Can the financial performance of the micro, small, and medium-sized enterprise production sector in Medan be a signal in the use of a leanness strategy?

Introduction

Globally, the growth of micro, small, and medium-sized enterprises (MSMEs) is higher than that of large companies, especially in Indonesia. Based on MSME data,¹ the growth of MSMEs in Indonesia from 2017 to 2018 was approximately 8.98 percent compared to large enterprises, which only grew by approximately 1.64 percent. The discussion on microenterprises (MEs) is challenging compared to small and medium-sized enterprises (SMEs) in various studies. There is a lack of information and data on microenterprises in the field regarding the fluctuating and inconsistent actions of microbusinesses. Furthermore, based on data, the SME sector plays a crucial and significant role as a driving force for the economy in creating new jobs, increasing the number of new entrepreneurs, and creating innovations at relatively economical and efficient costs.² Additionally, data and facts show that SMEs contribute, on average, to developing countries in the range of 20 to 50 percent of total GDP.^3,4 Based on Usaha Mikro Kecil Menengah (UMKM) data,¹ SMEs in Indonesia have contributed 23.21 percent of the total GDP. The city of Medan is the third-largest city in Indonesia.¹² It is the gateway to western Indonesia as a multi-cultural centre of trade, industry, and business since the Dutch East Indies era.^11,12 If grouped by production characteristics, it contains eleven groups in the production sector (see data file 12).¹⁰ Based on the Office of Medan City Cooperatives and SMEs Service database in 2020, the majority of the production sector of the MSMEs from the 466 identified in Medan City are in the handicrafts production group with 95 businesses, followed by the fabric production group with 79 businesses (see data file 12).¹⁰ Therefore, the studying and deepening of MSMEs' strategies in maintaining their performance conditions are needed to act as a barometer of crisis resistance in the macroeconomic sector, especially concerning MSMEs in Medan.

There are various business strategies for improving business performance that can lead to environmental friendliness, one of which is the ‘leanness production strategy’.⁵ The mechanism and standardization of lean production in seeing its effect on financial performance (company) are still in the early stages. Currently, the practice of lean production is considered the gold standard of modern operations and supply chain management.⁶ Lean refers to eliminating waste in operational practices regarding costs in obtaining resources.⁷ Additionally, Hofer et al.,⁶ revealed that the application of internal and external lean practises, such as a decrease in inventory, an increase in quality, and a shorter operating time, will increase operating income, which will improve financial performance. Nawanir et al.,⁸ defined lean as ‘lean manufacturing’, a manufacturing process without residue; however, there are not enough studies conducted in Indonesia that discuss lean manufacturing strategies.

The studies relating to MSMEs' strategic practices in Indonesia have not provided comprehensive information, as they lack a conceptual approach and do not consider the characteristics that exist in Indonesia in general. Additionally, the effect of the deepening of business strategy practices on MSMEs' financial performance in Medan city, the third-largest city in Indonesia, can be considered a novelty. This study empirically focuses on the leanness strategy used by MSMEs in Medan. External and internal leanness strategy practices are exogenous variables, and financial performance is a mediating variable. Thus, there are seven research hypotheses in this study:

Methods

Data analysis

The data analysis carried out in this study consisted of two sub-model tests, namely testing the measurement model or often referred to as the outer model and testing the structural model or often referred to as the inner model with 0.10 as its’ significant level. Before testing the outer model and inner model, it is necessary to test whether the arrow direction in each construct is formative or reflective. In this case, a test will be carried out on each exogenous and endogenous construct whether there is an intercorrelation on the indicators owned. If there is an indication of intercorrelation between indicators, it can be concluded that the construct is reflective vice versa.⁹ Then testing the measurement model or outer model is carried out to determine which indicators have a significant role in explaining latent variables with the partial least square algorithm mechanism with the provisions of whether the construction is formative or reflective. This was done more than twice to obtain significant indicators in explaining the latent variables in each construct. After obtaining strong and significant indicators for each construct, the next step is to test the inner model. This test is helpful to see the strength of the influence between latent variables through the path or R-square value between latent variables. Testing the inner model will be carried out using the bootstrapping mechanism. After testing the inner model and outer model, the last step is testing the model fit of the built model, with each construct having significant indicators. Before it is known empirically whether the construct will be formative or reflective, this study assumes that all constructs are in a reflective position. Simultaneous equation modelling - partial least squares regression analysis, assisted by Smart PLS 3.0 and SPSS 18, was used. In this case, the constructs of two exogenous variables, one endogenous variable and one mediation variable along with each latent variable's manifest variable, are presented in Figures 1–5.

Figure 1. Inner construct model.

Notes: ξ₁ = external lean practice, η₁ = financial performance, γ₂₁ = the power of ξ₁ in influencing η₂, γ₁₁ = the power of ξ₁ in influencing η₁, ξ₂ = internal lean practice, η₂ = the use of lean strategy, γ₁₁ = the power of ξ₁ in influencing η₁, γ₁₂ = the power of ξ₂ in influencing η₁, and β₂₁ = the power of η₁ in influencing η₂.

Figure 2. Outer construct model for ξ₁.

Notes: X₁ = supplier feedback, X₂ = supplier just in time, X₃ = supplier development, X₄ = customer involvement λ₄ = the strength reflective power of X₄ in influencing ξ₁, λ₃ = the strength reflective power of X₃ in influencing ξ₁, λ₂ = the strength reflective power of X₂ in influencing ξ₁, and λ₁ = the strength reflective power of X₁ in influencing ξ₁.

Figure 3. Outer construct model for ξ₂.

Notes: X₅ = pull system, X₆ = continue flow, X₇ = setup time reduction, X₈ = statistical process control, X₉ = employee involvement, X₁₀ = total productive maintenance, λ₁₀ = the strength reflective power of X₁₀ in influencing ξ₂, λ₉ = the strength reflective power of X₉ in influencing ξ₂, λ₈ = the strength reflective power of X₈ in influencing ξ₂, λ₇ = the strength reflective power of X₇ in influencing ξ₂, λ₆ = the strength reflective power of X₆ in influencing ξ₂, and λ₅ = the strength reflective power of X₅ in influencing ξ₂.

Figure 4. Outer construct model for η₁.

Notes: Y₁₈ = return on investment, Y₁₉ = return on assets, Y₂₀ = sell growth, Y₂₁ = total operating cost, λ₁₁ = the strength reflective power of Y₁₈ in influencing η₁, λ₁₂ = the strength reflective power of Y₁₉ in influencing η₁, λ₁₃ = the strength reflective power of Y₂₀ in influencing η₁, and λ₁₄ = the strength reflective power of Y₂₁ in influencing η₁.

Figure 5. Outer construct model for η₂.

Notes: LE₁ = pull production system, LE₂ = inventory delivery, LE₃ = business location, LE₄ = consumer needs, LE₅ = production time, λ₁₅ = the strength reflective power of LE₁ in influencing η₂, λ₁₆ = the strength reflective power of LE₂ in influencing η₂, λ₁₇ = the strength reflective power of LE₃ in influencing η₂, λ₁₈ = the strength reflective power of LE₄ in influencing η₂, and λ₁₉ = the strength reflective power of LE₅ in influencing η₂.

Results

The intercorrelation results for all constructs do not show a strong relationship with each other.¹⁰ Therefore, empirically the indicators that build the four constructs are formative, starting from external leans practice, internal leans practice, financial leans practice, and use of leans strategy. This report shows that testing the outer and inner models must be in line with the standard formative construct testing. Furthermore, examining the outer and inner models with formative constructs can only be analysed based on the bootstrapping calculation mechanism.⁹

In this case, based on bootstrapping results (see data file 5),¹⁰ to see the value of the indicator in each construct with a significant or insignificant value based on P-Values is through the tabulation of outer loadings. Based on the tabulation of outer loadings, the indicators (manifest variables) LE₂ (inventory delivery) and LE₃ (business location) have no significant value in explaining the use of leans strategy, with their P-Values values being 0.808 and 0.493, respectively (see data file 5).¹⁰ Moreover, in the external leans practice construct, only X₄ (customer involvement) has no significant value to explain the latent variable with a P-Value value of 0.588.¹⁰ Thus, only indicators LE₂, LE₃, and X₄ should be eliminated from each construct. Bootstrapping calculations are performed again after eliminating LE₂, LE₃, and X₄. Even though, the P-Value of LE₄ (consumer needs) on the outer loadings shows above 0.05, it can still be maintained because this study is exploratory with a significance level of 0.10 (see data file 5).¹⁰

The next step is to measure the strength of the inner model is through the path coefficient tabulation derived from the results of the bootstrapping calculation. In this case, it is known that the path between the external leans practice latent variable, and the use of leans strategy latent variable should be eliminated because the significance value is above 0.05 (see data file 13).¹⁰ Similarly, the latent variable financial performance on the latent variable use of leans strategy should be removed from the inner structural model because it has a significant value above 0.05 (see data file 13).¹⁰

After eliminating three indicators or manifest variables and two inner model paths that are not significant at the 0.0.05 level, the final bootstrapping result (see data file 5)¹⁰ is obtained with overall outer loadings (see data file 5)¹⁰ and path coefficients (see data file 5),¹⁰ which have a significance value below 0.05. Thus, from the path diagram figure²⁹ of the bootstrapping process, equation models can be made for this study's outer model and inner model.

Based on the results of the bootstrapping calculation, it is known that the P-Value values for hypotheses three (H3) and five (H5) are above 0.05, namely 0.529 and 0.932, respectively (see path coefficient data file 13),¹⁰ which means that they are not significant at 0.05 and should be excluded from the study. Because H3 and H5 do not meet the 0.05 significance requirement, then hypothesis six (H6) automatically cannot be continued for analysis because it is clearly proven that the practice of external leans strategy does not affect or cannot be a signal for the use of leans strategy by MSMEs. Furthermore, hypothesis seven (H7) cannot be accepted or continued for analysis because H5 is actually excluded from the study, so it can be interpreted that empirically financial performance cannot be a mediating variable between internal and external practice of leans on the use of leans strategy. So, the final modification result of the bootstrapping (see data file 5)¹⁰ concludes that the first, second, and fourth hypotheses are empirically acceptable. Therefore, financial performance cannot signal the practice of leanness strategy in MSMEs in Medan's production sector. However, it was found that internal lean practice predominantly influences the financial performance of MSMEs in Medan city compared to external lean practice. Furthermore, internal lean practice is the primary signal of the lean strategy in the MSME production sector in Medan city. The mechanism for obtaining these research results can be seen in Figure 6, which presents a flow diagram of outer model evaluation, and Figure 7, which presents a flow diagram of inner model evaluation. From 169 respondents who returned the questionnaire with complete entries, it is known that 51 respondents are small-sized businesses, five respondents are medium-sized businesses, and 113 respondents are micro-sized businesses. Then, most of the production sector of MSMEs in Medan city are handicraft producers, followed by fabric producers, convection producers, shoe manufacturers, and household furniture manufacturers (see data file 12).¹⁰

Figure 6. Outer model evaluation.

Figure 7. Inner model evaluation and final equation result.

Discussion

Based on the results of path coefficient analysis (see data file 13),¹⁰ it is proven that empirically the first hypothesis, second hypothesis, and fourth hypothesis are acceptable. Therefore, this study contributes to the theory of business strategy used by MSMEs in the production sector of Medan by highlighting and investigating the role of internal and external leans practice and financial performance. The study results obtained the importance of internal leans practice in influencing leans strategy and financial performance. In addition, external lean practice is known to affect the financial performance of MSMEs.

The first hypothesis has been proven to be acceptable that external leanness strategy practices carried out by MSMEs in Medan (production sector) affect the company's financial performance. In this case, the characteristics of external lean practice carried out by MSMEs in the production sector of the city of Medan validly consist of three indicators or characters, namely providing supplier feedback (X₁), suppliers just in time (X₂), and referred to as supplier development (X₃). So, based on the external leans practices character owned by MSMEs in the production sector, they can streamline and grow business financial performance. In contrast to the findings of the study conducted by Hofer et al.,⁶ that external leanness strategy practice is not significant in influencing business financial performance. Hofer et al.,⁶ states that financial performance will be good if cost reduction is realized, and cost reduction can be made if it is influenced by inventory leanness. However, some study results state that the external leanness strategy has a real significance in influencing financial performance.^5,20

The second and third hypotheses are empirically proven,¹⁰ where internal lean practice in MSMEs in the production sector has an influence value of 63.6 percent positively on financial performance and 77.6 percent on lean strategies.¹⁰ Furthermore, the six indicators of internal lean practice proved to be valid with P-Values < 0.10.¹⁰ Thus, the results of this study prove that the characteristics of MSMEs in the production sector of the city of Medan in running a business during 2020 are by the six indicators that build an internal lean practice, namely carrying out business in a pull system (X₅), continue flow (X₆), setup time reduction (X₇), statistical process control (X8), employee involvement (X₉), and total productive maintenance. Furthermore, studies conducted by Hofer et al.,⁶ and Fullerton and Wempe¹⁸ prove that there is a positive influence between internal leans practice on financial performance. However, if between internal leans practice and financial performance the inventory leans variable or non-financial performance measures, there will decrease direct influence by 25 percent. Furthermore, a study conducted by Hallgren, M. et al.,¹⁹ and Chanegrih, T.²⁰ show that the effort to practice leans strategy will result in more efficient financial performance than companies with agile characteristics. Therefore, it is empirically and theoretically proven that internal lean practice will positively affect the effectiveness of financial performance efficiently, which consists of five indicators, namely, return on investment (Y₁₈), return on assets (Y₁₉), cell growth (Y₂₀), and total operating cost (Y₂₁). Furthermore, something that is a novelty from this research is the proven internal lean practice as a strong signal in showing whether a business has used a leans strategy or not. In this case, the use of the leans strategy by MSMEs is seen from five indicators. However, only three indicators have proven significant as a characteristic of MSMEs business strategy in the production sector of Medan, namely, the use of a pull production system (LE₁), based on consumer needs (LE₄), and the use of time production (LE₅).

The empirical proof of this study showed that financial performance could not signal using a strategy that the MSMEs production sector of Medan used (see path coefficient data file 13).¹⁰ However, internal and external lean practice empirically could be a signal on the financial performance of MSMEs production sector in Medan, which internal leans practice has a strong influence compared to external leans practice. Thus, based on the path coefficient, the financial performance of MSMEs cannot be a mediating variable between external and internal leanness practices toward the use of leans strategy. Therefore, based on path diagram figure (see data file 2)¹⁰ the prediction equation model (inner model equation) for the use of a leanness strategy in MSMEs production sector Medan is only influenced by the practice of internal leanness: $\text{with the model equation is}\mathrm{as}\text{follows}{\eta }_2=0.776{\xi }_2$. Furthermore, empirically the equation model (inner model) for financial performance in MSMEs production sector in Medan is ${\eta }_1=0.155{\xi }_1+0.636{\xi }_2$. This study proves that the role of external and internal leans practice affects the financial condition and business strategy of MSMEs in the production sector of Medan.

This study can be categorized as a study of lean manufacturing practices because it combines the perspectives of just-in-time (JIT), total quality management (TQM), and total productive/preventive maintenance (TPM) to obtain productivity and quality levels through waste reduction.²² An emphasis on internal and external strategic leanness practices can improve financial performance and environmental protection because resources will be limited to what is needed and not to what is desired.⁵ These findings can act as a map for practitioners, academics, and the government to improve MSMEs' financial performance and assist MSMEs with their business strategies through internal business practices. Furthermore, this finding can be an entry point for investigating whether MSMEs in the production sector of Medan have characteristics of agile manufacturing. The characteristics of MSMEs with agile manufacturing are needed in the current situation; where the growth of transactions or trade globally is increasing, the environment is uncertain, the development of technology is rapidly getting faster, and the pressure of competition between manufacturers requires different practices and different tools to improve the process company operations. In this case, a study conducted by M. Khalfallah and L. Lakhal²² proves that lean manufacturing can explain whether a company has agile manufacturing practices where agile manufacturing is essential in viewing operational performance.

Conclusion

These findings can act as a map for practitioners, academics, and the government to improve MSMEs' financial performance and assist them with business strategies through internal business practices. Moreover, this study's impact can lead to an understanding of operations business strategies that directly or indirectly affect the environment. Finally, in-depth knowledge of business actors' operational systems will assist regional, state, and international policies to fight global warming and various environmental problems through waste reduction.

Data availability