An empirical analysis of inventory turnover performance in the Indian trading sector

Transcription

1 An empirical analysis of inventory turnover performance in the Indian trading sector Saurabh Chandra a & Rakesh Arrawatia b Abstract: a. Indian Institute of Management Indore, MP, India b. Institute of Rural Management Anand, Gujrat, India The Indian trading sector comprises of all the companies involved in the distribution and sale of goods to customers, in both wholesale and retail formats. The study aims at examining the determinants of inventory performance measures of Indian trading firms across various product segments. The study is based on econometric analysis of inventory measures using extensions to an inventory turnover model. The empirical model is implemented using financial data of a sample of 407 Indian trading firms for the period Panel data regression technique is employed for analysis. In tune with the existing literature on retail firms in US and Greek contexts, inventory turnover ratio is negatively correlated to Gross Margin, positively correlated with sales growth, capital intensity and company size. Hypothesis on negative correlation between inventory turnover and distribution expenses ratio was not supported by the analysis. The maximum variability in the inventory turnover ratio was explained by the model when considering fixed effects associated with different firms and across different years and product segment wise effects for each explanatory variable. As the study is based on aggregate analysis of financial data, it may not capture some of the important operational variables. This study could help managers in identifying the reasons for differences in inventory performance across different firms and in the same firm across time. It may help managers in making aggregate level inventory decisions. Being one among the few studies on trading firms in an emerging market, this study has the potential to stimulate further research in this region. We analyse the inventory behaviour of the wholesale and retail trading industry in Indian context. We introduce a new research hypothesis in our study as an addition to the models suggested in the literature. Keywords: Inventory management, Retail trade, Wholesale trade, Panel data, India 1. Introduction Trading industry is involved in distribution and sales of goods, both within and outside a country. The trading sector can be broadly divided into wholesale trading and retail trading. The wholesale trading sector mostly acts as intermediary in distribution of products and caters to retail and other wholesale firms. On the other hand, retail companies sell directly to final customers. Trading sector is one of the major contributors to GDP (Gross Domestic Product) and employment for a developing country like India. The contribution of trading sector to the GDP was about 14.2 % in as per factor prices. This sector has been growing at a rate of 8% per annum since in terms of gross value added (Kolli 2011). The retail sector in India is divided into the organized sector and unorganized sector. The organized retail is relatively new and expected to grow considerably in the coming years. The overall retail market in India is expected to reach about US$ 793 billion by the year Organized retail constituted 7% of total retail in the year (RPT report). Given the importance of this sector to India s economic growth, it is important to understand the drivers of business excellence in this sector. To our knowledge, the literature on India s trading sector is scant. A detailed search of academic and industry literature revealed a small body of work in the field of Indian wholesale industry, especially related to

2 practices in operations management. The retail trading sector has, although, received some attention from the academia. Additionally there is a rich body of industry literature on India s retail sector. Inventory forms a major part of a trading firm s assets. Investment in inventory forms almost 21.7% of the total assets of the sampled firms in our study. Indian firms operate under very complex supply chain structures owing to the current taxation regime and poor infrastructure (Shah 2009). Additionally due to the fact that this industry is involved in direct purchase and sale of goods, high customer service level is important for business success. Thus, inventory management is a major challenge for trading firms. Inventory performance measures, derived from firm s financial data, are extensively used by industry executives and academic researchers to evaluate supply chain performance of different manufacturing, wholesale and retail firms. Inventory turnover is the ratio of a firm s cost of goods sold to the average inventory level (Gaur et al. 2005). Inventory turnover (IT) is commonly used index to measure and compare inventory performance of different retail, wholesale and manufacturing companies, across firms and over time (Gaur et al., 2005; Rumiyantsev and Netessine, 2007; Kolias et al., 2011; and Grubor et al., 2013). Inventory number of days (IND) is the ratio of a firm s average inventory and cost of goods sold, and thus is the direct reciprocal of inventory turnover ratio. A higher inventory turnover may suggests lower average inventory number of days, lower average inventory held by a firm and/or smaller lot sizes ordered by the firm on an average. Indian firms mostly record and evaluate the inventory number of days as the index of inventory performance measurement, which is equivalent to measuring inventory turnover ratio. Many studies have tried to look at firm performance by analysing firm level data of inventories and other financial data. Event based studies were the earliest studies on impact of operational decision on firm performance (Hendricks and Singhal, 1996). Vergin (1998) analysed trends in inventory turnover ratios in fortune 500 companies. Studies after 2000 tried to look at relationship between turnover ratios and other business variables. Fleisch and Tellkamp (2005) explained the effect of inventory inaccuracy on supply chain performance. Gaur et al. (2005) demonstrate that inventory turnover ratio varies widely across industry segments and is influenced by variations in gross margin, sales surprise and capital intensity. So inventory turnover ratio should not be directly used for performance analysis, per se. They propose an econometric model to investigate the determinants of inventory turnover in US retail industry and consider gross margin, sales surprise and capital intensity as explanatory variables. It is suggested that changes in the explanatory variables should be incorporated with the inventory turnover data to evaluate the inventory productivity of a firm. The Japanese manufacturing practices motivated firms worldwide to reduce inventory levels throughout the supply chain. Reduction in inventory levels has been reported across various industries by several studies. Chen et al. (2005) demonstrate that inventory levels have been gradually reducing in US manufacturing firms from early 1980s to early 2000s. Gaur et al. (2005) also present gradual reduction in inventory levels across the retail firms, adjusting for the effects of certain explanatory variables. Rajagopalan and Malhotra (2001) mention reduction in raw materials and WIP inventory levels across US manufacturing firms, while no such significant reduction in finished goods inventory is reported. Gaur et al. (2005) propose that an accounting measure of inventory performance, inventory turnover ratio, varies widely across industries and within an industry. Thus it is important to identify and analyse the important determinants of inventory turnover in different industries. Several studies cater to the retail industry in this regards. Gaur et al. (2005) propose a panel data based, econometric model to analyse the

3 determinants of inventory turnover ratio in US retail firms from They propose an adjusted inventory turnover ratio to measure inventory performance across time and firms. Gaur and Keshavan (2009) extends the work by investigating the effects of firm size and sales growth on inventory turnover for US retailers. Similar study is carried out by Kolias et al. (2011) in the Greek retail sector and Grubor et al. (2013) in Serbian context. Many important results are presented in these studies. Rumyantsev and Netessine (2007) conduct a similar analysis on a cross-section of US firms across various industries to determine the determinants of inventory level using deterministic and stochastic inventory models suggested in academic literature. These studies suggest a negative correlation between inventory turnover ratio and gross margin. The other hypotheses suggest that inventory turnover ratio is positively correlated to capital intensity, sales surprise and company size in terms of previous year s sales. An additional hypothesis on effect of sales growth on inventory turnover ratio is proposed to be effective in the sales growth region, rather than the sales decline region. These studies divide the industry into retail segments based on standard industry classifications available. The fixed effects model has been found to be more effective with product segment wise coefficient estimates. A related body of research tries to evaluate the effect of inventory performance as an explanatory variable on the financial performance of the firms. One may refer to Cannon (2008) for a detailed study and literature body. The aim of this study is to investigate the determinants of inventory performance in the Indian trading industry. The paper uses publicly available financial data from CMIE s Prowess database to conduct an empirical investigation of inventory performance in Indian wholesale and retail trading services. Prowess is a database of publicly disclosed financial performance data maintained by the Centre for Monitoring Indian Economy (CMIE). This database is used in several empirical studies using financial data from India. We came across a few research papers that investigate the determinants of inventory performance as measured by inventory turnover ratio. As per our knowledge, this is the first instance in literature when this analysis is carried out for the whole trading sector, including wholesale and retail. Since major part of retail is held with unorganized sector, large wholesale firms and organized retail firms need to be studied as a group to understand the inventory performance of Indian trading industry. In this study we use a methodology similar to Gaur et al. (2005) and Kolias et al. (2011) to identify the determinants of inventory performance in Indian trading sector. Inventory turnover ratio is considered as the dependent variable in our study. Inventory number of days is a direct reciprocal of inventory turnover ratio and we need not consider it separately since it is expected to give symmetrical results. We take the explanatory variables as gross margin, sales growth ratio, and capital intensity. Firm size, measured as previous year s sales is included as a moderating variable in the model. We propose an additional explanatory variable as distribution expenses ratio, measured as the ratio of distribution expenses to sales. The main findings of our econometric analysis conform to the earlier studies in terms of relationship of important variables with the dependent variable. As per the earlier studies, gross margin is negatively correlated with inventory turnover, positively correlated to sales growth ratio and capital intensity. Inventory turnover ratio is positively moderated by company size. The distribution expenses ratio variable did not exhibit any significant relationship with inventory turnover ratio. Our model explains 31% of total firm variation of inventory turnover ratio.

4 In emerging markets like India, organized retail is not very prevalent and major part of distribution and sales of goods is still carried out by a combination of wholesale and retail companies. This study considers a larger subset of trading industry to understand the dynamics of inventory management in an emerging economy. This study could help managers in identifying the reasons for differences in inventory performance across different firms and in the same firm across time. It may help managers in making aggregate level inventory decisions. Being one among the few studies on trading firms in an emerging market, this study has the potential to stimulate further research in this region. In the next section we define the data and variables used in the study. Section-3 describes the detailed econometric analysis of inventory turnover model using the financial data of Indian trading firms. Results of the econometric analysis are presented in the following section. Section-5 presents an analysis of trends in inventory turnover ratio of Indian trading firms over the years. At last, we present the concluding remarks and the scope for future research in this area. 2. Data description and definition of variables We use publicly available financial data of Indian wholesale and retail trading firms for the 14 year period , drawn from annual income statements and balance sheets. These data are obtained from Centre for Monitoring Indian Economy s Prowess database. In this study we followed the classification, in terms of main product or service group, provided by the Prowess database along with NIC (National Industrial classification) code assigned to each product group by the Ministry of Statistics and Programme Implementation, India. Our data set includes 14 segments in the wholesale and retail trading industry in India. Table 1 lists the various product segments with their NIC codes along with number of firms in the original data and in the final data sample. We grouped some of the product groups together. The product group of trade in agricultural products is merged with trade in fertilizers and pesticides, because the trading in the three product groups is expected to follow the same trade patterns. Similarly, we have merged the retail outlets, departmental stores and shopping malls together in one group. This grouping enables us to increase the degrees of freedom by estimating one set of coefficients for all product groups related to single industry segment (Gaur et al. 2005). Our original dataset contains 2474 firms. We removed all the observations that had missing data or negative values of gross margin in any year during the period Initially we also included the data for the year 1999 to estimate some of the variables that needed previous year s sales as input. We did not include this data into the model. We also omit from our data set those firms that have less than five consecutive years of data available for any sub-period during , as there are too few observations for these firms to carry out time series analysis, as pointed out by Gaur et al. (2008). Finally, we get an unbalanced data set of 3357 observations across 407 firms. This gives an average of 8.25 years of data per firm.

5 Table 1: Classification of data into product groups S.I. No. Product group NIC codes No. of firms (Original sample) No. of firms (Final sample) 1 Agricultural products including fertilizers and 46101, pesticides 2 Trade in manufactured products Trade in minerals and energy sources Wholesale trade Trade in food products/tea/beverage Trade in textiles Drugs and medicine Transport equipment Electric machinery Non-electric m/c Dyes and paints Chemicals/other chemicals Other manufactured goods Retail trade 47711, Total Table 2 presents summary statistics of inventory as a ratio to total assets and sales for firms under each of the product categories and for all the firms together. The data described earlier in the section is used to estimate other variables required for the study. Table 2: Descriptive statistics for Inventory to Total Assets and Sales Prod. Segment #obs. Inventory to total assets Sales (Rs. Million) Mean s.d. Median Mean s.d. Median ,690 22, ,062 1, ,310 12, ,624 3, ,113 1,05, ,179 28, ,344 36, ,120 60, , , ,502 6, ,375 13, All ,019 44, Table 2: Descriptive statistics for Inventory to Total Assets and Sales presents the summary statistics of inventory turnover ratio, gross margin, capital intensity and distribution expenses to sales ratio for firms under each product category and of all the firms together. The data described above is used for model formulation and estimation. The variables are defined sequentially, as follows.

6 Table 3: Descriptive statistics for inventory turnover, gross margin, capital intensity and distribution expenses ratio P.S. #obs. Inventory turnover Gross margin Capital intensity Distribution expenses to Sales Mean s.d. Median Mea s.d. Median Mea s.d. Median Mean s.d. Median n n All Inventory turnover (IT) for a firm i, producing product segment s, at the year t, is defined as the ratio of the firm s cost of goods sold (COGS) in the year t to inventories (INV) held by the firm at year t. IT sit = COGS sit INV sit (1) This expression for inventory turnover comes directly as an application of Little s law to the financial data of a firm. As per this law, the average inventory held by a firm in a year is equal to the product of average flow rate of the products and average flow time of a single product through the system. Now the inventory data presented in the annual financial statement of a firm is the average inventory of a firm in monetary terms and the cost of goods sold of a firm is the flow rate in terms of Rupees per year. Consequently, the flow time of each unit of inventory, on an average, which is nothing but the inventory number of days (IND) is given as the ratio of inventories to the cost of goods sold (Cachon & Terwiesch, 2012). Thus, IND sit = INV sit ( Number of workingdays a year ) COGS sit (2)

7 IT is simply the inverse of IND, measured as the average number of times inventory is turned in a year. Thus, we can calculate the inventory turnover ratio using equation (1). For a firm i in segment s, Gross margin (GM) is defined as the ratio of sales (S) minus cost of goods sold (COGS) in a year t to the sales in year t. GM sit = S sit COGS sit S sit (3) Capital intensity (CI) of a firm i in segment s is defined as the ratio of net fixed assets (NFA) to the sum of net fixed assets (NFA) and inventories (INV) at year t. NFA sit CI sit = NFA sit +INV sit (4) Distribution expenses ratio (DER) of a firm i in a segment s is defined as the ratio of distribution expenses in year t to the sales in year t. DER sit = DE sit S sit (5) As a proxy for company size of a firm i in segment s in the year t, we have taken previous year sales figure, S t-1 as per Gaur and Keshavan (2009). As per argument, with increase in sales there would be economy of scale in operations leading to higher inventory turnover. An important explanatory variable considered in related literature is sales surprise, which is defined as the ratio of actual sales in a year to anticipated sales. Gaur et al. (2005) and Kolias et al. (2011) have estimated the anticipated sales as a sales forecast from historical data. Both the studies have used Holt linear exponential smoothing method for sales forecasting using historical data. In this study we have considered an explanatory variable, sales growth as a ratio of this year sales to previous year sales for sales surprise effect. The trading sector in India is highly volatile in terms of sales with an aggregate coefficient of variation of 7.4, referring to Table 2. We do not expect most of these firms to follow advanced business analysis tools like forecasting, as majority of these firms are run by traditional business houses. Thus we have chosen sales growth ratio as a proxy of sales surprise. SG sit = S sit S si(t 1) (6) The impact of sales growth on inventory turnover might change based on whether the company is operating in sales-decline region or sales-increase region (Kolias et al., 2011). To test this effect we have considered an explanatory variable censg, defined as follows:

8 S censg sit ={0,if log SG sit <0( S sit < S si(t 1) log SG sit,if log SG sit >0 (sit <S si (t 1 )) (7) 2.1 Hypothesis development A company having high relative distribution expenses is expected to order, in general, in higher lot sizes. This may lead to increase in average cycle stock. Additionally improved distribution efficiency is associated with higher economy of scale and better supply chain co-ordination. This type of firm is expected to have a lower average inventory level. As per EOQ inventory model, order size is directly proportional to ordering cost. Distribution expenses for many firms may be a part of ordering cost, which further suggests a negative relationship between inventory level and distribution expenses ratio of a firm. Thus we formulate the following hypothesis: HYPOTHESIS. Inventory turnover is negatively correlated with distribution expenses ratio. The presence of third party transporters and LTL (less than truck load) shipments for many companies may reduce the effect of distribution expenses on inventory levels. The acceptance or rejection of this hypothesis may throw some light on the distribution practices prevalent in Indian trading industry, across various product segments. Finally, we show the correlation coefficient between all the variables used in the study. Table 4: Correlation coefficients matrix for all the variables across all observations in the sample IT 1 IT GM SG DER SalesPrev censg GM SG DER SalesPrev censg Econometric Analysis We specify a set of models based on the results of the literature on inventory performance behaviour discussed earlier, step-by-step, to understand the relationship between the variables. We will explain the econometric analysis in the same sequence it was carried out. First, we start with a pooled model in which product segments do not have any effect of the relationship among variables. In other words, we assume a single regression coefficient associated with each explanatory variable, independent of the product segments. The model, M1, takes the following log-linear form:

9 (M1) (8) log IT sit =F i +C t +b 1 log GM sit +b 2 logci sit +b 3 log SG sit +b 4 logs si(t 1) +b 5 censg sit +b 6 log DE In the above model, i refers to a firm, s refers to the product segment, where the firm belongs, and t refers to the time, measured in years. The dependent variable, log IT sit, denotes the natural logarithm of inventory turnover of firm i belonging to segment s in the year t. The independent variables, log GM sit, log CI sit, log SG sit, log S si(t-1), and log DER sit denote the natural logarithms of gross margin, capital intensity, sales growth, previous year sales and distribution expenses ratio of the firm i belonging to product segment s in the year t. censg sit, as defined in equation-(7), represents whether the company experiences sales growth phase or sales decline phase. sit denotes the error term for the observation for year t for firm i in segment s. A log-linear model is taken, as the plots of GM, CI and SG with IT suggest log-linear relationships. A log-linear specification yields a lower prediction error than linear specification (Gaur et al., 2005). F i denotes the firm level effects, which captures hard to measure firm-level factors like managerial efficiency, location strategy, etc. Firm level effects are assumed to vary across firms but constant over time. The term C t denotes the period fixed effects, which are unobservable effects constant across firms but varying over time, like interest rates and prices. Introduction of these effects avoid inconsistency in results owing to omission of many omitted variables owing to limitations of data collection. In model M1, the parameters, b 1 to b 6 denote the coefficients to be estimated and are fixed for each explanatory variable, across all product segments. We tested the sample data on the model M1 using the pooled OLS estimator, random effect and fixed effect separately. The Lagrange multiplier test for panel models was conducted to test for individual effects (Breusch & Pagan, 1980). F-test was conducted to compare the within and the pooling models. To compare between random and fixed effect models, Hausman test for panel model was used (Hausman, 1978). To see the effect of product segmentation on the independent variable coefficients, we specify the following model: (M2) (9) log IT sit =F i +C t +b s 1 log GM sit +b s 2 log CI sit +b s 3 log SG sit +b s 4 logs si( t 1) +b s 5 censg sit +b s 6 log DE In model M2, the coefficients b s 1 to b s 6 vary across the product segments, but remain same for a single product category. The estimation of this model allows us to understand the variation in industry behaviour across the different product segments in trading industry. To estimate different coefficient for each product category, we introduce 14 dummy variables in the model. A dummy variable is represented as z s, where s = 1,2,, 14, represents each product category. A dummy variable z s associated with a product segment s has a value of 1 for each observation corresponding to the same product segment, while 0 value for all other observations. A column vector corresponding to each independent variable is multiplied element by element with each dummy vector to create a separate independent variable column for each product segment. This modification allows us to estimate separate coefficients for each independent variable as per each product segment. As in M1, we conduct different tests to determine the best estimator and identify issues with autocorrelation and heteroscedasticity.

10 We test an additional model to test the effects of multicollinearity between variables, GM and CI. Both these independent variables along with the dependent variable IT are functions of COGS and INV. We test another model with INV as dependent variable and add COGS as independent variable to take care scale effects. In this model CI is calculated as GFA divided by TA. (M3) (10) log INV sit =F i +C t +b s 1 loggm sit +b s 2 logci sit +b s 3 log SG sit +b s 4 log S si (t 1 ) +b s 5 censg sit +b s 6 log D All models are estimated assuming that the error term, sit, has first order autocorrelation and is segmentwise heteroscedastic, i.e. the variance of sit varies by the segment. 4. Results The pooled model is estimated first with results as shown in. This panel regression estimation yields an adjusted R 2 of 19.63%. F-test on the regression coefficients give a F-statistic equal to on 6 and 2944 d.f. with a negligible p-value. Thus the null hypothesis that all the regression coefficients are together equal to zero is rejected with a high level of significance. The panel regression for model M1 is carried out using the pooled or OLS estimator, fixed or within effects, and random effects. We conduct the Lagrange Multiplier tests for panel models to find the best estimator. The null hypothesis that there are no significant effects from the individuals is rejected at level of significance, suggesting that pooled OLS model is not a good fit for model M1 (Honda, 1985). F-test for individual effects shows that the within or fixed group model gives a better fit than the pooled OLS for the regression model M1. The F- statistic is at 406 and 2944 d.f. with a significance level. shows regression coefficients for the fixed effect panel test. Hausman test (Hausman, 1978) for comparing the random and fixed effects models rejects the null hypothesis, with a chi-square statistic of at 6 d.f. and level of significance, that firm-specific effects are uncorrelated with the independent variables and we conclude that the fixed effects are present in the model. Table 5: Coefficient estimates of model M1 Estimate Std. Error t-value Pr(> t ) LogGM LogCI LogSG LogS t censg LogDER

11 Next, we show the results of product segment wise regression analysis. Lagrangean Multiplier Test rejects the null hypothesis, at p < significance level, that pooled OLS gives a better estimate of the model. In other words there are significant individual effects on the observations. F-test for individual effects rejects the null hypothesis that individual firm effects are not significant, with an F-statistic of at 406 and 2866 df and p < level of significance. Hausman test to compare between fixed and random effects model cannot reject the null hypothesis that random effects are significant as compared to fixed effects. Thus we use random effect estimator to determine regression coefficients for the model M2. Table 6 gives the value of regression coefficients for product segment-wise panel regression, or for model M2. The adjusted R 2 associated with panel regression of model M2 is 31%, which is higher than the adjusted R 2 for pooled model M1. This proves that product wise segmentation of the coefficients gives a better fit of the model, or explains more variability in the dependent variable, log IT. F-test on all the coefficients together gives an F-statistic of 17.8 at 84 and 3272 df and level of significance as p < Thus, we can reject with a high level of significance that all coefficients of model M2 are equal to 0. To test for serial correlation in the panel model, we conduct Breusch-Godfrey/Wooldridge test (Breusch, 1978; Godfrey, 1978). The null hypothesis that serial correlation does not exist is rejected at p < significance level, with a chi-square statistics of at 5 df. Alternate hypothesis suggests serial correlation in idiosyncratic errors. Breusch-Pagan test is conducted to test for heteroscedasticity in linear models. The null hypothesis of this test suggests homoscedasticity in the model. The test yields a test statistic of at 490 df and a significance level of The test result points towards the presence of heteroscedasticity in the model. To control for autocorrelation and heteroscedasticity effects across individual observations, we generate a robust covariance matrix using the method proposed by Arellano (1987) to generate a fully general structure w.r.t. heteroscedasticity and cross-sectional correlation. Table 6: Coefficient estimates for model M2 Product segment coeff. (b) log GM log CI log SG* log S t-1 censg log DER S.E. coeff. (b) S.E. coeff. (b) S.E a a b a a b a a b b a b a b b a a b b b b a b a a a a b b b a a a a b coeff. (b) S.E. coeff. (b) S.E. coeff. (b) S.E.

12 a a a a a b b a, b Statistically significant at p < 0.01 and p < 0.10, respectively, for 2-tailed tests. * The estimates of log SG are derived after removing censg from the model. The coefficient of log GM is model M1 is negative with a level of statistical significance, p < This strongly supports the hypothesis on negative correlation between log IT with log GM. As we use a log-linear model, the coefficient of log GM gives the elasticity of inventory turns with respect to gross margin. As per the pooled model M1 results, a 1% change in gross margin is associated with an estimated % change in inventory turns. Across product segments, as per the results of model M2, the coefficient of log GM is negative in 12 out of 14 sectors, out of which 9 are statistically significant (p < 0.1). Among the positive coefficients, none are statistically significant. The product segments of Drugs and Medicines and non-electric machines exhibit an insignificant positive relationship of gross margin with inventory turns. Across segments, the coefficient estimate for log GM varies from for transport equipment to for retail outlets. The pooled coefficient of log CI is at a level of statistical significance, p < , as per model M1. This strongly supports the hypothesis on positive correlation of log IT with log CI. As per the results from the pooled model M1, a 1% change in capital intensity is associated with a 0.398% change in inventory turns across the trading firms. As per estimation results of model M2, across product segments the coefficient of log CI is positive for 13 out of 14 product segments, all of which are statistically significant (p < 0.01 for 11 segments and p< 0.1 for 2 segments). Only the product segment, Dyes and paints shows a negative coefficient, which is statistically insignificant. Across segments, the coefficient estimate for log CI varies from for trade in food products to for trade in drugs and medicine. The pooled coefficient of log SG is at a level of statistical significance, p < , as per model M1. This strongly supports the hypothesis on positive correlation of log IT with log SG. As per the results from the pooled model M1, a 1% change in capital intensity is associated with a 0.332% change in inventory turns across the trading firms. As per estimation results of model M2, across product segments the coefficient of log SG is positive for 10 out of 14 product segments, out of which 8 are statistically significant (p < 0.01 for 3 segments and p< 0.1 for 5 segments).the product segments trade in dyes and paints has negative estimate of coefficient of log SG, but statistically insignificant. The product segment retail trade has a negative estimate of coefficient, which is statistically significant (p < 0.1). Across segments, the coefficient estimate for log SG varies from for trade in transport equipment to for wholesale trade. The pooled coefficient of log S t-1 is at a level of statistical significance, p < , as per model M1. This supports the hypothesis on positive moderation effect of company size in terms of previous year s sales on inventory turns. The effect of company size is although smaller as compared to gross margin, capital intensity and sales growth explanatory variables. As per the results from the pooled model M1, a 1% change in capital intensity is associated with a 0.117% change in inventory turns across the trading firms. As per estimation results of model M2, across product segments the coefficient of log S t-1 is positive for 12 out of 14 product segments, out of which only two, agriculture products and transport equipment are statistically significant (p < 0.1).The product segments trade in dyes and paints, and

13 retail trade have negative estimates of coefficient of log S t-1. The negative coefficient estimate of product segment retail trade is statistically significant (p < 0.l). The explanatory variable censg and log DER do not show statistically significant coefficient estimates, as per pooled model M1. This rejects our hypothesis on negative correlation of inventory turnover with distribution expenses ratio. Model M2 coefficient estimates for censg show statistical significance for segments 6 (p < 0.1), 7 (p < 0.1), 10 (p < 0.1) and 11 (p < 0.001). The estimates of coefficients for these product segments had opposite signs for log SG and censg. The sum of values of coefficient estimates for log SG and censg for these product segments are 0.179, 0.723, 0.204, and respectively. Thus, for product segments of trades in textiles, drugs and medicines, non-electrical machines, and dyes and paints inventory turnover is more sensitive to changes in the sales growth rate when firms operate in the sales-declined range than in the sales-increased range. In the segment wise model M2, the estimate of log DER is positive and statistically significant for segment 4, wholesale trade (p < 0.01). For product segments 5 (food products), 10 (non-electric machines) and 12 (chemicals) the coefficient estimate is negative and statistically significant (p < 0.1). Thus only 3 out of 14 product segments support our hypothesis on a negative correlation of distribution expenses ratio with inventory turnover. To summarize our analysis so far, the inventory behaviour in Indian trading firms tends to follow similar behaviour as the related industry in the developed markets context. The variability in inventory turnover explained by the model is although not as high as reported in western literature. Our model could explain only 31% of the total variability in the inventory turnover, whereas western studies report more than 90% (Gaur et al., 2005; Kolias et al., 2011). This suggests there is wide variation in other variables not captured within the model. It may also suggest that the inventory management in Indian trading firms does not follow the systematic managerial practices as practiced in the USA or Greece. In spite of that our analysis does develop strong linkages between important variables. The inventory turnover is negatively correlated with gross margin and positively correlated with capital intensity and sales growth. Inventory turnover is also positively moderated by the firm size. The results do not support the hypotheses on the effect of sales growth on inventory turnover during the sales growth period or sales decline period. The hypothesis of inventory turnover having negative correlation with distribution expenses ratio did not get support from the model results. This may signal a possibility that most of the trading firms do not do in house transportation. Due to an availability of large number of trucking companies in India, there may be an availability of LTL (less than truckload) shipments with standard rates. Thus, it may not be additionally beneficial to exploit the economies of scale for firms having larger distribution costs. Due to a multilayer distribution network in India distribution expenses may be dependent on several other factors, which might not be related to inventory management as such. 5. Trends in inventory performance in the trading industry According to Gaur et al. (2005), inventory turnover should not be used per se in performance analysis, as it depends on other factors. Through our empirical analysis we derive that some of the variables like gross margin, capital intensity, sales growth and company size significantly influence the value of inventory turnover for a firm at any time period. Thus changes in gross margin, capital intensity, sales growth and company size should be incorporated in the evaluation of inventory productivity of a firm. This figure is

14 termed as adjusted inventory turnover (AIT) by Gaur et al. (2005). We estimate the trends in inventory performance across different product segments in the Indian trading industry by taking the adjusted inventory turnover as measurement. Since our model did not explain a high level of variability in the inventory turnover variable, there might be other factors influencing the variable, so we still cannot rely completely on the demonstrated trends in AIT to compare firms in terms of inventory performance. The value of AIT for firm i in segment s in year t is computed as follows: IT sit b 1 loggm sit b 2 logci sit b 3 logsg sit b 4 log S si (t 1 ) AIT sit =log log or, we can represent AIT as GM CI SG S (si (t 1) ) b 4 ( sit ) b 3 ( sit ) b 2 ( sit ) b 1 AIT sit =IT sit (11) To understand the trends in average AIT levels among the product groups, we plot an area chart as shown in Figure 1. The trends suggest some reduction in average overall AIT levels across all the product segments. At the individual segment level there is wide variation in AIT levels and does not reflect a general improvement in inventory performance levels. Figure 2 shows a plot of overall average AIT from the year 2000 to This too does not suggest any significant improvement in inventory performance levels.

15 Figure 1: Trends in average AIT of the product segments Total 2 0 Figure 2: Trend in overall average AIT level

16 6. Conclusion The aim of this study is to analyse the determinants of inventory performance of firms belonging to the wholesale and retail trading sector in India from the year This study was conducted on a sample of 407 Indian trading firms divided into 14 industry segments based on the products traded, as per NIC coding scheme. Panel data regression technique was employed in the study to analyse the relationship between independent variable, inventory turnover and dependent variables gross margin, capital intensity, sales growth, company size, and distribution expenses ratio. The results suggest that inventory turnover has a negative correlation with gross margin and a positive correlation with capital intensity, sales growth. Inventory turnover is positively moderated by the company size. No significant relationship could be established between inventory turnover and distribution expenses ratio. Firm level and time related fixed effects are shown to result in better model estimation. On partitioning the total variance of inventory turns into its components, it was found that a substantial variability was due to segment wise effects. Adjusted inventory turnover trends across different product segments do not suggest a significant improvement in inventory performance levels. This study has some potential limitations. We have used aggregate data from the CMIE Prowess database of Indian firms. Analysis carried out on such a data cannot capture operational aspects important in inventory management like product variety, different units of analysis, locational advantages/disadvantages. This analysis also cannot include other aspects like level of ICT sophistication developed by a firm, marketing strategy, supply chain disruptions, product life cycle lengths, etc., which may substantially alter inventory management practices. Limitations of this kind are standard in the empirical literature working with inventories (Rajagopalan and Malhotra, 2001; Gaur et al., 2005). The results of this study can be used to identify methods and practices to improve inventory performance among firms and over time. Factors and variables influencing inventory performance can be identified using industry surveys and can be included along with proxies like warehouse size, location, ERP implementation, etc. to develop a robust model explaining inventory behaviour. Detailed field studies can be done in emerging markets context to identify specific variables that effect inventory performance. Finally, we would like to point out that this study can be extended to analyse inventory intensive manufacturing firms as well.

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