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Multiple-method analysis of logistics costs

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  • Engblom, Janne
  • Solakivi, Tomi
  • Töyli, Juuso
  • Ojala, Lauri

Abstract

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.

Suggested Citation

  • Engblom, Janne & Solakivi, Tomi & Töyli, Juuso & Ojala, Lauri, 2012. "Multiple-method analysis of logistics costs," International Journal of Production Economics, Elsevier, vol. 137(1), pages 29-35.
  • Handle: RePEc:eee:proeco:v:137:y:2012:i:1:p:29-35
    DOI: 10.1016/j.ijpe.2012.01.007
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    References listed on IDEAS

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    3. Eroglu, Cuneyt & Hofer, Christian & Hofer, Adriana Rossiter & Hou, Young, 2023. "“Cultural inventories”: How dimensions of national culture moderate the effect of demand unpredictability on firm-level inventories," International Journal of Production Economics, Elsevier, vol. 264(C).
    4. Erik Hofmann & Juuso Töyli & Tomi Solakivi, 2022. "Working Capital Behavior of Firms during an Economic Downturn: An Analysis of the Financial Crisis Era," IJFS, MDPI, vol. 10(3), pages 1-20, July.
    5. Wang Bo & Dmytro Bugayko & Maria Grigorak, 2018. "Assessment of the national economy through the application of logistics costs," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 51-67,68-82.
    6. Liang, Wen Yau & Huang, Chun-Che & Lin, Yin-Chen & Chang, Tsun Hsien & Shih, Meng Hao, 2013. "The multi-objective label correcting algorithm for supply chain modeling," International Journal of Production Economics, Elsevier, vol. 142(1), pages 172-178.
    7. Wetzel, Philipp & Hofmann, Erik, 2019. "Supply chain finance, financial constraints and corporate performance: An explorative network analysis and future research agenda," International Journal of Production Economics, Elsevier, vol. 216(C), pages 364-383.

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