Skip to main content

Forming the Complex Model to Rate Transportation Indicators in Supply Chains

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 117))

Abstract

It has been proved by the latest research on key performance indicators (KPIs) of transportation services that their successful implementation into practice is possible only if there is a thorough database of indicators and the methodology of their calculation. To reach these goals, it is necessary to classify the indicators within the framework of the system which includes the two levels: the basic (the first) and the specific (the second) KPI. This division allows to form the complex of models to calculate the basic indicators, which characterize performance (e.g. performance per hour), time parameters, expenses, reliability, etc. The article provides the analysis of papers on the methods of transportation efficiency rating in supply chains and the ways of their development to increase the efficiency of transportation; the new approach to obtain analytic dependencies to calculate KPI of transportation on the basis of the integral (factorial) method of economic analysis; the examples of calculations of some KPIs of transportation. The suggested KPI models can be used to create programs aimed at the digitalization of transportation operations in supply chains.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Yatskiv (Jackiva), I., Nathanail, E., Savrasovs, M., Adamos, G., Mitropoulos, L.: Assessing knowledge level of stakeholders on transport interchange design and operation. Transport 33(3), 793–800 (2018). https://doi.org/10.3846/transport.2018.5400

  2. Dingil, A.E., Schweizer, J., Rupi, F., Stasiskiene, Z.: Transport indicator analysis and comparison of 151 urban areas, based on open source data. Eur. Transp. Res. Rev. 10, 58 (2018). https://doi.org/10.1186/s12544-018-0334-4

    Article  Google Scholar 

  3. Boone, T., Ganeshan, R., Jain, A., Sanders, N.R.: Forecasting sales in the supply chain: consumer analytics in the big data era. Int. J. Forecast. 35, 170–180 (2019). https://doi.org/10.1016/j.ijforecast.2018.09.003

    Article  Google Scholar 

  4. Heitz, A., Launay, P., Beziat, A.: Heterogeneity of logistics facilities: an issue for a better understanding and planning of the location of logistics facilities. Eur. Transp. Res. Rev. 11, 5 (2019). https://doi.org/10.1186/s12544-018-0341-5

    Article  Google Scholar 

  5. Tsami, M., Adamos, G., Nathanail, E., Budilovich (Budiloviča), E., Yatskiv (Jackiva), I., Magginas, V.: A decision tree approach for achieving high customer satisfaction at urban interchanges. Transp. Telecommun. 19(3), 194–202 (2018). https://doi.org/10.2478/ttj-2018-0016

  6. Kabashkin, I., Lučina, J.: Development of the model of decision support for alternative choice in the transportation transit system. Transp. Telecommun. 16(1), 61–72 (2015). https://doi.org/10.1515/ttj-2015-0007

    Article  Google Scholar 

  7. Bowersox, D.J., Closs, D.J., Cooper, M.B.: Supply Chain Logistics Management. McGraw-Hill Education, New York (2007)

    Google Scholar 

  8. Christopher, M.: Logistics and Supply Chain Management. FT Press, Upper Saddle River (2016)

    Google Scholar 

  9. Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning, and Operations. Pearson, London (2015)

    Google Scholar 

  10. Gattorna, Dj., Sergeev, V.I.: Supply Chain Management, 5th edn. Infra-M, Moscow (2008). (in Russian)

    Google Scholar 

  11. Grant, D., Lambert, D., Stock, J., Ellram, L.: Fundamentals of Logistics Management. McGraw-Hill Education, New York (2006)

    Google Scholar 

  12. Isoraite, M.: Analysis of transport performance indicators. Transport 20(3), 111–116 (2005)

    Article  Google Scholar 

  13. Leenders, M., Fearon, H.: Purchasing and Supply Management, 11th edn. Irwin, New York (1997)

    Google Scholar 

  14. Waters, D.: Logistics: An Introduction to Supply Chain Management. Palgrave Macmillan Ltd., London (2003)

    Google Scholar 

  15. APICS: Supply Chain Operations Reference Model, version 12 (2017). www.apics.org/apics-for-business/frameworks/scor12

  16. Bakanov, M., Sheremet, A.: Theory of Economic Analysis, 4th edn. Finance and Statistics, Moscow (2006). (in Russian)

    Google Scholar 

  17. Sheremet, A.: Theory of Economic Analysis. Infra-M, Moscow (2011). (in Russian)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Valery Lukinskiy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lukinskiy, V., Lukinskiy, V., Koroleva, E., Bazhina, D. (2020). Forming the Complex Model to Rate Transportation Indicators in Supply Chains. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2019. Lecture Notes in Networks and Systems, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-44610-9_25

Download citation

Publish with us

Policies and ethics