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Proposal of specific measures in the context of streamlining operating costs in a selected company

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Języki publikacji
EN
Abstrakty
EN
The aim of this research is to propose particular measures aimed at streamlining costs in terms of operations in a selected company. A specific proposal is based on the concept related to using services of the transport databank. As at least two out of five major carriers of the investigated company resell shipments to other carriers on such portals, the company could reduce the transport costs by entering shipments into the transport databank on its own. One out of three selected providers will be chosen using multi-criteria evaluation methods, specifically, the Analytic Hierarchy Process (hereinafter referred to as AHP) and the Base-criterion method. Determination of the weights of criteria will be carried out using the Saaty method of quantitative pair wise comparison and the Fuller pair wise comparison method. In this paper, the presented methods are applied for the example of a specific company specialized in the manufacture of metal storage racks and steel structures. In the Czech Republic, the company employs 65 employees in production, storage and administration. Based on the analytical evaluation of the current situation of the company, relevant measures will be proposed with a required effect on the effectiveness of such an enterprise.
Czasopismo
Rocznik
Strony
121--134
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
  • Institute of Technology and Business in České Budějovice, Faculty of Technology, Okružní 517/10, 370 01 České Budějovice, Czech Republic
  • Institute of Technology and Business in České Budějovice, Faculty of Technology, Okružní 517/10, 370 01 České Budějovice, Czech Republic
  • Lublin University of Technology, Faculty of Mechanical Engineering, Nadbystrzycka 36, 20-618 Lublin, Poland
Bibliografia
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  • 2. Yu, B. & Cai, M. & Li, Q. A λ-rough set model and its applications with TOPSIS method to decision making. Knowledge-Based Systems. 2019. Vol. 165. P. 420-431. DOI: 10.1016/j.knosys.2018.12.013.
  • 3. Venkatesh, V.G. & Zhang, A. & Deakins, E. & Luthra, S. & Mangla, S. A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research. 2019. Vol. 283. No. 1-2. P. 1517-1550. DOI: 10.1007/s10479-018-2981-1.
  • 4. Agarski, B. & Hadzistevic, M. & Budak, I. & Moraca, S. & Vukelic, D. Comparison of approaches to weighting of multiple criteria for selecting equipment to optimize performance and safety. International Journal of Occupational Safety and Ergonomics. 2019. Vol. 25. No. 2. P. 228-240. DOI: 10.1080/10803548.2017.1341126.
  • 5. Yu, C. & Shao, Y. & Wang, K. & Zhang, L. A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Systems with Applications. 2019. Vol. 121. P. 1-17. DOI: 10.1016/j.eswa.2018.12.010.
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  • 9. Chen, X. & Ding, Y. & Cory, C.A. & Hu, Y. & Wu, K. & Feng, X. A decision support model for subcontractor selection using a hybrid approach of QFD and AHP-improved grey correlation analysis. Engineering, Construction and Architectural Management. 2020. DOI: 10.1108/ECAM-12-2019-0715.
  • 10. Ikram, M. & Sroufe, R. & Zhang, Q. Prioritizing and overcoming barriers to integrated management system (IMS) implementation using AHP and G-TOPSIS. Journal of Cleaner Production. 2020. Vol. 254. No. 120121.
  • 11. Roy, P.K. & Shaw, K. A credit scoring model for SMEs using AHP and TOPSIS. International Journal of Finance and Economics. 2021. DOI:10.1002/ijfe.2425.
  • 12. Yadav, S. & Garg, D. & Luthra, S. Selection of third-party logistics services for internet of thingsbased agriculture supply chain management. International Journal of Logistics Systems and Management. 2020. Vol. 35. No. 2. P. 204-230. DOI: 10.1504/IJLSM.2020.104780.
  • 13. Phruksaphanrat, B. & Borisutiyanee, M. Ranking barriers and solutions of supply chain information technology adoption in Thai industrial sector by AHP and fuzzy TOPSIS. International Journal of Integrated Supply Management. 2019. Vol. 12. No. 3. P. 230-258. DOI:10.1504/IJISM.2019.099719.
  • 14. Droździel, P. & Wińska, M. & Madleňák, R. & Szumski, P. Optimization of the position of the local distribution centre of the regional post logistics network. Transport Problems. 2017. Vol. 12. No. 3. P. 43-50. DOI: 10.20858/tp.2017.12.3.4.
  • 15. Dinçer, S.E. Multi-criteria analysis of economic activity for European Union member states and candidate countries: TOPSIS and WSA applications. European Journal of Social Sciences. 2011. Vol. 21. No. 4. P. 563-572.
  • 16. Houska, M. Reply to the paper 'Multi-criteria analysis of economic activity for European Union member states and candidate countries: TOPSIS and WSA applications' by S.E. Dincer. European Journal of Social Sciences. 2012. Vol. 30. No. 2. P. 290-295.
  • 17. Duleba, S. & Moslem, S. Examining Pareto optimality in analytic hierarchy process on real Data: An application in public transport service development. Expert Systems with Applications. 2019. Vol. 116. P. 21-30. DOI: 10.1016/j.eswa.2018.08.049.
  • 18. Kauf, S. & Tłuczak, A. Allocation of logistic risk-investment in public-private-partnership – Use of fuzzy TOPSIS method. MATEC Web of Conferences. 2019. Vol. 184. DOI: 10.1051/matecconf/201818404025.
  • 19. Adetunji, O. & Bischoff, J. & Willy, C.J. Managing system obsolescence via multicriteria decision making. Systems Engineering. 2018. Vol. 21. No. 4. P. 307-321. DOI: 10.1002/sys.21436.
  • 20. Čarný, Š. & Šperka, A. & Zitrický, V. Multi-criteria Evaluation of Railway Transport Using Evaluation Method. LOGI – Scientific Journal on Transport and Logistics. 2020. Vol. 11. No. 2. P. 88-99. DOI: 10.2478/logi-2020-0018.
  • 21. Pelegrina, G.D. & Duarte, L.T. & Romano, J.M.T. Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems. Expert Systems with Applications. 2016. Vol. 122. P. 262-280. DOI: 10.1016/j.eswa.2019.01.008.
  • 22. Antuchevičiene, J. & Zavadskas, E.K. & Zakarevičius, A. Multiple criteria construction management decisions considering relations between criteria. Technological and Economic Development of Economy. 2010. Vol. 16. No. 1. P. 109-125. DOI: 10.3846/tede.2010.07.
  • 23. Chovancová, M. & Klapita, V. Modeling the supply process using the application of selected methods of operational analysis. Open Engineering. 2017. Vol. 7. No. 1. P. 50-54. DOI: 10.1515/eng-2017-0009.
  • 24. Behzadian, M. & Khanmohammadi Otaghsara, S. & Yazdani, M. & Ignatius, J. A state-of the-art. survey of TOPSIS applications. Expert Systems with Applications. 2012. Vol. 39. No. 17. P. 13051-13069. DOI: 10.1016/j.eswa.2012.05.056.
  • 25. Ejem, E.A. & Uka, C.M. & Dike, D.N. & Ikeogu, C.C. & Igboanusi, C.C. & Chukwu, O.E. Evaluation and selection of Nigerian third-party logistics service providers using multi-criteria decision models. LOGI – Scientific Journal on Transport and Logistics. 2021. Vol. 12. No. 1. P. 135-146. DOI: 10.2478/logi-2021-0013.
  • 26. Simić, V. & Lazarević, D. & Dobrodolac, M. Picture fuzzy WASPAS method for selecting last-mile delivery mode: A case study of Belgrade. European Transport Research Review. 2021. Vol. 13. No. 1. DOI: 10.1186/s12544-021-00501-6.
  • 27. Cables, E. & García-Cascales, M.S. & Lamata, M.T. The LTOPSIS: An alternative to TOPSIS decision-making approach for linguistic variables. Expert Systems with Applications. 2012. Vol. 39. No. 2. P. 2119-2126. DOI:10.1016/j.eswa.2011.07.119.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-def04362-2d0f-471f-8d54-b3a99fe4d5c6
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