KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya Üniversitesi İşletme Enstitüsü Dergisi / 2025 / Cilt: 7 Sayı: 2

Karar Verme Maliyetlerinin Optimizasyonu

Optimization of Decision-Making Costs

Murat Anbarcı(Sultanbeyli Belediyesi)0000-0003-0766-3088
Sayfa
234–255
Yayın Tarihi
2025-12-25
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Teorik bir vaka analizi aracılığıyla test edilen model, karar alma sürecinin erken aşamalarında yapılan koruyucu ve uygulayıcı yatırımların, toplam karar maliyetini düşürdüğünü ve karar kalitesini artırdığını göstermektedir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışma, karar verme süreçlerinin genellikle göz ardı edilen ancak stratejik öneme sahip bir boyutu olan karar verme maliyetleri üzerine odaklanmaktadır.
📊
Temel Bulgular & Veri:Model, karar verici tercihlerini ve stratejik öncelikleri gözeterek maliyet sapmalarını minimize etmeyi amaçlamaktadır.
💡
Sonuç & Katkı:Teorik bir vaka analizi aracılığıyla test edilen model, karar alma sürecinin erken aşamalarında yapılan koruyucu ve uygulayıcı yatırımların, toplam karar maliyetini düşürdüğünü ve karar kalitesini artırdığını göstermektedir.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Karar Verme Maliyetleri · Maliyet Optimizasyonu · Çok Amaçlı Hedef Programlama

Özet

Bu çalışma, karar verme süreçlerinin genellikle göz ardı edilen ancak stratejik öneme sahip bir boyutu olan karar verme maliyetleri üzerine odaklanmaktadır. Geleneksel kalite maliyeti yaklaşımları üretim ve operasyonel süreçlere yoğunlaşırken, karar alma sürecinin kendisinin neden olduğu doğrudan ve dolaylı maliyetler çoğu zaman sistematik biçimde ele alınmamaktadır. Bu boşluğu gidermek amacıyla çalışma, karar verme yaşam döngüsü boyunca ortaya çıkan Üretim, Uygulama, Başarısızlık, Koruma ve Müzakere maliyetleri kategorilerinde tanımlamaktadır. Söz konusu maliyetlerin dinamik ve karşılıklı bağımlı doğası nedeniyle, karar vericilerin bu unsurlar arasında denge kurabilmesi için Çok Amaçlı Hedef Programlama (ÇAHP) temelli bir optimizasyon modeli kullanılmıştır. Model, karar verici tercihlerini ve stratejik öncelikleri gözeterek maliyet sapmalarını minimize etmeyi amaçlamaktadır. Teorik bir vaka analizi aracılığıyla test edilen model, karar alma sürecinin erken aşamalarında yapılan koruyucu ve uygulayıcı yatırımların, toplam karar maliyetini düşürdüğünü ve karar kalitesini artırdığını göstermektedir. Bu kapsamda çalışma, karar verme süreçlerinin maliyetlerini ölçülebilir, yönetilebilir ve optimize edilebilir bir yapıya kavuşturarak literatüre yenilikçi bir katkı sunmaktadır.

Abstract

This study focuses on decision-making costs, a dimension of organizational processes that is often overlooked despite its strategic importance. While traditional quality cost approaches tend to concentrate on production and operational activities, they rarely account for the direct and indirect costs arising from the decision-making process itself in a systematic manner. To address this gap, the study identifies and categorizes decision-related costs across the decision-making lifecycle into five interrelated domains: Production, Implementation, Failure, Protection, and Negotiation Costs. Given the dynamic and interdependent nature of these cost components, a Multi-Objective Goal Programming (MOGP) model is used to support decision-makers in balancing trade-offs among them. The model aims to minimize cost deviations by incorporating decision-maker preferences and strategic priorities into the optimization process. Validated through a theoretical case study, the findings indicate that early-stage investments in protective and implementation activities significantly reduce total decision-making costs while improving the overall quality of decisions. Accordingly, this study makes a novel contribution to the literature by conceptualizing decision-making costs as quantifiable, manageable, and optimizable elements, thereby offering a new perspective for enhancing both decision efficiency and strategic resource allocation in organizations.

🔖

Bu Makaleye Atıf Yap

Anbarcı, M. (2025). Karar Verme Maliyetlerinin Optimizasyonu. Sakarya Üniversitesi İşletme Enstitüsü Dergisi, Cilt: 7 Sayı: 2, 234–255. https://doi.org/10.47542/sauied.1766844

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (72)

  1. Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. In P. Aghion & S. N. Durlauf (Eds.), Handbook of economic growth (Vol. 1A, pp. 385–472). Elsevier.
  2. Acquisti, A., Taylor, C. R., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442–492. https://doi.org/10.1257/jel.54.2.442
  3. Anderson, S. W., & Young, S. M. (1999). The impact of contextual and process factors on the evaluation of activity-based costing systems. Accounting, Organizations and Society, 24(7), 525–559. https://doi.org/10.1016/S0361-3682(99)00018-5
  4. Anthony, R. N., & Govindarajan, V. (2007). Management control systems (12th ed.). McGraw-Hill Education.
  5. Argyris, C., & Schön, D. A. (1978). Organizational learning: A theory of action perspective. Reading, MA: Addison-Wesley.
  6. Badri, M. A. (1999). Combining the analytic hierarchy process and goal programming for global facility location–allocation problem. International Journal of Production Economics, 62(3), 237–248. https://doi.org/10.1016/S0925-5273(98)00249-7
  7. Barberis, N. (2013). Thirty years of prospect theory in economics: A review and assessment. Journal of Economic Perspectives, 27(1), 173–196.
  8. Barzel, Y. (1989). Economic analysis of property rights. Cambridge University Press.
  9. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  10. Buchanan, J. M., & Tullock, G. (1962). The calculus of consent: Logical foundations of constitutional democracy. University of Michigan Press.
  11. Chang, D. Y. (2006). Application of goal programming for capital budgeting: A case study in the auto parts industry. International Journal of Production Economics, 100(1), 254–261.
  12. Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138–151. https://doi.org/10.1287/mnsc.1.2.138
  13. Cinelli, M., Kadziński, M., Gonzalez, M., & Słowiński, R. (2021). How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega, 96, 102261. https://doi.org/10.1016/j.omega.2020.102261
  14. Clemen, R. T., & Reilly, T. (2013). Making hard decisions with decisiontools (3rd ed.). Cengage Learning.
  15. Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405. https://doi.org/10.1111/j.1468-0335.1937.tb00002.x
  16. Crosby, P. B. (1979). Quality is free: The art of making quality certain. New York: McGraw-Hill.
  17. de Soto, H. (2000). The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. Basic Books.
  18. Deloitte. (2024). Optimizing the balance between performance and cost. https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/global-supply-chain-resilience- amid-disruptions.html
  19. Deming, W. E. (1986). Out of the crisis. MIT Press.
  20. Drucker, P. F. (1999). Management challenges for the 21st century. HarperBusiness.
  21. Gaissmaier, W., & Schooler, L. J. (2008). The smart potential behind probability matching. Cognition, 109(3), 416–422. https://doi.org/10.1016/j.cognition.2008.09.007
  22. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669. https://doi.org/10.1037/0033-295X.103.4.650
  23. Gigerenzer, G., & Todd, P. M. (1999). Simple heuristics that make us smart. Oxford University Press.
  24. Harless, D. W., & Camerer, C. F. (1994). The predictive utility of generalized expected utility theories. Econometrica, 62(6), 1251–1289. https://doi.org/10.2307/2951749
  25. Hayek, F. A. (1945). The Use of Knowledge in Society. The American Economic Review, 35(4), 519–530. http://www.jstor.org/stable/1809376
  26. Horngren, C. T., Datar, S. M., Rajan, M. V., & Wynder, M. (2013). Cost accounting: A managerial emphasis (15th ed.). Pearson.
  27. Hwang, B. G., Thomas, S. R., Haas, C. T., & Caldas, C. H. (2009). Measuring the impact of rework on construction cost performance. Journal of Construction Engineering and Management, 135(3), 187–198. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(187)
  28. Ignizio, J. P. (1976). Goal programming and extensions. Lexington, MA: Lexington Books.
  29. International Energy Agency (IEA). (2021). World energy outlook. https://www.iea.org/reports/world-energy-outlook-2021
  30. Josephson, P.-E., & Hammarlund, Y. (1999). The causes and costs of defects in construction. Automation in Construction, 8(6), 681–687. https://doi.org/10.1016/S0926-5805(98)00114-9
  31. Juran, J. M., & Gryna, F. M. (1993). Quality planning and analysis (3rd ed.). McGraw-Hill.
  32. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
  33. Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526. https://doi.org/10.1037/a0016755
  34. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
  35. Kaplan, R. S., & Atkinson, A. A. (1998). Advanced management accounting (3rd ed.). Prentice Hall.
  36. Kaplan, R. S., & Cooper, R. (1998). Cost & effect: Using integrated cost systems to drive profitability and performance. Harvard Business Press.
  37. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press.
  38. Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research, 40(13), 3165–3180. https://doi.org/10.1080/00207540210146062
  39. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Cambridge University Press.
  40. Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). Wiley.
  41. King, A. A., & Lenox, M. J. (2000). Industry self-regulation without sanctions: The chemical industry's responsible care program. Academy of Management Journal, 43(4), 698–716. https://doi.org/10.5465/1556362
  42. Kotter, J. P. (1996). Leading change. Harvard Business School Press.
  43. Laudon, K. C., & Laudon, J. P. (2021). Management information systems: Managing the digital firm (17th ed.). Pearson.
  44. Lyu, F. (2025). Decision optimization of manufacturing supply chain: A multi-objective mixed-integer approach. Sustainability.
  45. March, J. G. (1994). A primer on decision making: How decisions happen. Free Press.
  46. Meredith, J. R., & Mantel, S. J. (2011). Project management: A managerial approach (8th ed.). Hoboken, NJ: John Wiley & Sons.
  47. Mintzberg, H., Ahlstrand, B. W., & Lampel, J. (2005). Strategy safari: A guided tour through the wilds of strategic management. Simon and Schuster.
  48. Mintzberg, H., Raisinghani, D., & Théorêt, A. (1976). The structure of "unstructured" decision processes. Administrative Science Quarterly, 21(2), 246–275. https://doi.org/10.2307/2392045
  49. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press.
  50. Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510. https://doi.org/10.1016/j.im.2014.03.006
  51. Ortega, P. A., & Braun, D. A. (2013). Thermodynamics as a theory of decision-making with information-processing costs. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 469(2153), 20120683. https://doi.org/10.48550/arXiv.1204.6481
  52. Ostrom, E. (2005). Understanding institutional diversity. Princeton University Press.
  53. Rodrik, D. (2007). One economics, many recipes: Globalization, institutions, and economic growth. Princeton University Press.
  54. Romero, C. (1991). Handbook of critical issues in goal programming. Pergamon Press.
  55. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
  56. Shollo, A., & Galliers, R. D. (2015). Towards an understanding of the role of business intelligence systems in organizational knowing. Information Systems Journal, 26(4), 339–367. https://doi.org/10.1111/isj.12071
  57. Shu, L. (2023). Price optimization of closed-loop supply chain with uncertain demand under multi-channel cross influence. SAGE Open. https://journals.sagepub.com/doi/abs/10.1177/21582440231218065
  58. Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016). Inventory and production management in supply chains (4th ed.). CRC Press.
  59. Simon, H. A. (1947). Administrative behavior: A study of decision-making processes in administrative organizations. Macmillan.
  60. Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
  61. Simon, H. A. (1977). The new science of management decision (Rev. ed.). Prentice-Hall.
  62. Simon, H. A. (1997). Administrative behavior: A study of decision-making processes in administrative organizations (4th ed.). Free Press.
  63. Simons, R. (2000). Performance measurement and control systems for implementing strategy. Prentice Hall.
  64. Stiglitz, J. E. (1998). More instruments and broader goals: Moving toward the post-Washington Consensus. WIDER Annual Lectures.
  65. Triantaphyllou, E. (2000). Multi-criteria decision making methods: A comparative study. Springer.
  66. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
  67. Xu, Z. (2024). Frontiers and trends of supply chain optimization in the age of industry 4.0: An operations research perspective. https://link.springer.com/article/10.1007/s10479-024-05879-9
  68. Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision-making methods. International Journal of Operations Research, 10(2), 56–66.
  69. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
  70. Omolara, P. O., Agwubuo, C. C., Usoro, S. O., Obani, O. Q., Nwafor, K. C., & Ajayi, O. O. (2024). The impact of big data on financial decision-making: Mechanisms and managerial implications. International Journal of Science and Research Archive, 12(02), 821–827. https://doi.org/10.30574/ijsra.2024.12.2.1313
  71. Williamson, O. E. (1979). Transaction-Cost Economics: The Governance of Contractual Relations. Journal of Law and Economics, 22(2), 233–261. https://www.jstor.org/stable/725118
  72. Williamson, O. E. (1985). The economic institutions of capitalism. Free Press.