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Multi criteria decision making model for logistics processes in particular enterprise

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Warianty tytułu
Konferencja
7th International Conference System Safety: Human - Technical Facility - Environment, CzOTO 2018 (7 ; 12-14.12.2018 ; Zakopane, Poland)
Języki publikacji
EN
Abstrakty
EN
Relevant indicators and measurement methods should be assessed in order to contribute to optimize management of an enterprise. Business performance can be measured by various indicators and various business results could be assessed. Analyses of value chains should be focused on specification of so called bottle necks which mention those activities that disable to increase business margin. At the same time, these analyses show the inefficiency caused by oversized of some activities regarding to lower level of assurance or safety and lower performance of other business activities. Importance of multi criteria decision-making methods for evaluation of alternatives doesn´t lie in definite increasing of results objectivity although it should lead to that. Priority of this method lies mainly in simplification of manager´s decision making. It allows managers to arrange alternatives according to extensive file of criteria, it describes particular steps of solution and its logical sequence, this methodology also requires from managers to express their understanding of various criteria importance. All this process of solution is transparent, repeatable and there are evident starting assumptions and also how these assumptions, situations, criteria and incidents affect reached results.
Wydawca
Rocznik
Strony
522--531
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Technical University in Zvolen, Slovakia
Bibliografia
  • [1] Aláč, P., 2005. Viackriteriálne rozhodovanie v logistickom reťazci drevárskeho podniku. PhD thesis. TU vo Zvolene, Drevárska fakulta, 137 p.
  • [2] Balmat, J., Lafont, F., Maifret, R., Pessel, N., 2011. A decision-making system to maritime risk assessment. Ocean Engineering, 38(1), 171-176.
  • [3] Canbolat, Y., Chelst, K., Garg, N., 2007. Combining decision tree and MAUT for selecting a country for a global manufacturing facility. Omega, 35(3), 312-325.
  • [4] Chen, Y., Okudan, G., Riley, D., 2010. Decision support for construction method selection in concrete buildings: Prefabrication adoption and optimization. Automation in Construction, 19(6), 665-675.
  • [5] Fishburn, P., 1967. Conjoint measurement in utility theory with incomplete product sets. Journal of Mathematical Psychology, 4(1), 104-119.
  • [6] Fitzgerald, L., Johnston, R., Brignall, T. J., Silvestro, R., Voss, C., 1991. Performance measurement in service businesses. Chartered Institute of Management Accountants, London, UK.
  • [7] Flint, D., J., Larsson, E., Gammelgaard, B., Mentzer, J.T., 2005. Logistic Innovation: A customer value-oriented social process. Journal of Business Logistics, 26(1), 113–147, DOI: 10.1002/j.2158-1592.2005.tb00196.x, ISSN 0735-3766.
  • [8] Gibson, B.J., Mentzer, J.T., Cook, R.L., 2005. Supply Chain Management: The Pursuit of a Consensus Definition. Journal of Business Logistics, 26, 17–25, DOI: 10.1002/j.2158-1592.2005.tb00203.x.
  • [9] Gomez-Limon, J., Arriaza, M., Riesgo, L., 2003. An MCDM analysis of agricultural risk aversion. European Journal of Operational Research, 151(3), 569-585.
  • [10] Jeetendra, V. A., Krishnaiah Chetty, O. V., Prashanth Reddy, J., 2000. Petri nets for project management and resource levelling. The International Journal of Advanced Manufacturing Technology, 16(7), 516-520.
  • [11] Kahneman, D., 2012. Myšlení rychlé a pomalé. Jan Melvil publishing Brno, s.r.o., ISBN 978-80-87270-42-4, 542 p.
  • [12] Kanji, G.K., 2002. Performance measurement system. Total Quality Management, 13(5), 715–728.
  • [13] Kaplan, R., S., Norton, D.P, 1996. The balanced scorecard: Translating strategy into action. Harvard Business School Press, Boston, MA.
  • [14] Keegan, D.P., Eiler, R.G., Jones, C.R., 1989. Are your performance measures obsolete? Management Accounting, 70(12), 45–50.
  • [15] Keeney, R., 1977. The art of assessing multiattribute utility functions. Organizational Behavior and Human Performance, 19(2), 267-310.
  • [16] Kennerley, M., Neely, A. D., 2000. Performance measurement framework -A review. In Performance measurement-Past, present and future. Proceedings of the 2nd international conference on performance measurement. Cambridge, UK, 19–21 July.
  • [17] Khadam, I., Kaluarachchi, J., 2003. Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water. Environmental Impact Assessment Review, 23(6), 683-721.
  • [18] Konidari, P., Mavrakis, D., 2007. A multi-criteria evaluation method for climate change mitigation policy instruments. Energy Policy, 35(12), 6235-6257.
  • [19] Lakehal, A., Tachi, F., 2018. Probabilistic Assessment of Road Risks for Improving Logistics Processes. MATEC Web of Conferences 183, 01003, DOI: 10.1051/matecconf/201818301003.
  • [20] Langevin, A., Riopel, D., 2005. Logistics Systems – Design and Optimization. Springer, ISBN 0-387-24971-0, 387.
  • [21] Liu, R., Kumara A., Van der Aalst, W., 2007. A formal modelling approach for supply chain event management. Decision Support Systems 43, 761-778.
  • [22] Loken, E., 2007. Use of multi-criteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
  • [23] MacBryde, J., Radnor, Z., Shepherd, C., Günter, H., 2006. Measuring supply chain performance: current research and future directions. International Journal of Productivity and Performance Management, 55(3/4), 242-258, ISSN: 1741-0401.
  • [24] Neely, A., 2002. Business performance measurement. Cambridge University Press, Cambridge, MA.
  • [25] Saaty, T., 1980. The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill, New York.
  • [26] Song, C., Hong, Z., 2008. Time scorecard: an integrative performance measurement framework for time-based companies. In 4th International conference on wireless communications, networking and mobile computing (pp. 1–5). Dalian, China, 12–14, October.
  • [27] Zadeh, L., 1965. Fuzzy sets. Information and Control, 8(3), 338-353.
  • [28] Zhang, X., Lu, Q., Wu, T., 2011. Petri-net based applications for supply chain management: an overview. International. Journal of Production Research, 49(13), 3939-3961.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6e281a72-0ffe-4d01-b68b-ab130c6d2fa0
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