Evaluating Performance Measurement Metrics for Lean and Agile Supply Chain Strategies in Large Enterprises
The PLS-PM method using XLSTAT software has been adopted for outer and inner model analysis. XLSTAT is a statistical software used for various applications, including descriptive statistics, hypothesis testing, and regression analysis). Partial Least Squares (PLS) is a statistical method used in structural equation modelling (SEM) and regression analysis. In PLS-PM, the emphasis is on predicting the dependent variables rather than explaining their variance. This method is also applicable when dealing with small sample sizes.
4.2. Inner Model Analysis (Hypotheses Testing Using PLS-PM Method)
This section will present an in-depth understanding of the relationship between SC strategies and SC performance metrics. The degree to which a supply chain strategy influences a particular performance metric directly correlates with the importance of that metric within the strategy. This signifies that metrics closely aligned with a specific supply chain strategy hold greater significance for the overall success and effectiveness. Consequently, supply chain strategies are expected to prioritise practices aimed at enhancing these pivotal metrics. For instance, in a lean supply chain strategy focused on minimising waste and optimising efficiency, inventory turnover or lead time metrics assume heightened importance, prompting the implementation of practices geared towards inventory management and process streamlining. Similarly, in an agile supply chain strategy emphasising responsiveness and flexibility, metrics such as customer response time or product customisation capability become paramount, leading to adopting practices geared towards enhancing agility and adaptability within the supply chain. Therefore, the alignment of supply chain strategy and performance metrics underscores the need for tailored practices to optimise these critical metrics to drive overall strategy success. To validate the research hypotheses, the coefficient of determination, structural coefficient, and the percentage of the contribution to R2 will be calculated and supported by a graphical representation of the importance of each performance metrics group.
In the upcoming point, the analysis will delve into calculating the coefficient of determination, a pivotal statistical measure used to assess the strength of the relationship between variables in our study. This essential metric provides valuable insights into how one variable can be predicted by another, laying the groundwork for a deeper understanding of our research findings.
Table 5 reveals a coefficient of determination of 0.4901, indicating that the two types of strategies influence over 49% of the financial performance. These strategies account for around 20% of the efficiency performance distribution, meaning that 19.44% of the Efficiency Financial Performance (EFP) is attributable to the variation in ASCS and LSCS. Similarly, the inner model analysis results demonstrate a coefficient of determination of 0.5437 for the CSP variable. This implies that more than 54% of the CSP data distribution stems from variations in the lean and agile SC strategies. Lastly, according to the results depicted in Table 4, lean and agile SC strategies contribute to approximately 37% of the data distribution regarding performance in terms of flexibility.
Following the presentation of the coefficient of determination, the subsequent stage involves computing the structural coefficient for FIP, EFP, CSP, and FLP.
Table 6 indicates that financial performance is predominantly influenced by the lean strategy, as evidenced by its structural coefficient of 0.6354, contrasting with the insignificant coefficient of 0.0902 for the agile strategy. Additionally, the findings underscore the continued significance of the lean strategy in determining efficiency, with respective structural coefficients of 0.2863 and 0.1926 for agile SC. Consequently, the findings highlight the notable association between lean strategy, financial performance, and cost optimisation. On the other hand, the analysis reveals that the agile strategy holds greater importance in the customer service dimension, with a structural coefficient of 0.4778 compared to 0.3231 for the lean strategy, indicating a solid linkage between customer services and the agile strategy. Moreover, Table 5 further demonstrates that the agile SC strategy exerts a more substantial impact on flexibility, as evidenced by its higher structural coefficient of 0.5005 compared to 0.1435 for the lean strategy, emphasising the robust connection between FLP and the ASCS.
Following the determination of the structural coefficient, the subsequent step involves calculating the contribution percentage to R2 of the FIP, EFP, CSP, and FLP, providing additional insight into the variance explained by the model.
As indicated in Table 7 above, it becomes apparent that the lean SC strategy plays a predominant role in determining financial performance, accounting for over 90% of the R2. In comparison, approximately 10% is attributed to the agile SC strategy, emphasising the lean SC strategy’s focus on production costs and pricing to bolster sales. Additionally, the table illustrates that the lean SC strategy contributes more than 61% to the R2 of business efficiency, whereas the agile SC strategy’s contribution represents about 39% of the R2. Conversely, it is evident from Table 6 that, for customer service, the agile SC strategy’s contribution is more significant, exceeding 61%, while that of the LSCS variable represents approximately 39%. Table 6 further confirms the predominance of the agile SC strategy in enhancing flexibility, with a contribution rate to R2 exceeding 81%, while the proportion of LSCS represents approximately 19% of R2. Consequently, an agile SC strategy has a more pronounced influence on performance in terms of services and customer satisfaction compared to a lean SC strategy, which is more closely associated with financial performance and costs. Figure 2 highlights the correlation between each performance’s metrics and SC strategies.
In summary, the results of the inner model analysis conducted using structural equation modelling (SEM) based on the PLS-PM approach reveal that both supply chain (SC) strategies, namely lean and agile, exert a positive influence on the four dimensions of supply chain performance: financial performance, efficiency, customer service, and flexibility. The direct effect of the lean SC strategy on these dimensions was 0.6354, 0.2863, 0.3231, and 0.1435, respectively, whereas the direct impact of the agile SC strategy was 0.0902, 0.1926, 0.4778, and 0.5005, respectively. Based on these findings, we can categorise the performance dimensions into two groups: the first group encompasses financial performance and efficiency, wherein the direct effects of the lean SC strategy were greater than those of the agile SC strategy, with respective direct effects of 0.6354 and 0.2863 for the lean SC strategy compared to 0.0902 and 0.1926 for the agile SC strategy. In contrast, for the dimensions of the second group, namely customer service and flexibility, the impact of the agile SC strategy surpasses that of the lean SC strategy, with direct effects of 0.4778 and 0.5005 from the agile SC strategy, while those from the lean SC strategy are 0.3231 and 0.1435, respectively.
