Sürdürülebilir Operasyon Yönetimi Uygulamalarında Teknoloji Gelişmelerini Değerlendirmeye Yönelik Sürdürülebilirlik Ölçütleri

Öz

Aim: This study aims to identify and evaluate sustainability metrics that assess the environmental, economic, and social impacts of technological developments in operations management. It seeks to support sustainable decision-making in digital transformation processes.
Method: A literature review was conducted to identify relevant sustainability indicators. These metrics covering environmental (e.g., energy efficiency), social (e.g., employee safety, digital skills), and economic (e.g., cost-effectiveness) were further analyzed using the Fuzzy CRITIC method to assign objective weights and ensure a balanced, data-driven assessment.
Results: The Fuzzy CRITIC analysis revealed that Technology Payback Period, Digital Skills & Upskilling Rate, Return on Technology Investment, and Energy Efficiency Improvement were identified as the most critical metrics in evaluating the sustainability impact of digital technologies in operations management. 
Conclusion: The proposed metrics offer a more holistic approach to assessing technological advancements in sustainable operations. This study provides practical insights for researchers, practitioners, and policymakers seeking to align innovation with sustainability goals in real-world settings.

Anahtar Kelimeler

• Sustainability
• Operations Management
• Digitalization
• Performance
• Decision Making Models

Sustainability Metrics for Assessing Technology Developments in Sustainable Operations Management Practices

Öz

Amaç: Bu çalışma, operasyon yönetiminde teknolojik gelişmelerin çevresel, ekonomik ve sosyal etkilerini değerlendiren sürdürülebilirlik metriklerini tanımlamayı ve analiz etmeyi amaçlamaktadır. Dijital dönüşüm süreçlerinde sürdürülebilir karar alma mekanizmalarını desteklemek hedeflenmiştir.
Yöntem: İlgili sürdürülebilirlik göstergelerini belirlemek için bir literatür taraması yapılmıştır. Çevresel (örn. enerji verimliliği), sosyal (örn. çalışan güvenliği, dijital beceriler) ve ekonomik (örn. maliyet etkinliği) boyutları kapsayan bu metrikler, nesnel ağırlıklar atamak ve dengeli, veri odaklı bir değerlendirme sağlamak amacıyla Fuzzy CRITIC yöntemi ile analiz edilmiştir.
Bulgular: Fuzzy CRITIC analizi, dijital teknolojilerin operasyon yönetiminde sürdürülebilirliğe etkisinin değerlendirilmesinde teknoloji geri ödeme süresi, dijital beceri ve yetkinlik geliştirme oranı, teknoloji yatırımlarının geri dönüşü ve enerji verimliliği iyileşmesi metriklerinin en kritik göstergeler olarak öne çıktığını ortaya koymuştur.
Sonuç: Önerilen metrikler, teknolojik gelişmelerin sürdürülebilir operasyonlar açısından daha bütüncül bir şekilde değerlendirilmesini sağlamaktadır. Bu çalışma, yeniliklerin sürdürülebilirlik hedefleriyle uyumlu hâle getirilmesini amaçlayan araştırmacılar, uygulayıcılar ve politika yapıcılar için pratik çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Sürdürülebilirlik
• Operasyon Yönetimi
• Dijitalleşme
• Performans
• Karar Verme Modelleri

Kaynakça

1. AHI, P., & SEARCY, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. https://doi.org/10.1016/j.jclepro.2013.02.018
2. AKBARI, M., & HOPKINS, J. L. (2022). Digital technologies as enablers of supply chain sustainability in an emerging economy. Operations Management Research, 15(3), 689-710. https://doi.org/10.1007/s12063-021-00226-8
3. ANGELOPOULOS, K., DAVVETAS, V., & DIMITRIADIS, S. (2023). Digital transformation in operations: Redefining performance metrics for Industry 4.0. International Journal of Operations & Production Management, 43(2), 240–263. https://doi.org/10.1002/joom.1271.
4. ATIEH, A. A., ABU HUSSEIN, A., AL-JAGHOUB, S., ALHEET, A. F., & ATTIANY, M. (2025). The Impact of Digital Technology, Automation, and Data Integration on Supply Chain Performance: Exploring the Moderating Role of Digital Transformation. Logistics, 9(1), 11. https://doi.org/10.3390/logistics9010011
5. BUTTURI, M. A., NERI, A., MERCALLI, F., & GAMBERINI, R. (2025). Sustainability-oriented innovation in the textile manufacturing industry: Pre-consumer waste recovery and circular patterns. Environments, 12(3), 82. https://doi.org/10.3390/environments12030082
6. CAI, W., BU, K., ZHA, L., ZHANG, J., LAI, D., & BAO, H. (2025). Energy consumption of plant factory with artificial light: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 210, 115235. https://doi.org/10.48550/arXiv.2405.09643
7. CEN, L., HAN, Y., & WU, J. (2025). Equal employment opportunity in supply chains. Production and Operations Management, 34(3), 524-540. https://doi.org/10.1177/10591478241238968
8. CHARDINE-BAUMANN, E., & BOTTA-GENOULAZ, V. (2014). A framework for sustainable performance assessment of supply chain management practices. Computers & Industrial Engineering, 76, 138–147. https://doi.org/10.1016/j.cie.2014.07.029

9. CHOI, T., KUMAR, S., YUE, X., & CHAN, H. (2022). Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond. Production and Operations Management, 31(1), 9-31. https://doi.org/10.1111/poms.13622
10. DEMARTINI, M., EVANS, S., & TONELLI, F. (2019). Digitalization technologies for industrial sustainability. Procedia manufacturing, 33, 264-271. https://doi.org/10.1016/j.promfg.2019.04.032
11. DIAKOULAKI, D., MAVROTAS, G., & PAPAYANNAKIS, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
12. DICHABENG, P., RAPITSENYANE, Y., MOALOSI, R., RUELE, V., & SEALETSA, O. (2025). Development of Sustainability Indicators for Smart and Connected Digital Manufacturing Systems. In Digital Technology Enabled Circular Economy (pp. 84-98). CRC Press.
13. DINÇER, H., KRISHANKUMAR, R., YÜKSEL, S., & ECER, F. (2025). Evaluating smart grid investment drivers and creating effective policies via a fuzzy multi-criteria approach. Renewable and Sustainable Energy Reviews, 208, 115052. https://doi.org/10.1016/j.rser.2024.115052
14. DUAN, Y., KHOKHAR, M., RAZA, A., SHARMA, A., & ISLAM, T. (2025). The role of digital technology and environmental sustainability in circular supply chains based on the fuzzy TOPSIS model. Environment, Development and Sustainability, 1-32. https://doi.org/10.1007/s10668-024-05924-4
15. ELKINGTON, J. (1998). Cannibals with forks: The triple bottom line of 21st century business. New Society Publishers.
16. FATORACHIAN, H., & PAWAR, K. (2025). Enhancing Waste Management in Cold Supply Chains via Digital Transformation. Procedia Computer Science, 253, 167-176. https://doi.org/10.1016/j.procs.2025.01.080
17. FRANK, A. G., DALENOGARE, L. S., & AYALA, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. https://doi.org/10.1016/j.ijpe.2019.01.004
18. FU, W., ZHANG, M., ZHAO, X., GUO, H., & WANG, L. (2026). A two-stage supply chain ecosystem evolution model: the role of platformization. Production Planning & Control, 1-25. DOI:10.1080/09537287.2026.2625183
19. GAO, X., DONG, H., SU, C., DAI, Y., LIU, Y., PARKIN, I. P., Carmalt, C.J. & HE, G. (2025). Payback trade-offs from the electrolyte design between energy efficiency and lifespan in zinc-ion batteries. Energy & Environmental Science, 18(1), 13-18. https://doi.org/10.1039/D4EE03483J
20. GÖKALP, Y., & ETI, S. (2025). Priority strategy development with intuitionistic fuzzy DEMATEL method for reducing energy costs in hospitals. Journal of Soft Computing and Decision Analytics, 3(1), 26-32. https://doi.org/10.31181/jscda31202548
21. GUNASEKARAN, A., & KOBU, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819-2840. https://doi.org/10.1080/00207540600806513
22. GUNASEKARAN, A., PATEL, C., & MCGAUGHEY, R. E. (2001). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333–347. https://doi.org/10.1016/j.ijpe.2003.08.003
23. HAKTANIR, E., & KAHRAMAN, C. (2022). A novel integrated decision-making approach based on spherical fuzzy CRITIC and EDAS methods for prioritizing challenges of Industry 4.0. Journal of Intelligent & Fuzzy Systems, 42(3), 3129–3143. https://doi.org/10.3233/JIFS-210881
24. HALEEM, A., KHAN, S., PUNDIR, H., JAIN, A., UPADHYAY, P., & KHAN, M. I. (2021). Investigating barriers toward the implementation of circular economy: A fuzzy critic approach. Journal of Industrial Integration and Management, 6(01), 107-139. https://doi.org/10.1142/S2424862220500177
25. HARIRAM, N. P., MEKHA, K. B., SUGANTHAN, V., & SUDHAKAR, K. (2023). Sustainalism: An integrated socio-economic-environmental model to address sustainable development and sustainability. Sustainability, 15(13), 10682. https://doi.org/10.3390/su151310682
26. HATEFI, S. M., AHMADI, H., & TAMOŠAITIENĖ, J. (2025). Risk Assessment in Mass Housing Projects Using the Integrated Method of Fuzzy Shannon Entropy and Fuzzy EDAS. Sustainability, 17(2), 528. https://doi.org/10.3390/su17020528
27. HWANG, G., HAN, S., JUN, S., & PARK, J. (2014). Operational performance metrics in manufacturing process: based on SCOR model and RFID technology. International Journal of Innovation, Management and Technology, 5(1), 50-55. https://doi.org/10.7763/IJIMT.2014.V5.485
28. JIANING, P., BAI, K., SOLANGI, Y. A., MAGAZZINO, C., & AYAZ, K. (2024). Examining the role of digitalization and technological innovation in promoting sustainable natural resource exploitation. Resources Policy, 92(C). https://doi.org/10.1016/j.resourpol.2024.105036
29. KLEINDORFER, P. R., SINGHAL, K., & VAN WASSENHOVE, L. N. (2005). Sustainable operations management. Production and Operations Management, 14(4), 482–492. https://doi.org/10.1111/j.1937-5956.2005.tb00235.x
30. LAI, K. H., FENG, Y., & ZHU, Q. (2023). Digital transformation for green supply chain innovation in manufacturing operations. Transportation Research Part E: Logistics and Transportation Review, 175, 103145. https://doi.org/10.1016/j.tre.2023.103145
31. LIU, L., WU, X., & CHEN, G. (2023). Picture fuzzy interactional Bonferroni mean operators via strict triangular norms and applications to multi-criteria decision making. arXiv preprint, arXiv:2301.01192. https://doi.org/10.48550/arXiv.2301.01192
32. MACHADO, C. G., WINROTH, M., & DA SILVA, E. H. D. R. (2020). Sustainable manufacturing in Industry 4.0: An emerging research agenda. International Journal of Production Research, 58(5), 1462–1484. https://doi.org/10.1080/00207543.2019.1652777
33. MARTUSCELLI, L., FANTOZZI, I. C., LEONI, L., & SCHIRALDI, M. M. (2026). Artificial intelligence for operational excellence in operations management: a systematic literature review and classification framework in manufacturing. Production Planning & Control, 1-37. https://doi.org/10.1080/09537287.2026.2638482
34. MELNYK, S. A., STEWART, D. M., & SWINK, M. (2004). Metrics and performance measurement in operations management: dealing with the metrics maze. Journal of Operations Management, 22(3), 209-218. https://doi.org/10.1016/j.jom.2004.01.004
35. MOLLA, A., GEKARA, V., KARANASIOS, S., & SNELL, D. (2025). Modeling digital skills beyond the IT workforce: construct definition, measurement and impact on digitalization value. Information Technology & People, 38(3), 1461-1504. https://doi.org/10.1108/ITP-04-2023-0385
36. MUKHAMETZYANOV, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76–105. https://doi.org/10.31181/dmame2104076m
37. NEELY, A., GREGORY, M., & PLATTS, K. (1995). Performance measurement system design. International Journal of Operations & Production Management, 15(4), 80–116. https://doi.org/10.1108/01443570510633639
38. OPOKU, R. K., & LI, X. (2025). Sustainable management practices, operational and sustainable performance in manufacturing contexts: empirical evidence from a developing economy. Journal of Responsible Production and Consumption, 2(1), 48-82. https://doi.org/10.1108/JRPC-07-2024-0035
39. OZOBU, C. O., ADIKWU, F., ODUJOBI, O., ONYEKWE, F. O., & NWULU, E. O. (2025). Developing an AI-powered occupational health surveillance system for real-time detection and management of workplace health hazards. World Journal of Innovation and Modern Technology, 9(1), 156-185. https://doi.org/10.56201/wjimt.v9.no1.2025.pg156.185
40. SAOUD, A., LACHGAR, M., HANINE, M., EL DHIMNI, R., EL AZIZI, K., & MACHMOUM, H. (2025). decideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making. SoftwareX, 29, 102026. https://doi.org/10.1016/j.softx.2024.102026
41. TABAKU, E., VYSHKA, E., KAPÇIU, R., SHEHI, A., & SMAJLI, E. (2025). Utilizing artificial intelligence in energy management systems to improve carbon emission reduction and sustainability. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(1), 393-405. https://doi.org/10.22437/jiituj.v9i1.38665
42. YADAV, G., LUTHRA, S., JAKHAR, S. K., MANGLA, S. K., & RAI, D. P. (2020). A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case. Journal of Cleaner Production, 254, 120112. https://doi.org/10.1016/j.jclepro.2020.120112
43. YANDAMURI, U. S. (2026). AI-Enabled Workflow Automation and Predictive Analytics for Enterprise Operations Management. Management, 3(1), 15-24. Accessed Link: https://openurl.ebsco.com/EPDB%3Agcd%3A4%3A28970196/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A191468312&crl=c&link_origin=www.google.com
44. ZE, Y., & LOANG, O. K. (2025). The Role of Technological Innovation in Shaping Investment Strategies in Emerging Markets: A Study on Risk and Return Dynamics. In Unveiling Investor Biases That Shape Market Dynamics (pp. 273-296). IGI Global Scientific Publishing.