تجزیه و تحلیل تجربی از رفتار گردش موجودی در بخش خرده فروشی یونانی: 2000-2005
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20666||2011||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 133, Issue 1, September 2011, Pages 143–153
In this study we investigate the determinants of inventory turnover. The study is based on an econometric analysis of inventory behaviour using an inventory turnover model. The empirical implementation of the model was conducted on a sample of financial data for 566 Greek retail firms for the period 2000–2005. By employing panel data techniques it was found that inventory turnover ratio is negatively correlated with gross margin and positively correlated with capital intensity and a measure of sales surprise. Decomposing the variance into its components associated with year, firm and retail segment effects, we found that a substantial amount of inventory turns variability is due to segment-wise effects. Moreover, the inventory turnover reaction to different sales changes was also studied. It was estimated that changes in sales bring on bigger changes when firms operate in sales-declined region. These results are useful in identifying methods and applications to improve inventory performance among firms and over time.
Inventories have generally been the most difficult asset to be managed both for merchandising and manufacturing firms. Inventory management incorporates purchasing, financing and selling policies. The implementation of these diverse policies comprises conflicting functional objectives; e.g. the financial manager’s effort to minimize the inventory level is contradictory to the goal of minimizing the probability of inventory shortage as marketing manager desires. Inventory management deals, on one hand, by specifying, retaining and controlling the desirable inventory level, and on the other, by minimizing the total inventory cost. In other words, the problem of managing inventories is an optimization problem between overstocking and understocking cost. Shortage of inventory implies unsatisfied demand and sales shrinkage. Excessive inventories may lead to the cost of items storage, taxes and insurance, breakage, spoilage, deterioration and obsolescence and the opportunity cost of alternative capital investment as well. Moreover, in all firms, except those belonging to the financial and service sector, inventories represent a large proportion of current and total assets. For example, Gaur et al. (2005) report that in 2003, inventories in US retailing represent, on average, 36% of total assets and 53% of current assets. Likewise, our dataset on Greek retailers, during the period 2000–2005, show that inventories represent on average 38% of total assets and 51% of current assets. Generally, as it stems from the relevant literature, investment in inventory represents a significant amount of the total funds available in firms. Furthermore, comparison of inventory turns between firms are often the basis for managerial compensation (Shleifer, 1985). For these reasons inventory management receives great attention from market analysts, bankers and investors. A financial index that combines the cost of goods sold with inventories is the inventory turnover ratio, defined as the ratio of a firm’s cost of goods sold to its inventory level. This index shows how many times inventories are turned over during the accounting year. Hence, inventory turnover ratio can often be used as a comparative measure of inventory performance between firms, or in evaluating the effectiveness of inventory management. To our knowledge, there have been only a few research papers that investigate the determinants of inventory management as expressed by inventory turnover ratio. For example, Gaur et al. (2005) set up a methodology, which combines inventory turnover with other performance variables such as the gross margin, capital intensity and sales related variables. In this study we follow a similar methodology in order to identify the factors that determine inventory behaviour and affect their performance using a large sample of Greek retailing firms operating in the period 2000–2005. Our dataset consists of repeated observations on the same cross section of firms over time drawn from financial data of the firms’ annual income statements and annual balance sheets. Econometric analysis is based on the study of Gaur et al. (2005) for the U.S. retail sector. We have further extended this analysis by looking at the sales growth process in association with the inventory turnover ratio. The results of our econometric analysis confirm the findings of the previous studies as far as the importance of gross margin, capital intensity and sales surprise ratio is concerned. Our model explains 94.10% of the total variation as well as 91.46% of within-firm variation of inventory turnover ratio. Moreover, we estimate the impact of sales growth rate on inventory turns and found that when firms operate in “sales-declined region”, sales changes bring on bigger changes to the inventory turnover than in cases where firms operate in “sales-increased region”. It was found that a 1% increase in sales growth ratio is associated with an increase in inventory turnover of 0.46% in the former case, and only 0.26% in the latter. Besides, we investigate the importance of year, firm and segment effects on inventory turnover. By doing so, we find that the variation across segments accounts for 58% of the total variation, while 33% is due to the variation across firms. Finally, we estimated the inventory turnover trend over the entire period examined and found that it varies across firms. Our results are useful in operation and financial management and could help managers make aggregate level inventory decisions as well as identify the causes of differences in inventory turns between firms and over time. It should be noted that the present panel data econometric study is the first in the Greek literature on inventory behaviour and the results coming from it can stimulate future research into possible ways of effective inventory management.1 The remainder of this paper is organized as follows. In Section 2, a review of the relevant literature is presented and the determinants of inventory turnover ratio are discussed. In Section 3, the dataset is explained and a number of descriptive statistics are given. The econometric model is specified in Section 4, while Section 5 contains the main findings. In Section 6 we discuss the implications of our results for operating and financial strategies as well as the limitations of our study. We conclude the paper in Section 7 showing directions for future research.
نتیجه گیری انگلیسی
This study is an attempt to investigate the determinants of inventory turnover ratio. It was conducted on a sample of financial data for 566 Greek retail firms for the period 2000–2005. By employing panel data techniques it was found that inventory turnover ratio is negatively correlated with gross margin and positively correlated with capital intensity and a measure of sales surprise. Moreover, the inventory turnover reaction to sales changes was also studied when firms operate in “sales-declined region” as well as in “sales-increased region”. It was estimated that changes in sales ratio bring on bigger changes in the former case than in the latter one. Partitioning the total variance of inventory turns into its components, we found that a substantial amount of the variability is due to segment-wise effects. However, to address accurately the impacts of firm and segment effects on inventory turns, further empirical and theoretical research is required about the origins of the differences in the determinants of inventory turnover. These results are useful in identifying methods and applications to improve inventory performance among firms and over time. Therefore, our study may contribute to further research on the microeconomic characteristics of inventory turnover and its application in performance analysis and managerial decision making. Possible extensions of the model include the use of a longer time series dataset and the introduction of variables that improve the explanatory power of the model. Indicatively we suggest the introduction of variables like investments in buildings and in information systems, the level of interest-rates and the prices of products, the length of product life as well as the size of stores and warehouses. Such improvements will make the results from the inventories research more reliable and applicable in the areas of operation and financial management. It is finally pointed out that although our evidence comes from the retailing industry, the methodology could be as well applied for the investigation of inventory performance in the manufacturing industry.