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

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Logistics Research

Abstract

Although inventory reduction has been a major topic in production and operations management research for many years, there is a lack of empirically confirmed answers for questions such as: Have inventories in fully industrialized economies such as Germany decreased, overall, during the past decades? To the extent, inventory reductions were successfully realized, in which industries did they occur? Are there differences in inventory reduction achievements between raw materials, work-in-process, or finished goods? Are there measurable effects of inventory reductions upon the financial performance? To the best of our knowledge, this empirical study is the first one to investigate long-term inventory development on a firm as well as on industry level in a major European economy. It is based on data from German corporations and provides answers to the research questions stated above. The study’s findings indicate that total inventory to sales ratio decreased in a statistically significant extent in four out of six industry sectors during the time frame investigated. Further results suggest that the overall impact of inventory reductions to the financial performance of companies is only of a small degree.

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Notes

  1. For some applications, the inventory to sales ratio is multiplied by 12 months or 365 days providing a measure of inventory coverage for a given value of sales. A further advantage of the inventory to sales ratio is that it corrects for sector size. Finally, the analysis is only to a minor degree affected by changes in price levels provided that prices of outputs vary according to the prices of inputs.

  2. Hence, there is a deviation from total inventories reported in the balance sheets, which may also contain payments in advance to suppliers, for example.

  3. In order to save space, the intercept parameter estimates obtained are not reported. Only the trend coefficients (slope), together with t-statistics (P value) and coefficients of determination (R2) are reported.

  4. 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., soft drinks or wearing apparel), whereas in the chemical industry work-in-process and finished goods inventories are usually combined into one balance sheet item due to production conditions.

  5. Therefore, we conducted an exhaustive search using “WISO”, the largest German language database for business and economics research articles, and LexisNexis for finding German press articles (newspapers, periodicals, and trade publications). We constrained our search to “JIT”. The first German article on JIT accounted for in the WISO 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 distribution of press articles according to the LexisNexis database starts with the early 1990s and reached a local maximum in 1999. After a short decline, the number of press articles on JIT took off again until reaching their all time high in 2006.

  6. Most likely affected were firms such as Dürr, Koenig and Bauer, KUKA, Linde MAN, Siemens, and Triumph Adler. Therefore, their WP inventory to sales performance should be interpreted carefully.

  7. Furthermore, we found no significant link between the size of a firm (e.g., measured in sales) and its inventory performance.

  8. To demonstrate the potential extent of this cannibalization effect, we take a closer look at the ROI from 1994 of Sektkellerei Schloss Wachenheim AG: in this particular year, the firm has an ROI of −90.77%. Because the total inventories represent 62.89% of the total capital employed the results of the sensitivity analysis have such a deep impact that all positive effects of the remaining 12 years are eaten up. As a result, there is no recognizable increase in the mean 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 and the sensitivity analysis in the case of 50% reduction of total inventories would lift the mean ROI up to 18.87%. In this specific case, the effect can reduced to a minimum using the median.

References

  1. Bairam EI (1996) Disaggregate inventory-sales ratios over time: the case of US companies and corporations, 1976–92. Appl Econ Lett 3:167–169

    Article  Google Scholar 

  2. Balakrishnan R, Linsmeier TJ, Venkatachalam M (1996) Financial benefits from JIT adoption: effects of customer concentration and cost structure. Account Rev 71:183–205

    Google Scholar 

  3. Biggart TB, Gargeya VB (2002) Impact of JIT on inventory to sales ratios. Ind Manag Data Syst 102:197–202

    Article  Google Scholar 

  4. Blinder AS, Maccini LJ (1991) Taking stock: a critical assessment of recent research on inventories. J Econ Perspect 5:73–96

    Article  Google Scholar 

  5. Canjels E, Watson MW (1997) Estimating deterministic trends in the presence of serially correlated errors. Rev Econ Stat 79:184–200

    Article  Google Scholar 

  6. Cannon AR (2008) Inventory improvement and financial performance. Int J Prod Econ 115:581–593

    Article  Google Scholar 

  7. Chen H, Frank MZ, Wu OQ (2005) What actually happened to the Inventories of American Companies Between 1981 and 2000. Manag Sci 51:1015–1031

    Article  Google Scholar 

  8. De Haan J, Yamamoto M (1999) Zero inventory management: facts or fiction? Lessons from Japan. Int J Prod Econ 59:65–75

    Article  Google Scholar 

  9. Durbin J, Watson GS (1950) Testing for serial correlation in least squares regression. I. Biometrika 37:409–428

    MATH  MathSciNet  Google Scholar 

  10. Durbin J, Watson GS (1951) Testing for serial correlation in least squares regression. II. Biometrika 38:159–178

    Article  MATH  MathSciNet  Google Scholar 

  11. Greene WH (2008) Econometric analysis, 6th edn

  12. Hayes RH (1981a) Why Japanese factories work. Harvard Bus Rev 59:56–66

    Google Scholar 

  13. Hirsch AA (1996) Has inventory management in the US become more efficient and flexible? A macroeconomic perspective. Int J Prod Econ 45:37–46

    Article  Google Scholar 

  14. Huson M, Nanda D (1995) The impact of just-in-time manufacturing on firm performance in the US. J Operat Manag 12:297–310

    Article  Google Scholar 

  15. Kobayashi M (1985) Comparison of efficiencies of several estimators for linear regressions with autocorrelated errors. J Am Stat Assoc 80:951–953

    Article  Google Scholar 

  16. Lieberman MB, Demeester L (1999) Inventory reduction and productivity growth: linkages in the Japanese automotive industry. Manag Sci 45:466–485

    Article  MATH  Google Scholar 

  17. Little JD (1961) A proof of the Queuing formula: L = λW. Operat Res 9:383–387

    Article  MATH  MathSciNet  Google Scholar 

  18. Monden Y (1981a) What makes the Toyota production system really tick? Ind Eng 13:36–46

    Google Scholar 

  19. Monden Y (1981b) Adaptable Kanban system helps Toyota maintain just-in-time production. Ind Eng 13:29–46

    Google Scholar 

  20. Nahmias S (2009) Production and operations analysis, 6th edn

  21. Nakane J, Hall RW (1983) Management specs for stockless production. Harvard Bus Rev 61:84–91

    Google Scholar 

  22. Park RB, Mitchell BM (1980) Estimating the autocorrelated error model with trended data. J Econ 13:185–201

    Article  MATH  Google Scholar 

  23. Prais SJ, Winsten CB (1954) Trend estimation and serial correlation, Cowles Commission Discussion Paper Statistics, no. 383

  24. Savin NE, White KJ (1977) The Durbin–Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica 45:1989–1996

    Article  MATH  Google Scholar 

  25. Schonberger RJ (1982) Japanese manufacturing techniques

  26. Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and scheduling, 3rd edn

  27. Swamidass PM (2007) The effect of TPS on US manufacturing during 1981–1998: inventory increased or decreased as a function of plant performance. Int J Prod Res 45:3763–3778

    Article  MATH  Google Scholar 

  28. Tribó JA (2009) Firms’ stock market flotation: effects on inventory policy. Int J Prod Econ 118:10–18

    Article  Google Scholar 

  29. Vollmann TE, Berry WL, Whybark DC, Jacobs FR (2005) Manufacturing planning and control for supply chain management, 5th edn

  30. Wooldridge JM (2006) Introductory econometrics: a modern approach, 3rd edn

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Obermaier, R., Donhauser, A. Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005. Logist. Res. 1, 95–111 (2009). https://doi.org/10.1007/s12159-009-0014-9

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