Contextual factors on Toyota Way and Agile Manufacturing: an empirical investigation

Abstract

This research aims to examine the differences across selected contextual factors on the Toyota Way (TW), agile manufacturing (AM) and their sub-elements. These factors include firm size, nationality of firms, production approaches, IATF 16949 - an international quality management certification in the automotive industry, supplier tiers in supply chains, export levels, and import levels. A survey instrument was developed based on literature, then verified by experts and statistical techniques. ANOVA and independent samples t-test were used to explore the differences across contextual factors of 216 automotive part manufacturers on TW, AM, and their elements. It revealed that there were significant differences across firm size, nationality of firms, and export levels on overall TW, AM and most sub-elements. Insignificant differences across production approaches and IATF 16949 on overall TW, AM and their sub-elements were found. Firms with different contextual factors require different levels of TW, AM and sub-elements. Managers should apply TW and AM elements at suitable levels in accordance with their organizational contexts.This is one of the first studies to empirically investigate the differences across the contextual factors on TW, AM and sub-elements in the same paper from a socio-technical perspective. This study can be used as a basis for further research on integrative practices between lean and agile (leagile) manufacturing.

Introduction

Effective and efficient implementation of manufacturing strategies needs more than just the application of techniques and tools. Although manufacturing strategies in general have been introduced in various environments to improve performance, their contributions are not equal in every firm. They are partially influenced by contextual factors: firm size, production approaches, tiers in supply chains, import and export levels (Vanichchinchai 2021d). These contextual factors can be either barriers or driving forces behind failure or success of such implementation (Qamar and Hall 2018). Companies should not apply the same set of manufacturing strategies or apply them at the same level. They should simply introduce practices which are proper for their contexts (Anvari et al. 2011). Understanding contextual factors, therefore, is a must for successful manufacturing strategy implementation. Literature on a firm’s contextual factors is limited to certain factors e.g. size, country, ISO 9001 (Dellana et al. 2020; Bayo-Moriones et al. 2010; Shah and Ward 2003; Doolen and Hacker 2005). Other factors, e.g. nationality of firms, production approaches, tiers, export levels, or import levels, have not been sufficiently explored (Tortorella et al. 2017a; Marodin et al. 2016; Vanichchinchai 2020).

The concept of Lean Manufacturing (LM) was developed from the Toyota Production System (TPS). Even though the terms TPS and LM can be used interchangeably (Vanichchinchai 2019a), LM is a more general and contemporary term, especially for non-Toyota business partners. The Toyota Way (TW) evolved from TPS with emphasis on human factors in order to sustain continuous improvement. Existing TPS literature focuses on technical aspects rather than contextual factors (Vanichchinchai 2021c). Currently, TW, and Agility Management (AM) are two prominent strategies used to improve competitiveness, particularly in the automotive supply chain. TW focuses on waste elimination leading to lower costs in a stable production environment, while AM emphasizes a rapid response to turbulent changes in a business environment and customer demand (Fadaki et al. 2019). Recent literature has been interested in the alignment between LM and AM into leagile manufacturing (Piotrowicz et al. 2022; Ahmed 2022, Qamar and Hall 2018). Both LM and AM are socio-technical systems, which need integration between sociological and technical factors (Vanichchinchai 2021c; Franco and Landini 2022). Thus, contextual factors of those companies implementing TW and AM are critical to the success of TW and AM. Researchers have attempted to investigate the implications of context on firms’ key management practices such as total quality management, and service quality (Hendricks and Singhal 2001; Vanichchinchai 2021d). Empirical study that examines contextual factors on TW and AM in the same paper is scarce. As of the end of 2021, using the keyword “leagile manufacturing” in general, only 73 and 38 papers were found in Scopus and Web of Science, respectively.

The automotive industry is prominent throughout the world and in Thailand. In 2020, Thailand’s automotive industry contributed around 6% of GDP with production of 1.4 million cars, ranking 11th in the world. The government of Thailand has a national policy to maintain Thailand as the Detroit of Asia (News Directory 3, 2021). Recently, Qamar and Hall (2018) and Marodin et al. (2016) extensively reviewed literature and revealed that the automotive supply chain is an underdeveloped area with regard to organizational factors. This research, therefore, aims to explore empirically the differences across firms’ contextual factors (firm size, nationality of firms, production approaches, IATF 16949, supplier tiers in supply chains, export levels, import levels) on TW and AM in the automotive parts industry in Thailand. IATF 16949 - the International Automotive Task Force 16949 - is widely recognized for international quality management certification in the automotive industry. These were key contextual factors suggested in Tortorella et al. (2017a), Tortorella et al. (2017b), Marodin et al. (2016), Qamar and Hall (2018), Vanichchinchai (2020).

Literature review

Contextual factors on management strategies

Contextual factors present situational characteristics generally exogenous to the organization or manager (Tortorella et al. 2015). Researchers recommended that the application of management strategies should be varied depending on the context of the implementing organization (Vanichchinchai 2021d; Bhasin 2012; Sousa and Voss 2008; Pettersen 2009). Firms should apply only those practices, and at levels, which are proper to their context (Anvari et al. 2011). The literature explored links between organizational contexts and management practices, such as 5 S (Bayo-Moriones et al. 2010), total quality management (Terziovski and Samson 2000; Hendricks and Singhal 2001), lean supply chain management (Tortorella et al. 2017a, b), service quality and lean healthcare (Vanichchinchai 2021d), and supply chain relationships (Vanichchinchai 2020). Considering contextual factors, Tortorella et al. (2017a, b), Marodin et al. (2016), Vanichchinchai (2014), Vanichchinchai (2020), Netland and Ferdows (2014), Wickramasinghe and Wickramasinghe (2011) suggested further studies across key contextual factors: types of business, firm size, nationality of firms, production approaches, tiers, management certification systems, import and export levels.

Contextual factors on TW

TW is a socio-technical system, which aims to eliminate waste efficiently and sustainably by integrating soft factors into hard factors (Vanichchinchai 2021c; Tortorella et al. 2017a) comprehensively reviewed literature and concluded that research in lean implementation always neglected organizational contexts. Accordingly, Marodin et al. (2016) reported that most studies regarding LM and contextual factors were small-scale case studies or theoretical. Few organizational factors were empirically investigated. Existing findings are still inconclusive. Furlan et al. (2011) found no effect of firm size on LM in Asian, North American or European companies. Conversely, Dorval et al. (2019) revealed that culture and organizational factors affected lean implementation. Shah and Ward (2003) reported that although large firms had stronger LM, small companies can translate LM practices better into performance. Bhasin (2012) and Doolen and Hacker (2005) commented that company size mattered for LM. Marodin et al. (2016) examined the influence of organizational contexts on LM in the automotive industry in Brazil. They revealed that large firms applied lean more extensively than small and medium firms. First and second tier suppliers were leaner than those at higher tiers. In Brazilian firms, Tortorella et al. (2017a) found no increased difficulty in lean supply chain management implementation at higher tier levels. Large firms are more favorable toward lean application. However, the level of onshore suppliers was less influential than expected.Therefore, outsourcing to more cost effective offshore suppliers was relevant for lean supply chain management. Similarly, Tortorella et al. (2017b) confirmed that the organizational context significantly affected lean supply chain management implementation. Firm size and tier levels were influential across lean practices. In the manufacturing sector in Thailand, Vanichchinchai (2020) revealed significant differences across organizational contexts (i.e. firm size, nationality of firms, production approaches, export levels, nationalities of customers, and existence of supply chain management departments) on LM. Regarding the country of implementation, Marodin et al. (2016) and Marodin et al. (2017) recommended that differences in cultural, economic or social levels between developed and developing countries might affect lean supply chain management. Kull et al. (2014) reported that LM was most effective in countries that valued low assertiveness, high uncertainty avoidance, low future orientation, and low performance orientation. Panizzolo et al. (2012) concluded that research into LM in developing countries was superficial and much less than that in developed countries. Many studies revealed differences in LM implementation between developing countries and developed countries (Saurin and Ferreira 2009). Accordingly, Jasti and Kodali (2014) reported that 87% of LM research was conducted in developed countries, mainly the USA, Japan and Europe.

Contextual factors on AM

Although literature has asserted that automotive supply chains are lean, the ability to respond quickly and flexibly to sudden changes in a market environment is increasingly important (Qamar and Hall 2018; Sellitto and Mancio 2019). Like TW, AM is a socio-technical system blending soft and hard elements. Several AM studies focused on workforce characteristics rather than organizational contextual factors. Mani and Mishra (2020) identified characteristics and ingredients of an agile workforce. Radhakrishnan et al. (2021) investigated the impact of project team characteristics (i.e. team autonomy, team diversity, and client collaboration) on project agility. Franco and Landini (2022) explored the relationships between workforce agility and innovative performance.To develop organizational agility, Joiner (2019) suggested emphasis on three areas: strategic agility, operational agility, and leadership agility. However, he concentrated only on leadership agility at the personal level. Strategic agility and operational agility, which may be influenced by contextual factors at higher levels, received insufficient attention. In addition to workforce factors on agility, AM should be further explored from a more holistic perspective at the whole organizational context level. Recently, Qamar and Hall (2018) reported that those companies closer to customers in the lower tier adopted flexible practices that were more agile than lean. AM was developed after TPS, then studied after implementation. As a result, AM literature is limited (Qamar and Hall 2018). At the end of 2021 in the Web of Science database, 2,951 articles were found using the keyword “lean manufacturing”, while only 1,053 articles were found with the keyword “agile manufacturing”. As a result, literature across organizational factors on AM with large-scale samples in developing countries is more limited than that of LM. Recently, Vanichchinchai (2020) recommended exploring the implications of AM across the contextual factors of firms.

Research methodology

The measurement instrument for TW was developed from Liker (2004), and Shang and Pheng (2012). In accordance with the original Toyota Way 2001 developed by Fujio Cho, TW in this study consisted of five sub-constructs: challenge (4 items), kaizen (8 items), genchi genbutsu (4 items), respect (3 items), and teamwork (4 items). In Japanese, kaizen means continuous improvement, and genchi genbutsu means go and see for yourself to thoroughly understand the situation (Liker 2004). AM scales were adapted from Sharifi and Zhang (2000), Zimmermann et al. (2020), Fadaki et al. (2019), Ghobakhloo and Azar (2018), Shahin and Rezaei (2018) and Birhanu et al. (2018). According to Sharifi and Zhang (2000), AM was composed of responsiveness (4 items), capability (3 items), and speed & flexibility (3 items). The instrument was verified by two academic and two industry experts in the automotive parts industry to improve validity and applicability. The experts suggested that the term “excellent production practices” should be used instead of “Toyota Way” in this research project in order to avoid potential reluctance of non-Toyota suppliers to answer the questionnaire. The questionnaire and documents in this research were revised accordingly as shown in the appendix. A seven-point Likert scale was applied to evaluate the levels of TW and AM in the sampled companies. To comply with international guidelines for human research protection, the questionnaire was approved by the central institutional review board of the university.

The survey instrument was developed using Google Forms, and then sent via e-mail to automotive parts companies in the directory of the Federation of Thai Industries, Thai Auto Parts Manufacturers Association. Sellitto et al. (2020) reported that recommendations from relevant entities to the survey can increase the response rate. Therefore, organizations such as the Industrial Estate Authority of Thailand, the Thailand Productivity Institute, the Institute for Small and Medium Enterprises Development, and some automotive parts companies were asked to distribute the questionnaire to their partners via e-mail or social media. The targeted respondents were middle management and above with at least one year of experience in manufacturing-related departments. The returned questionnaires were screened for eligibility (e.g. position, experience, department, incomplete answers). The number of valid questionnaires was 216. The sample profiles are shown in Table 1.

Table 1.

Sample profiles

Characteristic	Count	%
Position
Executive management	77	35.65
Middle management	139	64.35
Nationality of firms
Thai	122	57.01
Japanese	60	28.04
Others	32	14.95
Missing	2
Number of employees
< 51	27	12.50
51–200	62	28.70
> 200	127	58.80

SPSS AMOS 20 was employed for data analysis in this research. Overall TW and overall AM of early (132, 61.1%) and late responses (84, 38.9%) were compared with independent samples t-test to examine non-response bias in accordance with Lambert and Harrington (1990). The overall TW and AM had p-values 0.644 and 0.713 at 95% confidence level, respectively. The insignificant differences between the early and late responses confirmed that the collected data was free from non-response bias.

Reliability and validity test

Skewness and kurtosis of every sub-construct met the acceptable values +/-3 confirming normality of the data (Coakes and Steed 2003). Items-total correlations were 0.617 to 0.865, above the cut-off value at 0.3. Cronbach’s alpha values of all measurement models were higher than 0.8 as displayed in Table 2. Validity of the measurement models was examined to explore how well the observed indicators serve as measurement instruments for the latent sub-constructs. All measurement frameworks were validated with confirmatory factor analysis with maximum likelihood so as to remove the items with weak loading coefficients. Standardized loading of items in all models was higher than cut-off value at 0.5 as shown in Table 2. Consequently, no items were deleted. The overall goodness-of-fit of all frameworks was examined with multiple fit indexes in order to ensure that the constructs and sub-constructs were fit or reasonably consistent with the collected data. should be lower than 3. Goodness of fit index (GFI), comparative fit index (CFI), non-normed fit index (NNFI) should be at least 0.9. Standardized root mean squared residual (SRMR) should be less than 0.1 (Hair et al. 1998). All developed frameworks met these requirements with p-value above 0.05 as shown in Table 2. Composite reliability (CR) of all models was above 0.8. Average variance extracted (AVE) of models exceeded 0.5 (Hair et al. 1998; Nunnally and Burnstein 1994). Therefore, validity of all measurement models was confirmed.

Table 2.

Multiple fit indexes

	Standardized loading	Cronbach alpha	CR	AVE	p-value		GFI	CFI	NNFI	SRMR
Toyota Way	0.824–0.975	0.947	0.953	0.804	0.943	0.128	0.999	1.000	1.008	0.0027
Challenge	0.594–0.859	0.837	0.821	0.540	0.081	2.516	0.989	0.991	0.974	0.0167
Kaizen	0.643–0.959	0.943	0.932	0.636	0.072	1.646	0.978	0.995	0.989	0.0197
Genchi genbutsu	0.843–0.911	0.934	0.936	0.785	0.580	0.545	0.997	1.000	1.004	0.0050
Respect	0.892–0.928	0.929	0.930	0.815	0.234	1.417	0.996	0.999	0.998	0.0051
Teamwork	0.842–0.933	0.936	0.937	0.790	0.735	0.308	0.999	1.000	1.006	0.0039
AM	0.856–0.862	0.894	0.894	0.739	0.361	0.835	0.997	1.000	1.001	0.0059
Responsiveness	0.570–0.970	0.875	0.855	0.606	0.740	0.110	1.000	1.000	1.010	0.0019
Capability	0.789–0.833	0.844	0.848	0.650	0.213	1.551	0.995	0.998	0.994	0.0119
Speed & flexibility	0.759–0.918	0.866	0.870	0.692	0.819	0.052	1.000	1.000	1.009	0.0022

Findings and discussions

ANOVA with significant pairwise comparison (Scheffe post-hoc analysis) was utilized to explore the differences across firm size, nationality of firms, and production approaches on TW and AM constructs and sub-constructs. An independent sample t-test was used to investigate the differences across IATF 16949, tiers in supply chains, export levels and import levels.

Firm size

Doolen and Hacker (2005), Bhasin (2012) and Tortorella et al. (2017a, b) found that firm size had an influence on LM. Conversely, Furlan et al. (2011) reported no effects of company size on LM in European, North American and Asian firms. Shah and Ward (2003) revealed that although large companies had more LM maturity, small companies translated LM practices into better performance. Al-Hyari et al. (2016) revealed that lean is applicable to private hospitals in Jordan without a significant difference in advantage depending on hospital size.

With reference to the classification by the Ministry of Industry of Thailand in 2019 (SME Development Bank of Thailand, 2019), company size in this study was classified according to the number of employees into large (> 200 employees), medium (51–200 employees), and small (< 51 employees) companies. As shown in Table 3, large companies had the highest mean values, whereas small companies had the lowest scores in every construct and sub-construct, except for capability. The mean differences were significant between large and small firms in TW, AM and every sub-construct except for respect, and between medium and small companies in kaizen, AM, agility, capability, and speed & flexibility. The findings were reasonable and confirmed that larger firms have more mature manufacturing practices due to their better resources and more sophisticated systems. Similarly, Tortorella et al. (2017a) and Marodin et al. (2016) revealed that large companies in Brazil were more favorable towards lean supply chain management than small and medium companies. Godinho Filho et al. (2016) commented that small firms in Brazil did not obviously understand LM concepts. Yadav et al. (2019) found that small and medium companies in India applied a limited number of LM practices.

Table 3.

Differences across firm size

	Small		Medium		Large		S & M	M & L	S & L
	Mean	SD	Mean	SD	Mean	SD	Sig.	Sig.	Sig.
TW	5.26	0.82	5.70	0.65	5.88	0.98	0.096	0.432	0.005**
Challenge	5.31	0.95	5.68	0.83	5.88	0.97	0.219	0.390	0.016*
Kaizen	5.08	0.92	5.78	0.78	5.96	1.00	0.005**	0.470	< 0.001***
Genchi genbutsu	5.18	1.11	5.48	0.83	5.70	1.04	0.407	0.386	0.049*
Respect	5.47	0.82	5.78	0.71	5.97	1.11	0.378	0.492	0.059
Teamwork	5.28	0.91	5.79	0.70	5.90	1.17	0.100	0.768	0.017*
AM	5.20	0.77	5.77	0.79	5.83	0.88	0.015*	0.901	0.002**
Responsiveness	5.18	0.83	5.54	0.94	5.74	0.96	0.253	0.391	0.021*
Capability	5.05	0.94	5.71	0.85	5.68	1.03	0.014*	0.977	0.010**
Speed & flexibility	5.37	0.93	6.06	0.83	6.08	0.91	0.004**	0.996	0.001***
Valid N	27		62		127

Note: * significant at 0.05, ** significant at 0.01, *** significant at 0.001. A double underlined value was the highest. An underlined value was second highest

Nationality of firms

Ownership of the firm partially represents its management style. Boonsathorn (2007) revealed that, compared with Americans, Thais preferred conflict avoidance management styles. Kull et al. (2014) reported that LM was most effective in cultures or countries that valued low assertiveness, high uncertainty avoidance, low future orientation, and low performance orientation. Accordingly, Marodin et al. (2017), Tortorella et al. (2017b), and Kull et al. (2014) advised studying the relationships between cultural aspects or management styles and lean supply chain management.

In this research, nationalities of firms were categorized into Japanese, Thai, and firms with other nationalities. Japanese firms had the highest mean scores, while Thai firms had the lowest ones in every construct and sub-construct as shown in Table 4.There were significant differences between Japanese and Thai firms in all TW and AM constructs and sub-constructs, between Japanese and other nationalities in kaizen, and between Thai and other nationalities in genchi genbutsu. According to general beliefs, Japanese firms had better automotive manufacturing systems, including LM and AM, than other countries (Womack et al. 1990; Liker 2004); while other firms, mostly from developed countries, had better production management than Thais. In addition, most Japanese and other automotive parts manufacturers that had invested in Thailand were large companies with more resources and commitment for TW and AM in accordance with firm size analysis.

Table 4.

Differences across nationality of firms

	Thai		Others		Japanese		Thai & Others	Others & Japanese	Thai & Japanese
	Mean	SD	Mean	SD	Mean	SD	Sig.	Sig.	Sig.
TW	5.53	0.84	5.76	0.88	6.18	0.87	0.578	0.178	< 0.001***
Challenge	5.54	0.89	5.72	0.90	6.17	0.96	0.809	0.171	< 0.001***
Kaizen	5.59	0.91	5.61	1.07	6.30	0.85	1.000	0.009**	< 0.001***
Genchi genbutsu	5.28	0.95	5.83	1.06	5.99	0.90	0.040*	0.892	< 0.001***
Respect	5.64	0.95	5.96	0.93	6.20	1.02	0.436	0.725	0.004**
Teamwork	5.57	0.99	5.71	1.03	6.25	1.00	0.924	0.112	0.001***
AM	5.53	0.83	5.76	0.79	6.11	0.86	0.566	0.302	< 0.001***
Responsiveness	5.40	0.86	5.63	1.03	5.98	0.98	0.668	0.392	0.002**
Capability	5.42	0.94	5.69	0.91	5.93	1.05	0.570	0.731	0.011*
Speed & flexibility	5.77	0.95	5.97	0.73	6.42	0.78	0.711	0.135	< 0.001***
Valid N	122		32		60

Note: * significant at 0.05, ** significant at 0.01, *** significant at 0.001, Missing data = 2. A double underlined value was the highest. An underlined value was second highest

Production approaches

Tortorella et al. (2017a) and Tortorella et al. (2017b) explored the effects of contextual factors on lean supply chain management, and recommended future study on production volume and production approaches. In this research, production approaches were divided into make-to-order (MTO), make-to-stock (MTS), and a mix of MTO & MTS. MTS had the highest mean values in every TW and AM construct and sub-construct as shown in Table 5. However, no significant differences were found across production approaches in all constructs and sub-constructs. This was different from Vanichchinchai (2020), who explored LM and supply chain relationships in all manufacturing industries as a whole. Vanichchinchai (2019a) classified quality management and supply chain management frameworks by scope of implementation (at operational level, or at all levels) and generalities in industries (in specific business, or in any businesses) into qualifier, extender, improver and winner. TW and AM are key operational strategies and are more specific in the automotive supply chain (Qamar and Hall 2018; Vanichchinchai 2019a). These findings can be explained by this research specifically focusing on only the automotive parts industry. Most automotive parts manufacturers applied operational assembly-to-order (ATO) strategy. ATO (push-pull) is a hybrid and combines the benefits of MTS (push) and MTO (pull). It produces and maintains common work-in-process at reasonable quantities at a decoupling point to gain efficient cost. When there are orders, the work-in-process will be completed as finished products according to the customer requirements with a shorter lead time (Simchi-Levi et al. 2009; Jacobs and Chase 2011). With this strategy, ATO aligns LM and AM concepts. It is capable of production with lower inventory cost than pure MTS, with faster delivery than pure MTO.

Table 5.

Differences across production approaches

	MTO		Both		MTS		MTO & Both	MTO & MTS	Both & MTS
	Mean	SD	Mean	SD	Mean	SD	Sig.	Sig.	Sig.
TW	5.63	0.90	5.82	0.94	6.07	0.54	0.311	0.140	0.550
Challenge	5.61	0.97	5.85	0.95	5.98	0.75	0.230	0.297	0.858
Kaizen	5.66	0.98	5.87	0.98	6.22	0.72	0.292	0.060	0.345
Genchi genbutsu	5.38	0.98	5.69	1.04	5.96	0.80	0.086	0.057	0.543
Respect	5.81	0.96	5.84	1.05	6.18	0.75	0.988	0.314	0.360
Teamwork	5.68	0.98	5.88	1.12	5.99	0.83	0.429	0.485	0.907
AM	5.71	0.86	5.70	0.89	6.07	0.73	0.999	0.236	0.228
Responsiveness	5.55	0.97	5.65	0.95	5.80	0.89	0.764	0.554	0.806
Capability	5.56	0.95	5.56	1.04	6.13	0.78	1.000	0.059	0.058
Speed & flexibility	6.01	0.90	5.90	0.94	6.27	0.87	0.684	0.529	0.265
Valid N	99		96		20

Note: Missing data = 1. A double underlined value was the highest. An underlined value was second highest

IATF 16949

International quality management certification is required for global business competition. Recently, Vanichchinchai (2022) explored the effects of ISO 9001 on LM and supply chain relationships in manufacturing industries. ISO 9001 is a generic quality management system, which is applicable for every business. IATF 16949 is an international operational quality management certificate in the automotive industry. The classification by Vanichchinchai (2019a), ISO 9001 and IATF 16949 is categorized as a qualifier and an improver framework, respectively. This research compared manufacturers with and without IATF 16949. Companies with IATF 16949 had higher mean scores than companies without IATF 16949 in every construct and sub-construct except for respect and teamwork as shown in Table 6. However, the differences were not significant. This was because IATF has a different main goal from TW and AM. IATF 16949 aims to assure quality standards through defect prevention (IATF, 2021), while TW focuses on cost through waste elimination, while AM emphasizes delivery performance (Fadaki et al. 2019). In addition, the concepts of quality management systems such as ISO 9001 or even IATF 16949 (formally ISO/TS 16949) have been in use for a long time. Although some automotive parts manufacturers in Thailand did not apply for IATF 16949 certification, most of them extensively apply quality management (e.g. ISO 9001, continuous improvement activity, quality control circle), which share some good production practices with IATF 16949. Vanichchinchai (2021d) found insignificant differences across hospitals with and without ISO 9001 on overall lean performance. Bacoup et al. (2018), Chiarini et al. (2020), Micklewright (2010), Blecken et al. (2011) did not confirm positive effects of ISO 9001 on LM.

Table 6.

Differences across firms with and without IATF 16949

	Without IATF 16949		With IATF 16949
	Mean	SD	Mean	SD	Sig.
TW	5.68	0.84	5.79	0.93	0.440
Challenge	5.67	0.93	5.79	0.95	0.389
Kaizen	5.64	0.95	5.88	0.97	0.091
Genchi genbutsu	5.46	1.02	5.62	1.00	0.272
Respect	5.85	0.85	5.85	1.05	0.991
Teamwork	5.80	0.87	5.79	1.11	0.944
AM	5.63	0.80	5.78	0.89	0.253
Responsiveness	5.50	0.90	5.66	0.97	0.241
Capability	5.54	0.92	5.64	1.02	0.520
Speed & flexibility	5.86	0.85	6.04	0.94	0.185
Valid N	68		148

Note: Missing data = 1. An underlined value was higher

Supplier tiers in supply chains

In the automotive supply chain, first tier suppliers are responsible for managing and controlling their suppliers in the next tiers. To be approved as first tier suppliers by automotive assemblers (the focal firms in supply chains), the suppliers must be sophisticated enough in management practices for just-in-time delivery. In the automotive supply chain in the UK, Qamar and Hall (2018) found lean firms at the higher upstream tiers, and agile firms at the lower downstream tiers.

Tiers in this study were classified as first tier suppliers and other tier suppliers. As shown in Table 7, first tier suppliers had higher mean values than other tiers in all constructs and sub-constructs. However, the values were insignificant except for responsiveness. Sezen et al. (2012) reported insignificant differences between adoption of LM between first and second tier Turkish automotive parts suppliers. This can be explained that although Thailand is a developing country, Thailand was ranked the 11th country in the world by automotive production volume in 2020. Thus, LM and AM, which were key strategies for automotive production became institutionalized throughout automotive supply chains in Thailand. To be certified in the first tier is more relevant to the component parts which the supplier produces than the levels of their TW and AM. Accordingly, in the US automotive industry, Choi (1999) reported insignificant differences in the levels of three out of four quality management practices between the first-tier and third-tier suppliers. He explained that quality management had been comprehensively applied in the US automotive supply chain as a developed country. For tiers in healthcare supply chains, Thailand’s healthcare system ranked 6th in the world in 2019 (CEO World, 2019). Similarly, Vanichchinchai (2021d) found insignificant differences across community hospitals, general hospitals and regional hospitals in Thailand on lean performance.

Table 7.

Differences across tiers in supply chains

	Other tiers		Tier 1
	Mean	SD	Mean	SD	Sig.
TW	5.64	0.87	5.83	0.92	0.125
Challenge	5.63	0.92	5.83	0.96	0.130
Kaizen	5.68	0.95	5.88	0.98	0.141
Genchi genbutsu	5.42	1.06	5.68	0.96	0.063
Respect	5.79	0.92	5.90	1.04	0.432
Teamwork	5.68	0.98	5.87	1.07	0.180
AM	5.63	0.85	5.81	0.87	0.126
Responsiveness	5.43	0.94	5.73	0.95	0.023*
Capability	5.54	0.95	5.65	1.02	0.414
Speed & flexibility	5.90	0.94	6.04	0.89	0.269
Valid N	88		128

Note: * significant at 0.05. An underlined value was higher

Export levels

Tortorella et al. (2017a) and Tortorella et al. (2017b) explored the impact of onshore suppliers on lean supply chain management from the inbound perspective. Thailand exports its automotive products worldwide with an export value of 919 billion THB in 2020. Exports continue to grow (News Directory 3, 2021). Therefore, the implications of domestic or international markets from the outbound perspective on LM and AM should be investigated.

Automotive parts manufacturers in this research were grouped into those exporting less than 10% of sales volume or below 10 million THB (non-exporters), and those exporting more than 10% of sales volumes or at least 10 million THB (exporters). As shown in Table 8, exporters had higher mean scores than non-exporters in every construct and sub-construct with significant differences in all constructs and sub-constructs, except for respect and speed & flexibility. This was because exporting markets had higher uncertainty and risks. Cost competition in international markets is more intense than in the domestic market. LM and AM are main strategies to help improve competitiveness of the automotive exporters in international markets via lower cost, faster speed, and more flexibility (Fadaki et al. 2019). Similar to company size analysis, most automotive part exporters were large firms with more commitment and resources for LM and AM.

Table 8.

Differences across export levels

	Non-Exporters		Exporters
	Mean	SD	Mean	SD	Sig.
TW	5.47	0.95	5.85	0.86	0.006**
Challenge	5.44	0.89	5.86	0.94	0.004**
Kaizen	5.46	1.02	5.92	0.93	0.002**
Genchi genbutsu	5.24	1.09	5.69	0.95	0.004**
Respect	5.67	1.11	5.92	0.94	0.111
Teamwork	5.54	1.11	5.88	1.00	0.033*
AM	5.48	0.86	5.83	0.85	0.009**
Responsiveness	5.30	0.92	5.72	0.94	0.004**
Capability	5.34	1.01	5.70	0.96	0.016*
Speed & flexibility	5.79	0.95	6.05	0.89	0.060
Valid N	57		159

Note: * significant at 0.05, ** significant at 0.01. An underlined value was higher

Import levels

Several LM studies reinforce the importance of proximity to suppliers for better interaction and collaboration. Tortorella et al. (2017a) investigated the effects of onshore suppliers on lean supply chain management. Tortorella et al. (2017b) reported that onshore suppliers were less influential than had been previously believed in Chi et al. (2009) and Garza-Reyes et al. (2015). Thus, they recommended that outsourcing to low cost offshore suppliers was not relevant for lean supply chain management implementation in Brazil as a developing country. Frohlich and Westbrook (2001) revealed that offshore supply offered more attractive price benefits. However, importing needed longer delivery lead times leading to higher inventory levels. Thus, importers were less likely to respond rapidly to customer demands and fluctuations (Tortorella et al. 2017a).

In this study, importers were automotive parts companies importing raw materials of at least 10% of the total raw material purchasing value or at least 10 million THB per year. The others were non-importers. Importers had higher mean values than non-exporters in every LM and AM construct and sub-construct as shown in Table 9. However, the scores were significantly different in only challenge and responsiveness. This was because manufacturing costs in Thailand as a developing country are usually low. Thus, the necessity to import from other lower cost countries is less intense than that in developed countries. The importers had more challenging targets to receive more price benefits from overseas suppliers. They emphasized responsiveness to react quickly to uncertainty from offshore sources.

Table 9.

Differences across import levels

	Non-Importer		Importer
	Mean	SD	Mean	SD	Sig.
TW	5.57	0.76	5.79	0.92	0.184
Challenge	5.41	0.86	5.82	0.95	0.021*
Kaizen	5.64	0.81	5.84	1.00	0.284
Genchi genbutsu	5.37	0.90	5.61	1.02	0.198
Respect	5.77	0.82	5.88	1.02	0.567
Teamwork	5.66	0.84	5.82	1.07	0.386
AM	5.54	0.73	5.77	0.89	0.148
Responsiveness	5.31	0.80	5.67	0.97	0.042*
Capability	5.39	0.91	5.65	1.00	0.150
Speed & flexibility	5.92	0.80	6.00	0.94	0.662
Valid N	57		159

Note: * significant at 0.05. An underlined value was higher

Implications

Although changes to some contextual factors such as production approaches, tiers in supply chains and import levels are difficult or unnecessary, partial manipulation of other factors are possible and should be carried out to improve TW and AM. Companies should apply TW and AM practices which are suitable to their contexts only. If there are changes in company contextual factors, managers should be aware and adjust their TW and AM practices accordingly.

Among the seven investigated contextual factors as summarized in Table 10, there were significant differences across firm size, nationality of firms, and export levels on most TW and AM elements. This study confirmed that TW and AM are more applicable to large firms with more resources. Therefore, it is recommended that if smaller automotive parts manufacturers aim to apply TW or AM more extensively, they should acquire more resources or increase budgets for implementation. When small firms enlarge their businesses by increasing employment, new staff will always have their own personal characteristics (e.g. working styles, beliefs, values, norms, skills), which may be different from existing staff. As has been learned from large companies, it is suggested that the small companies should invest more in practicing TW and AM. Both hard technical and soft cultural TW and AM training is needed to instill norms and working methods of the companies in the new staff.

Table 10.

Summary of differences across contextual factors

	A	B	C	D	E	F	G	Count
TW	0.004**	< 0.001***				0.006**		3
Challenge	0.012*	< 0.001***				0.004**	0.021*	4
Kaizen	< 0.001***	< 0.001***				0.002**		3
Genchi genbutsu	0.035*	< 0.001***				0.004**		3
Respect		0.002**						1
Teamwork	0.017*	< 0.001***				0.033*		3
AM	0.002**	< 0.001***				0.009**		3
Responsiveness	0.016*	0.001***			0.023*	0.004**	0.042*	5
Capability	0.007**	0.005**				0.016*		3
Speed & flexibility	0.001***	< 0.001***						2
Count (including constructs)	9	10	0	0	1	8	2	30

Note: * significant at 0.05, ** significant at 0.01, *** significant at 0.001. A = firm size, B = nationality of firms, C = production approaches, D = IATF 16949, E = supplier tiers in supply chains, F = export levels, G = import levels

Several researchers including Womack et al. (1990) and Liker (2004) explored automotive production systems worldwide and agreed that the Japanese production style is the most effective and efficient. Although there are no changes in company ownership, it is advised that Thai and other national automotive parts manufacturers should adopt the Japanese manufacturing style for lower costs and quicker responses to customer demand, as confirmed by the Japanese manufacturers in this study.

International business competition has become increasingly intense and full of uncertainty. This research suggested that if domestic sales-oriented automotive parts manufacturers have a policy to expand export markets, they should focus more on TW and AM to improve costs and responsiveness, as evidenced from sample companies.

In order to improve performance, adoption of management certification systems, especially those with clear guidelines, such as ISO 9001 and IATF 16949 is not difficult. Generally, those accreditation systems require affordable resources, effort and time. However, this research has found that specific quality assurance in the automotive industry i.e. IATF 16949 is insufficient to improve TW and AM. Currently, there is no universally accepted TW and AM certification or implementation guidelines. Therefore, automotive parts manufacturers may adapt existing TW and AM practices reported in literature to enhance performance. International professional organizations should initiate international TW and AM accreditation systems and promote them. This should be supported by customers or business associations in their industries to improve TW and AM capabilities in their supply chains.

Policymakers should promote TW and AM by offering incentives to firms such as financial subsidies, free consultation or training for TW and AM implementation. Priority should be offered to smaller firms, Thai firms and exporting firms. It should be noted that a change to a contextual factor to improve TW and AM will affect other performance. Suitable levels of contextual factor manipulation and its trade-offs must be carefully considered.

Conclusion

The automotive parts industry contributes significantly to the economies of the world including Thailand. TW and AM are outstanding manufacturing strategies especially in automotive supply chains. Their applications in various companies are different, and should be investigated. This research found significant differences across nationality of firms, firm size and export levels on overall TW and overall AM. Firm size, nationalities of firms, and export levels were the three most influential contextual factors on TW and AM elements. There were insignificant differences across production approaches and IATF 16949 on every TW and AM construct and sub-construct. Managers should practice TW and AM elements, which are suited to the contextual factors of their companies.

Although this research reported TW and AM in the same paper, their frameworks were separate. Future studies should develop leagile manufacturing models, and explore their implications in organizational contexts. Data in this study was gathered from one manager per company. Multiple respondents should be included in future research by collecting data from more than one manager per company to improve reliability. It should be noted that the questionnaire was collected during the worldwide COVID-19 pandemic. Most businesses, including automotive parts manufacturers, must adapt their business processes in order to reduce costs more efficiently or effectively react to unexpected uncertainties and changes from this outbreak. This may partially affect some results and practices, which may be different from normal situations. After COVID-19 is over, future research should be conducted on other contextual factors (e.g. nationalities of major customers and suppliers), on other management strategies (e.g. environment, resilience, safety and security), in other industries or other countries.

Appendix: Toyota Way

Challenge.

• Company has challenging goals.

• Company gives more importance to long-term goals than short-term financial goals.

• Company has clear plans towards goals.

• Staff have commitment to achieve goals.

Kaizen.

• Company treats problems as development opportunities for staff.

• Company encourages and empowers staff for continuous improvement.

• Company supports continuous improvement activities.

• Company has continuous learning and improvement.

• Company applies suitable tools/techniques for daily operations and improvement.

• Company has good working standards.

• Company has preventive measures for errors.

• Company uses reliable and suitable technologies.

Genchi genbutsu.

• Staff check information and facts by themselves.

• Information for decision making is accurate and correct.

• Staff analyze and thoroughly understand a situation before making decisions.

• Decision making is based on verified factual information and consideration of all options.

Respect.

• Company/staff respect the roles, responsibilities, capabilities and contributions of others (including customers and suppliers).

• Company/staff respect differences in idea, cultural and personal believes of others (including customers and suppliers).

• Company builds mutual understanding and trust among employees, customers and suppliers.

Teamwork.

• Company has skill development and training for staff.

• Company encourage staff to cooperate with others.

• Company gives opportunities for team members to do their best.

• Staff work as a team to achieve goals.

Agile Manufacturing.

Responsiveness.

• Company is capable of predicting market changes.

• Company is capable of perceiving market changes.

• Company is willing to change products according to customer demand.

• Company responds to changes in customer demand quickly.

Capability.

• Company has flexible facilities, equipment and workforce.

• Company has spare capacity to respond to volatile demand.

• Company selects suppliers based on flexibility and responsiveness.

Speed & flexibility.

• Company can reschedule production in response to changes in demand.

• Company delivers products to customers quickly.

• Company has delivery flexibility.

Funding

The author receives no financial support for the research, authorship, and/or publication of this article.

Declarations

Conflict of interest

The author declares no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

About the author

Associate Professor Assadej Vanichchinchai received his Ph.D in Management of Technology from the Asian Institute of Technology; M.Sc (with distinction) in Engineering Business Management from the University of Warwick; M.Eng in Engineering Management; B.Eng in Industrial Engineering from Chulalongkorn University; and LL.B from Ramkhamhaeng University. Currently, he is a lecturer at Mahidol University. He has also been appointed as an independent director in public companies and state enterprises including the Government Pharmaceutical Organization, the Public Warehouse Organization, the Police Printing Bureau, the Fish Marketing Organization, Saha Crane Corporation PLC. Dr. Assadej has been presented with many international and national academic and practitioner awards and recognitions such as the Emerald & European Foundation for Management Development Outstanding Doctoral Research Award; recognitions as outstanding papers from Emerald Group Publishing and SAGE Publications. There have also been many outstanding research awards from the National Research Council of Thailand and the National Institute of Development Administration; the National Consultant Award from the Ministry of Industry; and the Outstanding Academic Staff Award from the Association of Private Higher Education Institutes of Thailand, and others. He has published in multiple journals, such as International Journal of Production Research, Journal of Manufacturing Technology Management, Operations Management Research and International Journal of Lean Six Sigma. He is the corresponding author and can be contacted at: assadej_v@yahoo.com.