Bivariate Analysis of Subsamples

These five conclusions can be partly supported by the detailed data analysis of high performing companies in terms of ROIC. Therefore, H&M, which is the company with the highest ROIC in the sample, shows that its above-average high turnover rate influences strongly and significantly its cash conversion cycle, which means that its Supply Chain efficiency has high impact on how fast the company converts cash from sales into even more cash and therefore explains its outperformance in terms of ROIC (see Table 12).

Table 12: Correlation Matrix H&M

Note: *. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed). All results with a high significance level (p < 0.01) are marked in grey. The number

H&M Revenue

Growth H&M ROIC

H&M Gross Margin H&M Days Inventory H&M Payables Period H&M Asset Turnover H&M Cash Conversion Cycle Pearson Correlation -.511 -.594 -.419 1 .427 .446 .863** Sig. (2-tailed) .131 .092 .228 .218 .197 .001 N 10 9 10 10 10 10 10 H&M Days Inventory

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of observations is n = 10 corresponding to the observation period of 10 years.

Source: SPSS Output Table.

The findings from this analysis infer that the Supply Chain has the same significance regarding business success in terms of ROIC in the case of Esprit, which is the company with the second most efficient cash conversion cycle in the sample. It is evident, that the decrease of stock turnover days explains the ROIC strongly and significantly.

Table 13: Correlation Matrix Esprit

Note: *. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed). All results with a high significance level (p < 0.01) are marked in grey. The number of observations is between n = 8 and n = 9 due to missing values in the time series.

Source: SPSS Output Table

Although the asset turnover ratio is even more highly correlated, this is not a restriction or contradiction to the previous conclusion, regarding the relationship between stock turnover and ROIC, but an extension of it. This is explained by asset turnover calculation also taking into account the variable stock turnover (days), along with other measures, such as fixed and current assets and therefore plant and equipment, receivables and other assets are included, whereas the stock turnover rate exactly matches the Supply Chain efficiency (Lui & Lo, 2014, p. 197). The importance of the Supply Chain for the total asset turnover of the sample’s companies becomes even clearer regarding the correlation between days inventory and asset turnover. Here,

Esprit Revenue

Growth Esprit ROIC

Esprit Gross Margin Esprit Days Inventory Esprit Payables Period Esprit Asset Turnover Esprit Cash Conversion Cycle Pearson Correlation 1 .811* .626 -.796* -.362 .855** -.257 Sig. (2-tailed) .015 .097 .018 .378 .007 .538 N _{8 8 8 8 8 8 8} Pearson Correlation .811* 1 .661 -.879** -.423 .940** -.160 Sig. (2-tailed) .015 .053 .002 .256 .000 .680 N 8 9 9 9 9 9 9 Esprit ROIC Esprit Revenue Growth

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the total sample shows regarding the days inventory a strong negative and significant impact on asset turnover rate (seey is the determining function. ) which means, that the lower the days inventory value, i.e. the higher the inventory turnover rate, and the higher is the asset turnover. Therefore, it becomes evident, that Supply Chain efficiency is the determining function.

Table 14: Correlation of Days Inventory and Asset Turnover – Total Sample

Note: *. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed). All results with a high significance level (p < 0.01) are marked in grey. The number of observations is n = 10 corresponding to the observation period of 10 years.

Source: SPSS Output Table.

The descriptive analysis of American Apparel reveals that the company has the lowest revenue growth in the sample, and the third highest gross margin, but the lowest ROIC, and shows very poor performance, in terms of inventory turnover rate and in terms of cash conversion cycle (see Table 8). The cash conversion cycle performance particularly showed that the company needed 190 days to convert an input dollar into cash inflow through sales, shown in Figure 28).

The correlation matrix (see Table 15), suggests that the reason for this slow cash conversion cycle is highly and significantly associated with the slow inventory turnover rate.

Asset Turnover Average Total Pearson Correlation -.847** Sig. (2-tailed) .002 N 10 Days Inventory Average Total Sample

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Table 15: Correlation Matrix American Apparel

Note: *. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed). All results with a high significance level (p < 0.01) are marked in grey. The number of observations is between n = 7 and n = 8 due to missing values in the time series.

Source: SPSS Output Table.

These findings are also supported by the analysis of fast turnover/high margin companies and slow turnover/low margin companies (see Figure 26). The subsample of fast turnover/high margin companies consists of H&M, Esprit and Inditex and the subsample of slow turnover/low margin companies consists of ASICS, Adidas, Puma, and Under Armour.

Table 16: Correlation Matrix for Positioning Subsamples

Note: *. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed). All results with a high significance level (p < 0.01) are marked in grey. The number cases in the subsample “Fast Turnover/High Margin” is n = 3; the number cases in the subsample “Slow Turnover/Low Margin” is n = 4. Since the time series of all companies in each subsample were summed up for each variable. Thus, for each variable the number of total observations is n = 10 in the time period of ten years.

Source: SPSS Output Table.

American Apparel Revenue Growth American Apparel ROIC American Apparel Gross Margin American Apparel Days Inventory American Apparel Payables Period American Apparel Asset Turnover American Apparel Cash Conversion Cycle Pearson Correlation -.380 -.291 .276 1 .212 -.448 .972** Sig. (2-tailed) .400 .484 .508 .614 .265 .000 N 7 8 8 8 8 8 8 American Apparel Days Inventory Rev. Growth Average Total Sample in % yoy ROIC Average Total Sample Gross Margin Average Total Sample Days Inventory Average Total Sample Cash Conversion Cycle Average Total Sample Pearson Correlation -.614 -.825** -.390 1 .142 Sig. (2-tailed) .059 .006 .265 .696 N 10 9 10 10 10 Pearson Correlation -.763* -.301 .265 1 .885** Sig. (2-tailed) .010 .397 .458 .001 N 10 10 10 10 10 Days Inventory Fast Turnover/High Margin Days Inventory Slow Turnover- Turnover/Low Margin

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The findings infer that there is a significant, strongly negative correlation for the fast turnover/high margin companies meaning that the lower the value of the stock turnover (days inventory), the higher the ROIC. However, in the case of slow turnover/low margin companies, the correlation is not significant, it is weak r=0.301. However the variable stock turnover (days inventory) provides strong evidence for the variable cash conversion cycle, indicating that the higher the values of stock turnover (days), the higher the value for the cash conversion cycle, it takes the company longer to obtain cash inflow from the cash outflow. The interpretation of these results can related to the ROICs of the sample companies: whereas Esprit, Inditex, and H&M can accomplish above average ROIC, with H&M as the indisputable top performer, ASICS, Adidas, Puma, and Under Armour are either categorised as average performer or underperformer (see Figure 30). As a consequence, the interpretation leads to the deduction that Supply Chain Management is not merely a supporting business function but a key factor in business success in the fashion industry, and reinforced by the conclusions from the earlier sections of the statistical analysis.