Skip to main content

Proposition and Evaluation of an Integrative Indicator Implementation for Logistics Management

  • Conference paper
  • First Online:
Innovative Intelligent Industrial Production and Logistics (IN4PL 2023)

Abstract

The growing operation complexity has led companies to adopt many indicators, making complex the evaluation of the overall performance of logistics systems. Among several studies about logistics management, this is the first one to determine an overall logistics performance indicator and evaluate its impact for logistics management. The proposed methodology encompasses four main phases: the first one defines the management scope and the indicator set, the second applies statistical tools reaching an initial model for indicators aggregation, the third one determines the global performance model, and the last phase implements the integrative indicator with its scale. The methodology is implemented in an outbound process from a Brazilian company with eight logistics KPI’s. Principal Component Analysis (PCA) is used to stablish the relationships among indicators and an optimization tool is applied to define the integrative indicator scale. The global performance (GP) provided by the integrative indicator has demonstrated that even if important indicators have not reached their goal, it is possible to attain a good global performance with improvements in other areas. Thus, the framework demonstrates to be a useful solution for logistics performance management.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zinn, W., Goldsby, T.J.: Global supply chains: globalization research in a changing world. J. Bus. Logist. 41, 4–5 (2020). https://doi.org/10.1111/jbl.12241

    Article  Google Scholar 

  2. Dev, N.K., Shankar, R., Gupta, R., Dong, J.: Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture. Comput. Ind. Eng. 128, 1076–1087 (2019). https://doi.org/10.1016/j.cie.2018.04.012

    Article  Google Scholar 

  3. Javaid, M., Haleem, A., Suman, R.: Digital twin applications toward industry 4.0: a review. Cogn. Robot. 3, 71–92 (2023). https://doi.org/10.1016/j.cogr.2023.04.003

    Article  Google Scholar 

  4. Winkelhaus, S., Grosse, E.H.: Logistics 4.0: a systematic review towards a new logistics system. Int. J. Prod. Res. 58, 18–43 (2020). https://doi.org/10.1080/00207543.2019.1612964

    Article  Google Scholar 

  5. Johnson, A., McGinnis, L.: Performance measurement in the warehousing industry. IIE Trans. 43, 220–230 (2010). https://doi.org/10.1080/0740817X.2010.491497

    Article  Google Scholar 

  6. Clivillé, V., Berrah, L., Mauris, G.: Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method. Int. J. Prod. Econ. 105, 171–189 (2007). https://doi.org/10.1016/j.ijpe.2006.03.002

    Article  Google Scholar 

  7. Lohman, C., Fortuin, L., Wouters, M.: Designing a performance measurement system: a case study. Eur. J. Oper. Res. 156, 267–286 (2004). https://doi.org/10.1016/s0377-2217(02)00918-9

    Article  Google Scholar 

  8. Vascetta, M., Kauppila, P., Furman, E.: Aggregate indicators in coastal policy making: potentials of the trophic index TRIX for sustainable considerations of eutrophication. Sustain. Dev. 16, 282–289 (2008). https://doi.org/10.1002/sd.379

    Article  Google Scholar 

  9. Lauras, M., Marques, G., Gourc, D.: Towards a multi-dimensional project performance measurement system. Decis. Support. Syst. 48, 342–353 (2010). https://doi.org/10.1016/j.dss.2009.09.002

    Article  Google Scholar 

  10. Irfani, D.P., Wibisono, D., Basri, M.H.: Logistics performance measurement framework for companies with multiple roles. Meas. Bus. Excell. 23, 93–109 (2019). https://doi.org/10.1108/MBE-11-2018-0091

    Article  Google Scholar 

  11. Franceschini, F., Galetto, M., Maisano, D., Viticchi, L.: The condition of uniqueness in manufacturing process representation by performance/quality indicators. Qual. Reliab. Eng. Int. 22, 567–580 (2006). https://doi.org/10.1002/qre.762

    Article  Google Scholar 

  12. Fattahi, F., Nookabadi, A.S., Kadivar, M.: A model for measuring the performance of the meat supply chain. Br. Food J. 115, 1090–1111 (2013). https://doi.org/10.1108/BFJ-09-2011-0217

    Article  Google Scholar 

  13. Bai, C., Sarkis, J.: Supply-chain performance-measurement system management using neighbourhood rough sets. Int. J. Prod. Res. 50, 2484–2500 (2012). https://doi.org/10.1080/00207543.2011.581010

    Article  Google Scholar 

  14. Götz, L.N., Staudt, F.H., de Borba, J.L.G., Bouzon, M.: A framework for logistics performance indicators selection and targets definition: a civil construction enterprise case. Production 33, 1–18 (2023). https://doi.org/10.1590/0103-6513.20220075

    Article  Google Scholar 

  15. Berrah, L., Clivillé, V.: Towards an aggregation performance measurement system model in a supply chain context. Comput. Ind. 58, 709–719 (2007). https://doi.org/10.1016/j.compind.2007.05.012

    Article  Google Scholar 

  16. Swanson, D., Goel, L., Francisco, K., Stock, J.: An analysis of supply chain management research by topic. Supply Chain Manag. 23, 100–116 (2018). https://doi.org/10.1108/SCM-05-2017-0166

    Article  Google Scholar 

  17. Banomyong, R., Supatn, N.: Selecting logistics providers in Thailand: a shippers’ perspective. Eur. J. Mark. 45, 419–437 (2011). https://doi.org/10.1108/03090561111107258

    Article  Google Scholar 

  18. Gunasekaran, A., Kobu, B.: Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int. J. Prod. Res. 45, 2819–2840 (2007). https://doi.org/10.1080/00207540600806513

    Article  Google Scholar 

  19. Torabizadeh, M., Yusof, N.M., Ma’aram, A., Shaharoun, A.M.: Identifying sustainable warehouse management system indicators and proposing new weighting method. J. Clean. Prod. 248, 119190 (2020). https://doi.org/10.1016/j.jclepro.2019.119190

    Article  Google Scholar 

  20. Yazdani, M., Pamucar, D., Chatterjee, P., Chakraborty, S.: Development of a decision support framework for sustainable freight transport system evaluation using rough numbers. Int. J. Prod. Res. 58, 4325–4351 (2020). https://doi.org/10.1080/00207543.2019.1651945

    Article  Google Scholar 

  21. Ülgen, V.S., Forslund, H.: Logistics performance management in textiles supply chains: best-practice and barriers. Int. J. Product. Perform. Manag. 64, 52–75 (2015). https://doi.org/10.1108/IJPPM-01-2013-0019

    Article  Google Scholar 

  22. Ravelomanantsoa, M.S., Ducq, Y., Vallespir, B.: A state of the art and comparison of approaches for performance measurement systems definition and design. Int. J. Prod. Res. 57, 5026–5046 (2018). https://doi.org/10.1080/00207543.2018.1506178

    Article  Google Scholar 

  23. Barbosa, D.H., Musetti, M.A.: The use of performance measurement system in logistics change process: proposal of a guide. Int. J. Product. Perform. Manag. 60, 339–359 (2011). https://doi.org/10.1108/17410401111123526

    Article  Google Scholar 

  24. Dehghanian, F., Mansoor, S., Nazari, M.: A framework for integrated assessment of sustainable supply chain management. IEEE Int. Conf. Ind. Eng. Eng. Manag. 279–283 (2011). https://doi.org/10.1109/IEEM.2011.6117922

  25. Kucukaltan, B., Irani, Z., Aktas, E.: A decision support model for identification and prioritization of key performance indicators in the logistics industry. Comput. Human Behav. 65, 346–358 (2016). https://doi.org/10.1016/j.chb.2016.08.045

    Article  Google Scholar 

  26. Makris, D., Hansen, Z.N.L., Khan, O.: Adapting to supply chain 4.0: an explorative study of multinational companies. Supply Chain Forum 20, 116–131 (2019). https://doi.org/10.1080/16258312.2019.1577114

    Article  Google Scholar 

  27. Orozco-Crespo, E., Sablón-Cossío, N., Taboada-Rodríguez, C.M., Staudt, F.H.: Textile sector supply chain: comprehensive indicator for performance evaluation. Rev. Venez. Gerenc. 26, 574–591 (2021). https://doi.org/10.52080/rvgluz.26.e6.35

    Article  Google Scholar 

  28. Irfani, D.P., Wibisono, D., Basri, M.H.: Design of a logistics performance management system based on the system dynamics model. Meas. Bus. Excell. 23, 269–291 (2019). https://doi.org/10.1108/MBE-01-2019-0008

    Article  Google Scholar 

  29. Gupta, A., Singh, R.K.: Developing a framework for evaluating sustainability index for logistics service providers: graph theory matrix approach. Int. J. Product. Perform. Manag. ahead-of-p (2020). https://doi.org/10.1108/IJPPM-12-2019-0593

  30. Rodriguez, R.R., Saiz, J.J.A., Bas, A.O.: Quantitative relationships between key performance indicators for supporting decision-making processes. Comput. Ind. 60, 104–113 (2009). https://doi.org/10.1016/j.compind.2008.09.002

    Article  Google Scholar 

  31. Brundage, M.P., Bernstein, W.Z., Morris, K.C., Horst, J.A.: Using graph-based visualizations to explore key performance indicator relationships for manufacturing production systems. Procedia CIRP. 61, 451–456 (2017). https://doi.org/10.1016/j.procir.2016.11.176

    Article  Google Scholar 

  32. Mitroff, I.I., Betz, F., Pondy, L.R., Sagasti, F.: On managing science in the systems age: two schemas for the study of science as a whole systems phenomenon. Interfaces (Providence) 4, 46–58 (1974). https://doi.org/10.1287/inte.4.3.46

    Article  Google Scholar 

  33. Gentle, J.E.: Matrix Algebra – Theory, Computations, and Applications in Statistics. Springer, New York, NY, USA (2007)

    Google Scholar 

  34. Wainer, J.: Principal Components Analysis. Available at: http://www.ic.unicamp.br/~wainer/cursos/1s2013/ml/Lecture18_PCA.pdf. pp. 1–18 (2010)

  35. Westfall, P.: Comparison of Principal Components, Canonical Correlation, and Partial Least Squares for the Job Salience/Job Satisfaction data analysis. http://courses.ttu.edu/isqs6348-westfall/images/6348/PCA_CCA_PLS.pdf. 1–2 (2007)

  36. Manly, B.F.J.: Principal component analysis. In: Multivariate Statistical Methods: a Primer, pp. 75–90. Chapman & Hall/ CRC, Boca Raton, Florida, USA (2004)

    Google Scholar 

  37. Katchova, A.: Principal Component Analysis and Factor Analysis. https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis. pp. 1–10 (2013)

  38. Albishri, D.Y., Sundarakani, B., Gomisek, B.: An empirical study of relationships between goal alignment, centralised decision-making, commitment to networking and supply chain effectiveness using structural equation modelling. Int. J. Logist. Res. Appl. 23, 390–415 (2020). https://doi.org/10.1080/13675567.2019.1700219

    Article  Google Scholar 

  39. PennState, E.C. of S.: Lesson 7.4 - Interpretation of the Principal Components. STAT 505 Available https://onlinecourses.science.psu.edu/stat505/node/54. (2015)

  40. Jung, H.W.: Investigating measurement scales and aggregation methods in SPICE assessment method. Inf. Softw. Technol. 55, 1450–1461 (2013). https://doi.org/10.1016/j.infsof.2013.02.004

    Article  Google Scholar 

  41. Staudt, F.H., Alpan, G., di Mascolo, M., Rodriguez, C.M.T.: A scale definition for an integrated performance indicator. In: ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings, pp. 607–616. Lyon – France (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francielly Hedler Staudt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Staudt, F.H. et al. (2023). Proposition and Evaluation of an Integrative Indicator Implementation for Logistics Management. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-49339-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49338-6

  • Online ISBN: 978-3-031-49339-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics