42 Findings in Table 13 showed that the trend of average inventory turnover declined each year from 7.55 times in 2016 to 7.07 times in 2018. More than half of the food and beverage firms from 2016 to 2018 had inventory turnover ratios of more than 5 times, which meant that these manufacturers could convert their inventory into sales more than 5 times per year. The faster the better, since food and beverages are perishable goods with short shelf lives, and good inventory management could help to reduce the risk of expiration, damage, and out-of-date inventory. However, from 2016 to 2018, 30.55% to 44.44% of firms had inventory turnover ratios of less than 5 times, and the middle category has dropped from 50.00% to 36.11% in 2018. Lower inventory turnover means that they had high levels of inventory on hand, which is not a good idea for perishable goods with short shelf lives. The slower the inventory is being converted into sales, the lower the profits. 2016-2018 Current Ratio Current Ratio is determined by current assets divided by current liabilities. It represents the ability to pay short-term debts when they are due (Stickney et al., 2009). Table 14. 2016-2018 Current Ratio * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent 0.00 – 1.00 15 41.67 13 36.11 13 36.11 1.01 – 2.00 9 25.00 12 33.33 11 30.56 2.01 – Above 12 33.33 11 30.56 12 33.33 Total 36 100.00 36 100.00 36 100.00 Avg. Current Ratio 2018= 2.27| Avg. Current Ratio 2017= 2.35| Avg. Current Ratio 2016= 2.19 * Current Ratio equals Current Assets divided by Current Liabilities The findings in Table 14 indicate that from 2016 to 2018, 36% to 41% had current ratios lower or equal to 1.0. This means that many firms were having a hard to
43 pay off their short-term debts when they are due. During the same time period, over 30% of firms had current ratios of 2.01 or higher. While this may look good in general, having too high a current ratio may not be effective because this may be the result of holding high levels of inventory. More than half of the manufacturing firms in Table 11 had high amounts of inventory tied up in their current assets. This inflates their current ratios and increases the risk of inventory damage, out-of-date inventory, and losses. In addition, high levels of inventory tie up cash and thus may reduce firm profitability (Vinturella & Erickson, 2013). In 2018, firms’ current ratios of lower or equal to 1.0 increased to 41.67%. This may because in 2018 many firms increased their firm size in order to prepare for higher domestic and international demand for food and beverages. This expansion may have led to increased borrowing, which would have increased liabilities in 2018, and caused current ratios to decline. 4.2. 2016-2018 Model Analysis Food and Beverage Sector Characteristics 2016-2018 Model Summary The Cox and Snell R Square and Nagelkerke R Square reflect the likelihood that the variation in the dependent variable is explained by the independent variables (Reddy, Likassa & Asefa, 2015). Table 15. 2016-2018 Model Summary 2018 2017 2016 -2 Log likelihood 24.929 29.971 34.290 Cox & Snell R Square .387 .295 .205 Nagelkerke R Square .558 .425 .295 Table 15 indicates that from 2016 to 2018, the Cox and Snell R Square suggests that 20.5%, 29.5%, and 38.7%, respectively of variation in the probability
44 that food and beverage manufacturers’ use of inventory costing method was explained by firm size, inventory size, firm leverage, profitability, inventory turnover, and current ratio. In addition, from 2016 to 2018, Nagelkerke R Square indicated that 29.5%, 42.5%, and 55.8%, respectively of variation in the probability that food and beverage manufacturers’ use of inventory costing method was explained by firm size, inventory size, firm leverage, profitability, inventory turnover, and current ratio. 2016-2018 Hosmer-Lemeshow Test The Hosmer-Lemeshow test is a secondary test commonly used to measure the goodness of fit for logistic regression models (Zhang, 2016). It tests the hypothesis that observed data are significantly different from the data predicted by the model (the expected data), and measures these differences (Field, n.d). Thus, the Hosmer-Lemeshow test is a kind of Chi-Square test that measures how well the actual data fit with the values predicted by the model; it is shown with a p-value. A value greater than 0.05 (> 0.05) means that no significant differences or problems were found between the observed and expected values; the data display a reasonable level of fit with the model. If the p value is smaller than 0.05 (< 0.05), however, this indicates major differences between the observed and expected values. It means that the model is not a good fit – there are significant problems with it (Blog.ExcelMasterSeries.com, 2014). Table 16. 2016-2018 Hosmer-Lemeshow Test 2018 2017 2016 Chi-square 4.797 5.216 4.989 df 7 7 7 p-value .685 .634 .661
45 The results in Table 16 indicated that from 2016 to 2018, the p-values were larger than 0.05, so they are not significant. In other words, there was a 66.1%, 63.4%, and 68.5% chance that the observed values were not different from the expected values, so the model does not display goodness of fit problems. 2016-2018 Classification Table The Table 17 results indicate how many cases are correctly predicted by comparing the number of firms that use FIFO Method (FIFO=1) and the number of firms that use Average Cost Method (AC=0) predicted by the logistic regression model to the number actually observed. Table 17. 2016-2018 Classification Table 2018 2017 2016 FIFO AC Correct FIFO AC Correct FIFO AC Correct FIFO 6 4 60.0% 4 6 40.0% 2 8 20.0% AC 3 23 88.5% 3 23 88.5% 3 23 88.5% Overall Percentage 80.6% 75.0% 69.4% Results indicate that in 2016, the logistic regression model correctly classified 69.4% of the manufacturing firms that selected the Average Cost Method, and 31.6% of the manufacturing firms that selected FIFO Method. In 2017, the model correctly classified 75.0% of the manufacturing firms that selected the Average Cost Method, and 25.0% of the manufacturing firms that selected FIFO Method. In 2018, the model correctly classified 80.0% of the manufacturing firms that selected the Average Cost Method, and 20.0% that selected FIFO. 2016-2018 Logistic Equation for Variables, Food and Beverage Firms Tables 18.1-2 explain the relationship between the independent and dependent variables. They display information regarding an equation based on the characteristics
46 of the food and beverage sector using (B) coefficient data, standard error, Wald Chisquare test, degrees of freedom, p-value, and odds ratio. Table 18.1. 2016-2018 Logistic Equation for Variables, Food and Beverage Firms 2018 2017 2016 Constant B -2.888 -1.883 -1.662 S.E. 1.175 .775 .790 Wald 6.045 5.909 4.429 df 1 1 1 p-value .014 .015 .035 Odds Ratio .056 .152 .190 Firm size B -1.414 -.835 -1.927 S.E. 1.558 .663 2.421 Wald .824 1.586 .634 df 1 1 1 p-value .364 .208 .426 Odds Ratio .243 .434 .146 Leverage B -.326 -.046 .167 S.E. .764 .655 .723 Wald .182 .005 .053 df 1 1 1 p-value .670 .944 .817 Odds Ratio .722 .955 1.182 Inventory Size B -2.467 -2.504 -1.703 S.E. 1.269 1.460 1.088 Wald 5.850 2.941 2.449 df 1 1 1 p-value .052 .086 .118 Odds Ratio .085 .082 .182 The findings for food and beverage manufacturing firms in Table 18.1 found that from 2016 to 2018, firm size, leverage and inventory size did not have any significant influence (p-value > 0.05) on the inventory costing method. Some smaller companies (10 out of 36 firms) used the FIFO Method to manage their inventory, while most large companies tended to use an Average Cost Method.
47 However, (B) Coefficient value still showed a relationship between firm size, leverage and inventory size. It implied that from 2016 to 2018, for a one-unit increase in firm size, use of the FIFO Method decreased by -1.927, -.835 and -1.414, respectively. This can be interpreted to mean that larger food and beverage manufacturing firms were less likely to use the FIFO Method. Applying the FIFO Method in larger firms might consume more time than use of Average Cost Method. For firm leverage, the findings show that from 2016 to 2018, for every one unit increase in firm leverage use of the FIFO Method increased by .167, and decreased by -.046 and -.326, respectively. This shows that firm leverage was probably unrelated to choice of inventory method. In regards to the inventory size of manufacturing firms, the results showed that from 2016 to 2018, for a one unit increase in inventory size, use of the FIFO Method decreased by -1.703, -2.504 and -2.467, respectively. This can be interpreted to mean that increases in inventory size from 2016 to 2018 negatively unrelated to choice of inventory method, because use of the FIFO Method is more complex than use of the Average Cost Method. The odds ratios also implied that from 2016 to 2018, firm size was .146 times, .434 times, and .243 times, respectively less likely to influence use of the FIFO Method in place of the Average Cost Method. In addition, from 2016 to 2018, the odds ratio of firm leverage to inventory method were 1.182 times, .955 times, and .722 times, respectively. These results showed that higher levels of firm leverage in 2018 were less likely to be associated with use of the FIFO Method than the Average Cost Method.
48 From 2016 to 2018, the odds ratios of inventory size were very small, indicating little relationship if any between this variable and inventory method. The findings for food and beverage manufacturing firms continue in Table 18.2 found that only profitability in 2017 and 2018, inventory turnover in 2018, and current ratio in 2018 have significant influence (p-value <0.05) on the choice of inventory costing method. In other words, their regression (B) coefficients were significantly different from 0. Moreover, most odds ratios were also significantly larger or smaller than 1. Odds ratios greater than 1 showed a positive relationship, between the variables, while those less than 1 showed a negative relationship between them. Table 18.2. 2016-2018 Logistic Equation Variables, Food and Beverage Firms (Cont.) 2018 2017 2016 Profitability B 3.866 1.754 1.089 S.E. 1.598 .799 .661 Wald 5.850 4.823 2.710 Df 1 1 1 p-value .016* .028* .100 Odds Ratio 47.754 5.780 2.971 Inventory Turnover B -2.282 -1.272 -.654 S.E. 1.044 .685 .606 Wald 4.775 3.449 1.165 Df 1 1 1 p-value .029* .063 .280 Odds Ratio .102 .280 .520 Current Ratio B -4.480 -1.177 -.359 S.E. 2.228 .909 .605 Wald 4.042 1.677 .353 Df 1 1 1 p-value .044* .195 .553 Odds Ratio .011 .308 .698
49 Log(p/1-p)(2016)=-1.662-1.927(FirmSize2016)+.167(Leverage2016)- 1.703(InvSize2016)+1.089(Profitability2016)- .654(Inv.Turnover2016)+-.359(CurrentRatio2016) Log(p/1-p)(2017)=-1.883-.835(FirmSize2017)-.046(Leverage2017)-2.504(InvSize2017) +1.754(Profitability2017)-1.272(Inv.Turnover2017)- 1.177(Currentratio2017) Log(p/1-p)(2018)=-2.888-1.414(FirmSize2018)-.326(Leverage2018)- 25.467(InvSize2018)+3.866(Profitability2018)- 2.282(Inv.Turnover2018)-4.480(Currentratio2018) (B) Coefficient values showed that from 2016 to 2018, for a one unit increase in profitability, the likelihood that the FIFO Method was used increased by 1.089, 1.754 and 3.866, respectively. It indicated that increases in profitability from 2016 to 2018 might be a reason why food and beverage manufacturers selected the FIFO Method, since it produces higher net profits. For inventory turnover, the finding showed that from 2016 to 2018, for a one unit increase in inventory turnover, the likelihood that the FIFO Method was used decreased by -.654, -1.272 and -2.282 times, respectively. This indicated that inventory turnover was probably negatively related to choice of inventory method, even though the FIFO Method produces higher inventory turnover ratio. The results for the current ratio indicate that from 2016 to 2018, for every additional unit increase in current ratios, the likelihood that the FIFO Method was used decreased by -.359, -1.177 and -4.480, respectively. This indicates that strength of the current ratio was probably not a factor in choice of an inventory method, even though use of the FIFO Method produces a higher current ratio.
50 The (B) coefficient values are often converted into odd ratios for easier interpretation. The odds ratiosindicated that, from 2016 to 2018, profitability was 2.971 times, 5.780 times and 47.754 times, respectively more likely to influence use of the FIFO Method than use of the Average Cost Method. However, in 2018, odd ratios of profitability was very high. One limitation of using odds ratios in this analysis was the limited sample size, since usable data was collected from only 36 firms, and at least 100 samples are recommended (Ugon, Karlsson, & Klein, 2018). Small sample sizes tend to return values farther away from 1.0 when logistic regression is performed (Newsom, 2016). So attaching too much significance to these values may not be warranted. From 2016 to 2018, the odd ratios of inventory turnover were .520 times, .280 times and .102 times, respectively. This means that inventory turnover is unlikely to influence use of the FIFO Method instead of the Average Cost Method. Lastly, from 2016 to 2018, odds ratios of current ratio were .698 times, .308 times .011 times, respectively, so current ratio condition is unlikely to influence use of FIFO in place of the Average Cost Method. However, both Betas (B) and odds ratios for current ratios have declined a lot during the 3 years for the minority of food and beverage firms that use FIFO. On the other hand, the current ratios of the majority of firms that use Average Cost Methods increased from 2016 to 2018. However, decreases in current ratios do not always mean that firms were operating ineffectively. Users of the FIFO Method may try to reduce the amount of inventory that is kept on hand if it consists of perishable goods with short shelf lives, or to prevent damage to or expiration of such items.
51 4.3 2016-2018 Manufacturing Firms in the Steel Sector This part involves the description and analysis of characteristics of steel manufacturing firms using frequencies and a logistic model. 2016-2018 Inventory Costing Methods Results in Table 19 show that a majority (83.3%) of manufacturing firms in the steel sector used the Average Cost Method, and only 16.7% of such firms used the FIFO Method. This shows that though FIFO may be seen as a good inventory method that shows high net income and higher retained earnings, a majority of firms in the sector preferred to use an Average Cost Method. Table 19. 2016-2018 Inventory Costing Method 2018 2017 2016 Frequency Percent Frequency Percent Frequency Percent FIFO 4 16.7 4 16.7 4 16.7 Average Cost 20 83.3 20 83.3 20 83.3 Total 24 100.0 24 100.0 24 100.0 2016-2018 Firm Size Firm Size is determined by the total assets at year end. From 2016 to 2018, over 78% of steel manufacturers were relatively small firms, with 100 Million Baht (Approximately $3.225 Million) to 10,000 Million Baht (Approximately $322.5 Million) in total assets. Table 20. 2016-2018 Firm Size * 2018 2017 2016 In Million Baht Frequency Percent Frequency Percent Frequency Percent 100 – 10,000 18 78.26 19 79.17 19 79.17 10,001 – Above 5 21.74 5 20.83 5 20.83 Total 23 100.00 24 100.00 24 100.00 2018 Avg. Firm Size = 7,197 mil Baht; 2017 Size = 7,085 mil Baht; |2016 Size = 6,525 mil Baht
52 * Firm Size equals Total Assets However, the trend of average firm size was gradually increasing, from 6,525 million Baht (Approximately $210 Million) in 2016 to 7,197 million Baht ($232 Million) in 2018. In 2016, steel sector firms started to profit after experiencing price slumps in the previous year. In addition, steel prices increased starting at the beginning of the year because of rebounding demand from China due to investment in infrastructure projects (Bank of Thailand, 2016). The steel sector manufacturers in Thailand are mostly of small and medium size. In order to reduce risk and take advantage of economies of scale, large manufacturers usually combine steel production with the sale of billet and slab, together with intermediate products (Thailand Industry Outlook: Steel Industry, 2018). 2016-2018 Firm Financial Leverage Firm financial leverage (debt-to-equity) is determined by total liabilities divided by total equity; this ratio compares the level of borrowing with the level of capital invested by shareholders. Table 21. 2016-2018 Firm Financial Leverage * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent ≤1.00 15 65.22 18 75.00 17 70.83 >1.01 8 34.78 6 25.00 7 29.17 Total 23 100.0 24 100.00 24 100.00 Avg. Firm leverage 2018=6.10| Avg. firm leverage 2017=1.09| Avg. firm leverage 2016=1.25 * Firm Financial Leverage equals Debt divided by Equity From 2016 to 2018, over 65% of steel manufacturers had debt to equity ratios of less than or equal to 1.00. When this ratio is less than 1.00, it means that shareholders contributed more to the firm’s assets than creditors. When creditor and
53 shareholder contributions to firm assets are equal, then the ratio is equal to 1.00. This may be because over 70% of the steel firms were relatively small. This may be the reason why a majority of them were not able to borrow more. Average firm leverage from 2016 to 2018 was 1.25, 1.09 and 6.10, respectively. The reason why average firm leverage increased in 2018 may be because demand for long steel, wire rods, high tensile steel wire, and hot-rolled structural steel had increased. This was primarily due to expansion of the construction industry, government infrastructure construction, and the construction of private residences (Ministry of Industry, 2018). Therefore, this may have caused many manufacturers to try to borrow more to expand their businesses to support increasing market demand. 2016-2018 Inventory Size Inventory size is determined by ending inventory divided by total assets. This ratio indicates what percentage of total assets is tied up in inventory. Table 22. 2016-2018 Inventory Size * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent Less – 0.10 2 8.69 3 12.50 5 20.83 0.11 – 0.20 3 13.04 4 16.67 5 20.83 0.21 – Above 18 78.27 17 70.83 14 58.34 Total 23 100.0 24 100.00 24 100.00 Avg. Inv. Size 2018 = 0.27 | Avg. Inv. Size 2017 = 0.26 | Avg. Inv. Size 2016 = 0.22 * Inventory size equals Ending Inventory divided by Total Assets Findings in Table 22 showed that from 2016 to 2018, 50% to 78% of steel manufacturers carried inventory that was more than 20% of their total assets. Average inventory size from 2016 to 2018 was 22%, 26% and 27%, respectively. This meant that most firms held high levels of inventory. This maybe because steel products do
54 not have expiration dates and can be kept longer than food and beverage products, which have short shelf lives and are perishable. 2016-2018 Profitability Profitability (Return on Assets, or ROA) is determined by net income divided by average total assets, and indicates how effectively a company generates a profit through use of its total assets. Table 23. 2016-2018 Profitability * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent ≤ 0.00 11 47.83 7 29.00 6 25.00 0.01 – Above 12 52.17 17 71.00 18 75.00 Total 23 100.0 24 100.00 24 100.00 Average Profitability 2018= 0.02 | Average Profitability 2017 = 0.01| Average Profitability 2016 = 0.06 * Profitability (ROA) equals Net Income divided by Average Total Assets Findings in Table 23 show that over 70% of steel manufacturers had positive ROA ratios for 2016 and 2017, while in 2018 only about half of steel firms were profitable. The average profitability from 2016 to 2018 was 6%, 1% and 2%, respectively. Table 23 shows that from 2016 to 2018, 25% to 48% of steel firms suffered losses, while some only broke even. This may be because the production of flat steel, tin plate steel, and hot-rolled steel coil declined in 2018 because of cheaper imported products from China and Vietnam (Ministry of Industry, 2018). 2016-2018 Inventory Turnover Inventory Turnover is determined by the cost of goods sold divided by average inventory. It shows the number of times in a year a company can turn its inventory into sales. High levels of inventory tie up cash and reduces the firm’s profitability.
55 Table 24. 2016-2018 Inventory Turnover * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent ≤ 5.00 11 47.82 14 58.33 13 54.17 5.01 and above 12 52.18 10 41.67 11 45.83 Total 23 100.0 24 100.00 24 100.00 Avg. Inv. Turnover 2018 = 10.05 | Avg. Inv. Turnover 2017 = 7.09 | Avg. Inv. Turnover 2016 = 11.71 * Inventory Turnover equals Cost of Goods Sold divided by Average Inventory From 2016 to 2018, over 47% of steel manufacturers had annual ratios of less than 5 times. Many of them may have faced difficulties in turning inventory into sales. Moreover, from 2016 to 2018, less than 50% had annual ratios above 5 times; these firms were able to move their inventory quite well during the year. However in 2018, steel manufacturers showed a little improvement in this area. This may have been due to increasing sales in the construction industry, government infrastructure construction, and the construction of private residences, which boost demand for long steel, rod wire production, high tensile steel wire, and hot-rolled structural steel. Average inventory turnover ratios from 2016 to 2018 were 11.71 times, 7.09 times, and 10.05 times, respectively. 2016-2018 Current Ratio The Current Ratio is determined by current assets divided by current liabilities. It represents the ability to pay short-term debts when they are due (Stickney et al., 2009).
56 Table 25. 2016-2018 Current Ratios * 2018 2017 2016 Ratios Frequency Percent Frequency Percent Frequency Percent 0.00 – 1.00 6 26.09 6 25.00 6 25.00 1.01 – 2.00 7 30.43 8 33.33 6 25.00 2.01 – Above 10 43.48 10 41.67 12 50.00 Total 23 100.0 24 100.00 24 100.00 Avg. Current Ratio 2018 = 5.31| Avg. Current Ratio 2017 = 5.37| Avg. Current Ratio 2016 = 7.97 * Current Ratio equals Current Assets divided by Current Liabilities The findings in Table 25 show that from 2016 to 2018, about 25% of steel manufacturers had ratios from 0 to 1.0, and so they may have had difficulty to repay their short-term obligations. But this also means that during the same time period, over 75% of such firms had current ratios higher than 1.0, which means that these firms had adequate short-term liquidity. Average current ratios from 2016 to 2018 were 7.97, 5.37 and 5.31, respectively. Many firms had more than enough current assets to repay their short-term debts. Too high a current ratio may show that firms did not use their short-term liquidity to generate profits very well. Many firms may be carrying high levels of inventory, which can inflate the current ratio. 4.4. 2016-2018 Model Analysis for Steel Sector Characteristics 2016-2018 Model Summary The Cox and Snell R Square and Nagelkerke R Square reflect the likelihood that the variation in the dependent variable is explained by the independent variables (Reddy, Likassa & Asefa, 2015). Table 26. 2016-2018 Model Summary 2018 2017 2016 -2 Log likelihood 13.656 16.769 9.963 Cox & Snell R Square .281 .183 .385 Nagelkerke R Square .466 .309 .648
57 Table 26 indicates that from 2016 to 2018, the Cox and Snell R Square suggests that 38.5%, 18.3%, and 28.1%, respectively of variation in the probability that steel manufacturers’ use of inventory costing method was explained by firm size, inventory size, firm leverage, profitability, inventory turnover, and current ratio. In addition, from 2016 to 2018, Nagelkerke R Square indicated that 64.8%, 30.9%, and 46.6%, respectively of variation in the probability that steel manufacturers’ use of inventory costing method was explained by firm size, inventory size, firm leverage, profitability, inventory turnover, and current ratio. 2016-2018 Hosmer and Lemeshow Test The Hosmer-Lemeshow test is a secondary test commonly used to measure the goodness of fit for logistic regression models (Zhang, 2016). It tests the hypothesis that observed data are significantly different from the data predicted by the model (the expected data), and measures these differences (Field, n.d). Thus, the Hosmer-Lemeshow test is a kind of Chi-Square test that measures how well the actual data fit with the values predicted by the model; it is shown with a p-value. A value greater than 0.05 (> 0.05) means that no significant differences or problems were found between the observed and expected values; the data display a reasonable level of fit with the model. If the p value is smaller than 0.05 (< 0.05), however, this indicates major differences between the observed and expected values. It means that the model is not a good fit – there are significant problems with it. (Blog.ExcelMasterSeries.com, 2014).
58 Table 27. 2016-2018 Hosmer and Lemeshow Test 2018 2017 2016 Chi-square 2.977 5.833 2.456 df 8 8 8 p-value .936 .666 .964 The results in Table 27 indicated that from 2016 to 2018, the p-values were larger than 0.05, so they are not significant. In other words, there was a 96.4%, 66.6%, and 93.6% chance that the observed values were not different from the expected values, so the model does not display goodness of fit problems. 2016-2018 Classification Table The Table 28 results indicate how many cases are correctly predicted by comparing the number of firms that use the FIFO Method (FIFO=1) and the number of firms that use an Average Cost Method (AC=0) predicted by the logistic regression model to the number actually observed. Table 28. 2016-2018 Classification Table 2018 2017 2016 FIFO AC Correct FIFO AC Correct FIFO AC Correct FIFO 1 3 25.0% 1 3 25.0% 2 2 50.0% AC 0 19 100.0% 1 19 95.0% 1 19 95.0% Overall Percentage 87.0% 83.3% 87.5% Results indicate that for 2016, the logistic regression model correctly classified that 87.5% of steel manufacturers selected an Average Cost Method, and only 12.5% of such firms selected the FIFO Method. In 2017, the model correctly classified 83.3% of steel manufacturers that selected an Average Cost Method, and 16.7% that selected the FIFO Method. In 2018, the model correctly classified 87.0% of steel firms that selected an Average Cost Method, and 13.0% that selected the FIFO Method.
59 2016-2018 Logistic Equation for Variables, Steel Firms Tables 29.1-2 explain the relationship between the independent variables and dependent variables. They display information regarding an equation model based on the characteristic of steel sector manufacturing firms by using (B) coefficient data, standard error, Wald Chi-square test, degrees of freedom, p-value, and odds ratio. Table 29.1. 2016-2018 Logistic Equation for Variables, Steel Firms 2018 2017 2016 Constant B -2.720 -1.932 -5.343 S.E. 1.702 .740 4.665 Wald 2.555 6.819 1.312 df 1 1 1 p-value .110 .009 .252 Odds Ratio .066 .145 .005 Firm size B 2.035 .991 2.238 S.E. 1.492 1.230 2.710 Wald 1.861 .649 .682 df 1 1 1 p-value .173 .420 .409 Odds Ratio 7.651 2.694 9.378 Leverage B -2.102 -.588 -1.380 S.E. 4.318 1.023 1.656 Wald .237 .331 .695 df 1 1 1 p-value .626 .565 .405 Odds Ratio .122 .555 .252 Inventory Size B 1.362 .252 -2.797 S.E. 1.290 1.166 2.992 Wald 1.113 .047 .874 df 1 1 1 p-value .291 .829 .350 Odds Ratio 3.902 1.287 .061 The findings for steel manufacturers in Table 29.1 indicated that from 2016 to 2018, firm size, firm leverage and inventory size did not have any significant influence (p-value > 0.05) on the inventory costing method. A few smaller firms (4
60 out of 24 firms) used the FIFO Method to manage their inventory, while most companies tended to use an Average Cost Method. However, (B) Coefficient values still showed a relationship between firm size, firm leverage and inventory size. Regarding firm size, from 2016 to 2018 it was observed that for a one-unit increase in firm size, the likelihood of using the FIFO Method increased by 2.238, .991, and 2.035, respectively. However, no change in use of inventory method was observed during this time period. This might be because firm size in the steel sector was relatively small. For firm leverage, the findings showed that from 2016 to 2018, for every one unit increase in firm leverage, use of the FIFO Method decreased by -1.380, -.588, and -2.102, respectively. This shows that increases in firm leverage were probably unrelated to choice of inventory method. In regards to inventory size, the results showed that from 2016 to 2018, for a one unit increase in inventory size, use of the FIFO Method changed by -2.797, .252, and 1.362, respectively. This may be interpreted to mean that increases in inventory size are probably unrelated to choice of inventory method. In fact, use of the FIFO Method is more complex than use of the Average Cost Method. To interpret this using the odds ratio, the results showed that from 2016 to 2018, firm size was 9.378 times, 2.694 times, and 7.651 times, respectively more likely to influence usage of the FIFO Method than the Average Cost Method. In addition, from 2016 to 2018, the odds ratio of firm leverage to inventory method were .252 times, .555 times, and .122 times, respectively. These results showed that higher firm leverage was less likely to be associated with use of the FIFO Method than the Average Cost Method.
61 While from 2016 to 2018, the odds ratios of inventory size were .061 times, 1.287 times, and 3.902 times, respectively more likely to influence use of the FIFO Method than the Average Cost Method. However, in 2016 inventory size was more likely to influence use of the Average Cost Method than use of the FIFO Method. The odds ratios for inventory size have changed a lot during the 3 years because some steel manufacturers that used the FIFO Method had very high inventory turnover for the past 3 years. This may have been because of increased demand for long steel, rod wire production, high tensile steel wire, and hot-rolled structural steel during 2017 and 2018. The findings for steel manufacturers continue in Table 29.2 found that from 2016 to 2018, profitability, inventory turnover and current ratio did not have any significant influence (p-value > 0.05) on the inventory costing method. Table 29.2. 2016-2018 Logistic Equation for Variables, Steel Firms (Cont.) 2018 2017 2016 Profitability B 1.081 .131 1.232 S.E. .838 1.233 1.126 Wald 1.661 .011 1.196 df 1 1 1 p -value .197 .916 .274 Odds Ratio 2.947 1.140 3.427 Inventory Turnover B 1.601 -1.197 -8.061 S.E. 1.733 1.673 11.181 Wald .854 .512 .520 df 1 1 1 p -value .355 .474 .471 Odds Ratio 4.958 .302 .000 Current Ratio B .613 2.327 3.275 S.E. 1.574 1.834 3.387 Wald .152 1.610 .934 df 1 1 1 p -value .697 .205 .334 Odds Ratio 1.847 10.250 26.434
62 Log(p/1-p)(2016)=-5.343+2.238(FirmSize2016)-1.380(Leverage2016)- 2.797(InvSize2016)+1.232(Profitability2016)- 8.061(Inv.Turnover2016)+3.275(CurrentRatio2016) Log(p/1-p)(2017)=-2.720+.991(FirmSize2017)-.588(Leverage2017)- 1.362(InvSize2017)+.131(Profitability2017)-1.197(Inv.Turnover2017) + 2.327(Currentratio2017) Log(p/1-p)(2018)=-2.720+2.035(FirmSize2018)-2.102(Leverage2018) +1.362(InvSize2018)+1.081(Profitability2018) +1.601(Inv.Turnover2018)-.613(Currentratio2018) However, profitability, inventory turnover, and current ratio still provided helpful information. They showed that from 2016 to 2018, for a one-unit increase in profitability, the likelihood that the FIFO Method was used increased by 1.232, .131, and 1.081 respectively. This indicates that increases in profitability from 2016 to 2018 might be a reason why steel manufacturers would select the FIFO Method, since it produces higher net profits and higher retained earnings. For inventory turnover, the findings showed that from 2016 to 2018, for a oneunit increase in inventory turnover, the likelihood that the FIFO Method was used decreased by -8.061, -1.197, and increased by a factor of 1.601 respectively. This indicates that inventory turnover was probably unrelated to choice of inventory method. The results for the current ratios indicate that from 2016 to 2018, for a oneunit increase in current ratio, the likelihood that the FIFO Method was used increased by 3.275, 2.327 and .613, respectively. This indicated that strong current ratios might
63 lead steel manufacturers to select the FIFO Method, because it produces higher current ratios, which creditors favor. To interpret the odds ratios, the results showed that from 2016 to 2018, odds ratios for profitability were 3.427 times, 1.140 times, and 2.947 times respectively more likely to influence use of the FIFO Method than the Average Cost Method. In addition, from 2016 to 2018, odds ratios for inventory turnover were .000 times, .302 times, and 4.958 times respectively less likely to influence use of the FIFO Method than the Average Cost Method, except for 2018. Moreover, from 2016 to 2018, the odds ratios for current ratios were 26.434 times, 10.250 times and 1.847 times respectively more likely to influence use of the FIFO Method than the Average Cost Method. The odds ratios for current ratios have increased a lot during the past 3 years, especially for some steel manufacturers that use the FIFO Method. This may have been due to increases in inventory turnover that helped firms turn inventory into sales more quickly. Therefore, cash would have also increased, and firms may have manufactured more inventory in response to rising demand for long steel, rod wire production, high tensile steel wire, and hot-rolled structural steel during 2017 and 2018. This would also have caused current ratios to increase.
64 CHAPTER 5 CONCLUSIONS 5.1. Discussion and Conclusions This study examined Thai manufacturing firms in the food and beverage and steel sectors. It found that 72.2% of food and beverage firms and 83.3% of steel firms were using the Average Cost Method for their inventory, while only 16.7% and 27.8% of them respectively were using the FIFO Method. These results aligned with Garrison, Noreen, and Brewer (2019), who stated that although the FIFO Method gives more accurate cost of inventory figures than the weighted average method, it is more complex. However, the study’s findings were not aligned with Simeon and John (2018), who stated that for perishable inventories, companies may use the FIFO Method to show higher profits for the year than firms that use the Weighted Average Cost Method. Bragg (2005) stated that the FIFO Method helps to reduce risk of outdated cost inventory since the oldest items will be sold first. However, the findings still showed that most manufacturing firms in both sectors used the Average Cost Method. The findings also showed that many manufacturing firms in the food and beverage and steel sectors increased their total assets from 2016 to 2018. The bigger a firm’s size in terms of total assets, the higher was their liquidity. This indicated that these manufacturing firms had enough assets to meet their financial obligations when they are due. The findings also revealed that firm financial leverage in terms of debt-toequity ratios for both sectors increased in 2018. This result is consistent with those of Robinson, Pirie and Broihanh (2012) stated that using the Weighted Average Cost
65 Method results in a higher debt-to-equity ratio, since retained earnings are lower than when the FIFO Method is applied. However, the debt-to-equity ratio has its own advantages and disadvantages. For those companies that carry higher portion of equity than debt may lose their ownership because the shareholders have more power than the founders. On the other hand, companies that have more debt than equity may find it difficult to pay back their financial obligations when they are due. The findings also found that more than half of the manufacturing firms in food and beverage and steel sectors were operating using more equity funds rather than debt. This may not be too risky because the firms will pay the debt before paying the dividend to the shareholders and the shareholders will share the burden in case of a firm goes bankruptcy. However, many firms that prefer to borrow rather than raising funds from shareholders or having a debt-to-equity ratio that is higher than 1 may find it difficult to repay their debts when they are due. The amounts of inventory for both sectors increased in 2018. This may indicate that firms need to improve their inventory management in order to reduce the risk of expired, damaged, and out-of-date inventory. The higher the inventory that is being carried, the higher the risk. However, these results are consistent with those of Wisner, Tan, and Leong (2019), who found that inventory can be one of a firm’s most expensive assets. It may tie up more than 10 percent of total assets for some companies. In addition, manufacturing sector firms usually hold more inventory than service sector firms. Furthermore, profitability (ROA) for a majority of firms in both sectors was in the range between 1% and 10%, which is acceptable, though some firms performed at a rate higher than 10%. The lower the net profits, the lower the dividends declared for
66 investors will be, and the lower the retained earnings will be for future expansion, as well as debt repayment. The findings of Robinson, Greuning, Henry and Broihahn (2015) were consistent with these results, and stated that profitability is an indicator of how much a company is worth. Equity analysts value profitability a lot in their analyses, because it shows a firm’s competitive position in the market and how well it is managed. Earnings can be given to shareholders or reinvested in the firm; reinvested earnings improve solvency, and help it meet short-term obligations as they are due. The higher the ROA, the more income is earned by a given level of assets. Inventory turnover for food and beverage sector declined in 2018. Many companies had a hard time turning their inventory into sales revenue. Chang (2010) stated that inventory turnover (Cost of Goods Sold/Average Inventory) indicates how fast inventory during the year can be turned into sales. The faster the turnover, the more efficient the inventory management performance will be, since inventory items will be sold before they expire or are damaged. In 2018, the current ratio for 15 out of 36 manufacturing firms in the food and beverage sector declined to less than 1.0. This indicated that they had less liquidity to pay their short-term obligations, while the majority of steel firms had current ratios of more than 1.0 for three years. However, a majority of manufacturing firms in both sectors had current ratios of greater than 2.0, which is favorable. However, too much inventory tied up in current assets may inflate current ratios. Therefore, companies need to reduce the inventory held in stock to avoid higher risks of inventory damage and expired goods. Certified Public Accountants rated this ratio highest for measuring liquidity as to measure the ability of a firm meet its short-term financial obligations.
67 The benchmark of this ratio was at an average ratio of 2 to 1. However, some firms’ experience that a ratio of less than 2 to 1 is adequate, while others prefer a larger one. Firm size (total assets) for both sectors from 2016 to 2018 had no noticeable influence on the use of the FIFO Method. This is opposite to the finding of Gopalakrishnan (1994), who found that firm size (log of total assets) was likely to be positively related to FIFO Method. The results showed no differences between the food and beverage and steel sectors. Inventory size for both sectors in 2016 to 2018 did not influence use of the FIFO Method. Wisner, Tan, and Leong (2019) explained that inventory is one of the firm’s most expensive assets, while Bragg (2005) stated that use of the FIFO Method reduced risk of outdated inventory costs. However, the findings for this study did not support these ideas. The results showed no significant differences between the two sectors, which are different type of businesses. However, profitability (ROA) in the food and beverage sector in 2017 and 2018 had a significant influence on choice of the FIFO Method. This is in line with Simeon and John’s (2018) statement, who found a very significant relationship between choice of inventory valuation method and profitability. It also agrees with Robinson, Henry, Pirie, and Broihahn (2012), who explained that using the Weighted Average Cost Method would result in lower return-on-assets ratios, since the incremental profit added to net income has a bigger impact than the incremental increase to total assets. On the other hand, profitability (ROA) for the steel sector from 2016 to 2018 showed that profitability did not significantly influence choice of the FIFO Method. This finding was supported by Asgari, Mehdizade, and Hassani (2014) who explained
68 that there was no significant relationship between using FIFO for inventory accounting and profitability. Financial leverage (debt-equity ratio) for both sectors from 2016 to 2018 did not have any significant influence on the use of FIFO Method. These results aligned with the study of Zinkevičienė and Rudžionienėv (2005), whose findings did not support the hypothesis that the higher a firm’s financial leverage, the more likely were its managers to use the FIFO Method. This is similar to the study of Asgari, Mehdizade and Hassani (2014), who found no significant relationship between the inventory costing method and debt-equity ratios. However, the results show that there were better terms on borrowings. Even though use of FIFO gives high retained earnings that lower debt-to-equity ratios and the FIFO Method could increase firm credit ratings, the findings from both the sectors did not show any relationship between financial leverage and choice of the FIFO Method. Inventory turnover for the 2018 food and beverage sector influenced choice of the FIFO Method, while no statistically significant relationship was found for the steel sector from 2016 to 2018. This is in line with Wahlen, Baginski, and Bradshaw (2011), who stated that inventory turnover ratios gave better explanations regarding the turnover of inventory if FIFO was used. However, it is not in harmony with the Chartered Financial Analyst Institute findings, which reported that use of the Weighted Average Cost Method produced higher inventory turnover, higher cost of goods sold, and lower inventory than when using the FIFO Method. Current ratios for the food and beverage sector in 2018 influenced choice of the FIFO Method, while no statistically significant relationship was found for the steel sector from 2016 to 2018. These results are in line with the study of Troy (2008),
69 who found that the current ratio affected choice of the FIFO Method. Also, Robinson, Henry, Pirie, Broihanh (2012) added that using the Weighted Average Cost Method will result in lower current ratios, since this method results in a lower carrying value for inventory than FIFO. 5.2 Recommendations For further study, the researcher would like to suggest that anyone who is interested in conducting a similar study add a primary research component, and use both qualitative and quantitative methods to find the key factors that influence the choice of inventory costing methods. There might be other factors that can influence the choice of inventory costing method that the researcher did not investigate. In addition, there is a need to increase the sample size for manufacturing firms if the same results will be found. The two sectors have indicated that no matter how different the nature of their businesses, more than half of manufacturing firms in both sectors adopted an Average Cost Method rather than FIFO. In addition, the researcher recommends that potential researchers compare the manufacturing and service sectors to add to the body of knowledge and understanding. For further studies, researchers may consider companies that use more than one inventory costing method for all types of their inventories. 5.3 Limitations There were many difficulties that the researcher faced during the study. Firstly, since the research paper was done using secondary data. It was very difficult to gather information about the manufacturing firms selected for this study. The researcher found that more than half of the manufacturing firms in the manufacturing sectors were using different inventory costing methods for different type of
70 inventories; thus, an instrument was needed to find out the reasons for the use of multiple methods. Secondly, the researcher did not know the exact reasons why each manufacturing firm selected different inventory costing methods due to the limitation of secondary research.
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80 APPENDICES Appendix 1: 2014-2018 Production, Sales, Export, and Import for the Food Industry Source: Office of Industrial Economic, Ministry of Commerce Appendix 2: Q4/2017-Q4/2018 Sales Volume and Import Value Source: Office of Industrial Economics and the Iron and Steel Institute of Thailand
81 Appendix 3: Q4/2017-Q4/2018 Production Index