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Tytuł artykułu

Selection of an Optimal Network-Ranking Model to Achieve the Optimal Production Line Value Chain: a Case Study in the Textile Industry

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Opracowanie modelu rankingu sieciowego w celu stworzenia najlepszego łańcucha wielkości produkcji: studium przypadku w przemyśle tekstylnym
Języki publikacji
EN
Abstrakty
EN
Fulfilling needs and organisational resources with the least cost and highest quality is the main reason to achieve the optimal value chain. Application of most of the current techniques has merely been intended to choose the best scenario. However, industrial units need to build an ideal scenario as a value chain which focuses on intangible interstitial and hidden factors: good (good nature), bad (bad nature), fixed (obligatory nature) and free (not identifying their nature) and creates value. Therefore the model presented in this article answers this issue. First of all, we present a model based on the network approach of data envelopment analysis, then assess and rank the stages based on the scenarios for the stages forming the value chain, and finally the ideal decision unit is presented. For this reason, the general efficiency is designed with two natures; 1. input-centered (concentration on the costs) and 2. output-centered (concentration on the incomes).
PL
Głównym powodem tworzenia łańcucha wielkości jest zaspokojenie potrzeb i zasobów organizacyjnych przy jak najniższej cenie i najwyższej jakości. Zastosowanie większości obecnych technik miało jedynie na celu wybranie najlepszego scenariusza. Zakłady produkcyjne zmuszone są do budowania idealnego scenariusza, jako łańcucha wartości, który skupia się na wartościach niematerialnych i ukrytych: dobrych, złych, stałych (obowiązkowych) i wolnych (bez zidentyfikowanej natury). W pracy przedstawiono model oparty na wieloetapowej analizie danych.
Rocznik
Strony
93--99
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
Bibliografia
  • 1. Abramo G, Cicero T, and Andrea D’Angelo C. A field-standardized application of DEA to national-scale research assessment of universities. Journal of Informatics 2011; 5, 4: 618-628.
  • 2. Akther S, Fukuyama H and Weber W L. Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking. Omega 2013; 41, 1: 88–96.
  • 3. André F J, Herrero I and Riesgo L. A modified DEA model to estimate the importance of objectives with an application to agricultural economics. Omega 2010; 38, 5: 371-382.
  • 4. A. Jayant, Wadhwa S, Gupta P and Garg SK. Simulation modeling of outbound logistics of supply chain. International Journal of Industrial Engineering, 2012; 19(2): 90-100.
  • 5. Ayyaz Ahmad, Muhammad Ilyas Mazhar and Ian Howard. A framework for the adoption of rapid prototyping for SMEs: from strategic to operationa. International Journal of Industrial Engineering 2012; 19(3): 161-170.
  • 6. Banker R, Charnes A and Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 1984; 30, 9: 1078-1092.
  • 7. Barth W and Staat M. Restructuring the branch network of a bank: the dynamic perspective. International Journal of Business and Systems Research 2008; 2, 3: 272-284.
  • 8. Caballer-Tarazona M, Moya-Clemente I, Vivas-Consuelo D and Barrachina-Martínez I. A model to measure the efficiency of hospital performance. Mathematical and Computer Modelling 2010; 52, 7–8: 1095-1102.
  • 9. Chamodrakas I and Martakos D. A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks. Applied Soft Computing 2011; 11, 4: 3734-3743.
  • 10. Charnes A, Cooper WW and Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 23, 1: 429-444.
  • 11. Cook D, Zhu J, Bi G, and Yang F. Network DEA: additive efficiency decomposition. European Journal of Operational Research 2010; 207, 2: 1122-1129.
  • 12. Deville A. Branch banking network assessment using DEA: A benchmarking analysis—A note. Management Accounting Research 2009; 20, 4: 252-261.
  • 13. Farzipoor Saen R. Restricting weights in supplier selection decisions in the presence of dual-role factors. Applied Mathematical Modelling 2010; 34, 10: 2820-2830.
  • 14. Hatami-Marbini A, Saati S and Tavana M. An ideal-seeking fuzzy data envelopment analysis framework. Applied Soft Computing 2010; 10, 4: 1062-1070.2
  • 15. Jahanshahloo G R, Hosseinzadeh Lotfi F, Khanmohammadi M, Kazemimanesh M and Rezaie V, Ranking of units by positive ideal DMU with common weights. Expert Systems with Applications 2010; 37, 12: 7483-7488.
  • 16. Jahanshahloo G R, Hosseinzadeh Lotfi F, Rezaie V and Khanmohammadi M. Ranking DMUs by ideal points with interval data in DEA. Applied Mathematical Modelling 2011; 35, 1: 218–229.
  • 17. Kao C and Hwang SN. Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems 2010; 48, 1: 437-446.
  • 18. Liang F. Bayesian neural networks for nonlinear time series forecasting. Statistics and Computing 2005; 15(1): 13-29.
  • 19. Lewis H F, Lock K A and Sexton T R. Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901–2002. European Journal of Operational Research 2009; 197, 2: 731–740.
  • 20. Mirhedayatian S M, Azadi M and Farzipoor Saen R. A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics 2014: 147, Part B: 544-554.
  • 21. Nandy D. Efficiency study of Indian public sector banks - an application of data envelopment analysis and cluster analysis. International Journal of Business Performance Management 2012; 13, No. ¾: 312 – 329.
  • 22. Ramón N, Ruiz J L and Sirvent L. Common sets of weights as summaries of DEA profiles of weights: With an application to the ranking of professional tennis players. Expert Systems with Applications 2012; 39, 5: 4882-4889.
  • 23. Schaefer A, Burger A and Moormann J. Sophisticating business performance management for banks: using data envelopment analysis on business process level. International Journal of Business Performance Management 2012; 13, ¾: 227-243.
  • 24. Wang Y-M, Chin K-S and Luo Y. Cross-efficiency evaluation based on ideal and anti-ideal decision making units. Expert Systems with Applications 2011; 38, 8: 10312-10319.
  • 25. Wang N-S, Yi R-H and Wang W. Evaluating the performances of decision-making units based on interval efficiencies. Journal of Computational and Applied Mathematics 2008; 216, 2: 328–343.
  • 26. Yang C and Liu H-M. Managerial efficiency in Taiwan bank branches: A network DEA. Economic Modelling 2012; 29, 2: 450-461.
  • 27. Yu M M and Lin E T J. Efficiency and effectiveness in railway performance using a multiactivity network DEA model. Omega 2008, 36, 6: 1005-1017.3
  • 28. Zhao Y, Triantis K, Murray-Tuite, P and Edara P. Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach, Transportation Research Part E: Logistics and Transportation Review 2011, 47, 6: 1140–1159.
  • 29. Zhu J. Airlines performance via two-stage network DEA approach. Journal of CENTRUM Cathedra: The Business and Economics Research Journal 2011; 4, 2: 260-269.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-79dffea4-52a7-475c-81ab-28de3ba77c5d
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