تجزیه و تحلیل با روش های متعدد برای هزینه های لجستیک
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1438||2012||7 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 137, Issue 1, May 2012, Pages 29–35
Logistics costs comprise a significant and relevant proportion of business costs, often exceeding 10 per cent of company turnover. This article examines the differences and interdependencies in the self-reported logistics costs of manufacturing and trading companies operating in Finland. Total logistics costs are taken to consist of six individual components: transport, warehousing, inventory carrying, logistics administration, transport packaging, and indirect costs of logistics. The analysed panel data covers 241 companies identified from two surveys for the years 2005 and 2008. Logistics costs were explored through multiple methods including descriptive analysis, generalised linear mixed models (GLMM), and principal component analysis. The distributions of logistics costs measured as percentages of turnover were skewed and best described by the beta distribution. Time, the number of employees, turnover, industry, and level of internationalisation were shown to be statistically significant explanatory variables of logistics costs. Logistics costs tended to be lower in larger companies, although diseconomies of scale eventually prevail. The analysis also covers changes in costs between 2005 and 2008. In general, the results indicate the need for caution in interpreting changes in logistics costs, and for simultaneously controlling the effects of background variables.
Logistics costs comprise a significant and relevant proportion of business costs: depending on the method applied and the industry in question, their share of company turnover in developed economies tends to be at least 10 per cent. However, definitions of logistics costs are many, and vary considerably. For the purposes of this article total logistics costs are measured as a percentage of turnover and comprise six individual components: transport, warehousing, inventory carrying, (logistics) administration, (transport) packaging, and indirect costs of logistics. This classification resembles that used by Naula et al. (2006) and Töyli et al. (2008). Heskett et al. (1973) identify transportation, warehousing, inventory carrying and administration as components of logistics costs, a classification that has been widely used. Gunasekaran et al. (2001) include the opportunity cost of capital and storage, risk costs, and the possible costs of lost sales under inventory costs, thus combining the elements of inventory carrying and warehousing. Beamon (1999), on the other hand, distinguishes between operating costs and inventory costs, whereas Lambert and LaLonde (1976) separate warehousing and inventory carrying costs, but include some of the components of warehousing costs, such as the inventory service costs and storage space costs in inventory carrying. For more discussion about the definition of inventory-related costs, see also Stewart, 1995a and Stewart, 1995b, Lee and Billington (1992), and Levy (1997). In sum, these four cost components seem to be in general use on the one hand, but on the other hand authors tend to define the limits of the categories in unique, inconsistent ways. The literature identifies a wide range of logistics cost components in addition to transport, warehousing, inventory carrying and logistics administration. For example, Zeng and Rossetti (2003) add customs, risk and damage as well as handling and packaging to the list, and Ojala et al. (2007) include “other logistics costs” to reflect the fact that logistics costs can rarely be divided and measured accurately. Klaus and Kille (2007) and Klaus et al. (2010) single out order-entry costs from other administration costs as a separate component. The ambiguities are not limited to defining and understanding logistics cost components. They are also rife in the measurement approaches and the scope of analysis, which may vary from a single function or project within a firm to the entire company, a fraction of a supply chain, or even a demand–supply network. There appear to be three main measurement approaches. The first is statistics-based and uses various types of national accounts or industrial statistics to create an estimate for logistics costs as a share of GDP. Examples of its implementation include Elger et al, (2008), Wilson (2009) and Havenga (2010). Bowersox et al. (2003) and Rodrigues et al. (2005), applied econometric modelling based on this kind of data in order to estimate the GDP share of logistics costs worldwide. The so-called “Top 100” reports on European logistics markets compiled by Klaus et al. (2010) estimate the size of logistics expenditure, and ultimately logistics costs, in Europe based on statistical data on transport volumes, employment in logistics, and demand for logistics services. Second, van Damme and van der Zorn (1999) and Baykasoglu and Kaplanoglu (2008), for example, take an accounting and activity-based-costing approach. In a similar vein, Pirttilä and Huiskonen (1996) apply so-called mission costing. This technique also comprises the use of specific logistics-performance indicators in various resources-planning software products for enterprises, such as the SCOR model developed by the Supply Chain Council. These have enabled accounting-based comparisons across industries and countries, especially among larger firms using elaborate ERP systems (see also Pohlen et al., 2009). The third main measurement approach is survey-based, and logistics costs are assessed from self-reported company data. The surveys are usually questionnaire-based and result in estimates of logistics costs as a percentage of sales. The European Logistics Association and Kearney (2004, 2009) has used this approach, as have Naula et al. (2006), and Ojala et al. (2007). This article examines the differences and the interdependencies in self-reported logistics costs of manufacturing and trading companies operating in Finland. In order to assess temporal differences, panel data on 241 companies was collected through two surveys in the years 2005 and 2008. This survey-based approach was the only feasible measurement alternative that would yield sufficiently detailed information about the logistics cost components in a broad range of companies. Traditional assessments of logistics costs are based mainly on calculations and reports of their mean values among the target population or sample, related to a mixed set of background variables. The limitations of this method are acknowledged here (for a more detailed discussion about these limitations see, for example, Dodd et al., 2006), and a different method based on generalised linear mixed models (GLMMs) for panel data is applied. The differences of cost estimates through descriptive analysis and GLMM model based means are discussed. GLMMs take into account multivariate, longitudinal, and non-normality aspects of data, for example, and differ from descriptive analysis in which dependent variables are analysed alone in one- or two-dimensional space. GLMMs also allow the simultaneous analysis of statistical dependencies between logistics costs and several explanatory variables. Descriptive analysis may give misleading results because differences between the direct and indirect relationships among the variables remain unrecognised. Generalised Linear Models (GLMs) represent a class of fixed-effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts; McCullagh and Nelder, 1989). In GLMs all independent variables (effects) are assumed fixed which means that there are observations from every level of that effect. If there is only a sample of levels in the data, the effect is called random (McCulloch et al., 2001). For the purpose of this article, logistics cost components are treated as continuous variables with two observations nested within randomly selected companies. This necessitates the use of random effects in the model. GLMs, which assume that all observations are independent of each other, are not appropriate for the analysis of these correlated data structures, longitudinal panel data in particular (see de Leeuw and Meijer, 2008). Analysis of this kind of multilevel data requires the addition of random cluster and/or subject effects into the regression model in order to account for the correlation. The resulting model, with both fixed and random effects, is called a linear mixed model (LMM). Given that the preliminary descriptive analysis indicated non-normality of the logistics cost components, and the beta distribution turned out to be more suitable than gamma distribution, the beta distribution was used in the reported analysis. For this reason, the word ‘generalised’ was added to the method description (GLMM).
نتیجه گیری انگلیسی
This article examined the differences and the interdependencies in the logistics cost components and in the total logistics costs. The analyses covered the associations between time, turnover, numbers of employees, industry, and the degree of internationalisation and both the various components and the total costs. Related to the company size, measured as the number of employees or annual turnover, transport costs, warehousing costs, and total logistics costs showed a U-shaped pattern, whereas inventory carrying costs decreased and administration costs were unstable, but the trend was downward. This result could be partly attributable to economies of scale but also suggest that after certain size the diseconomies of scale may prevail. Total logistics costs, and warehousing costs, inventory carrying and packaging costs turned out to be statistically significantly higher for trading than for manufacturing companies. This result is contradictory to those reported in most previous studies both internationally and in Finland, most of which refer to lower costs in trading companies. The various cost components were also estimated to be higher among export companies than in companies operating either internationally, with production facilities abroad, or entirely in the domestic market. It may be that export companies face the challenges of international supply chains without being able to exploit the scale and location-specific advantages often associated with international firms. The analysis also covered changes in costs between 2005 and 2008. The largest relative and only statistically significant change was in administration costs, other changes being statistically insignificant. This result is surprising, given that administration costs could be considered the most inelastic of the components under study. These findings also contribute to the discussion in most of the studies reporting annual or biannual changes in costs. These studies tend to report absolute changes without sufficient testing of their statistical significance, and without simultaneously controlling for other background variables such as size and level of internationalisation. The findings reported here imply that changes in time should be interpreted cautiously, and there should be proper testing of whether or not they are statistically and practically significant. The GLMM-based approach and the use of beta distribution resulted in different estimated levels of logistics cost components than the descriptive mean values based on the data. The reason for this difference is the asymmetric nature of costs, which makes beta distribution a better modelling tool than the normally distributed measures. Moreover, multivariate modelling provides simultaneous analysis of logistics costs with several independent variables and their different interdependencies, thereby controlling for the effects and interactions of the independent variables, which in the case reported here were sometimes large. Descriptive analysis generally does not take into account the multi-dimensional nature of business. In estimates of logistics costs in two industries, for example, it ignores the fact that the companies concerned also differ in size and level of internationalisation. The implication is that traditional, one-dimensional methods should be used cautiously. The correlation structure of logistics cost changed between 2005 and 2008. All the components correlated in 2005, for example, whereas in 2008 transport costs formed an independent group. One reason for this may be that the strongest factors affecting transport costs (the development of fuel prices and freight rates) in 2008 came from outside the company, and were not attributable to strategic decisions and their effects on the cost structure. Except in the case of warehousing and indirect costs, the companies appear to have become more homogeneous in terms of logistics costs, or then the logistics costs could be determined more by external factors that are not under the control of an individual company. Evidence of this is in the declining standard deviations of total costs and transport, inventory carrying, administration, and packaging costs. The general implication is that a lot of work remains to be done in order to enhance understanding of this important yet elusive facet of logistics – namely the logistics costs. As the analysis on this article is based on only two points of time, the findings and conclusions would benefit from validation based on additional points of time.