustrated in Figure 2 was used to address RO2.
<figure><img src="image_21.png" /><figcaption>**Table 5.** Models for assessing LM maturity levels.</figcaption></figure>

<table><thead><tr><th rowspan="2">Model/author</th><th colspan="5">Maturity level</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>LAI-MIT<br>(Nightingale &amp; Mize, 2002)</td><td>Limited awareness and sporadic improvement efforts</td><td>Deployment of informal approaches that affect only specific areas</td><td>Development of systematic approaches with good sustainment in most areas</td><td>Ongoing continuous improvement within the enterprise</td><td>Fully deployed in the extended enterprise</td></tr><tr><td>CMM Version 1.1<br>(Paulk et al., 1993)</td><td>Initial</td><td>Repeatable</td><td>Defined</td><td>Managed</td><td>Optimized</td></tr><tr><td>BeLean Deployment<br>(CapGemini, 2008)</td><td>Taking control</td><td>Creating excellence</td><td></td><td></td><td></td></tr><tr><td>Lean Capability Model<br>(Jørgensen et al., 2007)</td><td>Sporadic manufacturing optimization</td><td>Basic understanding and implementation</td><td>Strategic alignment within a lean objective and organizational goals</td><td>Proactive lean culture</td><td>Lean extended to manufacturing enterprise</td></tr><tr><td>The Milestones of Lean Maturity<br>(Hines, 2013)</td><td>Reactive and firefighting mode</td><td>Formally deployed with team learnings</td><td>Deployed with dedicated teams and orientation to visions and values</td><td>Involvement of the majority of the team</td><td>Continuous improvement in daily habits with full involvement of members of the organization</td></tr></tbody></table>

Source: Adapted from Setianto and Haddud (2016).

<figure><img src="image_22.png" /><figcaption>**Figure 2.** Model of hypotheses used to address RO2.</figcaption></figure>

Notes: E1: independent variable related to the size of the company; E21: dependent variable related to the maturity level of LM non-technical practices; E22: dependent variable related to the maturity level of LM technical practices; E3: independent variable related to the administrative region of implementation of the company.

The arrows in Figure 2 indicate the posited relationship between the independent and dependent variables.

The following hypotheses were formulated:

H1: Hypotheses related to size.

* H1a: The size of company E1 affects its maturity level in non-technical practices E21.

* H1b: The size of company E1 affects its maturity level in technical practices E22.
<figure><img src="image_23.png" /></figure>

H2: Hypotheses related to the administrative region of implementation.

* H2a: The administrative region of implementation of company E3 affects its maturity level in non-technical practices E21.

* H2b: The administrative region of implementation of company E3 affects its maturity level in technical practices E22.

**3.2. Methodology**

The methodology of this study was structured into four sequential steps: (1) questionnaire development, (2) questionnaire validation, (3) data collection, and (4) data analysis and hypotheses testing.

**3.2.1. Questionnaire development**

A questionnaire instrument was used to collect the data. This instrument was critical as a data entry point because it determined the type and nature of the data to be analyzed. A survey questionnaire should be complete enough to meet the research objectives, reasonable for respondents in terms of the time taken to complete it, and unambiguous to avoid missing answers or confusing questions. Although questionnaires for LM assessment are available in the literature, a novel questionnaire, organized into three sections, was developed in this study to collect the necessary data to meet the research objectives. Supplementary Appendix A presents the complete questionnaire.

The first section of the questionnaire collected data related to the company's basic profile, such as its size, administrative region for implementation, and activity sector, using the North American Industry Classification System (NAICS). This general information does not reveal the identity of a specific company; thus, the respondents' anonymity was preserved. The information collected allowed an analysis of the relationship between company profile and LM maturity levels.

The second section of the questionnaire collected data on the extensiveness and degree of application of the technical LM practices implemented by the companies. A set of 27 associated technical practices were selected for the operation of the four groups of technical practices identified in subsection 2.2.2: JIT, TPM, TQM, and CI (see Table 3). Each technical practice corresponded to a specific question; therefore, this section included 27 questions starting with JIT, a group of practices aimed primarily at reducing flow times within the production system and response times from suppliers to customers (Sahoo, 2020).

When implementing JIT, an organization produces desirable items at the right time (Womack & Jones, 2003), which helps in reducing inventories, space utilization, and possible waste (Belekoukias et al., 2014). JIT is supported by a computer-aided production management system (Lyonnet, 2010) and consists of specific sets of practices and techniques, such as a pull production system, leveling or smoothing production rate, small lot size production, and setup time reduction (Bortolotti et al., 2015; Cua et al., 2001; Furlan et al., 2011; Shah & Ward, 2003). As PT1–PT8, shown in Table 3, are closed aligned to these objectives, they were selected as technical practices whose implementation in a manufacturing system demonstrates the operationalization of JIT. Control of setup time (PT2) and single-minute exchange of die (PT3) are technical practices that led to setup time reduction. Control of cycle time (PT1), stock management on demand or kanban (PT8), and enterprise resource planning (PT7) allow the control of the production flow. Finally, the leveling of the production rate (PT4), application of pull production (PT5), and production based on objectives (PT6) are practices that contribute to producing desirable items at the right time.

The main objective of TQM is customer satisfaction through product quality, which is imperative for any company wishing to recruit and retain customers. As such, TQM is a group of practices that is of capital importance in LM. PT9–PT16 were selected as practices to operationalize TQM because TQM is an integrated approach that includes quality control, quality assurance, and management of quality. Quality control is a set of actions that aim to verify the conformity of a product (Kelada, 1997). During manufacturing operations, visual control systems (PT9) and checklists (PT15) are technical practices that have been identified in the literature (Bai et al., 2019; Battistella et al., 2023; Garza-Reyes et al., 2018; Tomic et al., 2017) as contributing to reducing non-conformities.

Quality assurance comprises systematic activities, including the design of control plans to obtain sufficient confidence in a manufactured product. A poka-yoke or error prevention system (PT10), the
<figure><img src="image_24.png" /></figure>

implementation of work standards (PT11), statistical process control (PT12), and process capability measurement (PT16) prevent non-conformities and, therefore, confer an assurance that the product meets customers' exigencies. These practices are widely cited in the literature (Baillargeon, 2016; Belhadi et al., 2018; Fullerton et al., 2014; Siegel et al., 2019; Thanki & Thakkar, 2014; Tomic et al., 2017) as LM technical practices associated with TQM. Meanwhile, poka-yoke (PT10) can dramatically decrease the risk of producing defectives (Shingo, 1986). Work standards (PT11) ensure that jobs are broken down into elements and examined to determine the best method for each operation (Yash, 2016). This practice positively impacts all aspects of production, including quality. Statistical process control (PT12) is the application of statistical tools to monitor the variation of a process and detect deviations (Baillargeon, 2016). In complementarity with process capability measurement (PT16), manufacturing processes are well-controlled, helping prevent non-conformities. Management of quality includes planning, organizing, directing, and controlling activities to improve and maintain the quality of products (Kelada, 1997). The development of policies, manuals, and quality control plans (PT13) in tandem with organizing a quality circle or a dedicated multidisciplinary team to address quality issues in a manufacturing company (PT14) are examples of quality management practices cited in the literature (Baillargeon, 2016; Thanki & Thakkar, 2014; Woong Jin & Doolen, 2014).

TPM is defined as an operational approach to maintaining plants and equipment using practices such as autonomous and preventive maintenance, an emphasis on technology, and effective workplace design. This ensures that every machine involved in the production process works smoothly with an uninterrupted output rate (Belekoukias et al., 2014; Konecny & Thun, 2011). The main objective of TPM is to minimize equipment downtime through the total involvement of all employees. Equipment performance must be monitored through the continuous evaluation of overall equipment effectiveness (PT19) and by promoting autonomous maintenance by operators (PT22). The application of 5S (PT17), maintenance procedure (PT18), and the periodic inspections of machines (PT21) supported by a computerized maintenance management system (PT20) are basic strategies to prevent equipment downtime. These practices are regularly cited in the literature in reference to TPM (Balouei Jamkhaneh et al., 2018; Belekoukias et al., 2014; Singha Mahapatra & Shenoy, 2022; Tortorella et al., 2021; Velarde et al., 2011; Wang, 2011); therefore, PT17–PT22 were selected as practices to operationalize TPM.

The practices selected to operationalize CI were PT23–PT27 because implementing these practices in a manufacturing company contributes to improvements in operations. CI begins with identifying the current state of the system through value stream mapping (PT25) and pinpointing areas for improvement through waste identification (PT23). This is what leads to Kaizen projects (PT24) for the implementation of improvement actions. The Gemba Walk (PT27) is a managerial CI practice that aims to observe employees, enquire about their tasks, and identify productivity gains. This connects management to floor shop employees and makes them more sensitive to decisions about improvements. The Ishikawa method (PT26) was selected as a practice to identify the causes of waste or any other cause of reduced operations performance. CI aims to change the production environment for a better future through small but continual improvement (Lyonnet, 2010). PT23–PT27 are presented in the literature as practices that support CI (Chan & Tay, 2018; Hussain & Figueiredo, 2023; Micieta et al., 2021; Veres, 2020).

After selecting the technical practices, questionnaire items were formulated according to the definitions of these practices, their effects on operations and the work environment, and their characteristics and attributes. For example, one question was related to the degree of implementation of the 5S pillars (PT17), a practice associated with TPM. As the visible effects of the 5S in a maintenance workshop environment are cleanliness and easy access to work tools, the item formulated for this practice was: 'The maintenance workshop is regularly maintained, with easy access to all maintenance tools'. Another item related to evaluating the application of process capability measurement (PT16), a TQM practice: 'The capabilities of all production processes are known and regularly analyzed'. Answers to this question revealed the degree of implementation of PT16 in the respondents' companies.

The list of 27 selected technical practices was not exhaustive; however, their selection was based on their direct contribution to the operation of the group of practices to which they belong and their presence in LM literature. The associated questionnaire items were formulated to reveal the degree of their presence or implementation in each manufacturing company and evaluate the maturity level of the company's technical practices.
<figure><img src="image_25.png" /></figure>

The beauty of LM philosophy is that it couples disciplined personal behaviors with disciplined production methods (Emiliani, 1998). The third section of the questionnaire collected data on the degree to which disciplined personal behaviors and general human resource management, in other words, the non-technical practices of LM, were applied. For this section, a set of 20 non-technical practices was selected and structured into two groups, HRM and WTB, as shown in Table 4.

The HRM group comprised a shortlist of practices that were selected because they directly contribute to employee engagement, a requirement to succeed in TQM, TPM, and CI. Some have also been identified in the literature as universal HRM best practices in any situation (Tortorella & Fogliatto, 2014), and it is suggested that adopting them will lead to superior organizational performance. The preservation of employees' health and safety (PNT1) is clearly an HRM best practice, regardless of the context, profile, or activity sector of a company. In addition, PNT1 appears to be the most frequently cited HRM practice in the literature (Tortorella et al., 2017). The professional development of employees (PNT2), non-discrimination (PNT3), and employee empowerment and development (PNT4) are all sources of employee engagement; thus, they were included in the HRM group of practices.

The WTB group of non-technical practices was composed of lean behaviors, a concept analogous to LM. Lean behavior involves the minimization of waste associated with arbitrary or contradictory thoughts and actions that lead to defensive behavior, ineffective relationships, poor cooperation, and negative attitudes in the workplace (Emiliani, 1998). These practices were selected for the WTB group because they have been found to be positively correlated with the operational performance of work teams (Rousseau et al., 2006), which is the main objective of implementing LM in a company. For example, a high level of trust results in increased participation in a social group (Emiliani, 1998), which is a necessity for TQM, TPM, and CI.

Nevertheless, the list of selected non-technical practices was not exhaustive. PNT5 to PNT20 are lean behaviors, identified by Emiliani (1998) and Rousseau et al. (2006), that are required for more efficient work teams and, specifically, for LM operationalization. They were, thus, included in the list of non-technical practices. The associated questionnaire items were formulated based on the definitions and main characteristics of the behaviors or managerial practices drawn from the literature. For example, humility, a non-technical practice highlighted by Emiliani (1998), is characterized by employees admitting their weaknesses and insufficiency in the event they commit an error or display incompetence (Baldoni, 2009). The formulated item on this non-technical practice aimed to identify the degree to which employees exhibit this trait.

### 3.2.2. Questionnaire validation

The questionnaire was reviewed for content validity after the formulation of the initial items. The foremost way to assess the validity of a questionnaire is content validity, which examines the relevance of the content, its representativeness, and technical quality (Elangovan & Sundaravel, 2021). Content validity refers to the extent to which items in a questionnaire represent the entire theoretical construct the questionnaire is designed to assess (Shultz et al., 2020). A thorough review of the related literature and consultation with experts can contribute to ensuring the validity of a questionnaire's content (Elangovan & Sundaravel, 2021). The content validity of the present study's questionnaire was determined in five sequential steps: (1) auto-validation by the authors, (2) comparison with existing questionnaires in the literature, (3) expert consultation, (4) pilot test, and (5) ethics approval.

Auto-validation is a form of internal verification and control undertaken by authors. This initial validation step was completed to ensure that the authors trusted the clarity, ease of reading, and completion time of the initial version of the questionnaire. Based on the practices to be assessed, the authors organized themselves into two sub-groups that separately drafted two preliminary versions of the questionnaire. Thereafter, these two sub-groups meet for discussions to reach a consensus on formulating an initial version of the questionnaire. The main benefit of this first step was the simplification of the structure of items and a reduction of their length, which also reduced the time required to complete the initial version of the questionnaire.

The second and third validation steps were conducted to meet similar objectives but took different approaches. The objectives were to validate the representativeness, significance, and accuracy of each questionnaire items, as well as each technical and non-technical practice used to assess the LM maturity
<figure><img src="image_26.png" /></figure>

levels of the participating manufacturing companies. Questionnaires developed by Shah and Ward (2007), Lyonnet (2010), Tortorella and Fogliatto (2014), Bortolotti et al. (2015), Yash (2016), and Tortorella and Fettermann (2018) were compared with the present study's questionnaire in terms of both the practices assessed and extent to which the item formulations were similar. This validation step allowed us to conclude that the initial version of the questionnaire was not off-topic. For example, the application of pull production (PT5) was assessed in Tortorella and Fettermann (2018) questionnaire with the item, 'We use a pull production system', and in the present study's questionnaire with the item, 'Most, if not all, production facilities operate on a pull-flow basis'.

In the third step, expert consultation, the questionnaire was reviewed by three industrial engineering professionals. Two of them worked as industrial engineering technology professors and all had more than 10 years of experience in LM in various activity sectors. The expert consultation was facilitated by the pre-evaluation comparisons undertaken by the authors. One week after their solicitation, the experts provided feedback. Their comments and advice were used to improve the questionnaire: Some items were reformulated for more accuracy, and some items were added to render the questionnaire more comprehensive.

A pre-final version of the questionnaire was drafted after the expert consultation, and its readability and the time required to complete it were tested. The readability pilot test was conducted with nursing students who did not have a background in LM. The students asked no questions that indicated issues with readability during the test. The pilot test of the time taken to complete the questionnaire was completed by engineering students who were familiar with engineering and manufacturing concepts. The average time to complete the questionnaire was 30 minutes, which was judged as satisfactory.

In the final step, the questionnaire was submitted to the Ethics Committee of Polytechnique Montreal for approval. Ethics approval is mandatory for Polytechnique Montreal researchers who involve humans in their work and contributes, among other things, to ensuring respondent privacy is not violated. The committee evaluated and approved the pre-final version of the questionnaire (approval number: CER-2122-31-D) for distribution to manufacturing companies in Quebec. This became the final version because it was approved without reservation.

No universal standard for questionnaire validation is provided in the literature; however, following the steps described above ensured that this study's questionnaire was acceptable for data collection both in this research and future assessments of LM practices.

### 3.2.3. Data collection

Data were collected by distributing questionnaires to manufacturing companies in Greater Montreal via email and during direct meetings. Respondents were floor operations employees or first-level managers directly involved in daily manufacturing operations (production supervisors, team leaders, production technicians, or production managers). Some companies were contacted through the Polytechnique Montreal placement and internship service, and others were contacted through the LinkedIn social network, the Quebec business directory, professional associations of manufacturing companies, and the authors' professional networks. Some industrial engineering technology graduates from Ahuntsic College distributed questionnaires to companies where they were undertaking their end-of-study internships. Respondents provided their consent before participating in the survey. Thereafter, they were asked to select one option from a 5-point Likert scale, ranging from 'Strongly disagree' to 'Strongly agree', which corresponded to their assessment of the degree of application of each of the 27 technical LM practices and 20 non-technical LM practices in their company.

### 3.2.4. Data processing

Data processing was conducted to obtain the elements that satisfy the research objectives described in Section 1. Numerical values were assigned to the collected responses. The meanings assigned to each possible response to the questionnaire items are presented below.

* Strongly disagree: This practice may not exist in the company and is not applied.

* Disagree: The practice may exist in the company but is rarely applied.
<table><caption>**Table 6.** Numerical values for each possible response option.</caption><thead><tr><th></th><th>Strongly disagree</th><th>Disagree</th><th>Neither agree nor disagree</th><th>Agree</th><th>Strongly agree</th></tr></thead><tbody><tr><th>Numerical value</th><td>0</td><td>0.25</td><td>0.5</td><td>0.75</td><td>1</td></tr></tbody></table>

* Neither agree nor disagree: The practice exists in the company but is rarely applied once or twice.

* Agree: The practice exists in the company, and the extent of its application is good.

* Strongly agree: The practice exists in the company, and the extent of its application is very good or excellent.

The numerical value assigned to each possible response is presented in Table 6.

After assigning numerical values to the responses, each company's scores for technical and non-technical practices were evaluated by applying the factor-weighting method. The data table had the following configuration:

$$ \begin{pmatrix} E_1 \\ \vdots \\ E_m \end{pmatrix} = \begin{pmatrix} R_{11} & \cdots & R_{1n} \\ \vdots & \ddots & \vdots \\ R_{m1} & \cdots & R_{mn} \end{pmatrix}, \qquad (2) $$

$$ R_{mn} = W_n \times V_{mn}, \qquad (3) $$

where the variables were defined as follows:

$E_m$: identification of the company;

$R_{mn}$: score obtained by company m for practice n;

$V_{mn}$: numerical value obtained by company m for practice n;

$W_n$: weighting factor of practice n.

The reading of the data table (2) is as follows: company $E_1$ respectively obtained score $R_{11}$ ... $R_{1n}$ to practice 1 ... n.

The weighting factor applied to each practice depended on its degree of relevance within the practice group. However, judging the relevance of one practice in comparison to another is risky. For example, how is a checklist objectively more important than a poka-yoke for implementing TQM in a company?

In this study, technical practices were considered equally important regardless of the group to which they belonged and, thus, had the same weighting factors. The same approach was taken for non-technical practices. Consequently, the weighting factor for technical practices was 1/27, and that for non-technical practices was 1/20 because there were 27 technical practices and 20 non-technical practices.

The maturity index of each company for technical and non-technical practices was obtained by evaluating the overall average score for all technical and non-technical practices using the following relationships:

$$ IMPT_{E_m} = \frac{1}{N} \sum_{n=1}^{N} R_{mn}, \qquad (4) $$

where $IMPT_{E_m}$ denotes the maturity index of company m for technical practices, $R_{mn}$ represents the score obtained by company m for technical practice n, and N represents the number of technical practices in the questionnaire.

$$ IMPNT_{E_m} = \frac{1}{M} \sum_{n=1}^{M} R_{mn} \qquad (5) $$

where, $IMPNT_{E_m}$ denotes the maturity index of company m for non-technical practices, $R_{mn}$ represents the score obtained by company m for non-technical practice n, and M represents the number of non-technical practices in the questionnaire. The maximum maturity index for technical and non-technical practices were, respectively:

$$ IMPT_{E_m, \max} = \frac{1}{27} \sum_{n=1}^{27} R_{mn} = 0.037 \times 1 + \cdots + 0.037 \times 1 = 0.037 $$

$$ IMPNT_{E_m, \max} = \frac{1}{20} \sum_{n=1}^{20} R_{mn} = 0.05 \times 1 + \cdots + 0.05 \times 1 = 0.05 $$
<figure><img src="image_27.png" /><figcaption>**Table 7.** Scale of maturity levels associated with the range of maturity indices.</figcaption></figure>

<table><caption>**Table 8.** Configuration of a contingency table for Fisher's exact test.</caption><thead><tr><th>Maturity level</th><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><td>Index for technical practices</td><td>0-0.925</td><td>0.925-1.850</td><td>1.850-2.775</td><td>2.775-3.704</td></tr><tr><td>Index for non-technical practices</td><td>0-1.250</td><td>1.250-2.500</td><td>2.500-3.750</td><td>3.750-5.00</td></tr><tr><td>Signification</td><td>Very low</td><td>Low</td><td>High</td><td>Very high</td></tr></tbody></table>

<table><thead><tr><th>Y</th><th>X</th><th>1</th><th>...</th><th>j</th><th>...</th><th>k</th><th>Total</th></tr></thead><tbody><tr><td>1</td><td>n<sub>1,1</sub></td><td>...</td><td>...</td><td>n<sub>1,j</sub></td><td>...</td><td>n<sub>1,k</sub></td><td>n<sub>1,•</sub></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>i</td><td>n<sub>i,1</sub></td><td>...</td><td>...</td><td>n<sub>i,j</sub></td><td>...</td><td>n<sub>i,k</sub></td><td>n<sub>i,•</sub></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>h</td><td>n<sub>h,1</sub></td><td>...</td><td>...</td><td>n<sub>h,j</sub></td><td>...</td><td>n<sub>h,k</sub></td><td>n<sub>h,•</sub></td></tr><tr><td>Total</td><td>n<sub>•,1</sub></td><td>...</td><td>...</td><td>n<sub>•,j</sub></td><td>...</td><td>n<sub>•,•</sub></td><td>n<sub>•,•</sub> = n</td></tr></tbody></table>

Source: Cited from Bertrand and Maumy-Bertand (2011).

The evaluation of LM technical and non-technical maturity indices using this method corresponds to the factor-weighting method, which is also known as the 'weighted sum method'. This approach has been applied in comparable studies by Shah and Ward (2003) and Lyonnet (2010). It has the advantage of being mathematically simple for the results it offers (Shah, 2022).

Following the evaluation of the maturity indices, the four maturity levels presented in Table 7 were defined to position the level of each company. In the first two rows of Table 7, each range of evaluated maturity indices of technical and non-technical practices is associated with the maturity level indicated in the column headings. The last row is the nominal signification of each maturity level.

### 3.2.5. Hypothesis testing

Hypothesis testing was conducted to explore the relationship between the size of a company or its administrative region of implementation and the maturity levels of its technical and non-technical practices. Fisher's exact test was applied to contingency tables for testing. This test determines whether an association exists between two categorical variables in a contingency table. The test is exact because it checks for all possible permutations of the data (Giannini, 2005), and the obtained p-value can be calculated exactly rather than relying on an approximation, as with many other statistical tests. For large and well-balanced data sets, exact and asymptotic p-values are very similar. However, for small, sparse, unbalanced, and heavily tied data, the exact and asymptotic p-values can be quite different and may lead to opposite conclusions concerning the hypothesis of interest (Cyrus & Nitin, 1989). Fisher's exact test is used in cases where there are cells with an expected frequency of less than 5 and/or with small sample sizes. The test has no sample size restriction (Altman, 1996). Categorical variables were used in this study, and the sample size of valuable collected data was small, resulting in numerous cells in the contingency tables with a frequency less than 5. Thus, Fisher's exact test was selected for hypothesis testing rather than other tests.

Fisher's exact test is calculated by determining the probability of getting the observed frequency distribution by establishing and comparing it to all other possible distributions where the column and rows total of the contingency table remain the same as that of the observed distribution (Fisher et al., 2011). An explanation of the Fisher's exact test method, provided by Rosner (2011) and Bertrand and Maumy-Bertand (2011), can be summarized as follows:

Let X and Y, two discrete qualitative or quantitative variables and the contingency table of $n_{i,j}$ values be used for each of the h modalities of X and the k modalities of Y in the sample. Table 8 is the configuration of the contingency table.

In Table 8, the row and column margins denoted by $(n_{1,•},...,n_{h,•})$ and $(n_{•,1},...,n_{•,k})$ respectively indicate the distribution of numbers between h modalities of X and k modalities of Y. The values of $n_{1,1}, n_{1,2}, ..., n_{1,k}, ..., n_{h,k-1}, n_{h,k}$ serve to evaluate the relationship between X and Y. The hypotheses are formulated as follows:

$H_0: X$ and $Y$ are independent $H_1: X$ and $Y$ are dependent
If the null hypothesis $H_0$ is true, the probability of finding a table $\gamma$ belonging to $\Gamma$, the set of tables having the same row and column margins is given by Equation 6.

$$P(\gamma) = \frac{\prod_{i=1}^{h} n_{i, \bullet}! \prod_{j=1}^{k} n_{\bullet, j}!}{(\prod_{i=1}^{h} \prod_{j=1}^{k} n_{i, j}!) (\sum_{i=1}^{h} \sum_{j=1}^{k} n_{i, j})!} \quad (6)$$

The two-sided $\alpha$ significance level test leads to the rejection of the null hypothesis $H_0$ when $P_2$, the probability of finding a distribution as abnormal or more abnormal in a table $\gamma$ of $\Gamma$ than the observed $\gamma(\text{obs})$, calculated by Equation 7, is less or equal to $\alpha$.

$$P_2 = \sum_{D(\gamma) \ge D(\gamma(\text{obs}))} P(\gamma) = P(D(\gamma) \ge D(\gamma(\text{obs}))) \quad (7)$$

Where the function $\gamma \to D(\gamma)$ is defined as follows:

$$D(\gamma) = -2 \ln \left( \frac{(2\pi)^{\frac{(h-1)(k-1)}{2}}}{N^{\frac{(h-1)}{2}}} \prod_{i=1}^{h} n_{i, \bullet}^{\frac{h-1}{2}} \prod_{j=1}^{h} n_{\bullet, j}^{\frac{k-1}{2}} P(\gamma) \right) \quad (8)$$

For this study, the contingency tables presented in Appendix B were used to apply Fisher's exact test. These tables were derived from ascending hierarchical classification (AHC), a method for clustering similar data points or objects such that objects in the same cluster are more similar than to those in other groups. Among the existing hierarchical classification methods, Ward's aggregation method combined with the Euclidean distance to determine the degree of proximity has been widely used in previous classification studies (Lyonnet, 2010). This method was applied to group together companies in the present study's sample according to the maturity levels of their technical and non-technical practices. Companies in the same group or that had similar maturity levels were distinct by their profile used to fill contingency tables.

Given the size of the study's data set, commercial software such as SPSS, XLSTAT, or FactoMineR was appropriate to ensure high processing quality. The XLSTAT software developed by Lumivero (2023) was used to perform normality tests, AHC, and Fisher's exact tests. The results are presented in section 4.

# 4. Results and discussion

## 4.1. General characteristics of the population and the Greater Montreal manufacturing industry

Greater Montreal includes the administrative regions of Montreal, Montérégie, Laval, Lanaudière, and Laurentides and comprises 82 municipalities. The metropolis represents 47.79% of Quebec's population and 55% of its gross domestic product (GDP) (Institut de la Statistique du Québec, 2023; Montréal International, 2023). Relative to the entirety of Quebec from 2018–2019, the manufacturing industry in Greater Montreal had the following characteristics (Ministère de l'Économie, 2021):

* 59.38% of 13,664 manufacturing companies;

* 57.70% of 8,977 small and medium-sized¹ manufacturing companies;

* 61.48% of 497,700 manufacturing jobs;

* 62.6% of the manufacturing GDP;

* 49.22% of investments (construction, materials, and tools) in the manufacturing sector.

Given these characteristics, Greater Montreal represents the core of Quebec's manufacturing economy. Although the survey was initially planned for the entire province of Quebec, limiting it to this metropolitan region was reasonable because of the importance of the manufacturing industry there.

## 4.2. Sample characteristics

The survey was sent to over 600 companies, of which 35 voluntarily agreed to participate, representing a participation rate of 5.83%. This low participation rate was unsurprising. A survey of lean
<figure><img src="image_28.png" /></figure>

manufacturing implementation was conducted by Filho et al. (2016) in seven states of Brazil, with only 52 valid responses collected from among 1,912 targeted companies. In Quebec, Beaulieu-Paré (2011) administered a similar survey across the entire province, in which only 28 companies participated. The sample characteristics recorded were the size of the company, activity sector, and administrative region to which it belonged.

### 4.2.1. Size of company

A company's size is determined by the number of employees: small (<100), medium (>100 and <500), and large (>500), following Statistics Canada's criteria. The sample in this study comprised mainly small and medium-sized enterprises (SMEs), which accounted for 86% of the companies. This proportion of SMEs in the sample was close to that of businesses in Greater Montreal, which accounted for 57.7% of the SMEs in the province of Quebec.

### 4.2.2. Activity sector

The NAICS was used to characterize the companies according to the activity sector criteria. The sample comprised companies that manufacture finished or semi-finished products from local and imported raw materials or components. Nine activity sectors were represented in the sample. The most represented sectors were food (311), metal products (332), and transport equipment manufacturing (336). However, 46% of the companies did not report their NAICS codes.

### 4.2.3. Administrative region

The administrative region corresponds to the geographical location in which a company performs its industrial production activities. In total, 40% of the participating companies were located in Montreal, the heart of Greater Montreal.

## 4.3. Results for maturity levels

This section presents and discusses the results that contribute to addressing RO1, which sought to assess the maturity levels of the sample's technical and non-technical LM practices.

Figure 3 indicates the maturity index of the technical (blue) and non-technical practices (striped orange) of each company. The company ID is indicated on the horizontal axis, and the maturity index appears on the vertical axis. Table 9 presents the distribution of the maturity indices of technical and non-technical practices through their quartiles, standard deviations (Std), and means.

### 4.3.1. Maturity level of technical practices

At first glance, Figure 3 shows that the global mean index for technical practices was 51.554%, merely 1.554% above the low maturity threshold, according to Table 7. Table 9 indicates that 50% of the sample had a maturity index below 53.807%. Among these companies, 16 companies (45.714% of the sample) were below 50% and, thus, had a low maturity level. Figure 3 also shows that only one company had a maturity index above 75% and that 18 companies (51.428% of the sample) had a maturity index between 50% and 75%, that is, a high maturity level. The results of the maturity level of technical practices were mitigated. The technical practices of slightly over 50% of the sample had a high maturity level; however, up to 45.714% had a low maturity level at best, indicating that they adopted few LM technical practices in their operational management approaches.

The presence of 45.714% of companies with low maturity levels of technical practices in the sample may be due to a lack of awareness of LM practices among management and frontline employees. There are seven universities in Greater Montreal, only three of which offer regular training courses dedicated to LM at the graduate level. In undergraduate programs, in which most students are trained, limited attention is dedicated to LM. Consequently, some graduates are employed as operation leaders in manufacturing companies without being sensitized to LM, despite its importance in improving productivity. Most LM practices are not technically complex and do not necessarily require significant financial investments. Despite the technical adeptness demonstrated by the nature of the products designed by the manufacturing companies in the sample (aircraft, robots, etc.), their employees may simply have an
<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /><figcaption>Figure 3. Maturity index of the LM technical and non-technical practices of each company in the sample.</figcaption></figure>

<table><caption>Table 9. Descriptive statistics of the maturity indices for LM technical and non-technical practices.</caption><thead><tr><th></th><th>Mean</th><th>Std</th><th>Min</th><th>25%</th><th>50%</th><th>75%</th><th>Max</th></tr></thead><tbody><tr><td>Maturity index for technical practices</td><td>51.554</td><td>14.026</td><td>23.182</td><td>43.553</td><td>53.807</td><td>61.197</td><td>75.995</td></tr><tr><td>Maturity index for non-technical practices</td><td>74.000</td><td>10.816</td><td>48.75</td><td>69.375</td><td>76.25</td><td>81.875</td><td>88.750</td></tr></tbody></table>

inadequate awareness of LM and, thus, rarely adopt it. This first result partially addresses RO1 by explicitly determining the maturity level of the sample's technical practices. This mitigated result implies that the sample of companies in Greater Montreal needs to develop action plans to improve LM practice.

### 4.3.2. Maturity level of non-technical practices

Figure 3 and Table 9 show the maturity indices of non-technical practices for the 35 manufacturing companies in the sample. Using this index, the maturity level was deduced from Table 7. In contrast to the results for technical practices, the following observations were notable. The global mean index of the sample was 74%, which was close to the maximum index. Referring to the maturity-level scale in Table 7, the non-technical practices of 20 companies (57.142% of the sample) had a very high maturity level, those of 13 companies (37.142% of the sample) had a high maturity level, and those of only two companies had a low maturity level.

The results indicated that the non-technical practices of 94.285% of the companies had at least a high maturity level. This suggests that the sample companies adopted LM non-technical practices in their operational management approaches better than LM technical practices. This second result contributes to meeting RO1, specifically identifying the maturity levels of the non-technical practices employed by the sample of manufacturing companies in Greater Montreal. The high maturity level in this group of LM practices denotes an appropriate working climate to deploy LM technical practices and implies that the management of these companies should consolidate this capital.

### 4.4. Impact of company size or location on maturity levels

This section presents and discusses the results related to RO2, which aimed to explore the possible relationship between the size of a company or its administrative region of implementation and the maturity levels of its technical and non-technical practices.
<figure><img src="image_31.png" /><figcaption>**Table 10.** Results of normality tests of maturity indices for LM technical and non-technical practices.</figcaption></figure>

<table><thead><tr><th></th><th>Shapiro-Wilk test</th><th>Anderson-Darling test</th><th>Lilliefors test</th><th>Jarque-Bera test</th></tr></thead><tbody><tr><td>p-value for technical practices</td><td>0.384</td><td>0.365</td><td>0.174</td><td>0.656</td></tr><tr><td>p-value for non-technical practices</td><td>0.006</td><td>0.009</td><td>0.178</td><td>0.062</td></tr></tbody></table>

<figure><img src="image_32.png" /><figcaption>**Figure 4.** Classes C1, C2, and C3 of companies with similar maturity levels.</figcaption></figure>

<table><caption>**Table 11.** Number of companies by class relative to maturity level for LM technical practices.</caption><thead><tr><th>Class</th><th>1 - very low</th><th>2 - low</th><th>3 - high</th><th>4 - very high</th></tr></thead><tbody><tr><td>C1</td><td>0</td><td>3</td><td>3</td><td>0</td></tr><tr><td>C2</td><td>2</td><td>12</td><td>0</td><td>0</td></tr><tr><td>C3</td><td>0</td><td>0</td><td>15</td><td>0</td></tr></tbody></table>

### 4.4.1. Normality tests

Given the sample size of 35 companies, normality tests were conducted a priori on the maturity indices of technical and non-technical practices following Anderson and Darling (1954), Jarque and Bera (1980), Lilliefors (1967), and Shapiro and Wilk (1965). The results are presented in Table 10.

With a significance level of 5%, in the results of the Lilliefors and Jarque-Bera tests, the indices of both technical and non-technical practices followed a normal distribution, whereas in the results of the Shapiro-Wilk and Anderson-Darling tests, only the indices of technical practices followed a normal distribution. Although the Shapiro-Wilk normality test is widely recognized in the literature, considering the results of the normality tests for non-technical practices, the data from the indices of these practices roughly follow a normal distribution.

The results of the four tests were 50% positive and 50% negative; thus, the analysis and discussion regarding the impact of company size or location on maturity levels assume that the indices of the two groups of practices follow a normal distribution.

### 4.4.2. Classification of companies

To address RO2, it was relevant to identify the company profiles relative to their maturity levels. To do this, the companies were grouped according to similar maturity levels in technical and non-technical practices using AHC. Using Lumivero's (2023) XLSTAT software, a dendrogram illustrating classes of companies with the same profile was produced, as shown in Figure 4. The company IDs are shown on the horizontal axis and the grouping criteria on the vertical axis.

Three classes (C1, C2, and C3) emerged from the classification. Companies in the same class differed with regard to size and the administrative region of implementation. Tables 11 and 12 present the number of companies in each class with respect to the maturity levels for technical and non-technical practices, respectively.
<figure><img src="image_33.png" /><figcaption>**Table 12.** Number of companies by class relative to maturity level for LM non-technical practices.</figcaption></figure>

<table><caption>**Table 13.** Hypothesis testing results of the impact of company size on the maturity level of LM technical and non-technical practices.</caption><thead><tr><th>Class</th><th>1 - very low</th><th>2 - low</th><th>3 - high</th><th>4 - very high</th></tr></thead><tbody><tr><td>C1</td><td>0</td><td>3</td><td>3</td><td>0</td></tr><tr><td>C2</td><td>0</td><td>0</td><td>4</td><td>10</td></tr><tr><td>C3</td><td>0</td><td>0</td><td>6</td><td>9</td></tr></tbody></table>

<table><thead><tr><th></th><th>p-value for non-technical practices</th><th>p-value for technical practices</th></tr></thead><tbody><tr><td>H1: Hypothesis related to size</td><td>0.005</td><td>0.08</td></tr><tr><td>Significance level</td><td>0.05</td><td>0.05</td></tr></tbody></table>

In conjunction with the hypothesis tests, this classification guided the discussion in Sections 4.4.3 and 4.4.4 and highlighted specific characteristics of the companies that affected the maturity levels of their LM practices.

### 4.4.3. Impact of company size on maturity levels

The results of Fisher's exact tests for H1a and H1b, which predicted an impact of company size on the maturity of technical and non-technical practices, are presented in Table 13. Appendix B presents the contingency tables used for testing. The results contributed to addressing RO2.

The results indicated that H1a was accepted, suggesting that the maturity level of the non-technical practices of a company depends on its size. According to the classification in Table 12, the non-technical practices of many companies in class C2 (10 of 14 companies) had a very high level of maturity.

Class C2 was mainly composed of small companies (12 of the 14 companies). The majority of the companies in class C3 (9 of 15 companies) in Table 12 also had a very high level of maturity in non-technical practices. C3 comprised small (5 of 15 companies) and medium-sized (7 of 15 companies) companies. The results, thus, suggest that non-technical practices are best applied by SMEs.

Non-technical practices improve the quality of social interactions. In this study, the non-technical practices identified were related to morality (e.g. honesty, humility, respect, and discipline). In SMEs, human behavior is easily visible to all; therefore, employees do not risk displaying poor behavior. Additionally, SMEs sometimes have better sociability. In the literature, cognitivism postulates the existence of different mental states in individuals. These mental states are the internal dispositions (emotions or state of concentration) of a person at a given moment and play a determining role in their behavior (Steiner, 2005). A higher level of social interaction in SMEs may shape employees' emotions and mental states more effectively, leading to the wider adoption of non-technical LM practices.

The results shown in Table 13 indicate that H1b was rejected, suggesting that the maturity level of a company's technical practices does not depend on its size. Rajagopalan and Solaimani (2019) reported that small companies have the most difficulty in implementing LM. Consequently, their technical practices have low maturity levels. This may seem to contradict the results of the present study's hypothesis testing. However, the difference between the p-value and the threshold for rejecting the null hypothesis was 3%. In addition, among the 14 companies in class C2, the technical practices of which had, at best, a low level of maturity, up to 12 were small companies. Companies in class C3 with a high level of maturity in technical practices were mostly medium-sized (7 of 15 companies) or large (3 of 15 companies). The results suggest that the technical practices of medium-to-large companies have better levels of maturity, whereas those of small companies have low levels of maturity.

### 4.4.4. Impact of administrative region of implementation on maturity levels

The results of Fisher's exact tests for H2a and H2b, which predicted an impact of administrative region on the maturity of a company's technical and non-technical practices, are listed in Table 14. Appendix B presents the contingency tables used for testing. The results contributed to addressing RO2.

The results indicated that H2a was rejected, suggesting that the maturity level of non-technical practices does not depend on the administrative region of implementation. All administrative regions in the sample were located in Greater Montreal. Consequently, employees living in or near these areas were exposed to the same local culture. Companies in classes C2 and C3 generally had a high level of
<figure><img src="image_34.png" /><figcaption>**Table 14.** Hypothesis testing results of the impact of administrative regions on maturity level for LM technical and non-technical practices.</figcaption></figure>

<table><caption>**Table 15.** Results of Kaiser-Meyer-Olkin and Bartlett's tests for data of technical practices.</caption><thead><tr><th></th><th>p-value for non-technical practices</th><th>p-value for technical practices</th></tr></thead><tbody><tr><td>H2: Hypothesis related to administrative region</td><td>0.851</td><td>0.018</td></tr><tr><td>Significance level</td><td>0.05</td><td>0.05</td></tr></tbody></table>

<table><tbody><tr><td>Kaiser-Meyer-Olkin measure</td><td></td><td>0.4</td></tr><tr><td rowspan="3">Bartlett's test</td><td>Approximate chi-square</td><td>564.788</td></tr><tr><td>Degree of freedom</td><td>351</td></tr><tr><td>Significance</td><td>0.000</td></tr></tbody></table>

maturity in non-technical practices and were mainly located in Laval, Montérégie, and Montreal, the three main administrative regions of Greater Montreal. As such, the results imply that companies from different administrative regions are equally suited to apply non-technical practices.

However, the results also indicated that H2b was accepted, suggesting that the maturity level of technical practices depends on the administrative region. Most companies in class C3 (10 of 15) were located in Montreal, and those in class C2 were mainly located on the northern and southern shores of Montreal (five companies in Montérégie and six in Laval).

Considering that all companies in class C3 demonstrated a high level of maturity in technical practices, while those in class C2 had, at most, a low level of maturity, the administrative region of Montreal appears to be well-suited for implementing technical practices. The ecosystem in Montreal is more favorable for business operations than the outskirts, and it has a larger number of service companies. This is likely why companies in the administrative region of Montreal had higher maturity levels of technical practices.

### 4.5. Assessing the validity and reliability of the constructs

Previous hypothesis tests were conducted to examine the dependency relationship between the maturity levels of a company and its profile. This section aims to assess the validity and reliability of the constructs underlying the variables (practices) that were used to assess maturity levels.

Average variance extracted (AVE) assesses convergent validity. It is a measure of the amount of variable that is taken by a construct in relation to the amount of variance due to measurement error (Fornell & Larcker, 1981). Convergent validity indicates the level of correlation of multiple indicators of the same construct that agree (Noora, 2021). Reliability refers to the overall consistency of a test or a survey questionnaire. Composite reliability (CR) is a measure of internal consistency. According to Fornell and Larcker (1981), CR is an indicator of the shared variance among the observed variables used as an indicator of a construct. For a construct X, AVE and CR are determined by the following equations:

$$AVE(X) = \frac{1}{p} \left( \sum_{i=1}^{p} \lambda_{i}^{2} \right) \qquad (9)$$

$$CR(X) = \frac{\left(\sum_{i=1}^{p} \lambda_{i}\right)^{2}}{\left(\sum_{i=1}^{p} \lambda_{i}\right)^{2} - \sum_{i=1}^{p}\left(1-\lambda_{i}^{2}\right)} \qquad (10)$$

where:

* P : number of indicators of construct

* λᵢ : factor loading of the iᵗʰ indicator

#### 4.5.1. Quality of the sample data

Before evaluating AVE and CR, Kaiser-Meyer-Olkin (KMO) was used to measure the suitability of the sample data. Thereafter, Bartlett's test of sphericity was calculated to detect appropriateness. The results of the KMO measurement and Bartlett's test are shown in Tables 15 and 16, respectively.

The results for the sample data of technical practices indicate that KMO equals 0.4. In the literature, the threshold of good KMO is 0.6 or above, but some scholars set this cutoff value at 0.5 (Dziuban & Shirkey, 1974). Rigorously, this sample does not pass the test of suitability. The value of KMO is 0.1 point close to
<figure><img src="image_35.png" /><figcaption>**Table 16.** Results of Kaiser-Meyer-Olkin and Bartlett's tests for data of non-technical practices.</figcaption></figure>

<table><caption>**Table 17.** Composite reliability and average variance extracted for sample data of technical practices.</caption><tr><td>Kaiser-Meyer-Olkin measure</td><td></td><td>0.5</td></tr><tr><td rowspan="3">Bartlett's test</td><td>Approximate chi-square</td><td>363.736</td></tr><tr><td>Degree of freedom</td><td>190</td></tr><tr><td>Significance</td><td>0.000</td></tr></table>

<table><caption>**Table 18.** Composite reliability and average variance extracted for sample data of non-technical practices.</caption><thead><tr><td>Constructs</td><td>CR</td><td>AVE</td></tr></thead><tbody><tr><td>Construct #1</td><td>0.874</td><td>0.376</td></tr><tr><td>Construct #2</td><td>0.693</td><td>0.275</td></tr><tr><td>Construct #3</td><td>0.611</td><td>0.241</td></tr><tr><td>Construct #4</td><td>0.667</td><td>0.420</td></tr></tbody></table>

<table><thead><tr><td>Constructs</td><td>CR</td><td>AVE</td></tr></thead><tbody><tr><td>Construct #1</td><td>0.852</td><td>0.493</td></tr><tr><td>Construct #2</td><td>0.780</td><td>0.422</td></tr><tr><td>Construct #3</td><td>0.633</td><td>0.295</td></tr></tbody></table>

the limit 0.5. On the other hand, the Bartlett's test shows excellent results. The test value is 564.788, and the significance is 3.270e-12; therefore, the correlation matrix diverges significantly from the identity matrix. Even if the KMO does not meet the requirement value of 0.5, it is not close to 0 either. Otherwise, the sample successfully passed Bartlett's test. Consequently, it is more prudent to proceed with the evaluation of AVE and CR using the sample data of technical practices rather than discarding it.

The KMO of the sample data of non-technical practices is equal to the limit of 0.5; it fairly passes the test of suitability. Additionally, Bartlett's test also showed excellent results, with a test value of 363.736 and a significance of 5.096e-13. Therefore, the sample data of non-technical practices is adequate for AVE and CR evaluation.

### 4.5.2. Composite reliability and average variance extracted results

The evaluation of CR and AVE on the sample data used to assess the maturity levels of technical and non-technical practices are shown in Tables 17 and 18, respectively.

These results are based on the summaries of constructs available in Appendix C for technical practices and Appendix D for non-technical practices. Because a factor loading equal to or greater than 0.4 generally indicates that a variable can be attributed to the construct (Cutillo, 2019), this cutoff was used to attribute practices (variables) in different constructs. The technique that was used to highlight important variables is varimax rotation.

CR equal to 0.6 is acceptable for good reliability (Hair et al., 2010). If AVE is less than 0.5 but CR is higher than 0.6, then convergent validity is still adequate (Fornell & Larcker, 1981). The exploratory nature of this research and the results of CR and AVE for technical and non-technical sample data lead to the conclusion of the validity and reliability of the constructs underlying the variables (practices) that were used to assess maturity levels.

## 4.6. Discussion

This study investigated the status of LM implementation in Greater Montreal. While the maturity level of technical practices is mitigated, the results suggested that the sample of companies in Greater Montreal adopted more non-technical LM practices than technical LM practices in their operational management approaches.

In Brazil, Filho et al. (2016) also studied the status of LM implementation in SMEs. Their findings from a sample of 52 companies indicated that only some LM practices were being implemented in these companies because of a lack of understanding of LM tools and certain characteristics of the industrial environment (Filho et al., 2016). This implies that Brazilian SMEs are still struggling to implement LM technical practices, similar to those in the sample in Greater Montreal. Filho et al. (2016) used an adapted version of Shah and Ward (2007) questionnaire to collect data; thus, their findings only apply to technical practices. This differs from the results of the present study, which also include non-technical practices. Beyond Brazil,
<figure><img src="image_36.png" /></figure>

the maturity level of LM practices within Moroccan SMEs was assessed by Arabi et al. (2021). Using a cus-
tom design questionnaire on technical practices, they concluded that lean production has not yet been
adopted or applied by all Moroccan SMEs owing to a lack of skilled labor in LM, limited financial resources,
and poor management (Arabi et al., 2021). In total, 24% of Arabi et al. (2021) sample of 78 companies had
not yet applied lean, suggesting that Moroccan SMEs are characterized by a lower maturity of LM practices,
similar to those in the sample in Greater Montreal. The manufacturing companies of Brazil, Morocco, and
Greater Montreal have similarities in terms of the maturity level of their technical LM practices. All are
struggling to implement LM technical practices, with this similarity resulting from the common characteris-
tics of a lack of awareness or deep understanding of LM principles and tools.

In France, Chanegrih and Creusier (2015) conducted a study to evaluate the implementation of LM prac-
tices in manufacturing companies. Their sample of 144 companies indicated that internal lean was well
implemented in French companies, with a preponderance of classic and founding lean tools (Chanegrih &
Creusier, 2015). Internal lean refers to technical practices dedicated to daily operations management. In
comparison to the maturity level of the technical practices of the Greater Montreal companies in the pre-
sent study, Chanegrih and Creusier (2015) results suggest that those of French companies have a better
maturity level. The relative success of implementing LM practices in French companies results from their
integrated and coherent approach to implementation (Chanegrih & Creusier, 2015).

There is also a visible difference between the status of LM implementation in India and Greater
Montreal. Rajagopalan and Solaimani (2019) studied the status of LM implementation in India. They ana-
lyzed approximately 200 companies yearly between 2013 and 2016 and found an improvement in the
average 'current state' scores, indicating that LM leaders in the Indian industry improved their LM adop-
tion during these years (Rajagopalan & Solaimani, 2019). This result implies that Indian companies do
not lack awareness of LM technical practices, as seemed to be the case among the sample of companies
in Greater Montreal. It is impossible to measure the progress made by Greater Montreal manufacturing
companies in implementing LM technical practices as no previous studies have clearly shown the matur-
ity levels of LM practices in any region of Quebec. However, the positive progress of overall average
scores of LM implementation in India demonstrates the dynamism of LM practices implemented by
Indian companies. Greater Montreal companies need to prove the same dynamism.

Like many other assessments of LM in the literature, none of the above-cited studies have undertaken
an evaluation of the maturity level of non-technical practices. Consequently, it is impossible to differentiate
the present study's findings on non-technical practices from those of previous research. A rare evaluation
of the implementation of lean behaviors was performed in Malaysia by Puvanasvaran (2012). The results
from a sample of 53 companies showed an increment of lean behaviors index value from 0.67 to 0.773 out
of 1 in the category of respect for others (Puvanasvaran, 2012). The present study also indicates that lean
behaviors can enhance productivity. The lean behaviors evaluated in Malaysia by Puvanasvaran (2012) are
close to the practices related to WTB. Consequently, the high maturity level of non-technical practices in
the sample of manufacturing industry companies in Greater Montreal may not be an isolated case. More
studies are required to draw a comparative conclusion on this dimension of LM practices. However, the
results regarding the level of non-technical maturity denote that workplaces in the manufacturing industry
in Greater Montreal would be conducive to implementing LM technical practices.

**4.7. Limitations and recommendations for future research**

Although this study's results provided enough information to achieve its initial objectives, some limitations
were identified. The study's main limitation is its small sample size of 35 companies, which is not sufficient
to generalize the results to the entire population of manufacturing companies in Greater Montreal. Based
on the formula recommended by Taherdoost (2017), the representative sample size would have to be at
least 68 companies to generalize the results. Though the proportions of SMEs in the sample and in the
overall population are similar, applying oversampling techniques to increase the sample size would have
resulted in a less authentic conclusion. Thus, our results are statistically valid only for the sample.

The first recommendation for future research is to collect more data from manufacturing companies
in Greater Montreal to expand the sample size. Applying a similar methodology to the new data could
consolidate the findings of this study.
<figure><img src="image_37.png" /></figure>

LM and Industry 4.0 technologies have the same goal, that is, to improve, if not optimize, the oper-
ational performances of manufacturing companies. Therefore, this study should have been extended to an
evaluation of the implementation of Industry 4.0 technologies and their complementary effects on oper-
ational performance. In this way, companies taking the time to respond would have a more complete
evaluation tool for their manufacturing operations. Adding two more sections to the survey questionnaire
is the second recommendation for future studies. One section would cover items related to the application
of Industry 4.0 technologies, and the other would cover items related to operational performance.

The third recommendation is to use the modified questionnaire to periodically assess manufacturing
operations in the whole province of Quebec. This will promote LM across the province and beyond for
better productivity.

This study focused on evaluating the maturity levels without the unique factors that affect the adop-
tion of LM practices specific to manufacturing companies in Greater Montreal. In complementing this
study, the final recommendation is to conduct a study to explore these factors.

**5. Conclusions**

The main objectives of this study were to evaluate the maturity levels of the technical and non-technical
LM practices implemented in manufacturing companies in Greater Montreal and to investigate the pos-
sible relationship between company profiles and LM maturity levels. Four key findings emerged:

1. The technical LM practices of the sample of manufacturing companies had low maturity levels. This may be a reason for their low productivity.

2. The non-technical LM practices of the sample of manufacturing companies had high maturity levels. As these practices are essential for operationalizing LM, this finding indicates that the companies had a favorable environment for implementing actions that increase productivity.

3. Small companies may have the most difficulty applying LM technical practices. However, companies of this size adopt non-technical practices more easily.

4. Companies located in the administrative region of Montreal appear to be the best at applying technical practices.

The study's results provide valuable guidance for the development of solutions to consolidate existing
non-technical practices and raise awareness among frontline employees regarding technical practices.

This study is novel and significant because its survey questionnaire explicitly covered the non-technical facets of LM, with an emphasis on lean behaviors, in contrast to the questionnaires used in previous studies, which have focused only on the technical aspects of LM. Additionally, the study examined the current implementation of LM practices in Greater Montreal and highlighted the importance of this aspect of industrial engineering, which appears to be increasingly overshadowed by the arrival of Industry 4.0.

**Note**

1. Small and medium enterprises (SMEs) are those with 5 to 249 employees.

**Acknowledgements**

The authors thank the management of the internship and placement service at Polytechnique Montreal for distribut-
ing the survey to over 450 manufacturing company partners. The authors also thank the students of the industrial
engineering technology program at Ahuntsic College for their assistance in distributing the survey to the managers
of the companies where they were completing their internships.

**Disclosure statement**

No potential conflict of interest was reported by the author(s).
<figure><img src="image_38.png" /></figure>

## Author contributions

DDT: conception and design, analysis and interpretation of the data, drafting of the paper, revising the paper critically for intellectual content, writing-review, and editing; FL: conception and design, revising the paper critically for intellectual content, writing-review, and editing; SJB: supervision, revising the paper critically for intellectual content, final approval of the paper to be published; AZ: conception and design, revising the paper critically for intellectual content, writing-review, and editing, final approval of the paper to be published; MT: supervision, final approval of the paper to be published; TW: supervision, final approval of the paper to be published. All authors have agreed to be accountable for all aspects of the work.

## About the authors

<figure><img src="image_39.png" /></figure>

**Didérot Déraillet Tadja** is a Professional Engineer registered in the province of Quebec, Canada and graduated in mechanical engineering from École de technologie supérieure in 2011. After various engineering experiences in manufacturing and consulting, he completed a Master of industrial engineering in 2016 at Polytechnique Montréal. He teaches industrial engineering technology at Collège Ahuntsic since 2018. His research focuses on connecting lean manufacturing, industrial internet of things, artificial intelligence and project management for safety, productivity and human well-being in manufacturing plants. Didérot is also a dual PhD candidate in industrial engineering.

<figure><img src="image_40.png" /></figure>

**Fatma Lehyani** is Assistant Professor at the Higher Institute of Transport and Logistics, University of Sousse, Tunisia. Also, she is member of OLID laboratory, University of Sfax, Tunisia. She obtained her PhD degree in 2019 in Industrial Engineering from Grenoble Alpes University, France and in Management Sciences from the University Sfax, Tunisia. Her research activities deal with supply chain management, total quality management and knowledge management. She has been the advisor for several Master studies and has published over 20 referenced papers.

<figure><img src="image_41.png" /></figure>

**Professor Samuel-Jean Bassetto** works in the field of engineering and mathematics. Founder of the LABAC laboratory at Polytechnique Montréal, he is interested in developing technologies that are human centered. Professor Bassetto received his Bachelor of Engineering from ParisTech Arts et Métiers in 2001, his Master's degree in computer science and artificial intelligence from University of Lorraine also in 2001 and completed a Doctoral thesis in industrial engineering in 2005 from ParisTech. Professor Bassetto is recipient of the NSERC Synergy Award and has been founded by CNRS, ANR, NSERC, FRQ and MITACS in collaboration with more than 10 institutions. He has supervised more than 150 highly qualified personnel and published more than 60 leading scientific articles and conference.

<figure><img src="image_42.png" /></figure>

**Alaeddine Zouari** is Associate Professor of Industrial Engineering at Higher Institute of Industrial Management of Sfax and member of OLID laboratory, University of Sfax, Tunisia. He has got his PhD degree in 2007 in Industrial Engineering from Grenoble Institute of Technology, France and in Mechanical engineering from National School of engineers of Sfax, Tunisia. He has over 20 years of work experience in higher education and research. His research activities deal with product design, SCM, waste management, LCSA, QSE Management and TQM. He has been the advisor for 10 PhD studies and has published over 70 referenced papers.

<figure><img src="image_43.png" /></figure>

**Dr. Michel Tollenaere** received a PhD in Mechanical Engineering from Grenoble INP in 1991. He has been a Professor of Management of Information Systems at Grenoble Institute of Technology (Grenoble INP) since 1996 and does his research at G-SCOP. Dr. Tollenaere specialises in product lifecycle management and production quality management. He served as Director AIP Primeca from 2001 till 2007. He has led research team SIREP at G-SCOP from 2007 till 2013. He successfully led the Master's program MIT, speciality industrial engineering, from 2011 till 2014 and acted as head of industrial relations department from 2011 till 2013 at Grenoble Institute of Technology. Dr. Tollenaere has supervised 25 PhD students and has published 117 publications. He led many research contracts with industrial partners, as Airbus, Audros, EADS, Eurocopter, Peugeot, Renault, Schneider Electric and ST Microelectronics.
<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

**Tony Wong** holds a B.Eng. and Master's degrees from École de technologie supérieure in electrical engineering. He received his Ph.D. degree in computer engineering from École Polytechnique de Montréal in Canada. Dr. Wong joined the Systems Engineering department of École de technologie supérieure in 1999. His research interests are Edge Computing, Industrial Internet of Things, Multi-objective Evolutionary Algorithms, Machine Learning methods, and their application to service and manufacturing problems.

## ORCID

Didérot Déraillet Tadja http://orcid.org/0009-0002-6556-2613

## Data availability statement

Data are available upon request to the corresponding author.

## References

* Adam Smith Institute. (2023). *The wealth of nations*. https://www.adamsmith.org/the-wealth-of-nations

* Aij, K. H., & Teunissen, M. (2017). Lean leadership attributes: A systematic review of the literature. *Journal of Health Organization and Management*, 31(7-8), 713-729. https://doi.org/10.1108/JHOM-12-2016-0245

* Alefari, M., Salonitis, K., & Xu, Y. (2017). The role of leadership in implementing lean manufacturing. *Procedia CIRP*, 63, 756-761. https://doi.org/10.1016/j.procir.2017.03.169

* Almquist, E., Senior, J., & Bloch, N. (2016). The elements of value: Measuring—and delivering—what consumers really want. *Harvard Business Review*, 94(9), 47-53. https://hbr.org/2016/09/the-elements-of-value

* Alsyouf, I. (2009). Maintenance practices in Swedish industries: Survey results. *International Journal of Production Economics*, 121(1), 212-223. https://doi.org/10.1016/j.ijpe.2009.05.005

* Altman, D. (1996). Practical statistics for medical research. https://doi.org/10.1201/9780429258589

* Anderson, T. W., & Darling, D. A. (1954). A test of goodness of fit. *Journal of the American Statistical Association*, 49(268), 765-769. https://doi.org/10.1080/01621459.1954.10501232

* Arabi, S., Chafi, A., Bajjou, M. S., & El Hammoumi, M. (2021). Exploring lean production system adoption in the Moroccan manufacturing and non-manufacturing industries: Awareness, benefits and barriers. *International Journal of Automotive and Mechanical Engineering*, 18(4), 9312-9332. https://doi.org/10.15282/ijame.18.4.2021.14.071

* Bai, C., Satir, A., & Sarkis, J. (2019). Investing in lean manufacturing practices: An environmental and operational perspective. *International Journal of Production Research*, 57(4), 1037-1051. https://doi.org/10.1080/00207543.2018.1498986

* Baillargeon, G. (2016). *Maitrise statistique des procédés [Statistical process control]*. Éditions SMG.

* Baker, W. (2021). How to build a culture of generosity at work. *Greater Good Magazine* https://greatergood.berkeley.edu/article/item/how_to_build_a_culture_ofGenerosity_at_work

* Baldoni, J. (2009). Use humility to improve performance. *Harvard Business Review* https://hbr.org/2009/11/use-humility-to-improve-perfor

* Balouei Jamkhaneh, H., Khazaei Pool, J., Khaksar, S. M. S., Arabzad, S. M., & Verij Kazemi, R. (2018). Impacts of computerized maintenance management system and relevant supportive organizational factors on total productive maintenance. *Benchmarking: An International Journal*, 25(7), 2230-2247. https://doi.org/10.1108/BIJ-05-2016-0072

* Battistella, C., Fornasier, A., & Pessot, E. (2023). How can lean tools support the innovation process of SMEs? *Journal of Manufacturing Technology Management*, 34(6), 1004-1024. https://doi.org/10.1108/JMTM-12-2022-0449

* Beaulieu-Paré, N. (2011). Une méthodologie pour l'implantation et le maintien du Lean dans les entreprises manufacturières québécoises [A methodology for implementing and maintaining Lean in Quebec manufacturing compagnies] [Master's dissertation]. École de technologie supérieure]. Espace ÉTS. https://espace.etsmtl.ca/id/eprint/890/

* Belekoukias, I., Garza-Reyes, J. A., & Kumar, V. (2014). The impact of lean methods and tools on the operational performance of manufacturing organisations. *International Journal of Production Research*, 52(18), 5346-5366. https://doi.org/10.1080/00207543.2014.903348

* Belhadi, A., Sha'ri, Y. B. M., Touriki, F. E., & El Fezazi, S. (2018). Lean production in SMEs: Literature review and reflection on future challenges. *Journal of Industrial and Production Engineering*, 35(6), 368-382. https://doi.org/10.1080/21681015.2018.1508081

* Ben Romdhane, T., Badreddine, A., & Sansa, M. (2017). A new model to implement Six Sigma in small- and medium-sized enterprises. *International Journal of Production Research*, 55(15), 4319-4340. https://doi.org/10.1080/00207543.2016.1249430

* Bertrand, F., & Maumy-Bertand, M. (2011). Statistique pour les scientifiques: En 80 fiches [Statistics for scientists: In 80 sheets]. Dunod.
<figure><img src="image_46.png" /></figure>

Bhasin, S. (2011). Measuring the Leanness of an organization. *International Journal of Lean Six Sigma, 2*(1), 55-74.
https://doi.org/10.1108/2040146111119459

Bhasin, S., & Burcher, P. (2006). Lean viewed as a philosophy. *Journal of Manufacturing Technology Management, 17*(1), 56-72. https://doi.org/10.1108/17410380610639506

Bortolotti, T., Boscari, S., & Danese, P. (2015). Successful lean implementation: Organizational culture and soft lean practices. *International Journal of Production Economics, 160*, 182-201. https://doi.org/10.1016/j.ijpe.2014.10.013

CapGemini. (2008). *How to implement lean successfully and deliver results that last* https://www.capgemini.com/wpcontent/uploads/2017/07/b_How_to_implement_Lean阿拉丁成功和 deliver results that last.pdf

Cappelletti, L., Khalla, S., Noguera, F., Scouarnec, A., & Voynnet Fourboul, C. (2010). Toward a new trend of managing people through benevolence? *Management & Avenir*, n° 36(6), 263-283. https://doi.org/10.3917/mav.036.0263

Cardona, N., & Bribiescas, F. (2015). Respect for people: The forgotten principle in manufacturing implementation. *European Scientific Journal*, 11(13), 45-61.

Chalmers, A. F. (2013). *What is this thing called science?*. Hackett Publishing.

Chan, C. O., & Tay, H. L. (2018). Combining lean tools application in kaizen: A field study on the printing industry. *International Journal of Productivity and Performance Management*, 67(1), 45-65. https://doi.org/10.1108/IJPPM-09-2016-0197

Chanegrih, T., & Creusier, J. (2015). Le lean manufacturing dans l'industrie française: états des lieux et implications pratiques [Lean manufacturing in French industry: Current status and practical implications]. *Revue Française De Gestion Industrielle*, 34(4), 59-71. https://doi.org/10.53102/2015.34.04.831

Charest, É. (2014). Équité en emploi– Équité salariale, Legault, Marie-Josée Québec: Presses de l'Université du Québec [Employment equity – Pay equity, Legault, Marie-Josée Québec: Presses de l'Université du Québec]. 2013, 598 pages. ISBN: 978-2-7605-3719-4 Relations Industrielles, 69(1), 226-228. https://doi.org/10.7202/1024217ar

Chatelain, G. (2018). La bienveillance au travail, ça commence par des gestes simples [Kindness at work starts with simple gestures]. *Psychologies*. https://www.psychologies.com/Travail/Relations-professionnelles/Vie-de-bureau/Articles-et-Dossiers

Chavez, R., Gimenez, C., Fynes, B., Wiengarten, F., & Yu, W. (2013). Internal lean practices and operational performance. *International Journal of Operations & Production Management*, 33(5), 562-588. https://doi.org/10.1108/01443571311322724

Comer, D. R., & Sekerka, L. E. (2014). Taking time for patience in organizations. *Journal of Management Development*, 33(1), 6-23. https://doi.org/10.1108/JMD-11-2013-0132

Costa, L. B. M., Godinho Filho, M., Fredendall, L. D., & Devós Ganga, G. M. (2021). Lean Six Sigma in the food industry: Construct development and measurement validation. *International Journal of Production Economics*, 231, 107843. https://doi.org/10.1016/j.ijpe.2020.107843

Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. *Journal of Operations Management*, 19(6), 675-694. https://doi.org/10.1016/S0272-6963(01)00066-3

Cutillo, L. (2019). Parametric and multivariate methods. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), *Encyclopedia of bioinformatics and computational biology*. Elsevier.

Cyrus, R. M., & Nitin, R. P. (1989). IBM SPSS exact tests. IBM Corporation.

Deschamps, C. (2018). *Support aux gestionnaires: Outil d'accompagnement pour l'implantation d'amélioration continue opérationnelle* [Support for managers: Support tool for the implementation of continuous operational improvement]. *Rapport de Maitrise, Polytechnique Montréal*.

Deslauriers, J., Gagné, R., & Paré, J. (2020). Productivité et prospérité au Québec [Productivity and prosperity in Quebec] *Centre sur la Productivité et la Prospérité – Fondation Walter J*. https://cpp.hec.ca/productivite-et-prosperite-au-quebec-bilan-2020/

Dombrowski, U., Richter, T., & Krenkel, P. (2017). Interdependencies of industrie 4.0 & lean production systems: A use cases analysis. *Procedia Manufacturing*, 11, 1061-1068. https://doi.org/10.1016/j.promfg.2017.07.217

Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. *Psychological Bulletin*, 81(6), 358-361. https://doi.org/10.1037/h003631

Elangovan, N., & Sundaravel, E. (2021). Method of preparing a document for survey instrument validation by experts. *MethodsX*, 8, 101326. https://doi.org/10.1016/j.mex.2021.101326

Emiliani, M. L. (1998). Lean behaviors. *Management Decision*, 36(9), 615-631. https://doi.org/10.1108/00251749810239504

Farris, J. A., Van Aken, E. M., Doolen, T. L., & Worley, J. (2009). Critical success factors for human resource outcomes in Kaizen events: An empirical study. *International Journal of Production Economics*, 117(1), 42-65. https://doi.org/10.1016/j.ijpe.2008.08.051

Feldt, T., Kivimäki, M., Rantala, A., & Tolvanen, A. (2004). Sense of coherence and work characteristics: A cross-lagged structural equation model among managers. *Journal of Occupational and Organizational Psychology*, 77(3), 323-342. https://doi.org/10.1348/0963179041752655

Filho, M. G., Ganga, G. M., & Gunasekaran, A. (2016). Lean manufacturing in Brazilian small and medium enterprises: Implementation and effect on performance. *International Journal of Production Research*, 54(24), 7523-7545. https://doi.org/10.1080/00207543.2016.1201606
<figure><img src="image_47.png" /></figure>

Fisher, M. J., Marshall, A. P., & Mitchell, M. (2011). Testing differences in proportions. *Australian Critical Care: official Journal of the Confederation of Australian Critical Care Nurses, 24*(2), 133–138. https://doi.org/10.1016/j.aucc.2011.01.005

Flynn, B. B., Schroeder, R. G., & Sakakibara, S. (1995). The impact of quality management practices on performance and competitive advantage. *Decision Sciences, 26*(5), 659–691. https://doi.org/10.1111/j.1540-5915.1995.tb01445.x

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. *Journal of Marketing Research, 18*(1), 39–50. https://doi.org/10.1177/002224378101800104

Fullerton, R. R., Kennedy, F. A., & Widener, S. K. (2014). Lean manufacturing and firm performance: The incremental contribution of lean management accounting practices. *Journal of Operations Management, 32*(7-8), 414–428. https://doi.org/10.1016/j.jom.2014.09.002

Furlan, A., Vinelli, A., & Pont, G. (2011). Complementarity and lean manufacturing bundles: An empirical analysis. *International Journal of Operations & Production Management, 31*(8), 835–850. https://doi.org/10.1108/01443571111153067

Garza-Reyes, J. A., Kumar, V., Chaikittisilp, S., & Tan, K. H. (2018). The effect of lean methods and tools on the environmental performance of manufacturing organisations. *International Journal of Production Economics, 200*, 170–180. https://doi.org/10.1016/j.ijpe.2018.03.030

Gesinger, S. (2016). Experiential learning: Using Gemba walks to connect with employees. *Professional Safety, 61*(2), 33–36.

Giannini, E. H. (2005). Design, measurement, and analysis of clinical investigations. In J. T. Cassidy, R. E. Petty, R. M. Laxer, & C. B. Lindsley (Eds.), *Textbook of pediatric rheumatology* (5th ed., pp. 142–173). W.B. Saunders. https://doi.org/10.1016/B978-1-4160-0246-8.50012-7

Glickman, J. (2011). Be generous at work. *Harvard Business Review* https://hbr.org/2011/06/be-generous-at-work

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). *Multivariate Data Analysis*. Pearson Prentice Hall.

Halgeri, P., Pei, Z. J., Iyer, K. S., Bishop, K., & Shehadeh, A. (2008). ERP systems supporting lean manufacturing: A literature review. In *ASME 2008 International Manufacturing Science and Engineering Conference Collocated with the 3rd JSME/ASME International Conference on Materials and Processing* (pp. 541–549). ASME. https://doi.org/10.1115/MSEC_ICMP2008-72542

Hallam, C. (2003). Lean enterprise self-assessment as a leading indicator for accelerating transformation in the aerospace industry [Doctoral dissertation]. Massachusetts Institute of Technology. DSpace@MIT. https://dspace.mit.edu/handle/1721.1/29216

Hanson, R. (2018). Speak with honesty and sincerity. *Psychology Today*. https://www.psychologytoday.com/intl/blog/your-wise-brain/201807/speak-honesty-and-sincerity-0

Henderson Brower, H., Wayne Lester, S., & Korsgaard, A. (2017). Want your employees to trust you? Show you trust them. *Harvard Business Review* https://hbr.org/2017/07/want-your-employees-to-trust-you-show-you-trust-them

Hines, P. (2013). *Creating a lean business system*. SA Partners https://sapartners.com/wp-content/uploads/2012/08/Creating-a-Lean-Business-System-updated-by-DS.pdf

Hines, P., Holweg, M., & Rich, N. (2004). Learning to evolve. *International Journal of Operations & Production Management, 24*(10), 994–1011. https://doi.org/10.1108/01443570410558049

Hu, Q., Mason, R., Williams, S. J., & Found, P. (2015). Lean implementation within SMEs: A literature review. *Journal of Manufacturing Technology Management, 26*(7), 980–1012. https://doi.org/10.1108/JMTM-02-2014-0013

Huet, J.-M., & Lensen-Gillette, S. (2012). La cohérence au cœur de la performance [Consistency at the heart of performance]. *L'Expansion. L'Expansion Management Review, N°* 145(2), 32–40. https://doi.org/10.3917/emr.145.0032

Hussain, D., & Figueiredo, M. C. (2023). Improving the time-based performance of the preparatory stage in textile manufacturing process with value stream mapping. *Business Process Management Journal, 29*(3), 801–837. https://doi.org/10.1108/BPMJ-08-2022-0366

INSEE. (2020). *Valeur ajoutée* [Added value]. https://www.insee.fr/fr/accueil

Institut de la Statistique du Québec. (2023). *Faits saillants tirés du bilan démographique du Québec* [Highlights taken from the Quebec demographic report]. https://statistique.quebec.ca/fr/produit/publication/evolution-mouvement-structure-age-population-bilan-demographique

Jain, A., Bhatti, R. S., & Singh, H. (2015). OEE enhancement in SMEs through mobile maintenance: A TPM concept. *International Journal of Quality & Reliability Management, 32*(5), 503–516. https://doi.org/10.1108/IJQRM-05-2013-0088

Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. *Economics Letters, 6*(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5

Jasti, N. V. K., & Kodali, R. (2015). Lean production: Literature review and trends. *International Journal of Production Research, 53*(3), 867–885. https://doi.org/10.1080/00207543.2014.937508

Jituri, S., Fleck, B., & Ahmad, R. (2018). Lean OR ERP—A decision support system to satisfy business objectives. *Procedia CIRP, 70*, 422–427. https://doi.org/10.1016/j.procir.2018.02.048

Jørgensen, F., Matthiesen, R., Nielsen, J., & Johansen, J. (2007). Lean maturity, lean sustainability. In J. Olhager & F. Persson (Eds.), *Advances in production management systems. IFIP - The International Federation for Information Processing* (Vol. 246, pp. 371–378). Springer. https://doi.org/10.1007/978-0-387-74157-4_44

Kelada, J. (1997). *Comprendre et réaliser la qualité totale* [Understanding and achieving total quality]. Quafec.
<figure><img src="image_48.png" /></figure>

Khan, F. I., & Haddara, M. (2004). Risk-based maintenance (RBM): A new approach for process plant inspection and maintenance. *Process Safety Progress*, 23(4), 252–265. https://doi.org/10.1002/prs.10010

Klein, J. A. (2005). Operational discipline in the workplace. *Process Safety Progress*, 24(4), 228–235. https://doi.org/10.1002/prs.10099

Konecny, P. A., & Thun, J.-H. (2011). Do it separately or simultaneously: An empirical analysis of a conjoint implementation of TQM and TPM on plant performance. *International Journal of Production Economics*, 133(2), 496–507. https://doi.org/10.1016/j.ijpe.2010.12.009

Kumar, B., Mosher, H., Farag, A., & Swee, M. (2023). How can we champion diversity, equity and inclusion within Lean Six Sigma? Practical suggestions for quality improvement. *BMJ Quality & Safety*, 32(5), 296–300. https://doi.org/10.1136/bmjqs-2022-014892

Laroche, C. (2018). Un outil d'amélioration continue pour l'engagement: Habiletés, facilitateurs de déploiement, facteurs-clés d'engagement, transition et encadrement [A continuous improvement tool for engagement: Skills, deployment facilitators, key engagement factors, transition and coaching]. Rapport de Maitrise, Polytechnique Montréal.

Liker, J. (2004). *The Toyota way: 14 management principles from the world's greatest manufacturer*. McGraw-Hill Education.

Liker, J., & Rother, M. (2011). *Why lean programs fail*. Lean Enterprise Institute https://www.bus-ex.com/article/operations-why-lean-programs-fail

Lilius, J. M., Worline, M. C., Maitlis, S., Kanov, J., Dutton, J. E., & Frost, P. (2008). The contours and consequences of compassion at work. *Journal of Organizational Behavior*, 29(2), 193–218. https://doi.org/10.1002/job.508

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. *Journal of the American Statistical Association*, 62(318), 399–402. https://doi.org/10.1080/01621459.1967.10482916

Lumivero. (2023). *XLSTAT statistical and data analysis solution*. https://www.xlstat.com/fr

Lyonnet, B. (2010). *Amélioration de la performance industrielle: Vers un système de production Lean adapté aux entreprises du pôle de compétitivité Arve Industries Haute-Savoie Mont-Blanc* [Improvement of industrial performance: Towards a Lean production system adapted to companies in the Arve Industries Haute-Savoie Mont-Blanc competitiveness cluster] [Doctoral thesis]. Archives-Ouvertes. Université de Savoie. https://tel.archives-ouvertes.fr/tel-00655808/

Maasouman, M. A., & Demirli, K. (2015). Assessment of lean maturity level in manufacturing cells. *IFAC-PapersOnLine*, 48(3), 1876–1881. https://doi.org/10.1016/j.ifacol.2015.06.360

Marodin, G. A., Saurin, T. A., Tortorella, G. L., & Denicol, J. (2015). How context factors influence lean production practices in manufacturing cells. *The International Journal of Advanced Manufacturing Technology*, 79(5-8), 1389–1399. https://doi.org/10.1007/s00170-015-6944-2

Mayr, A., Weigelt, M., Kühl, A., Grimm, S., Erll, A., Potzel, M., & Franke, J. (2018). Lean 4.0: A conceptual conjunction of lean management and Industry 4.0. *Procedia CIRP*, 72, 622–628. https://doi.org/10.1016/j.procir.2018.03.292

Megha, B., & Srikantha Dath, T. N. (2024). Human resources practices as a moderator for inculcating lean thinking in IT organizations: An empirical approach. *Business Process Management Journal*, 30(1), 84–104. https://doi.org/10.1108/BPMJ-05-2022-0239

Micieta, B., Howaniec, H., Binasova, V., Kasajova, M., & Fusko, M. (2021). Increasing work efficiency in a manufacturing setting using Gemba walk. *European Research Studies Journal, XXIV*(Special Issue 4), 601–620. https://doi.org/10.35808/ersj/2792

Ministère de l’Économie. (2021). *Portrait du secteur manufacturier des régions du Québec* [Portrait of the manufacturing sector in the regions of Quebec]. Ministère de l’Économie, de l’Innovation et de l’Énergie https://www.economie.gouv.qc.ca/bibliotheques/etudes-et-analyses/analyses-du-secteur-manufacturier

Montréal International. (2023). *Le Grand Montréal en un coup d'œil* [Greater Montreal at a Glance]. https://www.montrealinternational.com/fr/investir/zones-geographiques/

Mostafa, S., Lee, S.-H., Dumrak, J., Chileshe, N., & Soltan, H. (2015). Lean thinking for a maintenance process. *Production & Manufacturing Research*, 3(1), 236–272. https://doi.org/10.1080/21693277.2015.1074124

Narasimhan, R., & Das, A. (2001). The impact of purchasing integration and practices on manufacturing performance. *Journal of Operations Management*, 19(5), 593–609. https://doi.org/10.1016/S0272-6963(01)00055-9

Nawanir, G., Kong Teong, L., & Norezam Othman, S. (2013). Impact of lean practices on operations performance and business performance: Some evidence from Indonesian manufacturing companies. *Journal of Manufacturing Technology Management*, 24(7), 1019–1050. https://doi.org/10.1108/JMTM-03-2012-0027

Negrão, L. L. L., Godinho Filho, M., & Marodin, G. (2016). Lean practices and their effect on performance: A literature review. *Production Planning & Control*, 28(1), 1–24. https://doi.org/10.1080/09537287.2016.1231853

Nightingale, D., & Mize, J. (2002). Development of a lean enterprise transformation maturity model. *Information Knowledge Systems Management*, 3(1), 15–30. https://doi.org/10.3233/IKS-2002-00047

Noora, S. (2021). Factor analysis as a tool for survey analysis. *American Journal of Applied Mathematics and Statistics*, 9(1), 4–11. https://doi.org/10.12691/ajams-9-1-2

Pakdil, F., & Leonard, K. M. (2015). The effect of organizational culture on implementing and sustaining lean processes. *Journal of Manufacturing Technology Management*, 26(5), 725–743. https://doi.org/10.1108/JMTM-08-2013-0112

Panwar, A., Nepal, B. P., Jain, R., & Rathore, A. P. S. (2015). On the adoption of lean manufacturing principles in process industries. *Production Planning & Control*, 26(7), 564–587. https://doi.org/10.1080/09537287.2014.936532
<figure><img src="image_49.png" /></figure>

Patel, B. S., Sambasivan, M., Panimalar, R., & Hari Krishna, R. (2022). A relational analysis of drivers and barriers of lean manufacturing. *The TQM Journal*, 34(5), 845–876. https://doi.org/10.1108/TQM-12-2020-0296

Paulk, M. (2002). Capability maturity model for software. In J. J. Marciniak (Ed.), *Encyclopedia of software engineering*. Wiley Online. https://doi.org/10.1002/0471028959.sof589

Paulk, M., Weber, C., Garcia, S., Chrissis, M., & Bush, M. (1993). *Key practices of the capability maturity model*. Software Engineering Institute https://apps.dtic.mil/sti/tr/pdf/ADA263432.pdf

Pearce, A., Pons, D., & Neitzert, T. (2018). Implementing lean: Outcomes from SME case studies. *Operations Research Perspectives*, 5, 94–104. https://doi.org/10.1016/j.orp.2018.02.002

Peart, N. (2019). Making work less stressful and more engaging for your employees. *Harvard Business Review* https://hbr.org/2019/11/making-work-less-stressful-and-more-engaging-for-your-employees

Pentland, A. S. (2012). The new science of building great teams. *Harvard Business Review* https://hbr.org/2012/04/the-new-science-of-building-great-teams

Pettersen, J. (2009). Defining lean production: Some conceptual and practical issues. *The TQM Journal*, 21(2), 127–142. https://doi.org/10.1108/17542730910938137

Puvanasvaran, A. P. (2012). Enhancing productivity through lean behavior. In A. López-Varela (Ed.), *Theoretical and methodological approaches to social sciences and knowledge management* (pp. 283–306). IntechOpen. https://doi.org/10.5772/38555

Radziwiłł, N. (2015). Lead with respect: A novel of lean practice. *Quality Management Journal*, 22(2), 61–61. https://doi.org/10.1080/10686967.2015.11918429

Rajagopalan, J., & Solaimani, S. (2019). Lean management in Indian industry: An exploratory research study using a longitudinal survey. *International Journal of Lean Six Sigma*, 11(3), 515–542. https://doi.org/10.1108/IJLSS-12-2017-0140

Randhawa, J. S., & Ahuja, I. S. (2017). 5S—A quality improvement tool for sustainable performance: Literature review and directions. *International Journal of Quality & Reliability Management*, 34(3), 334–361. https://doi.org/10.1108/IJQRM-03-2015-0045

Rasi, R. Z. R. M., Rakiman, U. S., & Ahmad, M. F. B. (2015). Relationship between lean production and operational performance in the manufacturing industry. *IOP Conference Series: Materials Science and Engineering*, 83(1), 012016. https://doi.org/10.1088/1757-899X/83/1/012016

Rosenberg, C., Walker, A., Leiter, M., & Graffam, J. (2021). Humor in workplace leadership: A systematic search scoping review. *Frontiers in Psychology*, 12, 610795. https://doi.org/10.3389/fpsyg.2021.610795

Rosner, B. (2011). *Fundamentals of biostatistics*. Brooks/Cole.

Rousseau, V., Aubé, C., & Savoie, A. (2006). Le fonctionnement interne des équipes de travail: Conception et mesure [Internal functioning of work teams: Conception and measurement]. *Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences du Comportement*, 38(2), 120–135. https://doi.org/10.1037/cjbs2006002

Sahoo, S. (2019). Lean manufacturing practices and performance: The role of social and technical factors. *International Journal of Quality & Reliability Management*, 37(5), 732–754. https://doi.org/10.1108/IJQRM-03-2019-0099

Sahoo, S. (2020). Assessing lean implementation and benefits within Indian automotive component manufacturing SMEs. *Benchmarking: An International Journal*, 27(3), 1042–1084. https://doi.org/10.1108/BIJ-07-2019-0299

Salari, A., Farsijani, H., Dori, B., & Hamidizadeh, R. (2014). The role of people systems in lean production in enhancing performance perceptions and work-related attitudes. *European Online Journal of Natural and Social Sciences*, 2(3), 2059. https://european-science.com/eojnss/article/view/1151

Sanders, A., Subramanian, K., Redlich, T., & Wulfsberg, J. (2017). Industry 4.0 and lean management—synergy or contradiction?. In H. Lödding, R. Riedel, K. D. Thoben, G. von Cieminski, & D. Kiritsis, (Eds.), *Advances in production management systems: The path to intelligent, collaborative and sustainable manufacturing* (Vol. 2, pp. 341–349). Springer. https://doi.org/10.1007/978-3-319-66926-7_39

Sangwa, N. R., & Sangwan, K. S. (2023). Leanness assessment of a complex assembly line using integrated value stream mapping: A case study. *The TQM Journal*, 35(4), 893–923. https://doi.org/10.1108/TQM-12-2021-0369

Santos Bento, S., & Tontini, G. (2018). Developing an instrument to measure lean manufacturing maturity and its relationship with operational performance. *Total Quality Management & Business Excellence*, 29(9-10), 977–995. https://doi.org/10.1080/14783363.2018.1486537

Secchi, R., & Camuffo, A. (2019). Lean implementation failures: The role of organizational ambidexterity. *International Journal of Production Economics*, 210, 145–154. https://doi.org/10.1016/j.ijpe.2019.01.007

Seppälä, E. (2015). Why compassion is a better managerial tactic than toughness. *Harvard Business Review*. https://hbr.org/2015/05/why-compassion-is-a-better-managerial-tactic-than-toughness

Setianto, P., & Haddud, A. (2016). A maturity assessment of lean development practices in manufacturing industry. *International Journal of Advanced Operations Management*, 8(4), 294–322. https://doi.org/10.1504/IJAOM.2016.084150

Sezer, O., Nault, K., & Klein, N. (2021). Don't underestimate the power of kindness at work. *Harvard Business Review*. https://hbr.org/2021/05/dont-underestimate-the-power-of-kindness-at-work

Shah, R., & Ward, P. (2007). Defining and developing measures of lean production. *Journal of Operations Management*, 25(4), 785–805. https://doi.org/10.1016/j.jom.2007.01.019

Shah, R., & Ward, P. T. (2003). Lean manufacturing: Context, practice bundles, and performance. *Journal of Operations Management*, 21(2), 129–149. https://doi.org/10.1016/S0272-6963(02)00108-0
<figure><img src="image_50.png" /></figure>

Shah, V. (2022). *Lean manufacturing: Fundamentals, tools, approaches, and Industry 4.0 integration*. CRC Press. https://doi.org/10.1201/9781003190332

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). *Biometrika*, 52(3-4), 591–611. https://doi.org/10.1093/biomet/52.3.4.591

Shingo, S. (1986). *Zero quality control: Source inspection and the poka-yoke system*. Productivity Press.

Shultz, K. S., Whitney, D., & Zickar, M. J. (2020). *Measurement theory in action: Case studies and exercises* (3rd ed.). Routledge. https://doi.org/10.4324/9781003127536

Siegel, R., Antony, J., Garza-Reyes, J. A., Cherrafi, A., & Lameijer, B. (2019). Integrated green lean approach and sustainability for SMEs: From literature review to a conceptual framework. *Journal of Cleaner Production*, 240, 118205. https://doi.org/10.1016/j.jclepro.2019.118205

Singh, A. P., & Awoke, N. F. (2023). Relationship between TPM practices and operational performance in soft drinks manufacturing industry. *Journal of Quality in Maintenance Engineering*, 29(4), 729–762. https://doi.org/10.1108/JQME-10-2022-0067

Singha Mahapatra, M., & Shenoy, D. (2022). Lean maintenance index: A measure of leanness in maintenance organizations. *Journal of Quality in Maintenance Engineering*, 28(4), 791–809. https://doi.org/10.1108/JQME-08-2020-0083

Steiner, P. (2005). Introduction cognitivisme et sciences cognitives. *Labyrinth*, 20(20), 13–39. https://doi.org/10.4000/labyrinth.754

STIQ. (2022). *Baromètre industriel Québécois* [Quebec industrial barometer] (13th ed.). STIQ. https://www.stiq.com/barometre-industriel-quebecois/

STIQ. (2023). *Baromètre industriel Québécois* [Quebec industrial barometer] (14th ed.). STIQ. https://www.stiq.com/barometre-industriel-quebecois/

Subramanian, N., Suresh, M., & William, J. A. (2023). Human-related lean practices for manufacturing SMEs' lean transformation: A systematic literature review. *Nankai Business Review International*. Advance Online Publication. https://doi.org/10.1108/NBRI-09-2022-0087

Tadja, D. D., Bassetto, S., Tollenaere, M., & Wong, T. (2021). Les objets connectés pour améliorer la culture de la production épurée: Revue de littérature et esquisse de solution applicable aux entreprises manufacturières [Industrial Internet of Things to improve lean manufacturing culture: Literature review and solution outline for manufacturing plants]. *Génie Industriel & Productique*, 4(1), 1–35. https://doi.org/10.21494/ISTE.OP.2021.0745

Taherdoost, H. (2017). Determining sample size: How to calculate survey sample size. *International Journal of Economics and Management Systems* 2. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3224205

Thanki, S. J., & Thakkar, J. (2014). Status of lean manufacturing practices in Indian industries and government initiatives. *Journal of Manufacturing Technology Management*, 25(5), 655–675. https://doi.org/10.1108/JMTM-05-2012-0057

Tomic, B., Spasojević, Brkić, V., Karapetrovic, S., Pokrajac, S., Milanović, D. D., Babić, B., & Djurdjevic, T. (2017). Organizational culture, quality improvement tools and methodologies, and business performance of a supply chain. *Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture*, 231(13), 2430–2442. https://doi.org/10.1177/0954405416629100

Tortorella, G. L., & Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. *International Journal of Production Research*, 56(8), 2975–2987. https://doi.org/10.1080/00207543.2017.1391420

Tortorella, G. L., & Fogliatto, F. S. (2014). Method for assessing human resources management practices and organisational learning factors in a company under lean manufacturing implementation. *International Journal of Production Research*, 52(15), 4623–4645. https://doi.org/10.1080/00207543.2014.881577

Tortorella, G. L., Fogliatto, F. S., Cauchick-Miguel, P. A., Kurnia, S., & Jurburg, D. (2021). Integration of industry 4.0 technologies into total productive maintenance practices. *International Journal of Production Economics*, 240, 108224. https://doi.org/10.1016/j.ijpe.2021.108224

Tortorella, G. L., Vergara, L. G. L., & Ferreira, E. P. (2017). Lean manufacturing implementation: An assessment method with regards to socio-technical and ergonomics practices adoption. *The International Journal of Advanced Manufacturing Technology*, 89(9-12), 3407–3418. https://doi.org/10.1007/s00170-016-9227-7

Van Dun, H., & Wilderom, C. P. M. (2016). Lean-team effectiveness through leader values and members' informing. *International Journal of Operations & Production Management*, 36(11), 1530–1550. https://doi.org/10.1108/IJOPM-06-2015-0338

Velarde, G. J., Pirraglia, A., van Dyk, H., & Saloni, D. E. (2011). Lean manufacturing in the US South Atlantic Region: An overview of the current state of implementation in the secondary wood industry. *International Wood Products Journal*, 2(1), 30–37. https://doi.org/10.1179/2042645311Y.0000000006

Veres, C. (2020). Conceptual model for introducing lean management instruments. *Procedia Manufacturing*, 46, 233–237. https://doi.org/10.1016/j.promfg.2020.03.034

Vivares, J. A., Sarache, W., & Hurtado, E. (2018). A maturity assessment model for manufacturing systems. *Journal of Manufacturing Technology Management*, 29(5), 746–767. https://doi.org/10.1108/JMTM-07-2017-0142

Wang, Q., Wang, H.-J., Jiang, L., Li, Z., & Lyu, S. C. (2024). Leader humor, workplace gossip, and employee authentic self-expression: Implications for employee proactive behaviors. *Current Psychology*, 43(16), 14335–14347. https://doi.org/10.1007/s12144-023-05443-x
<figure><img src="image_51.png" /></figure>

Wang, W. (2011). A joint spare part and maintenance inspection optimisation model using the Delay-Time concept. *Reliability Engineering & System Safety*, 96(11), 1535-1541. https://doi.org/10.1016/j.ress.2011.07.004

Womack, J., & Jones, D. (2003). *Lean thinking: Banish waste and create wealth in your corporation*. Free Press.

Womack, J., Jones, D., & Ross, D. (1990). *The machine that changed the world*. Free Press.

Woong Jin, H., & Doolen, L. (2014). A comparison of Korean and US continuous improvement projects. *International Journal of Productivity and Performance Management*, 63(4), 384-405. https://doi.org/10.1108/IJPPM-01-2013-0012

Yang, Y., & Yang, B. (2024). Employee participation in total productive maintenance: A bottom-up perspective. *International Journal of Quality & Reliability Management*, 41(1), 269-290. https://doi.org/10.1108/IJQRM-12-2022-0353

Yash, D. (2016). *Development of integrated model for improving productivity through lean manufacturing practices* [Doctoral dissertation]. Devi Ahilya Vishwavidyalaya]. Shodhganga. http://hdl.handle.net/10603/235244

Zajkowska, E. L. (2013). Contribution à l'implantation de la méthode Lean Six Sigma dans les Petites et Moyennes Entreprises pour l'amélioration des processus [Contribution to the implementation of the Lean Six Sigma method in Small and Medium Businesses for process improvement] [Doctoral dissertation, École centrale de Lille] Archives-Ouvertes. https://tel.archives-ouvertes.fr/tel-00795533/document

Zenger, J., & Folkman, J. (2019, February 5). The 3 elements of trust. *Harvard Business Review*. https://hbr.org/2019/02/the-3-elements-of-trust

Zhu, X., & Lin, Y. (2017). Does lean manufacturing improve firm value? *Journal of Manufacturing Technology Management*, 28(4), 422-437. https://doi.org/10.1108/JMTM-05-2016-0071