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Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005

Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations... Logist. Res. (2009) 1:95–111 DOI 10.1007/s12159-009-0014-9 OR IGINAL PAPER Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005 Robert Obermaier Æ Andreas Donhauser Received: 13 November 2008 / Accepted: 15 June 2009 / Published online: 2 July 2009 Springer-Verlag 2009 Abstract Although inventory reduction has been a major 1 The premise of inventory reduction as a driver topic in production and operations management research of business performance for many years, there is a lack of empirically confirmed answers for questions such as: Have inventories in fully Inventory reduction has been a major topic in production industrialized economies such as Germany decreased, and operations management research as well as in the overall, during the past decades? To the extent, inventory academic literature on logistics and supply chain man- reductions were successfully realized, in which industries agement for many years. Myriads of articles and case did they occur? Are there differences in inventory reduc- studies have been written about firm’s needs and efforts to tion achievements between raw materials, work-in-process, reduce inventories. In the operations research literature, or finished goods? Are there measurable effects of inven- numerous normative models were developed to determine tory reductions upon the financial performance? To the best optimal lot sizes and inventory levels. The belief that of our knowledge, this empirical study is the first one to inventory reflects waste and should be eliminated to investigate long-term inventory development on a firm as increase productivity is the fundamental premise of popular well as on industry level in a major European economy. It concepts such as ‘‘just-in-time’’ (JIT) or ‘‘zero inventory’’ is based on data from German corporations and provides [8, 21]. This article is motivated by the observation that, answers to the research questions stated above. The study’s despite a long tradition of research related to inventory findings indicate that total inventory to sales ratio issues, there is lack of empirically confirmed answers to decreased in a statistically significant extent in four out of questions such as: Have inventories in fully industrialized six industry sectors during the time frame investigated. countries such as Germany actually decreased, overall, Further results suggest that the overall impact of inventory during the past decades? Has inventory reduction devel- reductions to the financial performance of companies is oped differently for raw materials (RMs), work-in-process only of a small degree. (WP), or finished goods (FGs), respectively? Are there measurable effects of inventory reductions upon the Keywords Inventory  Manufacturing  Just-in-time  financial performance? Supply chain  Logistics  Time series analysis The study presented here is believed to be the first one to empirically investigate long-term inventory development in a major European economy. It provides answers to the research questions stated above, using firm level data from a sample of German corporations as opposed to aggregated industry level data. Nevertheless, it also analyzes inventory developments by industry sectors and by stages of the R. Obermaier (&)  A. Donhauser typical industrial value chain, i.e., RMs, WP, and FGs. Chair for Controlling and Logistics, The article is organized into six sections: the subsequent Institute for Business Administration, Sect. 2 reviews the existing body of literature and sum- University of Regensburg, Regensburg, Germany marizes major findings. In Sect. 3, we describe our research e-mail: robert.obermaier@wiwi.uni-regensburg.de 123 96 Logist. Res. (2009) 1:95–111 methodology as well as the data sources used and develop Lieberman and Demeester [16] studied 52 Japanese several hypotheses regarding inventory trends during the automotive companies over a time period from the late time frame investigated. The results are presented in Sect. 1960s to the early 1980s, shedding light on the link 4. Their implications will be discussed in Sect. 5.We between inventory and productivity: firms reducing conclude with limitations and further research opportuni- inventory substantially were able to improve labor pro- ties in Sect. 6. ductivity significantly. Chen et al. [7] created portfolios of firms based on their relative inventory performance and find abnormally high inventories associated with poor 2 Inventory performance in the academic literature stock market performance. Swamidass [27] argues that inventory holding could be a function of firms’ financial To the best of our knowledge there is no recent empirical performance: top performers decreased inventories sig- study concerned with inventory performance of firms of nificantly, whereas low performers surprisingly showed any major European economy. Regarding the US manu- increasing inventories. Cannon [6] also analyzes the link facturing industry, however, there are several studies between inventory and financial performance, finding no examining the development of inventory levels. relationship between improvements in inventory perfor- In their critical assessment of research on inventories, mance and improvements in overall firm performance. Blinder and Maccini [4, p. 79] state that the inventory to sales ratio of US companies’ inventories shows no decreasing trend between 1959 and 1986, a result ‘‘which 3 Research hypotheses and the method of analysis casts serious doubt on buffer stock theories of inventory behavior because computerization should have reduced the 3.1 Hypotheses need for inventories as buffers’’. This statement served as point of departure for a series of other studies primarily It is according to common sense that inventory policy concerned with inventory levels in the US. In contrast to has to deal with a number of trade-off decisions bal- Blinder and Maccini [4], Bairam [1] finds significant ancing demand and capacity as well as costs and cus- downtrends in inventory to sales ratios of individual US tomer service. However, high inventories are often seen manufacturing firms between 1976 and 1992. Hirsch [13] as poor operational performance in general because of registers an improvement in WP and RM inventories for tied-up capital, excess holding and carrying costs, and some sectors of the US manufacturing industry from the furthermore covering/hiding unnoticed or unsolved pro- late 1960s to the early 1990s (e.g., motor vehicles, rubber cess problems. Hence, to release cash for alternative uses and plastics). Having investigated the inventories of 7.433 and to uncover hidden problems by lowering inventory US manufacturing firms, Chen et al. [7, p. 1021] report that levels, JIT systems, in particular, have been widely while ‘‘the medians of RMs, FGs, and total inventory days established in different industries [12, 18, 19, 25]. drop, the means actually rise between 1981 and 2000’’, as Accordingly, we want to know, if inventories in German means may be influenced by outliers they are focusing on firms actually decreased during the time frame investi- medians. Recently, from a capital market view, using a gated. Thus, we set forth the following hypotheses. sample of US manufacturing firms for the period 1994– 2004, Tribo [28] finds evidence that after a firm was listed 3.1.1 Hypothesis 1 on the stock market it shows decreasing inventory levels. In addition to this kind of inventory studies, a second In each of the German firms examined, (a) total inventory stream of research is dedicated to the benefits of JIT adoption on inventory performance. Huson and Nanda [14] to sales ratios, (b) RM inventory to sales ratios, (c) WP inventory to sales ratios, and (d) FGs inventory to sales studied a sample of 55 firms that adopted JIT manu- facturing and find out that these firms increased their ratios show a decreasing trend between 1993 and 2005. inventory turnover subsequent to JIT implementation. 3.1.2 Hypothesis 2 Balakrishnan et al. [2] compare a sample of 46 JIT adopters with a sample of non-adopters of the same size and observe On an aggregated level we correspondingly formulate no significant effects on financial performance. Biggart and Hypothesis 2. Gargeya [3] find decreasing total and RM inventory to In each of the industries examined, (a) total inventory sales ratios after JIT implementation, whereas this does not to sales ratios, (b) RM inventory to sales ratios, (c) WP hold for WP and FGs inventories. inventory to sales ratios, and (d) FGs inventory to sales Finally, a third stream of research deals with the relationship of inventory and firm performance. ratios show a decreasing trend between 1993 and 2005. 123 Logist. Res. (2009) 1:95–111 97 3.1.3 Hypothesis 3 2005. All data used were taken from Thomson Financial’s Worldscope Global Database. In several cases, manual Further on, we are interested in the stage where inventory correction of data was required based on print or online reduction mainly has taken place: RMs, WP, or FGs. From versions of the firms’ annual financial reports due to false the production and operations management literature, we or implausible data from the data base. If this was not know that JIT production techniques focus mainly on possible, firms were eliminated from the sample. Further- reducing WP inventory and cycle times [20, 26, 29]. The more, to estimate the trend coefficients, firms were exclu- adoption of JIT purchasing principles is motivated by a ded when inventory data were not available for the whole desire to reduce RM inventories, as well. From Little’s [17] time frame. Finally, the annual time series data cover 100 ‘‘law’’ we can derive that a reduction of cycle time leads to firms listed at the German stock market. The firms in the lower WP inventories. Nevertheless, if customers refuse to sample can be assigned to the Standard Industrial Classi- accept early deliveries because of their ‘‘inventory con- fication (SIC) manufacturing division that includes firms sciousness’’, orders that are finished ahead of their due engaged in the mechanical or chemical transformation of dates are forced to wait in FGs inventory before shipping. materials or substances into new products. This division A relatively poor performance in FGs inventories may can be split into two groups. The first group covers firms further be expected due to increasing product variety, 20 B SIC B 29, which are mainly in the food products number of plants or warehouse locations under the condi- (SIC 20), textiles (SIC 22) and wearing apparel (SIC 23), tion of constant or growing customer service levels. and chemical (SIC 28) industries. The second group covers Furthermore, WP inventory seems to be more affected firms 30 B SIC B 39, including manufacturing firms pri- by factors within a firm’s control when compared to FGs marily in industries such as rubber and plastics (SIC 30), inventories. Hence, we expect WP (FGs) inventories to stones, clay, and glass (SIC 32), primary metal (SIC 33), perform relatively best (worst) and therefore we formulate fabricated metal products (SIC 34), machinery (SIC 35), Hypothesis 3. electronics and electrical equipment (SIC 36), transporta- In each of the German firms examined, (a) WP inven- tion equipment (SIC 37), measuring instruments (SIC 38), tory ratios when compared to RM inventory ratios, (b) WP and miscellaneous manufacturing (SIC 39) industries. inventory ratios when compared to FGs inventory ratios, and (c) RMs inventory ratios when compared to FGs 3.3 Method of analysis inventory ratios show a greater decreasing trend between 1993 and 2005. A linear regression model with time (i.e., year) as inde- pendent variable is applied to investigate the rate of change 3.1.4 Hypothesis 4 in inventory ratios over time. Because inventory varies among others with production and distribution levels, it is Correspondingly, on an aggregated level we formulate necessary to use relative inventory measures. A widely Hypothesis 4. used ratio is inventory to sales, which measures the In each of the industries examined, (a) WP inventory percentage of sales served from stock on hand. Let I and it ratios when compared to RM inventory ratios, (b) WP S denote the inventory and the sales, respectively, of firm it inventory ratios when compared to FGs inventory ratios, i in year t, the inventory to sales ratio is: and (c) RMs inventory ratios when compared to FGs it IS ¼ : ð1Þ it inventory ratios show a greater decreasing trend between it 1993 and 2005. A declining (rising) inventory to sales ratio over time means good (bad) news in so far as sales grow faster 3.2 Data and sample (slower) than stocks. The short-term expectation is that production rates will be increased (cut back). For the long- For analyzing inventory performance over time, the study term, decreasing trends in inventory to sales ratios may could be executed either on firm level using disaggregated indicate improved efficiency. In order to better understand data or on industry level using aggregated data. This study the degree of improvement at each of the different is based on disaggregated data on firm level, mainly to guard against an ‘‘aggregation bias’’, i.e., differently per- forming firms canceling each other out per sector. In the For some applications, the inventory to sales ratio is multiplied by 12 months or 365 days providing a measure of inventory coverage for majority of cases, firm level data are publicly available a given value of sales. A further advantage of the inventory to sales only for stock-listed corporations, which, of course, rep- ratio is that it corrects for sector size. Finally, the analysis is only to a resent just a fractional amount of all German companies. minor degree affected by changes in price levels provided that prices The sample chosen covers the time frame from 1993 to of outputs vary according to the prices of inputs. 123 98 Logist. Res. (2009) 1:95–111 inventory stages as well as potential shifts between them, iterated Prais–Winsten [23] estimation. Accordingly, we we analyze different inventory to sales ratios separately for found that the trend coefficients, which are statistically total inventories as well as its constituents: RM, WP, and significant according to the Prais–Winsten estimation, do not FGs. In order to focus on the material aspects of inventory differ greatly from the OLS estimates. This does not hold for development, it has to be emphasized that total inventory is the Cochrane–Orcutt estimation that we conducted, but defined here as the sum of these three components. which is inferior to the Prais–Winsten iteration, especially in Besides firm level data, we are also interested in the the case of a smaller time series sample size [5, 15, 22]. inventory trends of the corresponding industries. In order to Therefore, we will only report the Prais–Winsten estimators. calculate aggregate inventory to sales ratios in period t for a certain industry j, inventory held in the industry’s firms i = 1, 2, …, n, are summed up and then divided by the sum 4 Results of sales across the n firms: n 4.1 Descriptive statistics aggr it i¼1 IS ¼ P : ð2Þ jt it i¼1 For a brief overview of the firms analyzed, the means, We aggregate our data according to the SIC codes on a medians, and variation coefficients of the different inven- two digit basis. As we did not establish a class with less tory to sales ratios are given in Table 1. The variation then ten companies, the result of the aggregation spans six coefficients indicate the relative degree of movements industry classes, whereas we have merged the SIC codes 22 inside a company’s or a sector’s inventory ratios. Furthermore, Table 2 shows the means, medians, and and 23 together due to their similarity. To assess the corresponding overall trend coefficients variation coefficients for the sample’s industry groups for our sample over time, we applied the following simple according to the SIC codes. Because some SIC code classes linear regression model for total inventory levels as well as consist of less than ten firms, they are not listed here, for each of the three inventory types: whereas, the SIC codes 22 and 23 are merged due to their similarity. IS ¼ a þ b  t þ e ; ð3Þ it i it it To calculate means, medians, and variation coefficients In Eq. 3, t represents the time period (year), a the on an industry group level, we first determined the sum of intercept, and b the slope, i.e., the trend coefficient, of firm i. the weighted inventory to sales ratios of all firms within Because we applied regression analysis on time series data, one sector for each year of the time frame investigated. The we checked for first order autocorrelation of the residuals numbers shown in Table 2 are based on variable aggre- e using the Durbin–Watson test statistic [9, 10], which it gation weights; this means that the sales of a company for compares the ordinary least squares (OLS) residual for each year are divided by the sector’s total sales of the period t with the residual from the preceding period t - 1, corresponding year. and is defined as: T 2 4.2 Empirical tests ðÞ ^ e  ^ e t t1 t¼2 d ¼ : ð4Þ ^ e t¼1 t The results of our time series regression analysis for testing hypothesis 1 are provided in Table 3. Considering The Durbin–Watson test statistic can vary between 0 and hypothesis 1 (a) we find significantly decreasing (increas- 4. If the Durbin–Watson test statistic equals 2, there is ing) total inventory to sales ratios for 26 (22) firms. absolutely no first order autocorrelation. A d value Decreasing (increasing) RM inventory to sales ratios are significantly less (greater) than 2 indicates a positive diagnosed for 28 (29) firms [Hypothesis 1 (b)]. 41 (23) (negative) autocorrelation. Corresponding tables for firms show a significantly decreasing (increasing) trend in different sample sizes can be found in Durbin and Watson WP inventories [Hypothesis 1 (c)]. Finally, decreasing [10] and Savin and White [24]. Applying the Durbin–Watson test, we found first order autocorrelation in nearly all of the time series in the sample. As a consequence, OLS test statistics are no longer valid because standard errors are In order to save space, the intercept parameter estimates obtained are not reported. Only the trend coefficients (slope), together with biased and, therefore, causing serious misleading signals [11, t-statistics (P value) and coefficients of determination (R ) are reported. 30]. In order to take autocorrelation into account, we employ Six cases are rejections due to a trend coefficient of zero. That is, because some firms do not carry work-in-process inventories (e.g., Hence, there is a deviation from total inventories reported in the soft drinks or wearing apparel), whereas in the chemical industry balance sheets, which may also contain payments in advance to work-in-process and finished goods inventories are usually combined suppliers, for example. into one balance sheet item due to production conditions. 123 Logist. Res. (2009) 1:95–111 99 Table 1 Means, medians, and variation coefficients of inventory ratios 1993–2005 (sample) No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 1 20 A. Moksel AG 3.79 3.69 16.07 0.42 0.31 41.89 0.02 0.00 165.61 3.35 3.33 14.41 2 20 Actris AG 5.06 5.12 17.24 2.11 2.08 27.12 0.83 0.89 22.65 2.13 2.23 16.84 3 20 ADM Hamburg AG 11.15 11.37 19.64 8.94 9.45 19.95 0.25 0.28 18.74 1.96 2.03 30.49 4 20 Berentzen-Gruppe AG 12.00 11.46 16.76 2.84 2.75 25.75 3.21 3.21 41.25 5.95 5.36 28.33 5 20 Dom Brauerei AG 5.13 4.88 22.54 2.02 1.90 21.71 1.48 1.06 60.80 1.63 1.50 32.52 6 20 Frosta AG 15.64 15.91 9.20 6.42 6.23 26.34 2.54 2.71 37.10 6.68 6.57 9.62 7 20 Kulmbacher Brauerei AG 6.01 5.90 8.63 2.63 2.44 26.52 1.10 1.07 21.11 2.28 2.33 22.91 8 20 Mineralbrunnen AG 4.20 4.22 22.37 2.54 2.31 19.38 0.00 0.00 n. def. 1.66 1.47 31.44 920 Su ¨ dzucker AG 30.61 29.10 15.54 2.31 2.11 20.30 5.48 4.53 43.00 22.82 22.98 10.51 10 20 Sektkellerei Schloss 35.80 26.18 46.67 5.91 4.59 47.92 21.11 14.32 56.17 8.77 8.00 46.17 Wachenheim AG 11 20 Staatl. Mineralbrunnen AG 3.28 3.09 20.98 1.23 1.21 26.01 0.00 0.00 n. def. 2.05 2.02 26.60 12 20 VK Muehlen AG 9.83 10.02 17.74 6.91 7.12 20.07 0.23 0.29 59.78 2.68 2.58 23.20 13 22 Bremer Woll-Ka ¨mmerei AG 23.96 26.17 29.60 9.62 9.96 31.53 0.19 0.19 34.52 14.14 12.78 35.02 14 22 Gruschwitz Textilwerke AG 22.41 21.45 10.21 6.41 6.30 48.78 8.58 5.91 42.38 7.42 7.50 17.05 15 22 Kunert AG 34.39 35.34 6.04 5.18 4.93 10.06 5.55 5.78 21.99 23.67 24.05 10.54 16 22 Textilgruppe Hof AG 20.31 19.57 18.24 4.71 4.86 16.48 3.94 3.27 44.82 11.67 10.77 36.31 17 22 Vereinigte Filzfabriken AG 14.02 13.02 17.70 4.90 4.87 9.75 2.93 2.99 19.51 6.20 5.36 26.63 18 23 Adidas AG 19.18 20.86 22.09 0.64 0.64 47.47 0.15 0.13 38.57 18.39 20.46 24.10 19 23 Ahlers AG 20.30 20.67 15.37 7.09 7.10 17.48 0.67 0.69 36.17 12.54 12.75 15.77 20 23 Escada AG 18.89 19.61 17.57 3.08 3.11 18.92 2.16 1.84 27.76 13.66 15.22 27.24 21 23 Etienne Aigner AG 14.07 16.81 29.06 1.38 1.23 35.30 0.00 0.00 n. def. 12.69 14.78 30.15 22 23 Gerry Weber International AG 12.56 11.83 23.25 1.88 1.97 27.33 3.96 3.97 39.08 6.72 7.45 26.55 23 23 Hirsch AG 15.55 16.10 20.93 4.63 4.50 19.94 3.18 3.06 12.02 7.74 8.27 32.08 24 23 Hucke AG 10.60 10.91 13.84 5.33 5.61 16.53 1.08 1.44 92.92 4.18 4.05 17.32 25 23 Hugo Boss AG 17.32 16.31 13.29 4.49 4.46 13.88 0.84 0.86 14.27 11.99 11.27 20.15 26 23 Puma AG 18.61 18.80 18.81 0.24 0.13 86.88 3.59 4.28 70.96 14.77 15.14 12.00 27 23 Triumph International AG 24.46 24.21 6.11 4.10 4.12 12.30 3.97 3.99 13.38 16.40 16.82 8.08 28 28 Altana AG 12.73 12.72 8.76 4.14 4.09 9.36 1.93 1.93 13.40 6.66 6.64 12.70 29 28 BASF AG 14.12 14.46 7.78 2.20 2.73 51.22 0.22 0.22 41.17 11.69 11.50 15.28 30 28 Bayer Aktiengesellschaft 20.34 20.53 3.38 3.67 3.51 10.62 0.00 0.00 n. def. 16.68 16.85 3.33 31 28 Beiersdorf AG 13.44 14.02 8.36 3.43 3.35 19.38 1.02 0.98 17.26 8.99 8.45 12.44 32 28 Biotest AG 44.01 45.60 17.28 11.47 10.79 33.48 23.88 23.11 32.46 8.66 8.50 9.02 33 28 Fresenius SE 11.75 9.56 32.35 2.77 2.03 44.64 1.67 1.39 41.34 7.31 6.16 26.31 34 28 Fuchs Petrolub AG 12.97 12.60 6.05 5.38 5.37 6.18 0.62 0.59 11.56 6.97 6.85 8.22 35 28 Henkel KGaA 12.00 12.91 12.75 3.81 4.01 18.09 1.11 1.32 45.64 7.08 7.24 8.76 36 28 Linde AG 17.55 19.03 31.70 2.88 3.01 22.26 8.02 9.38 55.61 6.65 6.54 11.15 37 28 Merck KGaA 19.69 19.44 11.87 4.22 4.30 20.57 0.00 0.00 n. def. 15.48 15.14 10.43 38 28 Su ¨ d Chemie AG 17.03 16.88 6.88 6.10 6.00 9.53 3.11 3.01 17.37 7.82 7.98 9.19 39 28 Schering AG 19.56 19.25 11.67 4.08 3.96 12.29 8.00 7.86 13.77 7.48 7.29 12.38 40 30 Continental AG 12.32 11.86 18.79 3.21 3.26 9.36 1.53 1.51 19.18 7.58 7.13 25.65 41 30 Ehlebracht AG 11.88 12.94 28.36 4.64 4.29 25.85 2.13 2.41 45.14 5.12 5.56 37.78 42 30 New York-Hamburger Gummi- 17.72 17.97 9.52 4.01 3.91 15.76 6.30 6.21 17.25 7.41 7.56 20.50 Waaren Compagnie AG 43 30 Simona AG 18.71 18.79 8.14 4.88 5.12 14.22 0.00 0.00 n. def. 13.82 13.81 8.67 44 30 WERU AG 6.44 6.38 13.29 5.53 5.43 16.07 0.35 0.33 27.25 0.56 0.54 21.82 123 100 Logist. Res. (2009) 1:95–111 Table 1 continued No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 45 32 BHS tabletop AG 16.75 16.53 23.75 2.94 2.56 33.80 1.53 1.66 48.55 12.28 12.01 21.86 46 32 Didier-Werke AG 16.87 17.32 19.59 5.24 5.44 23.77 3.52 3.23 34.60 8.11 6.96 29.88 47 32 Dyckerhoff AG 9.93 10.01 9.66 4.34 3.99 13.77 1.56 1.41 27.13 4.03 4.10 24.46 48 32 Erlus AG 9.19 10.01 41.32 1.52 1.65 30.41 0.30 0.31 16.92 7.36 7.85 45.61 49 32 Heidelbergcement AG 10.27 10.25 5.79 5.26 5.28 9.45 1.32 1.27 20.92 3.69 3.53 12.78 50 32 Keramag AG 10.07 10.19 8.82 0.72 0.61 34.31 0.54 0.38 51.87 8.82 8.86 8.52 51 32 Pilkington Deutschland AG 7.48 7.86 22.68 1.86 1.68 31.13 0.39 0.21 90.13 5.24 5.33 22.67 52 32 Rosenthal AG 29.22 27.77 16.24 2.62 2.66 16.31 6.08 5.20 70.23 20.52 21.03 16.43 53 32 Saint Gobain Oberland AG 13.76 13.17 21.84 3.59 3.08 52.32 0.38 0.22 97.38 9.79 9.64 11.60 54 32 SGL Carbon AG 27.57 26.78 9.32 7.22 7.10 12.37 14.66 14.46 9.05 5.68 5.64 13.66 55 32 Sto AG 7.76 7.79 6.50 2.00 1.95 8.57 0.18 0.16 32.00 5.59 5.58 7.81 56 32 Teutonia Zementwerk AG 16.49 15.59 17.60 8.73 8.48 19.37 4.87 4.26 34.37 2.89 2.51 35.47 57 32 Villeroy and Boch AG 26.19 25.95 7.20 3.75 3.75 6.50 4.02 3.56 16.85 18.42 18.20 9.61 58 33 Norddeutsche Affinerie AG 15.89 16.20 14.41 5.82 5.80 13.61 6.31 6.03 21.50 3.76 3.77 32.01 59 34 Innotec TSS AG 14.39 14.66 13.96 6.04 6.21 18.62 6.12 5.87 37.72 2.23 2.32 36.60 60 34 Salzgitter AG 16.82 16.25 9.81 4.43 4.22 25.44 3.47 3.77 22.89 8.92 9.04 7.13 61 34 WMF AG 25.06 25.00 8.31 3.58 3.61 11.06 2.82 2.80 8.79 18.66 18.79 9.61 62 35 Alexanderwerk AG 37.82 37.18 30.69 3.78 4.01 31.50 25.52 23.72 47.27 8.52 6.57 65.12 63 35 Bertold Hermle AG 18.32 17.15 25.43 3.27 3.29 67.97 8.80 5.56 56.15 6.25 6.60 31.04 64 35 Deutz AG 32.44 26.71 44.24 11.13 11.35 13.62 15.69 10.05 84.28 5.62 5.40 21.55 65 35 Du ¨ rkopp Adler AG 28.70 28.51 8.89 7.22 7.01 25.82 9.09 9.14 13.28 12.39 12.36 18.99 66 35 Du ¨ rr AG 15.25 15.03 72.70 2.23 2.16 16.37 12.86 12.03 89.27 0.16 0.05 90.25 67 35 GEA Group AG 10.62 10.32 18.17 2.13 2.46 29.64 4.74 4.51 30.34 3.75 3.93 16.13 68 35 Gildemeister AG 29.52 26.16 33.74 10.10 8.80 34.76 11.69 8.31 66.41 7.72 7.84 25.38 69 35 Jagenberg AG 19.34 21.00 25.92 4.48 4.02 20.86 12.18 13.50 26.93 2.68 2.77 57.44 70 35 Junghenrich AG 11.36 10.19 20.50 5.41 4.69 24.30 1.79 1.91 52.49 4.15 4.18 13.63 71 35 Kloeckner-Werke AG 18.10 15.57 37.52 6.33 5.53 31.06 9.58 7.74 54.91 2.19 2.01 28.94 72 35 Koenig and Bauer AG 34.08 36.71 16.60 6.37 6.23 32.46 27.48 28.33 15.19 0.23 0.14 93.12 73 35 Krones AG 12.74 11.92 27.93 3.44 3.12 37.08 5.70 5.62 20.58 3.60 3.37 40.63 74 35 KSB AG 19.60 20.36 13.41 6.07 5.92 8.21 8.38 8.67 27.00 5.16 5.26 12.14 75 35 KUKA AG 26.87 27.20 22.05 5.75 5.87 10.69 18.73 19.19 28.53 2.40 2.24 17.79 76 35 Rheinmetall AG 20.28 20.29 18.76 5.40 4.83 21.80 10.94 11.53 32.57 3.94 3.41 23.80 77 35 Sartorius AG 18.29 18.39 15.19 3.83 3.83 14.02 5.47 5.75 15.72 8.99 8.70 22.80 78 35 Triumph Adler AG 15.97 15.82 28.00 1.71 1.90 61.98 2.55 1.64 103.03 11.70 12.52 35.11 79 35 Vossloh AG 21.40 18.16 28.43 7.64 7.89 29.76 8.00 6.96 54.91 5.76 6.64 49.07 80 36 Brilliant AG 22.83 23.02 10.35 6.44 7.38 57.35 1.89 1.87 64.04 14.51 13.43 23.68 81 36 Ceag AG 20.28 21.02 19.51 6.60 6.72 17.96 3.70 3.45 59.15 9.98 10.13 27.54 82 36 Leifheit AG 15.60 15.20 13.21 4.02 3.66 28.17 1.85 1.65 37.33 9.73 8.95 23.49 83 36 M.tech AG 20.15 18.58 28.14 5.17 5.02 18.02 14.52 12.20 35.28 0.46 0.00 115.87 84 36 Schweizer Electronic AG 11.26 11.40 8.38 4.68 4.42 16.01 3.61 3.61 15.13 2.97 2.96 36.56 85 36 Sedlbauer AG 15.98 14.59 21.55 8.61 7.40 30.69 4.60 4.35 18.01 2.77 2.51 27.25 86 36 Vogt Electronic AG 17.82 17.18 22.43 8.77 8.76 19.55 4.39 3.62 38.74 4.66 3.62 50.76 87 37 Audi AG 6.31 5.94 16.88 1.36 1.38 19.64 1.42 1.45 16.87 3.54 3.44 32.96 88 37 BBS Fahrzeugtechnik AG 22.10 21.96 14.04 5.22 4.91 22.16 5.76 5.54 20.05 11.12 11.53 21.18 89 37 BMW AG 12.06 12.07 12.49 1.36 1.37 10.43 1.75 1.77 16.01 8.95 9.09 14.33 90 37 Hymer AG 21.75 20.35 15.26 8.94 8.99 19.74 1.75 1.76 15.52 11.06 10.05 18.73 123 Logist. Res. (2009) 1:95–111 101 Table 1 continued No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 91 37 MAN AG 37.18 36.60 13.46 3.76 3.71 7.89 18.76 18.39 18.85 14.66 16.34 39.41 92 37 Porsche AG 11.01 10.86 16.57 1.46 1.21 50.90 3.41 3.39 43.21 6.15 6.65 23.74 93 37 Progress-Werke Oberkirch AG 14.44 14.15 16.88 5.20 4.78 27.74 6.29 6.14 31.43 2.95 3.06 15.41 94 37 Schaltbau Holding AG 25.10 25.89 20.00 9.00 8.45 14.93 12.08 11.30 31.65 4.01 3.74 28.18 95 37 Veritas AG 9.84 9.48 16.48 3.57 3.30 25.12 2.64 2.45 46.89 3.63 3.56 21.25 96 37 Volkswagen AG 11.37 10.83 13.21 2.24 2.22 7.01 1.67 1.45 19.14 7.47 7.33 20.52 97 37 Wanderer-Werke AG 22.62 23.79 12.48 4.61 3.85 29.63 6.53 6.58 13.79 11.48 11.96 19.45 98 38 Draegerwerk AG 21.91 23.28 17.76 5.30 5.68 19.86 6.70 7.22 32.15 9.92 10.36 16.77 99 38 Siemens AG 15.01 14.07 17.82 2.98 2.96 9.86 7.27 5.23 44.05 4.76 4.67 16.21 100 39 Johann F. Behrens AG 26.47 26.29 6.97 6.54 5.91 26.18 1.90 2.21 75.73 18.03 18.12 8.33 Table 2 Means, medians, and variation coefficients of inventory ratios 1993–2005 (SIC code classes) SIC TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 20 18.41 18.39 13.74 3.24 3.27 7.59 3.06 2.79 40.01 12.12 12.23 9.67 22/23 18.90 19.48 11.84 2.51 2.32 30.57 1.34 1.28 22.27 15.05 14.39 11.84 28 16.20 16.25 6.87 3.16 3.04 8.72 1.26 1.32 21.29 11.78 11.49 8.66 32 13.71 13.43 4.30 4.65 4.48 8.75 2.79 2.77 7.56 6.28 6.27 10.13 35 17.21 16.82 11.03 4.53 4.68 10.45 8.77 9.01 21.45 3.90 3.81 7.32 37 13.57 13.63 6.41 2.11 2.05 6.77 3.43 3.17 20.13 8.03 7.84 14.47 Total 15.08 15.32 3.65 2.91 2.90 3.34 3.82 3.83 8.90 8.36 8.40 3.61 (increasing) FGs inventories are significant for 24 (22) A negative value in the WP versus RMs (FGs) column firms [Hypothesis 1 (d)]. indicates that WP inventories performed better [i.e., show a On an aggregated level, the results of our time series higher (lower) decreasing (increasing) trend] when com- regression analysis for testing hypothesis 2 are provided in pared to RMs (FGs) and a negative value in the RMs versus Table 4. Considering hypothesis 2 (a), total inventory to FGs column indicates that RMs inventories performed sales ratios decrease (increase) to a significant extent in better when compared to FGs. Considering Hypothesis 3 four (one) sector(s). Decreasing (increasing) RM inventory (a), WP inventory ratios compared to RM inventory ratios performed significantly better (worse) in 42 (17) firms. In to sales ratios can be observed in one (two) industry sec- tor(s) [Hypothesis 2 (b)], while three sectors show a sig- 34 (25) cases, a significantly better (worse) development of nificantly constant trend with a slope of 0. Regarding WP the WP inventory ratio can be noticed [Hypothesis 3 (b)] inventories [Hypothesis 2 (c)], the regression analysis when compared to the corresponding FGs inventory ratio. results in four (one) sector(s) with a significantly decreas- RMs inventory ratios showed a better (worse) performance ing (increasing) behavior. Decreasing (increasing) FGs for 26 (28) firms [Hypothesis 3 (c)] when compared to FGs inventories are significant for two (one) industries inventory ratios. On an aggregated level, the results of our [Hypothesis 2 (d)]. time series regression analysis for testing hypothesis 4 are To answer the question at which stages inventory provided in Table 6, comparing the trend coefficients of reduction mainly has taken place, we proceed with testing different inventory stages between 1993 and 2005 for each hypothesis 3, comparing the trend coefficients of different SIC class. inventory stages between 1993 and 2005 for each firm (see Testing hypothesis 4 (a) WP inventory ratios performed Table 5). better (worse) in four (two) sectors when compared to RM 123 102 Logist. Res. (2009) 1:95–111 Table 3 Overall trend coefficients 1993–2005 No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 1 20 A. Moksel AG -0.056 0.400 0.072 -0.017 0.332 0.094 0.004* 0.091 0.259 -0.045 0.404 0.071 2 20 Actris AG -0.022 0.798 0.007 0.033 0.570 0.033 -0.033** 0.046 0.341 -0.020 0.484 0.050 3 20 ADM Hamburg AG -0.144 0.545 0.038 -0.080 0.683 0.017 -0.002 0.500 0.047 -0.069 0.194 0.162 4 20 Berentzen-Gruppe AG 0.322* 0.084 0.270 0.152*** 0.000 0.730 -0.205 0.187 0.167 0.364*** 0.002 0.625 5 20 Dom Brauerei AG 0.183*** 0.004 0.579 -0.053 0.207 0.154 0.080 0.328 0.096 0.108*** 0.001 0.666 6 20 Frosta AG -0.166 0.156 0.190 -0.292** 0.040 0.358 0.240*** 0.000 0.785 -0.068 0.190 0.165 7 20 Kulmbacher Brauerei AG 0.049 0.241 0.134 0.061 0.285 0.113 -0.039** 0.039 0.361 0.035 0.384 0.076 8 20 Mineralbrunnen AG 0.217*** 0.001 0.694 0.097*** 0.004 0.575 0.000*** 0.000 0.000 0.120*** 0.000 0.743 920 Su ¨ dzucker AG 0.775 0.133 0.211 0.108** 0.013 0.473 0.516** 0.018 0.443 0.138 0.630 0.024 10 20 Sektkellerei Schloss Wachenheim AG -3.583*** 0.002 0.646 -0.485** 0.019 0.438 -2.576*** 0.002 0.650 -0.469 0.133 0.211 11 20 Saatl. Mineralbrunnen AG 0.155*** 0.001 0.696 0.030 0.390 0.075 0.000*** 0.000 0.000 0.128*** 0.000 0.856 12 20 VK Muehlen AG 0.280** 0.038 0.363 0.221** 0.041 0.354 0.018* 0.095 0.253 0.041 0.553 0.036 13 22 Bremer Woll-Ka ¨mmerei AG -1.515*** 0.003 0.597 -0.624*** 0.008 0.526 -0.013*** 0.001 0.713 -0.867** 0.031 0.387 14 22 Gruschwitz Textilwerke AG -0.026 0.908 0.001 0.631*** 0.001 0.688 -0.635** 0.036 0.371 0.023 0.837 0.004 15 22 Kunert AG -0.026 0.849 0.004 -0.087* 0.051 0.329 -0.312*** 0.000 0.850 0.369** 0.022 0.424 16 22 Textilgruppe Hof AG 0.715** 0.018 0.442 0.087 0.189 0.166 -0.395*** 0.002 0.648 1.016*** 0.000 0.769 17 22 Verenigte Filzfabriken AG 0.528*** 0.003 0.596 0.087*** 0.005 0.554 0.106*** 0.009 0.512 0.348*** 0.005 0.568 18 23 Adidas AG -0.932*** 0.000 0.725 0.030 0.275 0.118 0.004 0.573 0.033 -0.976*** 0.001 0.702 19 23 Ahlers AG -0.574** 0.018 0.442 -0.212** 0.040 0.356 -0.032 0.267 0.121 -0.376*** 0.009 0.509 20 23 Escada AG 0.629* 0.051 0.329 -0.058 0.339 0.092 -0.110** 0.024 0.415 0.753** 0.017 0.449 21 23 Etienne Aigner AG 0.886*** 0.007 0.531 0.092** 0.047 0.340 0.000*** 0.000 0.000 0.793** 0.011 0.494 22 23 Gerry Weber International AG -0.048 0.894 0.002 -0.084* 0.088 0.263 -0.151 0.394 0.074 0.244 0.119 0.226 23 23 Hirsch AG 0.460 0.209 0.153 0.034 0.737 0.012 0.011 0.764 0.009 0.379 0.114 0.230 24 23 Hucke AG -0.151 0.280 0.115 -0.171*** 0.001 0.655 0.009 0.938 0.001 -0.006 0.929 0.001 25 23 Hugo Boss AG 0.497** 0.012 0.487 -0.097* 0.064 0.303 0.004 0.702 0.015 0.589*** 0.000 0.749 26 23 Puma AG 0.148 0.724 0.013 -0.052*** 0.001 0.716 0.429* 0.077 0.280 -0.189 0.343 0.090 27 23 Triumph International AG 0.097 0.580 0.032 -0.097*** 0.006 0.544 -0.013 0.782 0.008 0.212 0.103 0.243 28 28 Altana AG -0.166* 0.088 0.263 -0.076*** 0.009 0.509 0.021 0.284 0.114 -0.101 0.236 0.137 29 28 BASF AG -0.212** 0.037 0.366 0.207** 0.042 0.353 -0.016* 0.076 0.281 -0.415*** 0.001 0.678 30 28 Bayer Aktiengesellschaft 0.067 0.109 0.236 0.049* 0.093 0.256 0.000*** 0.000 0.000 0.014 0.662 0.020 31 28 Beiersdorf AG -0.135 0.339 0.092 -0.174*** 0.000 0.904 -0.041*** 0.008 0.519 0.076 0.546 0.038 32 28 Biotest AG 1.600*** 0.004 0.585 -0.252 0.564 0.034 1.848*** 0.000 0.796 0.025 0.782 0.008 33 28 Fresenius SE -0.777** 0.018 0.442 -0.249** 0.022 0.425 -0.118* 0.080 0.275 -0.412*** 0.008 0.524 34 28 Fuchs Petrolub AG 0.092 0.307 0.104 -0.008 0.795 0.007 0.015*** 0.009 0.514 0.067 0.301 0.106 Logist. Res. (2009) 1:95–111 103 Table 3 continued No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 35 28 Henkel KGaA -0.254** 0.027 0.402 -0.154*** 0.001 0.710 -0.107*** 0.005 0.559 0.012 0.806 0.006 36 28 Linde AG -1.304*** 0.001 0.652 -0.129*** 0.006 0.543 -0.992*** 0.003 0.591 -0.154** 0.010 0.498 37 28 Merck KGaA -0.444** 0.018 0.444 -0.116 0.172 0.178 0.000*** 0.000 0.000 -0.325*** 0.007 0.536 38 28 Su ¨ d Chemie AG -0.041 0.734 0.012 -0.004 0.958 0.000 0.051 0.368 0.082 -0.099* 0.051 0.329 39 28 Schering AG -0.459* 0.096 0.253 -0.091 0.109 0.236 -0.172 0.263 0.123 -0.168** 0.025 0.410 40 30 Continental AG -0.568*** 0.000 0.856 -0.012 0.605 0.028 -0.068*** 0.000 0.804 -0.488*** 0.000 0.896 41 30 Ehlebracht AG -0.410 0.255 0.127 -0.232** 0.024 0.415 0.039 0.775 0.009 -0.215 0.314 0.101 42 30 New York-Hamburger Gummi-Waaren Compagnie AG -0.025 0.891 0.002 0.033 0.558 0.035 0.162 0.101 0.246 -0.230 0.159 0.188 43 30 Simona AG 0.132 0.348 0.088 0.114** 0.043 0.349 0.000*** 0.000 0.000 0.021 0.851 0.004 44 30 WERU AG 0.177*** 0.000 0.814 0.190*** 0.000 0.786 -0.004 0.605 0.028 -0.007 0.249 0.130 45 32 BHS tabletop AG 0.572 0.161 0.187 0.191** 0.030 0.390 -0.043 0.639 0.023 0.426* 0.089 0.262 46 32 Didier-Werke AG -0.296 0.269 0.120 -0.104 0.374 0.080 0.177 0.127 0.217 -0.392** 0.024 0.413 47 32 Dyckerhoff AG 0.057 0.411 0.069 0.106** 0.015 0.463 0.065* 0.075 0.283 -0.125 0.218 0.147 48 32 Erlus AG 0.866*** 0.007 0.533 0.107*** 0.000 0.786 0.009** 0.031 0.386 0.744** 0.012 0.486 49 32 Heidelbergcement AG 0.041 0.411 0.069 0.061 0.357 0.085 -0.040* 0.053 0.325 0.023 0.666 0.019 50 32 Keramag AG 0.150** 0.016 0.459 0.051*** 0.006 0.541 0.051* 0.064 0.302 0.049 0.375 0.079 51 32 Pilkington Deutschland AG -0.135 0.495 0.048 -0.122*** 0.009 0.515 -0.071*** 0.010 0.504 0.051 0.721 0.013 52 32 Rosenthal AG 0.690* 0.094 0.255 -0.055 0.149 0.196 0.711** 0.042 0.353 -0.195 0.578 0.032 53 32 Saint Gobain Oberland AG 0.275 0.327 0.096 0.242 0.180 0.172 0.046 0.248 0.131 -0.020 0.833 0.005 54 32 SGL Carbon AG 0.042 0.878 0.002 0.137* 0.088 0.263 -0.084 0.462 0.055 -0.002 0.979 0.000 55 32 Sto AG -0.052 0.354 0.086 0.005 0.727 0.013 0.004 0.541 0.038 -0.050 0.223 0.145 56 32 Teutonia Zementwerk AG 0.395*** 0.000 0.719 0.308*** 0.005 0.570 0.180** 0.035 0.374 -0.090 0.385 0.076 57 32 Villeroy and Boch AG -0.269 0.186 0.168 -0.010 0.703 0.015 -0.135** 0.011 0.489 -0.111 0.619 0.026 58 33 Norddeutsche Affinerie AG -0.319** 0.045 0.343 0.051 0.350 0.088 -0.109 0.259 0.125 -0.261*** 0.000 0.758 59 34 Innotec TSS AG 0.099 0.594 0.029 0.247*** 0.002 0.634 -0.360 0.175 0.176 0.095 0.237 0.137 60 34 Salzgitter AG 0.398*** 0.000 0.774 0.279*** 0.000 0.795 0.172*** 0.002 0.650 -0.060 0.210 0.152 61 34 WMF AG 0.005 0.981 0.000 0.071*** 0.007 0.539 -0.006 0.805 0.006 -0.060 0.734 0.012 62 35 Alexanderwerk AG -0.535 0.540 0.039 -0.030 0.810 0.006 -1.490 0.154 0.192 0.653 0.238 0.136 63 35 Bertold Hermle AG -0.741 0.146 0.199 0.554*** 0.000 0.878 -1.128*** 0.002 0.642 -0.150 0.509 0.045 64 35 Deutz AG -3.061*** 0.002 0.644 -0.233* 0.090 0.260 -2.530** 0.011 0.489 -0.232* 0.058 0.313 65 35 Du ¨ rkopp Adler AG -0.156 0.467 0.054 0.454*** 0.000 0.898 -0.243*** 0.003 0.614 -0.390* 0.057 0.316 66 35 Du ¨ rr AG -2.430*** 0.001 0.660 0.060* 0.098 0.250 -2.503*** 0.002 0.649 0.032*** 0.001 0.695 67 35 GEA Group AG -0.090 0.564 0.034 0.082 0.174 0.176 -0.159 0.205 0.155 -0.029 0.540 0.039 68 35 Gildemeister AG -1.777* 0.062 0.306 -0.638** 0.035 0.373 -1.525* 0.059 0.313 0.336** 0.035 0.372 104 Logist. Res. (2009) 1:95–111 Table 3 continued No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 69 35 Jagenberg AG -0.986*** 0.002 0.619 -0.039 0.610 0.027 -0.737*** 0.000 0.807 -0.222* 0.097 0.252 70 35 Jungheinrich AG -0.464*** 0.002 0.636 -0.284*** 0.001 0.689 -0.223*** 0.000 0.811 0.056 0.199 0.159 71 35 Kloeckner-Werke AG 1.193** 0.022 0.421 0.359** 0.044 0.347 0.862** 0.029 0.395 -0.025 0.612 0.027 72 35 Koenig and Bauer AG -1.273*** 0.001 0.658 -0.443*** 0.005 0.563 -0.815** 0.016 0.454 -0.011 0.680 0.018 73 35 Krones AG 0.542* 0.100 0.248 0.268*** 0.009 0.510 0.049 0.661 0.020 0.244* 0.054 0.324 74 35 KSB AG -0.530*** 0.006 0.547 0.077** 0.042 0.351 -0.545*** 0.000 0.819 -0.060 0.388 0.075 75 35 KUKA AG -1.174*** 0.006 0.549 -0.110* 0.050 0.332 -1.025*** 0.006 0.545 -0.053 0.154 0.192 76 35 Rheinmetall AG -0.787** 0.030 0.391 0.131 0.305 0.104 -0.893*** 0.000 0.844 0.013 0.917 0.001 77 35 Sartorius AG -0.594** 0.011 0.491 0.075 0.167 0.182 -0.203*** 0.000 0.763 -0.457*** 0.005 0.568 78 35 Triumph Adler AG 0.654 0.259 0.125 -0.231*** 0.009 0.515 -0.292 0.321 0.098 0.937*** 0.002 0.647 79 35 Vossloh AG 0.244 0.725 0.013 0.311 0.148 0.197 0.556 0.209 0.153 -0.631*** 0.000 0.719 80 36 Brilliant AG -0.224 0.128 0.216 -0.679* 0.058 0.314 -0.255* 0.061 0.308 0.619** 0.030 0.391 81 36 Ceag AG -0.146 0.728 0.013 -0.037 0.743 0.011 -0.476*** 0.002 0.633 0.347 0.232 0.139 82 36 Leifheit AG 0.002 0.993 0.000 -0.311*** 0.004 0.579 -0.176*** 0.000 0.965 0.447** 0.023 0.420 83 36 M.tech AG -0.326 0.483 0.050 -0.081 0.308 0.103 -0.384 0.378 0.078 0.121*** 0.001 0.708 84 36 Schweizer Electronic AG 0.143* 0.070 0.292 0.026 0.692 0.016 -0.044 0.463 0.055 0.153* 0.094 0.255 85 36 Sedlbauer AG -0.337 0.311 0.102 -0.128 0.600 0.028 -0.079 0.242 0.134 -0.153*** 0.001 0.669 86 36 Vogt Eectronic AG -0.878*** 0.000 0.776 -0.015 0.894 0.002 -0.398*** 0.000 0.767 -0.496** 0.013 0.477 87 37 Audi AG 0.247*** 0.000 0.752 0.046* 0.071 0.289 -0.043* 0.061 0.307 0.228** 0.012 0.484 88 37 BBS Fahrzeugtechnik AG -0.453* 0.099 0.249 -0.055 0.577 0.032 -0.002 0.980 0.000 -0.409* 0.062 0.307 89 37 BMW AG 0.194 0.214 0.150 -0.004 0.778 0.008 0.031 0.276 0.117 0.167 0.207 0.154 90 37 Hymer AG -0.247 0.468 0.054 0.075 0.661 0.020 -0.054*** 0.001 0.685 -0.300* 0.066 0.299 91 37 MAN AG 0.336 0.525 0.042 -0.044* 0.092 0.258 -0.721*** 0.007 0.528 1.048** 0.046 0.342 92 37 Porsche AG -0.079 0.673 0.019 -0.156** 0.027 0.400 -0.103 0.485 0.050 0.172 0.255 0.127 93 37 Progress-Werke Oberkirch AG 0.168 0.571 0.033 -0.233* 0.064 0.303 0.344* 0.072 0.288 0.028 0.616 0.026 94 37 Schaltbau Holding AG -0.641 0.155 0.191 -0.067 0.490 0.049 -0.335 0.420 0.066 -0.239*** 0.003 0.592 95 37 Veritas AG -0.093 0.613 0.026 0.168** 0.029 0.395 -0.185** 0.033 0.380 -0.021 0.793 0.007 96 37 Volkswagen AG -0.026 0.897 0.002 -0.017 0.298 0.107 -0.067** 0.010 0.497 0.039 0.843 0.004 97 37 Wanderer-Werke AG -0.399 0.114 0.230 -0.180 0.178 0.173 -0.122* 0.082 0.272 0.062 0.837 0.004 98 38 Draegerwerk AG -0.936*** 0.000 0.823 -0.071 0.526 0.041 -0.459*** 0.004 0.589 -0.409*** 0.000 0.773 99 38 Siemens AG 0.474** 0.046 0.342 -0.013 0.631 0.024 0.682*** 0.005 0.560 -0.199*** 0.000 0.930 100 39 Johann F. Behrens AG 0.120 0.580 0.032 0.394*** 0.002 0.632 -0.275** 0.015 0.464 -0.007 0.970 0.000 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) Logist. Res. (2009) 1:95–111 105 Table 4 Overall trend coefficients for SIC classes 1993–2005 SC TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 20 0.606*** 0.001 0.679 0.044*** 0.006 0.542 0.301*** 0.001 0.671 0.262*** 0.004 0.576 22/23 -0.439*** 0.006 0.544 -0.194*** 0.000 0.885 -0.067** 0.011 0.494 -0.170 0.313 0.101 28 -0.272*** 0.000 0.904 -0.011*** 0.000 0.955 -0.055*** 0.007 0.535 -0.253*** 0.000 0.840 32 -0.093** 0.010 0.497 0.089** 0.018 0.445 -0.027 0.106 0.240 -0.144*** 0.000 0.887 35 -0.275* 0.095 0.254 0.086** 0.033 0.379 -0.347** 0.019 0.437 -0.014 0.591 0.030 37 -0.026 0.815 0.006 -0.023* 0.098 0.250 -0.173*** 0.000 0.849 0.150 0.201 0.158 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) Table 5 Difference in regression coefficients 1993–2005 between inventory stages Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 1 20 A. Moksel AG 0.020 0.246 0.132 0.047 0.378 0.078 0.029 0.497 0.047 2 20 Actris AG -0.068 0.287 0.112 -0.017 0.612 0.027 0.053 0.140 0.205 3 20 ADM Hamburg AG 0.079 0.683 0.017 0.068 0.196 0.161 -0.012 0.939 0.001 4 20 Berentzen-Gruppe AG -0.383** 0.045 0.344 -0.532*** 0.008 0.527 -0.199* 0.054 0.324 5 20 Dom Brauerei AG 0.077 0.526 0.041 -0.069 0.520 0.043 -0.159*** 0.009 0.511 6 20 Frosta AG 0.530*** 0.004 0.587 0.304*** 0.000 0.811 -0.193 0.258 0.126 7 20 Kulmbacher Brauerei AG -0.100* 0.056 0.317 -0.068 0.199 0.159 0.026 0.778 0.008 8 20 Mineralbrunnen AG -0.097*** 0.004 0.575 -0.120*** 0.000 0.743 -0.019 0.396 0.073 9 20 Sektkellerei Schloss Wachenheim AG -2.065*** 0.007 0.537 -2.080*** 0.004 0.571 -0.015 0.909 0.001 10 20 Staatl. Mineralbrunnen AG -0.030 0.390 0.075 -0.128*** 0.000 0.856 -0.093** 0.033 0.379 11 20 Su ¨ d Zucker AG 0.407** 0.022 0.423 0.368** 0.040 0.359 -0.029 0.910 0.001 12 20 VK Muehlen AG -0.202* 0.060 0.311 -0.027 0.725 0.013 0.168 0.156 0.191 13 22 Bremer Woll-Ka ¨mmerei AG 0.609*** 0.010 0.506 0.853** 0.033 0.379 0.236 0.557 0.036 14 22 Gruschwitz Textilwerke AG -1.245*** 0.001 0.652 -0.655 0.117 0.227 0.595*** 0.010 0.504 15 22 Kunert AG -0.244** 0.014 0.472 -0.656*** 0.000 0.734 -0.478** 0.014 0.467 16 22 Textilgruppe Hof AG -0.482*** 0.002 0.643 -1.399*** 0.000 0.933 -0.926*** 0.001 0.688 17 22 Vereinigte Filzfabriken AG 0.021 0.549 0.037 -0.258*** 0.002 0.644 -0.288*** 0.009 0.508 18 23 Adidas AG -0.032 0.182 0.171 0.981*** 0.001 0.699 1.011*** 0.001 0.675 19 23 Ahlers AG 0.181 0.101 0.246 0.354** 0.011 0.496 0.168* 0.051 0.330 20 23 Escada AG -0.040 0.669 0.019 -0.825** 0.020 0.434 -0.809*** 0.003 0.594 21 23 Etienne Aigner AG -0.092** 0.047 0.340 -0.793** 0.011 0.494 -0.703** 0.018 0.442 22 23 Gerry Weber International AG -0.072 0.585 0.031 -0.484*** 0.000 0.841 -0.351** 0.012 0.484 23 23 Hirsch AG -0.011 0.877 0.003 -0.356* 0.087 0.264 -0.351** 0.047 0.339 24 23 Hucke AG 0.183 0.203 0.157 0.014 0.911 0.001 -0.154* 0.079 0.276 25 23 Hugo Boss AG 0.101** 0.045 0.343 -0.590*** 0.000 0.773 -0.693*** 0.000 0.928 26 23 Puma AG 0.478* 0.057 0.316 0.609*** 0.000 0.906 0.140 0.487 0.050 27 23 Triumph International AG 0.094** 0.016 0.456 -0.229** 0.041 0.354 -0.303** 0.018 0.444 28 28 Altana AG 0.098*** 0.008 0.521 0.120 0.193 0.163 0.036 0.640 0.023 29 28 BASF AG -0.222** 0.042 0.351 0.399*** 0.001 0.686 0.630*** 0.002 0.626 30 28 Bayer AG -0.049* 0.093 0.256 -0.014 0.662 0.020 0.039 0.367 0.082 31 28 Beiersdorf AG 0.135*** 0.000 0.965 -0.120 0.325 0.097 -0.241* 0.067 0.297 32 28 Biotest AG 2.048*** 0.005 0.559 1.755*** 0.000 0.736 -0.290 0.513 0.044 33 28 Fresenius SE 0.133*** 0.002 0.623 0.288*** 0.002 0.652 0.152*** 0.003 0.613 123 106 Logist. Res. (2009) 1:95–111 Table 5 continued Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 34 28 Fuchs Petrolub AG 0.026 0.367 0.082 -0.050 0.418 0.067 -0.055 0.365 0.083 35 28 Henkel KGaA 0.038 0.169 0.181 -0.125*** 0.001 0.670 -0.167*** 0.000 0.784 36 28 Linde AG -0.846*** 0.009 0.509 -0.825*** 0.006 0.553 0.009 0.784 0.008 37 28 Merck KGaA 0.116 0.172 0.178 0.325*** 0.007 0.536 0.180** 0.036 0.370 38 28 Schering AG -0.051 0.579 0.032 0.083 0.424 0.065 0.099** 0.030 0.389 39 28 Sud Chemie AG 0.052 0.489 0.049 0.157** 0.032 0.382 0.100 0.238 0.136 40 30 Continental AG -0.056*** 0.008 0.520 0.420*** 0.000 0.890 0.476*** 0.000 0.909 41 30 Ehlebracht AG 0.228 0.179 0.172 0.274** 0.010 0.497 0.026 0.898 0.002 42 30 New York-Hamburger Gummi-Waaren 0.174*** 0.008 0.519 0.390* 0.064 0.302 0.238 0.200 0.158 Compagnie AG 43 30 SIMONA AG -0.114** 0.043 0.349 -0.021 0.851 0.004 0.086 0.431 0.063 44 30 WERU AG -0.195*** 0.000 0.726 0.002 0.824 0.005 0.197*** 0.000 0.796 45 32 BHS tabletop AG -0.229*** 0.002 0.640 -0.485*** 0.009 0.514 -0.222 0.200 0.158 46 32 Didier-Werke AG 0.287** 0.048 0.337 0.582** 0.011 0.491 0.285* 0.067 0.297 47 32 Dyckerhoff AG -0.029* 0.081 0.274 0.170 0.211 0.151 0.200 0.146 0.199 48 32 Erlus AG -0.097*** 0.000 0.762 -0.734** 0.012 0.484 -0.630** 0.021 0.428 49 32 Heidelbergcement AG -0.115* 0.076 0.282 -0.074 0.217 0.148 0.066 0.574 0.033 50 32 Keramag AG -0.001 0.951 0.000 0.003 0.967 0.000 0.004 0.954 0.000 51 32 Pilkington Deutschland AG 0.050** 0.037 0.365 -0.118 0.364 0.083 -0.171 0.135 0.209 52 32 Rosenthal AG 0.732* 0.052 0.326 1.064* 0.051 0.330 0.140 0.682 0.017 53 32 Saint Gobain Oberland AG -0.180 0.226 0.143 0.067 0.470 0.053 0.234* 0.084 0.270 54 32 SGL CARBON AG -0.204*** 0.005 0.557 -0.046 0.341 0.091 0.153*** 0.006 0.545 55 32 Sto AG 0.001 0.949 0.000 0.054 0.144 0.201 0.049 0.183 0.170 56 32 Teutonia Zementwerk AG -0.123 0.181 0.172 0.276* 0.096 0.253 0.350* 0.091 0.259 57 32 Villeroy and Boch AG -0.114* 0.087 0.265 -0.024 0.921 0.001 0.086 0.715 0.014 58 33 Norddeutsch Affinerie AG -0.159* 0.090 0.260 0.150 0.163 0.185 0.314*** 0.000 0.759 59 34 Innotec TSS AG -0.645** 0.036 0.370 -0.490 0.151 0.194 0.161* 0.094 0.254 60 34 Salzgitter AG -0.112* 0.088 0.263 0.232** 0.011 0.488 0.340*** 0.000 0.805 61 34 WMF AG -0.089** 0.032 0.381 0.049 0.770 0.009 0.138 0.420 0.066 62 35 Alexanderwerk AG -1.500 0.178 0.174 -2.281 0.109 0.236 -0.710 0.146 0.199 63 35 Bertold Hermle AG -1.690*** 0.000 0.800 -1.043*** 0.000 0.875 0.678** 0.015 0.463 64 35 Deutz AG -2.136** 0.046 0.340 -2.283** 0.023 0.417 -0.014 0.859 0.003 65 35 Durkopp Adler AG -0.717*** 0.000 0.857 0.146 0.376 0.079 0.867*** 0.002 0.645 66 35 Durr AG -2.550*** 0.002 0.639 -2.528*** 0.002 0.648 0.028 0.359 0.084 67 35 GEA Group AG -0.236 0.118 0.226 -0.128 0.226 0.143 0.114 0.159 0.188 68 35 Gildemeister AG -0.920 0.139 0.205 -1.966** 0.037 0.367 -1.023*** 0.008 0.521 69 35 Jagenberg AG -0.697*** 0.000 0.742 -0.525*** 0.002 0.646 0.177** 0.031 0.387 70 35 Junghenrich AG 0.044 0.412 0.068 -0.271*** 0.000 0.889 -0.302*** 0.001 0.658 71 35 Kloeckner-Werke AG 0.504* 0.053 0.326 0.875** 0.042 0.353 0.414** 0.039 0.360 72 35 Koenig and Bauer AG -0.368 0.256 0.127 -0.815** 0.021 0.428 -0.429** 0.014 0.469 73 35 Krones AG -0.211*** 0.000 0.721 -0.192*** 0.001 0.652 0.023 0.704 0.015 74 35 KSB AG -0.619*** 0.000 0.892 -0.470*** 0.000 0.887 0.166** 0.041 0.356 75 35 KUKA AG -0.934*** 0.006 0.548 -0.977*** 0.008 0.520 -0.047 0.239 0.135 76 35 Rheinmetall AG -1.034*** 0.000 0.895 -0.891*** 0.000 0.835 0.139 0.133 0.211 77 35 Sartorius AG -0.283*** 0.000 0.871 0.247** 0.047 0.339 0.523*** 0.000 0.743 78 35 Triumph Adler AG -0.068 0.773 0.009 -1.227*** 0.000 0.804 -1.098*** 0.000 0.750 79 35 Vossloh AG 0.252 0.393 0.074 1.231*** 0.005 0.564 0.990*** 0.000 0.795 123 Logist. Res. (2009) 1:95–111 107 Table 5 continued Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 80 36 Brilliant AG 0.439 0.172 0.178 -0.828** 0.026 0.404 -1.242* 0.054 0.322 81 36 Ceag AG -0.449*** 0.001 0.656 -0.757** 0.028 0.398 -0.359 0.107 0.239 82 36 Leifheit AG 0.118 0.159 0.188 -0.633*** 0.004 0.586 -0.687*** 0.000 0.725 83 36 M.tech AG -0.317 0.454 0.057 -0.512 0.244 0.133 -0.203*** 0.005 0.563 84 36 Schweizer Electronic AG -0.053 0.394 0.073 -0.181 0.152 0.194 -0.122 0.420 0.066 85 36 Sedlbauer AG 0.064 0.723 0.013 0.074 0.232 0.139 0.012 0.960 0.000 86 36 Vogt Electronic AG -0.399** 0.026 0.405 0.089 0.625 0.025 0.446* 0.073 0.286 87 37 Audi AG -0.093*** 0.000 0.922 -0.261** 0.020 0.431 -0.178* 0.084 0.269 88 37 BBS Fahrzeugtechnik AG 0.077 0.630 0.024 0.459*** 0.003 0.600 0.366 0.143 0.202 89 37 BMW AG 0.034 0.302 0.106 -0.135 0.219 0.147 -0.173 0.201 0.158 90 37 Hymer AG -0.128 0.434 0.062 0.251* 0.088 0.263 0.370*** 0.001 0.686 91 37 MAN AG -0.678*** 0.009 0.511 -1.679** 0.010 0.497 -1.101** 0.030 0.389 92 37 Porsche AG 0.053 0.796 0.007 -0.260 0.144 0.201 -0.318 0.105 0.241 93 37 Progress-Werke Oberkirch AG 0.580*** 0.003 0.607 0.320* 0.075 0.283 -0.263*** 0.009 0.506 94 37 Schaltbau Holding AG -0.286 0.399 0.072 -0.086 0.856 0.003 0.181 0.221 0.146 95 37 Veritas AG -0.372*** 0.000 0.757 -0.184 0.102 0.245 0.206*** 0.005 0.558 96 37 Volkswagen AG -0.048 0.155 0.191 -0.101 0.608 0.027 -0.052 0.794 0.007 97 37 Wanderer-Werke AG 0.115 0.224 0.144 -0.154 0.659 0.020 -0.229 0.523 0.042 98 38 Draegerwerk AG -0.372* 0.083 0.271 -0.049 0.771 0.009 0.315** 0.010 0.497 99 38 Siemens AG 0.699*** 0.002 0.642 0.878*** 0.002 0.651 0.191*** 0.000 0.728 100 39 Johann F. Behrens AG -0.669*** 0.001 0.696 -0.232 0.143 0.202 0.411 0.149 0.197 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) inventory ratios. A better (worse) performance of WP latter decreasing, whereas the machinery industry shows a inventory ratios is found in two (two) industries [Hypothesis peculiar reduction in WP inventory to sales ratios. Somewhat 4 (b)] when compared to FGs inventory ratios. Finally, RMs surprisingly, the transportation equipment industry stands inventory ratios show a higher (lower) decreasing or lower out due to no significant change in total inventory to sales (higher) increasing trend in one (three) sector class(es) ratios, showing only significantly decreasing WP invento- [Hypothesis 4 (c)] when compared to FGs inventory ratios. ries. But an in-depth analysis of FGs inventories reveals an increase in the second half of our time frame investigated which results in a similar pattern in total inventories, 5 Discussion of results explaining their non-significant regression results. Observing our results on firm level, a somewhat mixed Regarding our results on an aggregated level, we find sig- picture emerges, contrasting the common belief about broad nificantly decreasing total inventory to sales ratios in the efforts on inventory reduction during the 1990s until present textile and wearing apparel, chemical, machinery, and in German corporations. This is even more surprising when stones, clay, and glass industry. The food industry shows we take into account the emerging interest on JIT techniques significantly increasing total inventory to sales ratios, which during the time frame investigated (see Fig. 1). is mainly due to increasing FGs and WP inventory to sales ratios. RM inventories remained nearly stable, and therefore Therefore, we conducted an exhaustive search using ‘‘WISO’’, the largest German language database for business and economics research performing relatively ‘‘better’’ when compared to the other articles, and LexisNexis for finding German press articles (newspapers, inventory stages. The inventory performance in the textile periodicals, and trade publications). We constrained our search to industry can be traced back to the fact of significantly ‘‘JIT’’. The first German article on JIT accounted for in the WISO decreasing RMs and WP inventories, whereas RMs per- database was published in 1982. A first peak in the distribution can be seen around 1989 with a significant decline until 2007. In contrast, the formed relatively better than WP inventories. The chemical distribution of press articles according to the LexisNexis database starts industry owes its inventory reduction mainly to decreased with the early 1990s and reached a local maximum in 1999. After a short FGs. Stones, clay, and glass show contrary developments in decline, the number of press articles on JIT took off again until reaching RMs and FGs inventories: the former are increasing, the their all time high in 2006. 123 108 Logist. Res. (2009) 1:95–111 Table 6 Difference in regression coefficients 1993–2005 between inventory stages for SIC classes SIC WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 20 0.256*** 0.003 0.612 0.031 0.574 0.033 -0.214*** 0.010 0.506 22/23 0.137*** 0.000 0.796 0.100 0.590 0.030 -0.032 0.858 0.003 28 -0.044** 0.024 0.412 0.195*** 0.002 0.624 0.243*** 0.000 0.829 32 -0.122** 0.012 0.488 0.121*** 0.000 0.819 0.245*** 0.000 0.785 35 -0.415** 0.011 0.489 -0.343*** 0.010 0.504 0.102*** 0.003 0.613 37 -0.149*** 0.001 0.689 -0.334** 0.013 0.473 -0.178 0.121 0.223 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) 600 Table 7 Sensitivity analysis for SIC classes JIT (LexisNexis) JIT (WISO) SIC ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 20 6.62 6.78 7.55 4.70 4.78 5.01 22/23 12.56 12.97 14.93 4.83 4.97 5.66 28 9.58 9.73 10.40 10.05 10.16 10.61 32 6.99 7.07 7.41 7.51 7.63 8.16 35 3.67 3.76 4.13 4.46 4.57 5.06 37 5.43 5.50 5.83 7.95 8.14 8.96 [ 7.48 7.64 8.38 6.17 6.30 6.91 In the literature reviewed, we find ongoing efforts iden- tifying a relationship between inventory and financial per- 1980 1985 1990 1995 2000 2005 formance. This is due to the ‘‘critical argument on behalf of inventory reduction… that it will improve the financial Fig. 1 Distribution of JIT articles 1980–2007 (source: LexisNexis and WISO database) position of firms’’ [7, p. 1025]. Following this paradigm, inventories are not seen as residua of production and oper- Nevertheless, half of the firms significantly decreasing ations activities, but as important contributors to a firm’s total inventories are covered by SIC codes 34–39 (metal overall success. Nevertheless, executing several regression fabrication, machinery, electrical equipment, and trans- analyses considering return-on-investment (ROI) or operat- portation equipment), thus belonging to industries that are ing margin, we found no evidence for such a relationship. notorious for their use of JIT techniques [27]. Correspondingly, Cannon [6] recently finds no link between It has to be noted that within the time frame analyzed, inventory improvements and firm performance. To grasp several firms changed from national (according to German some helpful insights about the relationship between Commercial Code, HGB) to International Financial inventory reduction and financial performance, we per- Reporting Standards (IFRS). We scrutinized for possible formed a sensitivity analysis. We tested on an aggregated and conversion effects, resulting in structural interruptions in disaggregated level to what extent the ROI could be the data. As a cause, in the majority of cases we identified improved by lowering total inventories ceteris paribus by the accounting of long-term construction contracts, which 10% (50%). Using the mean to determine the ROI for the are no longer reported under inventories but under accounts time frame investigated on an aggregate level, the highest receivable. Accordingly, we found evidence for such con- enhancement for a 10% (50%) total inventory reduction can version effects mainly in decreasing WP inventories in the be reached in the textile industry with an ROI increase of machinery industry. 0.41 (2.37) percent points. In the transportation industry and Most likely affected were firms such as Durr, Koenig and Bauer, KUKA, Linde MAN, Siemens, and Triumph Adler. Therefore, their Furthermore, we found no significant link between the size of a firm WP inventory to sales performance should be interpreted carefully. (e.g., measured in sales) and its inventory performance. Number of articles Logist. Res. (2009) 1:95–111 109 Table 8 Sensitivity analysis for firms with the best inventory performance No. SIC Firm b ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 1 20 Sektkellerei Schloss Wachenheim AG -3.583 7.24 7.30 7.24 6.58 6.77 7.63 2 35 Deutz AG -3.061 1.70 1.75 2.00 2.35 2.45 2.91 335 Du ¨ rr AG -2.430 3.58 3.67 4.12 4.58 4.68 5.13 4 35 Gildemeister AG -1.777 3.85 3.98 4.61 6.09 6.28 7.19 5 22 Bremer Woll-Kammerei AG -1.515 -1.58 -1.58 -1.53 -1.45 -1.50 -1.75 6 28 Linde AG -1.304 6.43 6.54 6.99 6.38 6.47 6.83 7 35 Koenig and Bauer AG -1.273 3.82 3.95 4.59 4.73 4.88 5.60 8 35 KUKA AG -1.174 5.00 5.18 6.08 4.85 5.02 5.83 9 35 Jagenberg AG -0.986 -0.66 -0.68 -0.80 -2.05 -2.09 -2.29 10 38 Draegerwerk AG -0.936 5.20 5.34 5.99 5.46 5.61 6.32 [ 3.46 3.55 3.93 4.79 4.95 5.71 Table 9 Sensitivity analysis for firms with the worst inventory performance No. SIC Firm b ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 1 28 Biotest AG 1.600 5.57 5.77 6.72 5.72 5.94 7.08 2 35 Kloeckner-Werke AG 1.193 5.54 5.67 6.24 4.76 4.86 5.31 3 23 Etienne Aigner AG 0.886 9.64 9.73 10.10 6.17 6.30 6.87 4 32 Erlus AG 0.886 8.68 8.77 9.20 8.82 8.94 9.45 5 22 Textilgruppe Hof AG 0.715 3.56 3.65 4.09 4.37 4.47 4.94 6 32 Rosenthal AG 0.690 -1.08 -1.13 -1.41 2.41 2.49 2.90 7 23 Escada AG 0.629 4.26 4.39 4.98 7.17 7.40 8.48 8 35 Krones AG 0.542 9.26 9.43 10.16 9.28 9.43 10.09 9 22 Vereinigte Filzfabriken AG 0.528 17.22 17.82 20.71 15.56 16.16 19.14 10 23 Hugo Boss AG 0.497 24.03 24.82 28.58 25.09 25.93 29.89 [ 8.67 8.89 9.94 6.67 6.85 7.78 the stone, clay, and glass industry, this effect is with a gain of first result, it can be stated that the sample firms with a 0.08 (0.41) percent points negligibly small. Using the median better inventory performance do not excel in terms of the for calculating the aggregated ROI over time, one gets a financial performance. The sensitivity analysis underlines completely different result concerning the best performing this observation. The impact on the mean (median) ROI industry, but the improvement effects are even smaller (see by a 10% total inventory reduction leads to a 0.09% also Table 7). (0.13) points improvement for the top ten firms in con- On a disaggregate level, we performed a sensitivity trast to 0.22% (0.14) points for the bottom ten firms. analysis for the ten firms with the highest and lowest This effect even becomes stronger for a 50% total significant inventory reduction over the time frame inventory reduction resulting in an ROI improvement of observed (see Tables 8, 9). Comparing the current state 0.47% (0.71) points for the top ten firms, in comparison of the top ten firms with the bottom ten firms regarding to 1.27% (0.75) points for the bottom ten firms. the financial performance, a completely different picture Conducting a sensitivity analysis, it has to be kept in emerges. While the top ten firms have a mean (median) mind that for years with a negative ROI the reduction of ROI of 3.46% (4.79%), the bottom ten firms stand out total inventories leads to an even smaller ROI. Because the mean (median) ROI is used for the time frame investigated, with a considerably higher ROI of 8.67% (6.67%). As a 123 110 Logist. Res. (2009) 1:95–111 potential improvement effects might be canceled out by inventory levels or the impact of postponement strategies on extraordinary results in one specific year. different inventory stages; or the effects of global sourcing In general, we see that the potential contributions of strategies, outsourcing or off-shoring production activities inventory improvements to the financial performance of on inventory holding. Increasing and more variable lead firms have only been small. These findings might give a times due to longer transportation would result in higher direction for further research, seeing inventory not so much stocks. Furthermore, the analysis of changes in factor prices as a predictor for financial performance but as what it as well as concentration tendencies in several industries on mainly is: a ‘‘buffer’’ which allows firms to smooth pro- inventory performance could be helpful to explain industry- duction levels, to shift production to periods with produc- specific developments. From a financial accounting per- tion costs expected to be relatively low, or as precaution for spective, further research is needed to better understand stock-outs. This insight can also be fruitful for managers, degree and direction of possible conversion effects on as inventory improvements are not necessarily a reliable inventory holdings reported under local versus international indicator for a firm’s overall performance. accounting standards. Finally, to better understand the dif- ferent causes for the inventory development analyzed, our research could be pursued using case study research design. 6 Conclusion Generating extensive examinations of each case could explore similar patterns of firms with high or low inventory Having analyzed inventory performance of 100 German performance or within different industries, for example. We corporations between 1993 and 2005, our findings indicate did not offer this research, but paved the way. that the total inventory to sales ratio decreased in a sta- tistically significant extent in four out of six industry sec- tors during the time frame investigated. On a firm level, we find that half of the firms with a significant decrease in total References inventories are based on industry sectors that are especially 1. Bairam EI (1996) Disaggregate inventory-sales ratios over time: known for their use of JIT techniques. Further, we pointed the case of US companies and corporations, 1976–92. Appl Econ out that potential contributions of inventory reductions to Lett 3:167–169 the financial performance of firms are only of a small 2. Balakrishnan R, Linsmeier TJ, Venkatachalam M (1996) Finan- degree. cial benefits from JIT adoption: effects of customer concentration and cost structure. Account Rev 71:183–205 There are several limitations regarding the empirical 3. Biggart TB, Gargeya VB (2002) Impact of JIT on inventory to findings presented above and the conclusions derived from sales ratios. Ind Manag Data Syst 102:197–202 them. Some of these limitations raise further research 4. Blinder AS, Maccini LJ (1991) Taking stock: a critical assess- opportunities. As discussed above, the cause and effect ment of recent research on inventories. J Econ Perspect 5:73–96 5. Canjels E, Watson MW (1997) Estimating deterministic trends in relationship between inventory holdings and financial per- the presence of serially correlated errors. Rev Econ Stat 79:184– formance (et vice versa) is still nebulous. While it is clear that, ceteris paribus, lower inventories cause higher return 6. Cannon AR (2008) Inventory improvement and financial per- on assets, this relationship does not necessarily hold in the formance. Int J Prod Econ 115:581–593 7. Chen H, Frank MZ, Wu OQ (2005) What actually happened to real world which does not offer a ceteris paribus opportunity the Inventories of American Companies Between 1981 and 2000. in most cases. As mentioned before, a good inventory policy Manag Sci 51:1015–1031 necessarily deals with trade-off decisions. Inventory hold- 8. De Haan J, Yamamoto M (1999) Zero inventory management: ing costs money but is not always bad. Accordingly, it facts or fiction? Lessons from Japan. Int J Prod Econ 59:65–75 9. Durbin J, Watson GS (1950) Testing for serial correlation in least would be interesting to investigate the links, e.g., between squares regression. I. Biometrika 37:409–428 higher customer service levels or better quality control and 10. Durbin J, Watson GS (1951) Testing for serial correlation in least squares regression. II. Biometrika 38:159–178 11. Greene WH (2008) Econometric analysis, 6th edn To demonstrate the potential extent of this cannibalization effect, 12. Hayes RH (1981a) Why Japanese factories work. Harvard Bus we take a closer look at the ROI from 1994 of Sektkellerei Schloss Rev 59:56–66 Wachenheim AG: in this particular year, the firm has an ROI of 13. Hirsch AA (1996) Has inventory management in the US become -90.77%. Because the total inventories represent 62.89% of the total more efficient and flexible? A macroeconomic perspective. Int capital employed the results of the sensitivity analysis have such a J Prod Econ 45:37–46 deep impact that all positive effects of the remaining 12 years are 14. Huson M, Nanda D (1995) The impact of just-in-time manufac- eaten up. As a result, there is no recognizable increase in the mean turing on firm performance in the US. J Operat Manag 12:297– ROI even if total inventory would be reduced by 50%. If we would exclude this specific year, a mean ROI of 15.40% could be achieved 15. Kobayashi M (1985) Comparison of efficiencies of several and the sensitivity analysis in the case of 50% reduction of total estimators for linear regressions with autocorrelated errors. J Am inventories would lift the mean ROI up to 18.87%. In this specific Stat Assoc 80:951–953 case, the effect can reduced to a minimum using the median. 123 Logist. Res. (2009) 1:95–111 111 16. Lieberman MB, Demeester L (1999) Inventory reduction and 24. Savin NE, White KJ (1977) The Durbin–Watson test for serial productivity growth: linkages in the Japanese automotive indus- correlation with extreme sample sizes or many regressors. try. Manag Sci 45:466–485 Econometrica 45:1989–1996 17. Little JD (1961) A proof of the Queuing formula: L = kW. 25. Schonberger RJ (1982) Japanese manufacturing techniques Operat Res 9:383–387 26. Silver EA, Pyke DF, Peterson R (1998) Inventory management 18. Monden Y (1981a) What makes the Toyota production system and production planning and scheduling, 3rd edn really tick? Ind Eng 13:36–46 27. Swamidass PM (2007) The effect of TPS on US manufacturing 19. Monden Y (1981b) Adaptable Kanban system helps Toyota during 1981–1998: inventory increased or decreased as a function maintain just-in-time production. Ind Eng 13:29–46 of plant performance. Int J Prod Res 45:3763–3778 20. Nahmias S (2009) Production and operations analysis, 6th edn 28. Tribo ´ JA (2009) Firms’ stock market flotation: effects on 21. Nakane J, Hall RW (1983) Management specs for stockless inventory policy. Int J Prod Econ 118:10–18 production. Harvard Bus Rev 61:84–91 29. Vollmann TE, Berry WL, Whybark DC, Jacobs FR (2005) 22. Park RB, Mitchell BM (1980) Estimating the autocorrelated error Manufacturing planning and control for supply chain manage- model with trended data. J Econ 13:185–201 ment, 5th edn 23. Prais SJ, Winsten CB (1954) Trend estimation and serial 30. Wooldridge JM (2006) Introductory econometrics: a modern correlation, Cowles Commission Discussion Paper Statistics, approach, 3rd edn no. 383 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Logistics Research Springer Journals

Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005

Logistics Research , Volume 1 (2) – Jul 2, 2009

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Engineering; Engineering Economics, Organization, Logistics, Marketing; Logistics; Industrial and Production Engineering; Simulation and Modeling; Operation Research/Decision Theory
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Abstract

Logist. Res. (2009) 1:95–111 DOI 10.1007/s12159-009-0014-9 OR IGINAL PAPER Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005 Robert Obermaier Æ Andreas Donhauser Received: 13 November 2008 / Accepted: 15 June 2009 / Published online: 2 July 2009 Springer-Verlag 2009 Abstract Although inventory reduction has been a major 1 The premise of inventory reduction as a driver topic in production and operations management research of business performance for many years, there is a lack of empirically confirmed answers for questions such as: Have inventories in fully Inventory reduction has been a major topic in production industrialized economies such as Germany decreased, and operations management research as well as in the overall, during the past decades? To the extent, inventory academic literature on logistics and supply chain man- reductions were successfully realized, in which industries agement for many years. Myriads of articles and case did they occur? Are there differences in inventory reduc- studies have been written about firm’s needs and efforts to tion achievements between raw materials, work-in-process, reduce inventories. In the operations research literature, or finished goods? Are there measurable effects of inven- numerous normative models were developed to determine tory reductions upon the financial performance? To the best optimal lot sizes and inventory levels. The belief that of our knowledge, this empirical study is the first one to inventory reflects waste and should be eliminated to investigate long-term inventory development on a firm as increase productivity is the fundamental premise of popular well as on industry level in a major European economy. It concepts such as ‘‘just-in-time’’ (JIT) or ‘‘zero inventory’’ is based on data from German corporations and provides [8, 21]. This article is motivated by the observation that, answers to the research questions stated above. The study’s despite a long tradition of research related to inventory findings indicate that total inventory to sales ratio issues, there is lack of empirically confirmed answers to decreased in a statistically significant extent in four out of questions such as: Have inventories in fully industrialized six industry sectors during the time frame investigated. countries such as Germany actually decreased, overall, Further results suggest that the overall impact of inventory during the past decades? Has inventory reduction devel- reductions to the financial performance of companies is oped differently for raw materials (RMs), work-in-process only of a small degree. (WP), or finished goods (FGs), respectively? Are there measurable effects of inventory reductions upon the Keywords Inventory  Manufacturing  Just-in-time  financial performance? Supply chain  Logistics  Time series analysis The study presented here is believed to be the first one to empirically investigate long-term inventory development in a major European economy. It provides answers to the research questions stated above, using firm level data from a sample of German corporations as opposed to aggregated industry level data. Nevertheless, it also analyzes inventory developments by industry sectors and by stages of the R. Obermaier (&)  A. Donhauser typical industrial value chain, i.e., RMs, WP, and FGs. Chair for Controlling and Logistics, The article is organized into six sections: the subsequent Institute for Business Administration, Sect. 2 reviews the existing body of literature and sum- University of Regensburg, Regensburg, Germany marizes major findings. In Sect. 3, we describe our research e-mail: robert.obermaier@wiwi.uni-regensburg.de 123 96 Logist. Res. (2009) 1:95–111 methodology as well as the data sources used and develop Lieberman and Demeester [16] studied 52 Japanese several hypotheses regarding inventory trends during the automotive companies over a time period from the late time frame investigated. The results are presented in Sect. 1960s to the early 1980s, shedding light on the link 4. Their implications will be discussed in Sect. 5.We between inventory and productivity: firms reducing conclude with limitations and further research opportuni- inventory substantially were able to improve labor pro- ties in Sect. 6. ductivity significantly. Chen et al. [7] created portfolios of firms based on their relative inventory performance and find abnormally high inventories associated with poor 2 Inventory performance in the academic literature stock market performance. Swamidass [27] argues that inventory holding could be a function of firms’ financial To the best of our knowledge there is no recent empirical performance: top performers decreased inventories sig- study concerned with inventory performance of firms of nificantly, whereas low performers surprisingly showed any major European economy. Regarding the US manu- increasing inventories. Cannon [6] also analyzes the link facturing industry, however, there are several studies between inventory and financial performance, finding no examining the development of inventory levels. relationship between improvements in inventory perfor- In their critical assessment of research on inventories, mance and improvements in overall firm performance. Blinder and Maccini [4, p. 79] state that the inventory to sales ratio of US companies’ inventories shows no decreasing trend between 1959 and 1986, a result ‘‘which 3 Research hypotheses and the method of analysis casts serious doubt on buffer stock theories of inventory behavior because computerization should have reduced the 3.1 Hypotheses need for inventories as buffers’’. This statement served as point of departure for a series of other studies primarily It is according to common sense that inventory policy concerned with inventory levels in the US. In contrast to has to deal with a number of trade-off decisions bal- Blinder and Maccini [4], Bairam [1] finds significant ancing demand and capacity as well as costs and cus- downtrends in inventory to sales ratios of individual US tomer service. However, high inventories are often seen manufacturing firms between 1976 and 1992. Hirsch [13] as poor operational performance in general because of registers an improvement in WP and RM inventories for tied-up capital, excess holding and carrying costs, and some sectors of the US manufacturing industry from the furthermore covering/hiding unnoticed or unsolved pro- late 1960s to the early 1990s (e.g., motor vehicles, rubber cess problems. Hence, to release cash for alternative uses and plastics). Having investigated the inventories of 7.433 and to uncover hidden problems by lowering inventory US manufacturing firms, Chen et al. [7, p. 1021] report that levels, JIT systems, in particular, have been widely while ‘‘the medians of RMs, FGs, and total inventory days established in different industries [12, 18, 19, 25]. drop, the means actually rise between 1981 and 2000’’, as Accordingly, we want to know, if inventories in German means may be influenced by outliers they are focusing on firms actually decreased during the time frame investi- medians. Recently, from a capital market view, using a gated. Thus, we set forth the following hypotheses. sample of US manufacturing firms for the period 1994– 2004, Tribo [28] finds evidence that after a firm was listed 3.1.1 Hypothesis 1 on the stock market it shows decreasing inventory levels. In addition to this kind of inventory studies, a second In each of the German firms examined, (a) total inventory stream of research is dedicated to the benefits of JIT adoption on inventory performance. Huson and Nanda [14] to sales ratios, (b) RM inventory to sales ratios, (c) WP inventory to sales ratios, and (d) FGs inventory to sales studied a sample of 55 firms that adopted JIT manu- facturing and find out that these firms increased their ratios show a decreasing trend between 1993 and 2005. inventory turnover subsequent to JIT implementation. 3.1.2 Hypothesis 2 Balakrishnan et al. [2] compare a sample of 46 JIT adopters with a sample of non-adopters of the same size and observe On an aggregated level we correspondingly formulate no significant effects on financial performance. Biggart and Hypothesis 2. Gargeya [3] find decreasing total and RM inventory to In each of the industries examined, (a) total inventory sales ratios after JIT implementation, whereas this does not to sales ratios, (b) RM inventory to sales ratios, (c) WP hold for WP and FGs inventories. inventory to sales ratios, and (d) FGs inventory to sales Finally, a third stream of research deals with the relationship of inventory and firm performance. ratios show a decreasing trend between 1993 and 2005. 123 Logist. Res. (2009) 1:95–111 97 3.1.3 Hypothesis 3 2005. All data used were taken from Thomson Financial’s Worldscope Global Database. In several cases, manual Further on, we are interested in the stage where inventory correction of data was required based on print or online reduction mainly has taken place: RMs, WP, or FGs. From versions of the firms’ annual financial reports due to false the production and operations management literature, we or implausible data from the data base. If this was not know that JIT production techniques focus mainly on possible, firms were eliminated from the sample. Further- reducing WP inventory and cycle times [20, 26, 29]. The more, to estimate the trend coefficients, firms were exclu- adoption of JIT purchasing principles is motivated by a ded when inventory data were not available for the whole desire to reduce RM inventories, as well. From Little’s [17] time frame. Finally, the annual time series data cover 100 ‘‘law’’ we can derive that a reduction of cycle time leads to firms listed at the German stock market. The firms in the lower WP inventories. Nevertheless, if customers refuse to sample can be assigned to the Standard Industrial Classi- accept early deliveries because of their ‘‘inventory con- fication (SIC) manufacturing division that includes firms sciousness’’, orders that are finished ahead of their due engaged in the mechanical or chemical transformation of dates are forced to wait in FGs inventory before shipping. materials or substances into new products. This division A relatively poor performance in FGs inventories may can be split into two groups. The first group covers firms further be expected due to increasing product variety, 20 B SIC B 29, which are mainly in the food products number of plants or warehouse locations under the condi- (SIC 20), textiles (SIC 22) and wearing apparel (SIC 23), tion of constant or growing customer service levels. and chemical (SIC 28) industries. The second group covers Furthermore, WP inventory seems to be more affected firms 30 B SIC B 39, including manufacturing firms pri- by factors within a firm’s control when compared to FGs marily in industries such as rubber and plastics (SIC 30), inventories. Hence, we expect WP (FGs) inventories to stones, clay, and glass (SIC 32), primary metal (SIC 33), perform relatively best (worst) and therefore we formulate fabricated metal products (SIC 34), machinery (SIC 35), Hypothesis 3. electronics and electrical equipment (SIC 36), transporta- In each of the German firms examined, (a) WP inven- tion equipment (SIC 37), measuring instruments (SIC 38), tory ratios when compared to RM inventory ratios, (b) WP and miscellaneous manufacturing (SIC 39) industries. inventory ratios when compared to FGs inventory ratios, and (c) RMs inventory ratios when compared to FGs 3.3 Method of analysis inventory ratios show a greater decreasing trend between 1993 and 2005. A linear regression model with time (i.e., year) as inde- pendent variable is applied to investigate the rate of change 3.1.4 Hypothesis 4 in inventory ratios over time. Because inventory varies among others with production and distribution levels, it is Correspondingly, on an aggregated level we formulate necessary to use relative inventory measures. A widely Hypothesis 4. used ratio is inventory to sales, which measures the In each of the industries examined, (a) WP inventory percentage of sales served from stock on hand. Let I and it ratios when compared to RM inventory ratios, (b) WP S denote the inventory and the sales, respectively, of firm it inventory ratios when compared to FGs inventory ratios, i in year t, the inventory to sales ratio is: and (c) RMs inventory ratios when compared to FGs it IS ¼ : ð1Þ it inventory ratios show a greater decreasing trend between it 1993 and 2005. A declining (rising) inventory to sales ratio over time means good (bad) news in so far as sales grow faster 3.2 Data and sample (slower) than stocks. The short-term expectation is that production rates will be increased (cut back). For the long- For analyzing inventory performance over time, the study term, decreasing trends in inventory to sales ratios may could be executed either on firm level using disaggregated indicate improved efficiency. In order to better understand data or on industry level using aggregated data. This study the degree of improvement at each of the different is based on disaggregated data on firm level, mainly to guard against an ‘‘aggregation bias’’, i.e., differently per- forming firms canceling each other out per sector. In the For some applications, the inventory to sales ratio is multiplied by 12 months or 365 days providing a measure of inventory coverage for majority of cases, firm level data are publicly available a given value of sales. A further advantage of the inventory to sales only for stock-listed corporations, which, of course, rep- ratio is that it corrects for sector size. Finally, the analysis is only to a resent just a fractional amount of all German companies. minor degree affected by changes in price levels provided that prices The sample chosen covers the time frame from 1993 to of outputs vary according to the prices of inputs. 123 98 Logist. Res. (2009) 1:95–111 inventory stages as well as potential shifts between them, iterated Prais–Winsten [23] estimation. Accordingly, we we analyze different inventory to sales ratios separately for found that the trend coefficients, which are statistically total inventories as well as its constituents: RM, WP, and significant according to the Prais–Winsten estimation, do not FGs. In order to focus on the material aspects of inventory differ greatly from the OLS estimates. This does not hold for development, it has to be emphasized that total inventory is the Cochrane–Orcutt estimation that we conducted, but defined here as the sum of these three components. which is inferior to the Prais–Winsten iteration, especially in Besides firm level data, we are also interested in the the case of a smaller time series sample size [5, 15, 22]. inventory trends of the corresponding industries. In order to Therefore, we will only report the Prais–Winsten estimators. calculate aggregate inventory to sales ratios in period t for a certain industry j, inventory held in the industry’s firms i = 1, 2, …, n, are summed up and then divided by the sum 4 Results of sales across the n firms: n 4.1 Descriptive statistics aggr it i¼1 IS ¼ P : ð2Þ jt it i¼1 For a brief overview of the firms analyzed, the means, We aggregate our data according to the SIC codes on a medians, and variation coefficients of the different inven- two digit basis. As we did not establish a class with less tory to sales ratios are given in Table 1. The variation then ten companies, the result of the aggregation spans six coefficients indicate the relative degree of movements industry classes, whereas we have merged the SIC codes 22 inside a company’s or a sector’s inventory ratios. Furthermore, Table 2 shows the means, medians, and and 23 together due to their similarity. To assess the corresponding overall trend coefficients variation coefficients for the sample’s industry groups for our sample over time, we applied the following simple according to the SIC codes. Because some SIC code classes linear regression model for total inventory levels as well as consist of less than ten firms, they are not listed here, for each of the three inventory types: whereas, the SIC codes 22 and 23 are merged due to their similarity. IS ¼ a þ b  t þ e ; ð3Þ it i it it To calculate means, medians, and variation coefficients In Eq. 3, t represents the time period (year), a the on an industry group level, we first determined the sum of intercept, and b the slope, i.e., the trend coefficient, of firm i. the weighted inventory to sales ratios of all firms within Because we applied regression analysis on time series data, one sector for each year of the time frame investigated. The we checked for first order autocorrelation of the residuals numbers shown in Table 2 are based on variable aggre- e using the Durbin–Watson test statistic [9, 10], which it gation weights; this means that the sales of a company for compares the ordinary least squares (OLS) residual for each year are divided by the sector’s total sales of the period t with the residual from the preceding period t - 1, corresponding year. and is defined as: T 2 4.2 Empirical tests ðÞ ^ e  ^ e t t1 t¼2 d ¼ : ð4Þ ^ e t¼1 t The results of our time series regression analysis for testing hypothesis 1 are provided in Table 3. Considering The Durbin–Watson test statistic can vary between 0 and hypothesis 1 (a) we find significantly decreasing (increas- 4. If the Durbin–Watson test statistic equals 2, there is ing) total inventory to sales ratios for 26 (22) firms. absolutely no first order autocorrelation. A d value Decreasing (increasing) RM inventory to sales ratios are significantly less (greater) than 2 indicates a positive diagnosed for 28 (29) firms [Hypothesis 1 (b)]. 41 (23) (negative) autocorrelation. Corresponding tables for firms show a significantly decreasing (increasing) trend in different sample sizes can be found in Durbin and Watson WP inventories [Hypothesis 1 (c)]. Finally, decreasing [10] and Savin and White [24]. Applying the Durbin–Watson test, we found first order autocorrelation in nearly all of the time series in the sample. As a consequence, OLS test statistics are no longer valid because standard errors are In order to save space, the intercept parameter estimates obtained are not reported. Only the trend coefficients (slope), together with biased and, therefore, causing serious misleading signals [11, t-statistics (P value) and coefficients of determination (R ) are reported. 30]. In order to take autocorrelation into account, we employ Six cases are rejections due to a trend coefficient of zero. That is, because some firms do not carry work-in-process inventories (e.g., Hence, there is a deviation from total inventories reported in the soft drinks or wearing apparel), whereas in the chemical industry balance sheets, which may also contain payments in advance to work-in-process and finished goods inventories are usually combined suppliers, for example. into one balance sheet item due to production conditions. 123 Logist. Res. (2009) 1:95–111 99 Table 1 Means, medians, and variation coefficients of inventory ratios 1993–2005 (sample) No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 1 20 A. Moksel AG 3.79 3.69 16.07 0.42 0.31 41.89 0.02 0.00 165.61 3.35 3.33 14.41 2 20 Actris AG 5.06 5.12 17.24 2.11 2.08 27.12 0.83 0.89 22.65 2.13 2.23 16.84 3 20 ADM Hamburg AG 11.15 11.37 19.64 8.94 9.45 19.95 0.25 0.28 18.74 1.96 2.03 30.49 4 20 Berentzen-Gruppe AG 12.00 11.46 16.76 2.84 2.75 25.75 3.21 3.21 41.25 5.95 5.36 28.33 5 20 Dom Brauerei AG 5.13 4.88 22.54 2.02 1.90 21.71 1.48 1.06 60.80 1.63 1.50 32.52 6 20 Frosta AG 15.64 15.91 9.20 6.42 6.23 26.34 2.54 2.71 37.10 6.68 6.57 9.62 7 20 Kulmbacher Brauerei AG 6.01 5.90 8.63 2.63 2.44 26.52 1.10 1.07 21.11 2.28 2.33 22.91 8 20 Mineralbrunnen AG 4.20 4.22 22.37 2.54 2.31 19.38 0.00 0.00 n. def. 1.66 1.47 31.44 920 Su ¨ dzucker AG 30.61 29.10 15.54 2.31 2.11 20.30 5.48 4.53 43.00 22.82 22.98 10.51 10 20 Sektkellerei Schloss 35.80 26.18 46.67 5.91 4.59 47.92 21.11 14.32 56.17 8.77 8.00 46.17 Wachenheim AG 11 20 Staatl. Mineralbrunnen AG 3.28 3.09 20.98 1.23 1.21 26.01 0.00 0.00 n. def. 2.05 2.02 26.60 12 20 VK Muehlen AG 9.83 10.02 17.74 6.91 7.12 20.07 0.23 0.29 59.78 2.68 2.58 23.20 13 22 Bremer Woll-Ka ¨mmerei AG 23.96 26.17 29.60 9.62 9.96 31.53 0.19 0.19 34.52 14.14 12.78 35.02 14 22 Gruschwitz Textilwerke AG 22.41 21.45 10.21 6.41 6.30 48.78 8.58 5.91 42.38 7.42 7.50 17.05 15 22 Kunert AG 34.39 35.34 6.04 5.18 4.93 10.06 5.55 5.78 21.99 23.67 24.05 10.54 16 22 Textilgruppe Hof AG 20.31 19.57 18.24 4.71 4.86 16.48 3.94 3.27 44.82 11.67 10.77 36.31 17 22 Vereinigte Filzfabriken AG 14.02 13.02 17.70 4.90 4.87 9.75 2.93 2.99 19.51 6.20 5.36 26.63 18 23 Adidas AG 19.18 20.86 22.09 0.64 0.64 47.47 0.15 0.13 38.57 18.39 20.46 24.10 19 23 Ahlers AG 20.30 20.67 15.37 7.09 7.10 17.48 0.67 0.69 36.17 12.54 12.75 15.77 20 23 Escada AG 18.89 19.61 17.57 3.08 3.11 18.92 2.16 1.84 27.76 13.66 15.22 27.24 21 23 Etienne Aigner AG 14.07 16.81 29.06 1.38 1.23 35.30 0.00 0.00 n. def. 12.69 14.78 30.15 22 23 Gerry Weber International AG 12.56 11.83 23.25 1.88 1.97 27.33 3.96 3.97 39.08 6.72 7.45 26.55 23 23 Hirsch AG 15.55 16.10 20.93 4.63 4.50 19.94 3.18 3.06 12.02 7.74 8.27 32.08 24 23 Hucke AG 10.60 10.91 13.84 5.33 5.61 16.53 1.08 1.44 92.92 4.18 4.05 17.32 25 23 Hugo Boss AG 17.32 16.31 13.29 4.49 4.46 13.88 0.84 0.86 14.27 11.99 11.27 20.15 26 23 Puma AG 18.61 18.80 18.81 0.24 0.13 86.88 3.59 4.28 70.96 14.77 15.14 12.00 27 23 Triumph International AG 24.46 24.21 6.11 4.10 4.12 12.30 3.97 3.99 13.38 16.40 16.82 8.08 28 28 Altana AG 12.73 12.72 8.76 4.14 4.09 9.36 1.93 1.93 13.40 6.66 6.64 12.70 29 28 BASF AG 14.12 14.46 7.78 2.20 2.73 51.22 0.22 0.22 41.17 11.69 11.50 15.28 30 28 Bayer Aktiengesellschaft 20.34 20.53 3.38 3.67 3.51 10.62 0.00 0.00 n. def. 16.68 16.85 3.33 31 28 Beiersdorf AG 13.44 14.02 8.36 3.43 3.35 19.38 1.02 0.98 17.26 8.99 8.45 12.44 32 28 Biotest AG 44.01 45.60 17.28 11.47 10.79 33.48 23.88 23.11 32.46 8.66 8.50 9.02 33 28 Fresenius SE 11.75 9.56 32.35 2.77 2.03 44.64 1.67 1.39 41.34 7.31 6.16 26.31 34 28 Fuchs Petrolub AG 12.97 12.60 6.05 5.38 5.37 6.18 0.62 0.59 11.56 6.97 6.85 8.22 35 28 Henkel KGaA 12.00 12.91 12.75 3.81 4.01 18.09 1.11 1.32 45.64 7.08 7.24 8.76 36 28 Linde AG 17.55 19.03 31.70 2.88 3.01 22.26 8.02 9.38 55.61 6.65 6.54 11.15 37 28 Merck KGaA 19.69 19.44 11.87 4.22 4.30 20.57 0.00 0.00 n. def. 15.48 15.14 10.43 38 28 Su ¨ d Chemie AG 17.03 16.88 6.88 6.10 6.00 9.53 3.11 3.01 17.37 7.82 7.98 9.19 39 28 Schering AG 19.56 19.25 11.67 4.08 3.96 12.29 8.00 7.86 13.77 7.48 7.29 12.38 40 30 Continental AG 12.32 11.86 18.79 3.21 3.26 9.36 1.53 1.51 19.18 7.58 7.13 25.65 41 30 Ehlebracht AG 11.88 12.94 28.36 4.64 4.29 25.85 2.13 2.41 45.14 5.12 5.56 37.78 42 30 New York-Hamburger Gummi- 17.72 17.97 9.52 4.01 3.91 15.76 6.30 6.21 17.25 7.41 7.56 20.50 Waaren Compagnie AG 43 30 Simona AG 18.71 18.79 8.14 4.88 5.12 14.22 0.00 0.00 n. def. 13.82 13.81 8.67 44 30 WERU AG 6.44 6.38 13.29 5.53 5.43 16.07 0.35 0.33 27.25 0.56 0.54 21.82 123 100 Logist. Res. (2009) 1:95–111 Table 1 continued No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 45 32 BHS tabletop AG 16.75 16.53 23.75 2.94 2.56 33.80 1.53 1.66 48.55 12.28 12.01 21.86 46 32 Didier-Werke AG 16.87 17.32 19.59 5.24 5.44 23.77 3.52 3.23 34.60 8.11 6.96 29.88 47 32 Dyckerhoff AG 9.93 10.01 9.66 4.34 3.99 13.77 1.56 1.41 27.13 4.03 4.10 24.46 48 32 Erlus AG 9.19 10.01 41.32 1.52 1.65 30.41 0.30 0.31 16.92 7.36 7.85 45.61 49 32 Heidelbergcement AG 10.27 10.25 5.79 5.26 5.28 9.45 1.32 1.27 20.92 3.69 3.53 12.78 50 32 Keramag AG 10.07 10.19 8.82 0.72 0.61 34.31 0.54 0.38 51.87 8.82 8.86 8.52 51 32 Pilkington Deutschland AG 7.48 7.86 22.68 1.86 1.68 31.13 0.39 0.21 90.13 5.24 5.33 22.67 52 32 Rosenthal AG 29.22 27.77 16.24 2.62 2.66 16.31 6.08 5.20 70.23 20.52 21.03 16.43 53 32 Saint Gobain Oberland AG 13.76 13.17 21.84 3.59 3.08 52.32 0.38 0.22 97.38 9.79 9.64 11.60 54 32 SGL Carbon AG 27.57 26.78 9.32 7.22 7.10 12.37 14.66 14.46 9.05 5.68 5.64 13.66 55 32 Sto AG 7.76 7.79 6.50 2.00 1.95 8.57 0.18 0.16 32.00 5.59 5.58 7.81 56 32 Teutonia Zementwerk AG 16.49 15.59 17.60 8.73 8.48 19.37 4.87 4.26 34.37 2.89 2.51 35.47 57 32 Villeroy and Boch AG 26.19 25.95 7.20 3.75 3.75 6.50 4.02 3.56 16.85 18.42 18.20 9.61 58 33 Norddeutsche Affinerie AG 15.89 16.20 14.41 5.82 5.80 13.61 6.31 6.03 21.50 3.76 3.77 32.01 59 34 Innotec TSS AG 14.39 14.66 13.96 6.04 6.21 18.62 6.12 5.87 37.72 2.23 2.32 36.60 60 34 Salzgitter AG 16.82 16.25 9.81 4.43 4.22 25.44 3.47 3.77 22.89 8.92 9.04 7.13 61 34 WMF AG 25.06 25.00 8.31 3.58 3.61 11.06 2.82 2.80 8.79 18.66 18.79 9.61 62 35 Alexanderwerk AG 37.82 37.18 30.69 3.78 4.01 31.50 25.52 23.72 47.27 8.52 6.57 65.12 63 35 Bertold Hermle AG 18.32 17.15 25.43 3.27 3.29 67.97 8.80 5.56 56.15 6.25 6.60 31.04 64 35 Deutz AG 32.44 26.71 44.24 11.13 11.35 13.62 15.69 10.05 84.28 5.62 5.40 21.55 65 35 Du ¨ rkopp Adler AG 28.70 28.51 8.89 7.22 7.01 25.82 9.09 9.14 13.28 12.39 12.36 18.99 66 35 Du ¨ rr AG 15.25 15.03 72.70 2.23 2.16 16.37 12.86 12.03 89.27 0.16 0.05 90.25 67 35 GEA Group AG 10.62 10.32 18.17 2.13 2.46 29.64 4.74 4.51 30.34 3.75 3.93 16.13 68 35 Gildemeister AG 29.52 26.16 33.74 10.10 8.80 34.76 11.69 8.31 66.41 7.72 7.84 25.38 69 35 Jagenberg AG 19.34 21.00 25.92 4.48 4.02 20.86 12.18 13.50 26.93 2.68 2.77 57.44 70 35 Junghenrich AG 11.36 10.19 20.50 5.41 4.69 24.30 1.79 1.91 52.49 4.15 4.18 13.63 71 35 Kloeckner-Werke AG 18.10 15.57 37.52 6.33 5.53 31.06 9.58 7.74 54.91 2.19 2.01 28.94 72 35 Koenig and Bauer AG 34.08 36.71 16.60 6.37 6.23 32.46 27.48 28.33 15.19 0.23 0.14 93.12 73 35 Krones AG 12.74 11.92 27.93 3.44 3.12 37.08 5.70 5.62 20.58 3.60 3.37 40.63 74 35 KSB AG 19.60 20.36 13.41 6.07 5.92 8.21 8.38 8.67 27.00 5.16 5.26 12.14 75 35 KUKA AG 26.87 27.20 22.05 5.75 5.87 10.69 18.73 19.19 28.53 2.40 2.24 17.79 76 35 Rheinmetall AG 20.28 20.29 18.76 5.40 4.83 21.80 10.94 11.53 32.57 3.94 3.41 23.80 77 35 Sartorius AG 18.29 18.39 15.19 3.83 3.83 14.02 5.47 5.75 15.72 8.99 8.70 22.80 78 35 Triumph Adler AG 15.97 15.82 28.00 1.71 1.90 61.98 2.55 1.64 103.03 11.70 12.52 35.11 79 35 Vossloh AG 21.40 18.16 28.43 7.64 7.89 29.76 8.00 6.96 54.91 5.76 6.64 49.07 80 36 Brilliant AG 22.83 23.02 10.35 6.44 7.38 57.35 1.89 1.87 64.04 14.51 13.43 23.68 81 36 Ceag AG 20.28 21.02 19.51 6.60 6.72 17.96 3.70 3.45 59.15 9.98 10.13 27.54 82 36 Leifheit AG 15.60 15.20 13.21 4.02 3.66 28.17 1.85 1.65 37.33 9.73 8.95 23.49 83 36 M.tech AG 20.15 18.58 28.14 5.17 5.02 18.02 14.52 12.20 35.28 0.46 0.00 115.87 84 36 Schweizer Electronic AG 11.26 11.40 8.38 4.68 4.42 16.01 3.61 3.61 15.13 2.97 2.96 36.56 85 36 Sedlbauer AG 15.98 14.59 21.55 8.61 7.40 30.69 4.60 4.35 18.01 2.77 2.51 27.25 86 36 Vogt Electronic AG 17.82 17.18 22.43 8.77 8.76 19.55 4.39 3.62 38.74 4.66 3.62 50.76 87 37 Audi AG 6.31 5.94 16.88 1.36 1.38 19.64 1.42 1.45 16.87 3.54 3.44 32.96 88 37 BBS Fahrzeugtechnik AG 22.10 21.96 14.04 5.22 4.91 22.16 5.76 5.54 20.05 11.12 11.53 21.18 89 37 BMW AG 12.06 12.07 12.49 1.36 1.37 10.43 1.75 1.77 16.01 8.95 9.09 14.33 90 37 Hymer AG 21.75 20.35 15.26 8.94 8.99 19.74 1.75 1.76 15.52 11.06 10.05 18.73 123 Logist. Res. (2009) 1:95–111 101 Table 1 continued No. SIC Firm TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 91 37 MAN AG 37.18 36.60 13.46 3.76 3.71 7.89 18.76 18.39 18.85 14.66 16.34 39.41 92 37 Porsche AG 11.01 10.86 16.57 1.46 1.21 50.90 3.41 3.39 43.21 6.15 6.65 23.74 93 37 Progress-Werke Oberkirch AG 14.44 14.15 16.88 5.20 4.78 27.74 6.29 6.14 31.43 2.95 3.06 15.41 94 37 Schaltbau Holding AG 25.10 25.89 20.00 9.00 8.45 14.93 12.08 11.30 31.65 4.01 3.74 28.18 95 37 Veritas AG 9.84 9.48 16.48 3.57 3.30 25.12 2.64 2.45 46.89 3.63 3.56 21.25 96 37 Volkswagen AG 11.37 10.83 13.21 2.24 2.22 7.01 1.67 1.45 19.14 7.47 7.33 20.52 97 37 Wanderer-Werke AG 22.62 23.79 12.48 4.61 3.85 29.63 6.53 6.58 13.79 11.48 11.96 19.45 98 38 Draegerwerk AG 21.91 23.28 17.76 5.30 5.68 19.86 6.70 7.22 32.15 9.92 10.36 16.77 99 38 Siemens AG 15.01 14.07 17.82 2.98 2.96 9.86 7.27 5.23 44.05 4.76 4.67 16.21 100 39 Johann F. Behrens AG 26.47 26.29 6.97 6.54 5.91 26.18 1.90 2.21 75.73 18.03 18.12 8.33 Table 2 Means, medians, and variation coefficients of inventory ratios 1993–2005 (SIC code classes) SIC TI RM WP FG Mean Median Varc Mean Median Varc Mean Median Varc Mean Median Varc (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 20 18.41 18.39 13.74 3.24 3.27 7.59 3.06 2.79 40.01 12.12 12.23 9.67 22/23 18.90 19.48 11.84 2.51 2.32 30.57 1.34 1.28 22.27 15.05 14.39 11.84 28 16.20 16.25 6.87 3.16 3.04 8.72 1.26 1.32 21.29 11.78 11.49 8.66 32 13.71 13.43 4.30 4.65 4.48 8.75 2.79 2.77 7.56 6.28 6.27 10.13 35 17.21 16.82 11.03 4.53 4.68 10.45 8.77 9.01 21.45 3.90 3.81 7.32 37 13.57 13.63 6.41 2.11 2.05 6.77 3.43 3.17 20.13 8.03 7.84 14.47 Total 15.08 15.32 3.65 2.91 2.90 3.34 3.82 3.83 8.90 8.36 8.40 3.61 (increasing) FGs inventories are significant for 24 (22) A negative value in the WP versus RMs (FGs) column firms [Hypothesis 1 (d)]. indicates that WP inventories performed better [i.e., show a On an aggregated level, the results of our time series higher (lower) decreasing (increasing) trend] when com- regression analysis for testing hypothesis 2 are provided in pared to RMs (FGs) and a negative value in the RMs versus Table 4. Considering hypothesis 2 (a), total inventory to FGs column indicates that RMs inventories performed sales ratios decrease (increase) to a significant extent in better when compared to FGs. Considering Hypothesis 3 four (one) sector(s). Decreasing (increasing) RM inventory (a), WP inventory ratios compared to RM inventory ratios performed significantly better (worse) in 42 (17) firms. In to sales ratios can be observed in one (two) industry sec- tor(s) [Hypothesis 2 (b)], while three sectors show a sig- 34 (25) cases, a significantly better (worse) development of nificantly constant trend with a slope of 0. Regarding WP the WP inventory ratio can be noticed [Hypothesis 3 (b)] inventories [Hypothesis 2 (c)], the regression analysis when compared to the corresponding FGs inventory ratio. results in four (one) sector(s) with a significantly decreas- RMs inventory ratios showed a better (worse) performance ing (increasing) behavior. Decreasing (increasing) FGs for 26 (28) firms [Hypothesis 3 (c)] when compared to FGs inventories are significant for two (one) industries inventory ratios. On an aggregated level, the results of our [Hypothesis 2 (d)]. time series regression analysis for testing hypothesis 4 are To answer the question at which stages inventory provided in Table 6, comparing the trend coefficients of reduction mainly has taken place, we proceed with testing different inventory stages between 1993 and 2005 for each hypothesis 3, comparing the trend coefficients of different SIC class. inventory stages between 1993 and 2005 for each firm (see Testing hypothesis 4 (a) WP inventory ratios performed Table 5). better (worse) in four (two) sectors when compared to RM 123 102 Logist. Res. (2009) 1:95–111 Table 3 Overall trend coefficients 1993–2005 No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 1 20 A. Moksel AG -0.056 0.400 0.072 -0.017 0.332 0.094 0.004* 0.091 0.259 -0.045 0.404 0.071 2 20 Actris AG -0.022 0.798 0.007 0.033 0.570 0.033 -0.033** 0.046 0.341 -0.020 0.484 0.050 3 20 ADM Hamburg AG -0.144 0.545 0.038 -0.080 0.683 0.017 -0.002 0.500 0.047 -0.069 0.194 0.162 4 20 Berentzen-Gruppe AG 0.322* 0.084 0.270 0.152*** 0.000 0.730 -0.205 0.187 0.167 0.364*** 0.002 0.625 5 20 Dom Brauerei AG 0.183*** 0.004 0.579 -0.053 0.207 0.154 0.080 0.328 0.096 0.108*** 0.001 0.666 6 20 Frosta AG -0.166 0.156 0.190 -0.292** 0.040 0.358 0.240*** 0.000 0.785 -0.068 0.190 0.165 7 20 Kulmbacher Brauerei AG 0.049 0.241 0.134 0.061 0.285 0.113 -0.039** 0.039 0.361 0.035 0.384 0.076 8 20 Mineralbrunnen AG 0.217*** 0.001 0.694 0.097*** 0.004 0.575 0.000*** 0.000 0.000 0.120*** 0.000 0.743 920 Su ¨ dzucker AG 0.775 0.133 0.211 0.108** 0.013 0.473 0.516** 0.018 0.443 0.138 0.630 0.024 10 20 Sektkellerei Schloss Wachenheim AG -3.583*** 0.002 0.646 -0.485** 0.019 0.438 -2.576*** 0.002 0.650 -0.469 0.133 0.211 11 20 Saatl. Mineralbrunnen AG 0.155*** 0.001 0.696 0.030 0.390 0.075 0.000*** 0.000 0.000 0.128*** 0.000 0.856 12 20 VK Muehlen AG 0.280** 0.038 0.363 0.221** 0.041 0.354 0.018* 0.095 0.253 0.041 0.553 0.036 13 22 Bremer Woll-Ka ¨mmerei AG -1.515*** 0.003 0.597 -0.624*** 0.008 0.526 -0.013*** 0.001 0.713 -0.867** 0.031 0.387 14 22 Gruschwitz Textilwerke AG -0.026 0.908 0.001 0.631*** 0.001 0.688 -0.635** 0.036 0.371 0.023 0.837 0.004 15 22 Kunert AG -0.026 0.849 0.004 -0.087* 0.051 0.329 -0.312*** 0.000 0.850 0.369** 0.022 0.424 16 22 Textilgruppe Hof AG 0.715** 0.018 0.442 0.087 0.189 0.166 -0.395*** 0.002 0.648 1.016*** 0.000 0.769 17 22 Verenigte Filzfabriken AG 0.528*** 0.003 0.596 0.087*** 0.005 0.554 0.106*** 0.009 0.512 0.348*** 0.005 0.568 18 23 Adidas AG -0.932*** 0.000 0.725 0.030 0.275 0.118 0.004 0.573 0.033 -0.976*** 0.001 0.702 19 23 Ahlers AG -0.574** 0.018 0.442 -0.212** 0.040 0.356 -0.032 0.267 0.121 -0.376*** 0.009 0.509 20 23 Escada AG 0.629* 0.051 0.329 -0.058 0.339 0.092 -0.110** 0.024 0.415 0.753** 0.017 0.449 21 23 Etienne Aigner AG 0.886*** 0.007 0.531 0.092** 0.047 0.340 0.000*** 0.000 0.000 0.793** 0.011 0.494 22 23 Gerry Weber International AG -0.048 0.894 0.002 -0.084* 0.088 0.263 -0.151 0.394 0.074 0.244 0.119 0.226 23 23 Hirsch AG 0.460 0.209 0.153 0.034 0.737 0.012 0.011 0.764 0.009 0.379 0.114 0.230 24 23 Hucke AG -0.151 0.280 0.115 -0.171*** 0.001 0.655 0.009 0.938 0.001 -0.006 0.929 0.001 25 23 Hugo Boss AG 0.497** 0.012 0.487 -0.097* 0.064 0.303 0.004 0.702 0.015 0.589*** 0.000 0.749 26 23 Puma AG 0.148 0.724 0.013 -0.052*** 0.001 0.716 0.429* 0.077 0.280 -0.189 0.343 0.090 27 23 Triumph International AG 0.097 0.580 0.032 -0.097*** 0.006 0.544 -0.013 0.782 0.008 0.212 0.103 0.243 28 28 Altana AG -0.166* 0.088 0.263 -0.076*** 0.009 0.509 0.021 0.284 0.114 -0.101 0.236 0.137 29 28 BASF AG -0.212** 0.037 0.366 0.207** 0.042 0.353 -0.016* 0.076 0.281 -0.415*** 0.001 0.678 30 28 Bayer Aktiengesellschaft 0.067 0.109 0.236 0.049* 0.093 0.256 0.000*** 0.000 0.000 0.014 0.662 0.020 31 28 Beiersdorf AG -0.135 0.339 0.092 -0.174*** 0.000 0.904 -0.041*** 0.008 0.519 0.076 0.546 0.038 32 28 Biotest AG 1.600*** 0.004 0.585 -0.252 0.564 0.034 1.848*** 0.000 0.796 0.025 0.782 0.008 33 28 Fresenius SE -0.777** 0.018 0.442 -0.249** 0.022 0.425 -0.118* 0.080 0.275 -0.412*** 0.008 0.524 34 28 Fuchs Petrolub AG 0.092 0.307 0.104 -0.008 0.795 0.007 0.015*** 0.009 0.514 0.067 0.301 0.106 Logist. Res. (2009) 1:95–111 103 Table 3 continued No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 35 28 Henkel KGaA -0.254** 0.027 0.402 -0.154*** 0.001 0.710 -0.107*** 0.005 0.559 0.012 0.806 0.006 36 28 Linde AG -1.304*** 0.001 0.652 -0.129*** 0.006 0.543 -0.992*** 0.003 0.591 -0.154** 0.010 0.498 37 28 Merck KGaA -0.444** 0.018 0.444 -0.116 0.172 0.178 0.000*** 0.000 0.000 -0.325*** 0.007 0.536 38 28 Su ¨ d Chemie AG -0.041 0.734 0.012 -0.004 0.958 0.000 0.051 0.368 0.082 -0.099* 0.051 0.329 39 28 Schering AG -0.459* 0.096 0.253 -0.091 0.109 0.236 -0.172 0.263 0.123 -0.168** 0.025 0.410 40 30 Continental AG -0.568*** 0.000 0.856 -0.012 0.605 0.028 -0.068*** 0.000 0.804 -0.488*** 0.000 0.896 41 30 Ehlebracht AG -0.410 0.255 0.127 -0.232** 0.024 0.415 0.039 0.775 0.009 -0.215 0.314 0.101 42 30 New York-Hamburger Gummi-Waaren Compagnie AG -0.025 0.891 0.002 0.033 0.558 0.035 0.162 0.101 0.246 -0.230 0.159 0.188 43 30 Simona AG 0.132 0.348 0.088 0.114** 0.043 0.349 0.000*** 0.000 0.000 0.021 0.851 0.004 44 30 WERU AG 0.177*** 0.000 0.814 0.190*** 0.000 0.786 -0.004 0.605 0.028 -0.007 0.249 0.130 45 32 BHS tabletop AG 0.572 0.161 0.187 0.191** 0.030 0.390 -0.043 0.639 0.023 0.426* 0.089 0.262 46 32 Didier-Werke AG -0.296 0.269 0.120 -0.104 0.374 0.080 0.177 0.127 0.217 -0.392** 0.024 0.413 47 32 Dyckerhoff AG 0.057 0.411 0.069 0.106** 0.015 0.463 0.065* 0.075 0.283 -0.125 0.218 0.147 48 32 Erlus AG 0.866*** 0.007 0.533 0.107*** 0.000 0.786 0.009** 0.031 0.386 0.744** 0.012 0.486 49 32 Heidelbergcement AG 0.041 0.411 0.069 0.061 0.357 0.085 -0.040* 0.053 0.325 0.023 0.666 0.019 50 32 Keramag AG 0.150** 0.016 0.459 0.051*** 0.006 0.541 0.051* 0.064 0.302 0.049 0.375 0.079 51 32 Pilkington Deutschland AG -0.135 0.495 0.048 -0.122*** 0.009 0.515 -0.071*** 0.010 0.504 0.051 0.721 0.013 52 32 Rosenthal AG 0.690* 0.094 0.255 -0.055 0.149 0.196 0.711** 0.042 0.353 -0.195 0.578 0.032 53 32 Saint Gobain Oberland AG 0.275 0.327 0.096 0.242 0.180 0.172 0.046 0.248 0.131 -0.020 0.833 0.005 54 32 SGL Carbon AG 0.042 0.878 0.002 0.137* 0.088 0.263 -0.084 0.462 0.055 -0.002 0.979 0.000 55 32 Sto AG -0.052 0.354 0.086 0.005 0.727 0.013 0.004 0.541 0.038 -0.050 0.223 0.145 56 32 Teutonia Zementwerk AG 0.395*** 0.000 0.719 0.308*** 0.005 0.570 0.180** 0.035 0.374 -0.090 0.385 0.076 57 32 Villeroy and Boch AG -0.269 0.186 0.168 -0.010 0.703 0.015 -0.135** 0.011 0.489 -0.111 0.619 0.026 58 33 Norddeutsche Affinerie AG -0.319** 0.045 0.343 0.051 0.350 0.088 -0.109 0.259 0.125 -0.261*** 0.000 0.758 59 34 Innotec TSS AG 0.099 0.594 0.029 0.247*** 0.002 0.634 -0.360 0.175 0.176 0.095 0.237 0.137 60 34 Salzgitter AG 0.398*** 0.000 0.774 0.279*** 0.000 0.795 0.172*** 0.002 0.650 -0.060 0.210 0.152 61 34 WMF AG 0.005 0.981 0.000 0.071*** 0.007 0.539 -0.006 0.805 0.006 -0.060 0.734 0.012 62 35 Alexanderwerk AG -0.535 0.540 0.039 -0.030 0.810 0.006 -1.490 0.154 0.192 0.653 0.238 0.136 63 35 Bertold Hermle AG -0.741 0.146 0.199 0.554*** 0.000 0.878 -1.128*** 0.002 0.642 -0.150 0.509 0.045 64 35 Deutz AG -3.061*** 0.002 0.644 -0.233* 0.090 0.260 -2.530** 0.011 0.489 -0.232* 0.058 0.313 65 35 Du ¨ rkopp Adler AG -0.156 0.467 0.054 0.454*** 0.000 0.898 -0.243*** 0.003 0.614 -0.390* 0.057 0.316 66 35 Du ¨ rr AG -2.430*** 0.001 0.660 0.060* 0.098 0.250 -2.503*** 0.002 0.649 0.032*** 0.001 0.695 67 35 GEA Group AG -0.090 0.564 0.034 0.082 0.174 0.176 -0.159 0.205 0.155 -0.029 0.540 0.039 68 35 Gildemeister AG -1.777* 0.062 0.306 -0.638** 0.035 0.373 -1.525* 0.059 0.313 0.336** 0.035 0.372 104 Logist. Res. (2009) 1:95–111 Table 3 continued No. SC Firm TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 69 35 Jagenberg AG -0.986*** 0.002 0.619 -0.039 0.610 0.027 -0.737*** 0.000 0.807 -0.222* 0.097 0.252 70 35 Jungheinrich AG -0.464*** 0.002 0.636 -0.284*** 0.001 0.689 -0.223*** 0.000 0.811 0.056 0.199 0.159 71 35 Kloeckner-Werke AG 1.193** 0.022 0.421 0.359** 0.044 0.347 0.862** 0.029 0.395 -0.025 0.612 0.027 72 35 Koenig and Bauer AG -1.273*** 0.001 0.658 -0.443*** 0.005 0.563 -0.815** 0.016 0.454 -0.011 0.680 0.018 73 35 Krones AG 0.542* 0.100 0.248 0.268*** 0.009 0.510 0.049 0.661 0.020 0.244* 0.054 0.324 74 35 KSB AG -0.530*** 0.006 0.547 0.077** 0.042 0.351 -0.545*** 0.000 0.819 -0.060 0.388 0.075 75 35 KUKA AG -1.174*** 0.006 0.549 -0.110* 0.050 0.332 -1.025*** 0.006 0.545 -0.053 0.154 0.192 76 35 Rheinmetall AG -0.787** 0.030 0.391 0.131 0.305 0.104 -0.893*** 0.000 0.844 0.013 0.917 0.001 77 35 Sartorius AG -0.594** 0.011 0.491 0.075 0.167 0.182 -0.203*** 0.000 0.763 -0.457*** 0.005 0.568 78 35 Triumph Adler AG 0.654 0.259 0.125 -0.231*** 0.009 0.515 -0.292 0.321 0.098 0.937*** 0.002 0.647 79 35 Vossloh AG 0.244 0.725 0.013 0.311 0.148 0.197 0.556 0.209 0.153 -0.631*** 0.000 0.719 80 36 Brilliant AG -0.224 0.128 0.216 -0.679* 0.058 0.314 -0.255* 0.061 0.308 0.619** 0.030 0.391 81 36 Ceag AG -0.146 0.728 0.013 -0.037 0.743 0.011 -0.476*** 0.002 0.633 0.347 0.232 0.139 82 36 Leifheit AG 0.002 0.993 0.000 -0.311*** 0.004 0.579 -0.176*** 0.000 0.965 0.447** 0.023 0.420 83 36 M.tech AG -0.326 0.483 0.050 -0.081 0.308 0.103 -0.384 0.378 0.078 0.121*** 0.001 0.708 84 36 Schweizer Electronic AG 0.143* 0.070 0.292 0.026 0.692 0.016 -0.044 0.463 0.055 0.153* 0.094 0.255 85 36 Sedlbauer AG -0.337 0.311 0.102 -0.128 0.600 0.028 -0.079 0.242 0.134 -0.153*** 0.001 0.669 86 36 Vogt Eectronic AG -0.878*** 0.000 0.776 -0.015 0.894 0.002 -0.398*** 0.000 0.767 -0.496** 0.013 0.477 87 37 Audi AG 0.247*** 0.000 0.752 0.046* 0.071 0.289 -0.043* 0.061 0.307 0.228** 0.012 0.484 88 37 BBS Fahrzeugtechnik AG -0.453* 0.099 0.249 -0.055 0.577 0.032 -0.002 0.980 0.000 -0.409* 0.062 0.307 89 37 BMW AG 0.194 0.214 0.150 -0.004 0.778 0.008 0.031 0.276 0.117 0.167 0.207 0.154 90 37 Hymer AG -0.247 0.468 0.054 0.075 0.661 0.020 -0.054*** 0.001 0.685 -0.300* 0.066 0.299 91 37 MAN AG 0.336 0.525 0.042 -0.044* 0.092 0.258 -0.721*** 0.007 0.528 1.048** 0.046 0.342 92 37 Porsche AG -0.079 0.673 0.019 -0.156** 0.027 0.400 -0.103 0.485 0.050 0.172 0.255 0.127 93 37 Progress-Werke Oberkirch AG 0.168 0.571 0.033 -0.233* 0.064 0.303 0.344* 0.072 0.288 0.028 0.616 0.026 94 37 Schaltbau Holding AG -0.641 0.155 0.191 -0.067 0.490 0.049 -0.335 0.420 0.066 -0.239*** 0.003 0.592 95 37 Veritas AG -0.093 0.613 0.026 0.168** 0.029 0.395 -0.185** 0.033 0.380 -0.021 0.793 0.007 96 37 Volkswagen AG -0.026 0.897 0.002 -0.017 0.298 0.107 -0.067** 0.010 0.497 0.039 0.843 0.004 97 37 Wanderer-Werke AG -0.399 0.114 0.230 -0.180 0.178 0.173 -0.122* 0.082 0.272 0.062 0.837 0.004 98 38 Draegerwerk AG -0.936*** 0.000 0.823 -0.071 0.526 0.041 -0.459*** 0.004 0.589 -0.409*** 0.000 0.773 99 38 Siemens AG 0.474** 0.046 0.342 -0.013 0.631 0.024 0.682*** 0.005 0.560 -0.199*** 0.000 0.930 100 39 Johann F. Behrens AG 0.120 0.580 0.032 0.394*** 0.002 0.632 -0.275** 0.015 0.464 -0.007 0.970 0.000 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) Logist. Res. (2009) 1:95–111 105 Table 4 Overall trend coefficients for SIC classes 1993–2005 SC TI RM WP FG 2 2 2 2 b P value R b P value R b P value R b P value R 20 0.606*** 0.001 0.679 0.044*** 0.006 0.542 0.301*** 0.001 0.671 0.262*** 0.004 0.576 22/23 -0.439*** 0.006 0.544 -0.194*** 0.000 0.885 -0.067** 0.011 0.494 -0.170 0.313 0.101 28 -0.272*** 0.000 0.904 -0.011*** 0.000 0.955 -0.055*** 0.007 0.535 -0.253*** 0.000 0.840 32 -0.093** 0.010 0.497 0.089** 0.018 0.445 -0.027 0.106 0.240 -0.144*** 0.000 0.887 35 -0.275* 0.095 0.254 0.086** 0.033 0.379 -0.347** 0.019 0.437 -0.014 0.591 0.030 37 -0.026 0.815 0.006 -0.023* 0.098 0.250 -0.173*** 0.000 0.849 0.150 0.201 0.158 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) Table 5 Difference in regression coefficients 1993–2005 between inventory stages Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 1 20 A. Moksel AG 0.020 0.246 0.132 0.047 0.378 0.078 0.029 0.497 0.047 2 20 Actris AG -0.068 0.287 0.112 -0.017 0.612 0.027 0.053 0.140 0.205 3 20 ADM Hamburg AG 0.079 0.683 0.017 0.068 0.196 0.161 -0.012 0.939 0.001 4 20 Berentzen-Gruppe AG -0.383** 0.045 0.344 -0.532*** 0.008 0.527 -0.199* 0.054 0.324 5 20 Dom Brauerei AG 0.077 0.526 0.041 -0.069 0.520 0.043 -0.159*** 0.009 0.511 6 20 Frosta AG 0.530*** 0.004 0.587 0.304*** 0.000 0.811 -0.193 0.258 0.126 7 20 Kulmbacher Brauerei AG -0.100* 0.056 0.317 -0.068 0.199 0.159 0.026 0.778 0.008 8 20 Mineralbrunnen AG -0.097*** 0.004 0.575 -0.120*** 0.000 0.743 -0.019 0.396 0.073 9 20 Sektkellerei Schloss Wachenheim AG -2.065*** 0.007 0.537 -2.080*** 0.004 0.571 -0.015 0.909 0.001 10 20 Staatl. Mineralbrunnen AG -0.030 0.390 0.075 -0.128*** 0.000 0.856 -0.093** 0.033 0.379 11 20 Su ¨ d Zucker AG 0.407** 0.022 0.423 0.368** 0.040 0.359 -0.029 0.910 0.001 12 20 VK Muehlen AG -0.202* 0.060 0.311 -0.027 0.725 0.013 0.168 0.156 0.191 13 22 Bremer Woll-Ka ¨mmerei AG 0.609*** 0.010 0.506 0.853** 0.033 0.379 0.236 0.557 0.036 14 22 Gruschwitz Textilwerke AG -1.245*** 0.001 0.652 -0.655 0.117 0.227 0.595*** 0.010 0.504 15 22 Kunert AG -0.244** 0.014 0.472 -0.656*** 0.000 0.734 -0.478** 0.014 0.467 16 22 Textilgruppe Hof AG -0.482*** 0.002 0.643 -1.399*** 0.000 0.933 -0.926*** 0.001 0.688 17 22 Vereinigte Filzfabriken AG 0.021 0.549 0.037 -0.258*** 0.002 0.644 -0.288*** 0.009 0.508 18 23 Adidas AG -0.032 0.182 0.171 0.981*** 0.001 0.699 1.011*** 0.001 0.675 19 23 Ahlers AG 0.181 0.101 0.246 0.354** 0.011 0.496 0.168* 0.051 0.330 20 23 Escada AG -0.040 0.669 0.019 -0.825** 0.020 0.434 -0.809*** 0.003 0.594 21 23 Etienne Aigner AG -0.092** 0.047 0.340 -0.793** 0.011 0.494 -0.703** 0.018 0.442 22 23 Gerry Weber International AG -0.072 0.585 0.031 -0.484*** 0.000 0.841 -0.351** 0.012 0.484 23 23 Hirsch AG -0.011 0.877 0.003 -0.356* 0.087 0.264 -0.351** 0.047 0.339 24 23 Hucke AG 0.183 0.203 0.157 0.014 0.911 0.001 -0.154* 0.079 0.276 25 23 Hugo Boss AG 0.101** 0.045 0.343 -0.590*** 0.000 0.773 -0.693*** 0.000 0.928 26 23 Puma AG 0.478* 0.057 0.316 0.609*** 0.000 0.906 0.140 0.487 0.050 27 23 Triumph International AG 0.094** 0.016 0.456 -0.229** 0.041 0.354 -0.303** 0.018 0.444 28 28 Altana AG 0.098*** 0.008 0.521 0.120 0.193 0.163 0.036 0.640 0.023 29 28 BASF AG -0.222** 0.042 0.351 0.399*** 0.001 0.686 0.630*** 0.002 0.626 30 28 Bayer AG -0.049* 0.093 0.256 -0.014 0.662 0.020 0.039 0.367 0.082 31 28 Beiersdorf AG 0.135*** 0.000 0.965 -0.120 0.325 0.097 -0.241* 0.067 0.297 32 28 Biotest AG 2.048*** 0.005 0.559 1.755*** 0.000 0.736 -0.290 0.513 0.044 33 28 Fresenius SE 0.133*** 0.002 0.623 0.288*** 0.002 0.652 0.152*** 0.003 0.613 123 106 Logist. Res. (2009) 1:95–111 Table 5 continued Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 34 28 Fuchs Petrolub AG 0.026 0.367 0.082 -0.050 0.418 0.067 -0.055 0.365 0.083 35 28 Henkel KGaA 0.038 0.169 0.181 -0.125*** 0.001 0.670 -0.167*** 0.000 0.784 36 28 Linde AG -0.846*** 0.009 0.509 -0.825*** 0.006 0.553 0.009 0.784 0.008 37 28 Merck KGaA 0.116 0.172 0.178 0.325*** 0.007 0.536 0.180** 0.036 0.370 38 28 Schering AG -0.051 0.579 0.032 0.083 0.424 0.065 0.099** 0.030 0.389 39 28 Sud Chemie AG 0.052 0.489 0.049 0.157** 0.032 0.382 0.100 0.238 0.136 40 30 Continental AG -0.056*** 0.008 0.520 0.420*** 0.000 0.890 0.476*** 0.000 0.909 41 30 Ehlebracht AG 0.228 0.179 0.172 0.274** 0.010 0.497 0.026 0.898 0.002 42 30 New York-Hamburger Gummi-Waaren 0.174*** 0.008 0.519 0.390* 0.064 0.302 0.238 0.200 0.158 Compagnie AG 43 30 SIMONA AG -0.114** 0.043 0.349 -0.021 0.851 0.004 0.086 0.431 0.063 44 30 WERU AG -0.195*** 0.000 0.726 0.002 0.824 0.005 0.197*** 0.000 0.796 45 32 BHS tabletop AG -0.229*** 0.002 0.640 -0.485*** 0.009 0.514 -0.222 0.200 0.158 46 32 Didier-Werke AG 0.287** 0.048 0.337 0.582** 0.011 0.491 0.285* 0.067 0.297 47 32 Dyckerhoff AG -0.029* 0.081 0.274 0.170 0.211 0.151 0.200 0.146 0.199 48 32 Erlus AG -0.097*** 0.000 0.762 -0.734** 0.012 0.484 -0.630** 0.021 0.428 49 32 Heidelbergcement AG -0.115* 0.076 0.282 -0.074 0.217 0.148 0.066 0.574 0.033 50 32 Keramag AG -0.001 0.951 0.000 0.003 0.967 0.000 0.004 0.954 0.000 51 32 Pilkington Deutschland AG 0.050** 0.037 0.365 -0.118 0.364 0.083 -0.171 0.135 0.209 52 32 Rosenthal AG 0.732* 0.052 0.326 1.064* 0.051 0.330 0.140 0.682 0.017 53 32 Saint Gobain Oberland AG -0.180 0.226 0.143 0.067 0.470 0.053 0.234* 0.084 0.270 54 32 SGL CARBON AG -0.204*** 0.005 0.557 -0.046 0.341 0.091 0.153*** 0.006 0.545 55 32 Sto AG 0.001 0.949 0.000 0.054 0.144 0.201 0.049 0.183 0.170 56 32 Teutonia Zementwerk AG -0.123 0.181 0.172 0.276* 0.096 0.253 0.350* 0.091 0.259 57 32 Villeroy and Boch AG -0.114* 0.087 0.265 -0.024 0.921 0.001 0.086 0.715 0.014 58 33 Norddeutsch Affinerie AG -0.159* 0.090 0.260 0.150 0.163 0.185 0.314*** 0.000 0.759 59 34 Innotec TSS AG -0.645** 0.036 0.370 -0.490 0.151 0.194 0.161* 0.094 0.254 60 34 Salzgitter AG -0.112* 0.088 0.263 0.232** 0.011 0.488 0.340*** 0.000 0.805 61 34 WMF AG -0.089** 0.032 0.381 0.049 0.770 0.009 0.138 0.420 0.066 62 35 Alexanderwerk AG -1.500 0.178 0.174 -2.281 0.109 0.236 -0.710 0.146 0.199 63 35 Bertold Hermle AG -1.690*** 0.000 0.800 -1.043*** 0.000 0.875 0.678** 0.015 0.463 64 35 Deutz AG -2.136** 0.046 0.340 -2.283** 0.023 0.417 -0.014 0.859 0.003 65 35 Durkopp Adler AG -0.717*** 0.000 0.857 0.146 0.376 0.079 0.867*** 0.002 0.645 66 35 Durr AG -2.550*** 0.002 0.639 -2.528*** 0.002 0.648 0.028 0.359 0.084 67 35 GEA Group AG -0.236 0.118 0.226 -0.128 0.226 0.143 0.114 0.159 0.188 68 35 Gildemeister AG -0.920 0.139 0.205 -1.966** 0.037 0.367 -1.023*** 0.008 0.521 69 35 Jagenberg AG -0.697*** 0.000 0.742 -0.525*** 0.002 0.646 0.177** 0.031 0.387 70 35 Junghenrich AG 0.044 0.412 0.068 -0.271*** 0.000 0.889 -0.302*** 0.001 0.658 71 35 Kloeckner-Werke AG 0.504* 0.053 0.326 0.875** 0.042 0.353 0.414** 0.039 0.360 72 35 Koenig and Bauer AG -0.368 0.256 0.127 -0.815** 0.021 0.428 -0.429** 0.014 0.469 73 35 Krones AG -0.211*** 0.000 0.721 -0.192*** 0.001 0.652 0.023 0.704 0.015 74 35 KSB AG -0.619*** 0.000 0.892 -0.470*** 0.000 0.887 0.166** 0.041 0.356 75 35 KUKA AG -0.934*** 0.006 0.548 -0.977*** 0.008 0.520 -0.047 0.239 0.135 76 35 Rheinmetall AG -1.034*** 0.000 0.895 -0.891*** 0.000 0.835 0.139 0.133 0.211 77 35 Sartorius AG -0.283*** 0.000 0.871 0.247** 0.047 0.339 0.523*** 0.000 0.743 78 35 Triumph Adler AG -0.068 0.773 0.009 -1.227*** 0.000 0.804 -1.098*** 0.000 0.750 79 35 Vossloh AG 0.252 0.393 0.074 1.231*** 0.005 0.564 0.990*** 0.000 0.795 123 Logist. Res. (2009) 1:95–111 107 Table 5 continued Nr. SIC Firm WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 80 36 Brilliant AG 0.439 0.172 0.178 -0.828** 0.026 0.404 -1.242* 0.054 0.322 81 36 Ceag AG -0.449*** 0.001 0.656 -0.757** 0.028 0.398 -0.359 0.107 0.239 82 36 Leifheit AG 0.118 0.159 0.188 -0.633*** 0.004 0.586 -0.687*** 0.000 0.725 83 36 M.tech AG -0.317 0.454 0.057 -0.512 0.244 0.133 -0.203*** 0.005 0.563 84 36 Schweizer Electronic AG -0.053 0.394 0.073 -0.181 0.152 0.194 -0.122 0.420 0.066 85 36 Sedlbauer AG 0.064 0.723 0.013 0.074 0.232 0.139 0.012 0.960 0.000 86 36 Vogt Electronic AG -0.399** 0.026 0.405 0.089 0.625 0.025 0.446* 0.073 0.286 87 37 Audi AG -0.093*** 0.000 0.922 -0.261** 0.020 0.431 -0.178* 0.084 0.269 88 37 BBS Fahrzeugtechnik AG 0.077 0.630 0.024 0.459*** 0.003 0.600 0.366 0.143 0.202 89 37 BMW AG 0.034 0.302 0.106 -0.135 0.219 0.147 -0.173 0.201 0.158 90 37 Hymer AG -0.128 0.434 0.062 0.251* 0.088 0.263 0.370*** 0.001 0.686 91 37 MAN AG -0.678*** 0.009 0.511 -1.679** 0.010 0.497 -1.101** 0.030 0.389 92 37 Porsche AG 0.053 0.796 0.007 -0.260 0.144 0.201 -0.318 0.105 0.241 93 37 Progress-Werke Oberkirch AG 0.580*** 0.003 0.607 0.320* 0.075 0.283 -0.263*** 0.009 0.506 94 37 Schaltbau Holding AG -0.286 0.399 0.072 -0.086 0.856 0.003 0.181 0.221 0.146 95 37 Veritas AG -0.372*** 0.000 0.757 -0.184 0.102 0.245 0.206*** 0.005 0.558 96 37 Volkswagen AG -0.048 0.155 0.191 -0.101 0.608 0.027 -0.052 0.794 0.007 97 37 Wanderer-Werke AG 0.115 0.224 0.144 -0.154 0.659 0.020 -0.229 0.523 0.042 98 38 Draegerwerk AG -0.372* 0.083 0.271 -0.049 0.771 0.009 0.315** 0.010 0.497 99 38 Siemens AG 0.699*** 0.002 0.642 0.878*** 0.002 0.651 0.191*** 0.000 0.728 100 39 Johann F. Behrens AG -0.669*** 0.001 0.696 -0.232 0.143 0.202 0.411 0.149 0.197 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) inventory ratios. A better (worse) performance of WP latter decreasing, whereas the machinery industry shows a inventory ratios is found in two (two) industries [Hypothesis peculiar reduction in WP inventory to sales ratios. Somewhat 4 (b)] when compared to FGs inventory ratios. Finally, RMs surprisingly, the transportation equipment industry stands inventory ratios show a higher (lower) decreasing or lower out due to no significant change in total inventory to sales (higher) increasing trend in one (three) sector class(es) ratios, showing only significantly decreasing WP invento- [Hypothesis 4 (c)] when compared to FGs inventory ratios. ries. But an in-depth analysis of FGs inventories reveals an increase in the second half of our time frame investigated which results in a similar pattern in total inventories, 5 Discussion of results explaining their non-significant regression results. Observing our results on firm level, a somewhat mixed Regarding our results on an aggregated level, we find sig- picture emerges, contrasting the common belief about broad nificantly decreasing total inventory to sales ratios in the efforts on inventory reduction during the 1990s until present textile and wearing apparel, chemical, machinery, and in German corporations. This is even more surprising when stones, clay, and glass industry. The food industry shows we take into account the emerging interest on JIT techniques significantly increasing total inventory to sales ratios, which during the time frame investigated (see Fig. 1). is mainly due to increasing FGs and WP inventory to sales ratios. RM inventories remained nearly stable, and therefore Therefore, we conducted an exhaustive search using ‘‘WISO’’, the largest German language database for business and economics research performing relatively ‘‘better’’ when compared to the other articles, and LexisNexis for finding German press articles (newspapers, inventory stages. The inventory performance in the textile periodicals, and trade publications). We constrained our search to industry can be traced back to the fact of significantly ‘‘JIT’’. The first German article on JIT accounted for in the WISO decreasing RMs and WP inventories, whereas RMs per- database was published in 1982. A first peak in the distribution can be seen around 1989 with a significant decline until 2007. In contrast, the formed relatively better than WP inventories. The chemical distribution of press articles according to the LexisNexis database starts industry owes its inventory reduction mainly to decreased with the early 1990s and reached a local maximum in 1999. After a short FGs. Stones, clay, and glass show contrary developments in decline, the number of press articles on JIT took off again until reaching RMs and FGs inventories: the former are increasing, the their all time high in 2006. 123 108 Logist. Res. (2009) 1:95–111 Table 6 Difference in regression coefficients 1993–2005 between inventory stages for SIC classes SIC WP vs. RM WP vs. FG RM vs. FG 2 2 2 b P value R b P value R b P value R 20 0.256*** 0.003 0.612 0.031 0.574 0.033 -0.214*** 0.010 0.506 22/23 0.137*** 0.000 0.796 0.100 0.590 0.030 -0.032 0.858 0.003 28 -0.044** 0.024 0.412 0.195*** 0.002 0.624 0.243*** 0.000 0.829 32 -0.122** 0.012 0.488 0.121*** 0.000 0.819 0.245*** 0.000 0.785 35 -0.415** 0.011 0.489 -0.343*** 0.010 0.504 0.102*** 0.003 0.613 37 -0.149*** 0.001 0.689 -0.334** 0.013 0.473 -0.178 0.121 0.223 t statistic (*P \ 0.1, **P \ 0.05, ***P \ 0.01) 600 Table 7 Sensitivity analysis for SIC classes JIT (LexisNexis) JIT (WISO) SIC ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 20 6.62 6.78 7.55 4.70 4.78 5.01 22/23 12.56 12.97 14.93 4.83 4.97 5.66 28 9.58 9.73 10.40 10.05 10.16 10.61 32 6.99 7.07 7.41 7.51 7.63 8.16 35 3.67 3.76 4.13 4.46 4.57 5.06 37 5.43 5.50 5.83 7.95 8.14 8.96 [ 7.48 7.64 8.38 6.17 6.30 6.91 In the literature reviewed, we find ongoing efforts iden- tifying a relationship between inventory and financial per- 1980 1985 1990 1995 2000 2005 formance. This is due to the ‘‘critical argument on behalf of inventory reduction… that it will improve the financial Fig. 1 Distribution of JIT articles 1980–2007 (source: LexisNexis and WISO database) position of firms’’ [7, p. 1025]. Following this paradigm, inventories are not seen as residua of production and oper- Nevertheless, half of the firms significantly decreasing ations activities, but as important contributors to a firm’s total inventories are covered by SIC codes 34–39 (metal overall success. Nevertheless, executing several regression fabrication, machinery, electrical equipment, and trans- analyses considering return-on-investment (ROI) or operat- portation equipment), thus belonging to industries that are ing margin, we found no evidence for such a relationship. notorious for their use of JIT techniques [27]. Correspondingly, Cannon [6] recently finds no link between It has to be noted that within the time frame analyzed, inventory improvements and firm performance. To grasp several firms changed from national (according to German some helpful insights about the relationship between Commercial Code, HGB) to International Financial inventory reduction and financial performance, we per- Reporting Standards (IFRS). We scrutinized for possible formed a sensitivity analysis. We tested on an aggregated and conversion effects, resulting in structural interruptions in disaggregated level to what extent the ROI could be the data. As a cause, in the majority of cases we identified improved by lowering total inventories ceteris paribus by the accounting of long-term construction contracts, which 10% (50%). Using the mean to determine the ROI for the are no longer reported under inventories but under accounts time frame investigated on an aggregate level, the highest receivable. Accordingly, we found evidence for such con- enhancement for a 10% (50%) total inventory reduction can version effects mainly in decreasing WP inventories in the be reached in the textile industry with an ROI increase of machinery industry. 0.41 (2.37) percent points. In the transportation industry and Most likely affected were firms such as Durr, Koenig and Bauer, KUKA, Linde MAN, Siemens, and Triumph Adler. Therefore, their Furthermore, we found no significant link between the size of a firm WP inventory to sales performance should be interpreted carefully. (e.g., measured in sales) and its inventory performance. Number of articles Logist. Res. (2009) 1:95–111 109 Table 8 Sensitivity analysis for firms with the best inventory performance No. SIC Firm b ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 1 20 Sektkellerei Schloss Wachenheim AG -3.583 7.24 7.30 7.24 6.58 6.77 7.63 2 35 Deutz AG -3.061 1.70 1.75 2.00 2.35 2.45 2.91 335 Du ¨ rr AG -2.430 3.58 3.67 4.12 4.58 4.68 5.13 4 35 Gildemeister AG -1.777 3.85 3.98 4.61 6.09 6.28 7.19 5 22 Bremer Woll-Kammerei AG -1.515 -1.58 -1.58 -1.53 -1.45 -1.50 -1.75 6 28 Linde AG -1.304 6.43 6.54 6.99 6.38 6.47 6.83 7 35 Koenig and Bauer AG -1.273 3.82 3.95 4.59 4.73 4.88 5.60 8 35 KUKA AG -1.174 5.00 5.18 6.08 4.85 5.02 5.83 9 35 Jagenberg AG -0.986 -0.66 -0.68 -0.80 -2.05 -2.09 -2.29 10 38 Draegerwerk AG -0.936 5.20 5.34 5.99 5.46 5.61 6.32 [ 3.46 3.55 3.93 4.79 4.95 5.71 Table 9 Sensitivity analysis for firms with the worst inventory performance No. SIC Firm b ROI (Mean) (%) ROI (Median) (%) Reduction of TI Reduction of TI 0% -10% -50% 0% -10% -50% 1 28 Biotest AG 1.600 5.57 5.77 6.72 5.72 5.94 7.08 2 35 Kloeckner-Werke AG 1.193 5.54 5.67 6.24 4.76 4.86 5.31 3 23 Etienne Aigner AG 0.886 9.64 9.73 10.10 6.17 6.30 6.87 4 32 Erlus AG 0.886 8.68 8.77 9.20 8.82 8.94 9.45 5 22 Textilgruppe Hof AG 0.715 3.56 3.65 4.09 4.37 4.47 4.94 6 32 Rosenthal AG 0.690 -1.08 -1.13 -1.41 2.41 2.49 2.90 7 23 Escada AG 0.629 4.26 4.39 4.98 7.17 7.40 8.48 8 35 Krones AG 0.542 9.26 9.43 10.16 9.28 9.43 10.09 9 22 Vereinigte Filzfabriken AG 0.528 17.22 17.82 20.71 15.56 16.16 19.14 10 23 Hugo Boss AG 0.497 24.03 24.82 28.58 25.09 25.93 29.89 [ 8.67 8.89 9.94 6.67 6.85 7.78 the stone, clay, and glass industry, this effect is with a gain of first result, it can be stated that the sample firms with a 0.08 (0.41) percent points negligibly small. Using the median better inventory performance do not excel in terms of the for calculating the aggregated ROI over time, one gets a financial performance. The sensitivity analysis underlines completely different result concerning the best performing this observation. The impact on the mean (median) ROI industry, but the improvement effects are even smaller (see by a 10% total inventory reduction leads to a 0.09% also Table 7). (0.13) points improvement for the top ten firms in con- On a disaggregate level, we performed a sensitivity trast to 0.22% (0.14) points for the bottom ten firms. analysis for the ten firms with the highest and lowest This effect even becomes stronger for a 50% total significant inventory reduction over the time frame inventory reduction resulting in an ROI improvement of observed (see Tables 8, 9). Comparing the current state 0.47% (0.71) points for the top ten firms, in comparison of the top ten firms with the bottom ten firms regarding to 1.27% (0.75) points for the bottom ten firms. the financial performance, a completely different picture Conducting a sensitivity analysis, it has to be kept in emerges. While the top ten firms have a mean (median) mind that for years with a negative ROI the reduction of ROI of 3.46% (4.79%), the bottom ten firms stand out total inventories leads to an even smaller ROI. Because the mean (median) ROI is used for the time frame investigated, with a considerably higher ROI of 8.67% (6.67%). As a 123 110 Logist. Res. (2009) 1:95–111 potential improvement effects might be canceled out by inventory levels or the impact of postponement strategies on extraordinary results in one specific year. different inventory stages; or the effects of global sourcing In general, we see that the potential contributions of strategies, outsourcing or off-shoring production activities inventory improvements to the financial performance of on inventory holding. Increasing and more variable lead firms have only been small. These findings might give a times due to longer transportation would result in higher direction for further research, seeing inventory not so much stocks. Furthermore, the analysis of changes in factor prices as a predictor for financial performance but as what it as well as concentration tendencies in several industries on mainly is: a ‘‘buffer’’ which allows firms to smooth pro- inventory performance could be helpful to explain industry- duction levels, to shift production to periods with produc- specific developments. From a financial accounting per- tion costs expected to be relatively low, or as precaution for spective, further research is needed to better understand stock-outs. This insight can also be fruitful for managers, degree and direction of possible conversion effects on as inventory improvements are not necessarily a reliable inventory holdings reported under local versus international indicator for a firm’s overall performance. accounting standards. Finally, to better understand the dif- ferent causes for the inventory development analyzed, our research could be pursued using case study research design. 6 Conclusion Generating extensive examinations of each case could explore similar patterns of firms with high or low inventory Having analyzed inventory performance of 100 German performance or within different industries, for example. We corporations between 1993 and 2005, our findings indicate did not offer this research, but paved the way. that the total inventory to sales ratio decreased in a sta- tistically significant extent in four out of six industry sec- tors during the time frame investigated. On a firm level, we find that half of the firms with a significant decrease in total References inventories are based on industry sectors that are especially 1. Bairam EI (1996) Disaggregate inventory-sales ratios over time: known for their use of JIT techniques. 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Logistics ResearchSpringer Journals

Published: Jul 2, 2009

References