Sürdürülebilir Üretimin Önündeki Engeller ve Çözüm Önerileri

Abstract

Son yıllarda hızla artan nüfus ve buna bağlı olarak sürekli artış gösteren talep, bunun yanında hızla tükenen kaynaklar ve küresel ısınma, çevre kirliliği, enerji fiyatlarının artması gibi fiziki koşulların kötüleşmeye başlaması küresel çapta sürdürülebilirlik farkındalığının artmasını sağlamıştır. Böylece üretim işletmeleri karlılığın ötesinde, tüm paydaşların refahını içeren bütünsel bir yaklaşımı benimsemek zorunda kalmıştır. Bu yaklaşımı temsil eden sürdürülebilir üretim; olumsuz çevresel etkileri en aza indiren, enerji ve doğal kaynakları koruyan, çalışan ve tüketiciler için güvenli bir ortam sağlamayı amaçlayan bir anlayışa sahiptir. Özellikle işletmeler ve ülkeler açısından sürdürülebilir performans artışlarına yaptığı önemli katkılar sayesinde bu yaklaşım son on yılda oldukça önemli hale gelmiştir. Sürdürülebilir performans artışlarının sağlanabilmesi noktasında sürdürülebilir üretimin önündeki engelleri ortadan kaldırılması gerekmektedir. Bu çalışma ile sürdürülebilir üretimin önündeki engellerin belirlenmesi ve bu engellerin önem düzeylerine göre önceliklendirilmesi amaçlanmaktadır. Bunun yanında, bu engellerin ortadan kaldırılmasına yönelik çözüm önerilerini önem düzeyine göre belirlemek çalışmanın bir diğer amacını oluşturmaktadır. Yapılan literatür araştırmasıyla belirlenen 8 engelleyici faktör Entropi yöntemi kullanılarak, belirlenen 9 çözüm önerisi SWARA yöntemiyle değerlendirilmiştir. Elde edilen bulgular finansal kısıtlamaların sürdürülebilir üretimin önündeki en önemli engel olduğunu göstermektedir. Yine sonuçlar, sürdürülebilir üretim için yasal mevzuatın güçlü bir şekilde uygulanması ve destekleyici yasaların çıkartılması önerisinin en önemli çözüm yolu olduğunu göstermiştir.

Keywords

• Sürdürülebilir Üretim
• Entropi
• SWARA

Obstacles To Sustainable Production And Suggestions For Solutions

Abstract

In recent years, the rapidly increasing population and the constantly increasing demand, as well as rapidly depleting resources and the deterioration of physical conditions such as global warming, environmental pollution, and increase in energy prices have increased the awareness of sustainability on a global scale. Thus, manufacturing businesses have had to adopt a holistic approach that includes the welfare of all stakeholders, beyond profitability. Sustainable production that represents this approach; has an understanding that minimizes negative environmental effects, protects energy and natural resources, and aims to provide a safe environment for employees and consumers. This approach has become very important in the last ten years, especially thanks to its significant contributions to sustainable performance increases for businesses and countries. In order to achieve sustainable performance increases, it is necessary to remove the obstacles to sustainable production. With this study, it is aimed to determine the obstacles in front of sustainable production and to prioritize these obstacles according to their importance levels. In addition, it is another aim of the study to determine the solution proposals for the elimination of these obstacles according to the level of importance. The 8 inhibitory factors determined by the literature research were evaluated using the Entropy method, and the 9 solution proposals determined were evaluated with the SWARA method. The findings show that financial constraints are the most important obstacle to sustainable production. Again, the results showed that the strong implementation of legal regulations and the enactment of supportive laws for sustainable production is the most important solution.

Keywords

• Sustainable Production
• Entropy
• SWARA

References

1. Aboagyewaa-Ntiri J., Mintah K. (2016). Challenges and Opportunities For The Textile Industry in Ghana: A Study Of The Adinkra Textile Sub-Sector. International Business Research, 9 (2), pp. 127-136.
2. Almeida, C.M., Bonilla, S.H., Giannetti, B.F. and Huisingh, D. (2013). Cleaner Production İnitiatives And Challenges For A Sustainable World: An İntroduction To This Special Volume. Journal of Cleaner Production, 47, pp. 1-10.
3. Ameknassi, L., Ait- Kadi, D. and Reza, N. (2016). Integration of logistics outsourcing decisions in a green supply chain design: a stochastic multi-objective multi-period multi-product programming model, International Journal of Production Economics, 182, pp. 165-184.
4. Bhandari, D., Singh, R.K. and Garg, S.K. (2019). Prioritisation and evaluation of barriers intensity for implementation of cleaner technologies: Framework for sustainable production. Resources, Conservation and Recycling, 146, pp. 156-167.
5. Bhanot, N., Rao, P.V. and Deshmukh, S.G. (2017). An integrated approach for analysing the enablers and barriers of sustainable manufacturing. Journal of Cleaner Production., 142, pp. 4412-4439.
6. Cai, W. and Lai, K. (2021). Sustainability assessment of mechanical manufacturing systems in the industrial sector. Renew. Sustain. Energy Rev., 135, pp. 110-169.
7. Cao, S., Yuan, L., Zheng, H and Wang, X. (2015). Research of the Risk Factors of China’s Unsustainable Socioeconomic Development: Lessons for Other Nations. Social Indicators Research, 123, pp. 337-347.
8. Chen, J., Sohal, A.S. and Prajogo, D.I. (2013). Supply chain operational risk mitigation: a collaborative approach. International Journal of Production Research, 51 (7), pp. 2186-2199.

9. Chen, P. (2020). Effects of The Entropy Weight on TOPSIS. Expert Systems With Applications, 168, pp. 114186.
10. Dangelico, R.M. (2015). Green Product Innovation: Where we are and Where we are Going. Business Strategy Environment, 25 (8), pp. 560-576.
11. Dhull, S. and Narwal, M. (2018). Prioritizing the Drivers of Green Supply Chain Management in Indian Manufacturing Industries Using Fuzzy TOPSIS Method: Government, Industry, Environment, and Public Perspectives. Process Integration Optimization Sustainability, 2, pp. 47-60.
12. Diabat, A., Kannan, D. and Mathiyazhagan, K. (2014). Analysis of enablers for implementation of sustainable supply chain management- A textile case. Journal of Cleaner Production, 83, pp. 391-403.
13. Eisenmenger, N., Pichler, M., Krenmayr, N., Noll, D., Plank, B., Schalmann, E., Theres, M. and Simone, W. (2020). The sustainable development goals prioritize economic growth over sustainable resource use: a critical reflection on the SDGs from a socio _ ecological perspective Sustain. Sci. Sachs, 2012.
14. Gardas, B.B., Raut, R.D. and Narkhede, B. (2018). Modelling the challenges to sustainability in the textile and apparel (T & A) sector: a Delphi- DEMATEL approach. Sustainable Production and Consumption, 15 (2018), pp. 96-108.
15. Ghobakhloo, M. (20108). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29, pp. 910-936.
16. Gimenez, C., Sierra, V. and Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International Journal of Production Economics, 140, pp. 149-159.
17. Govindan, K., Kaliyan M., Kannan, D. and Haq, A.N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Economics, 147, pp. 555-568.
18. Govindan, K., Muduli, K., Devika, K. and Barve, A. (2016). Investigation of the influential strength of factors on adoption of green supply chain management practices: An Indian mining scenario. Resources, Conservation and Recycling, 107, pp. 185-194.
19. Hamalainen, M., Mohajeri, B. and Nyberg, T. (2018). Removing barriers to sustainability research on personal fabrication and social manufacturing. Journal of Cleaner Production, 180, pp. 666-681.
20. Heijungs, R., Huppes, G. and Guinee, J. B. (2010). Life Cycle Assessment and Sustainability Analysis of Products, Materials and Technologies: Toward a Scientific Framework for Sustainability Life Cycle Analysis. Polymer Degradation and Stability, 95 (3), pp. 422–428.
21. Jabbour C.J.C., Jugend D., de Sousa Jabbour A.B.L., Govindan K., Kannan D. and Leal Filho W. (2018). There is no carnival without samba: Revealing barriers hampering biodiversity-based R&D and eco-design in Brazil. Journal of Environmental Management, 206 (1), pp. 236-245.
22. Keršulienė, V., Zavadskas, E.K. and Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11, pp. 243-258.
23. Lorek, S. and Spangenberg, J.H. (2014). Sustainable consumption within a sustainable economy–beyond green growth and green economies. Journal of Cleaner Production, 63, pp. 33-44.
24. Luthra, S., Mangla, S.K., Xu, L. and Diabat, A. (2016). Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain. International Journal of Production Economics, 181, pp. 342-349.
25. Maghsoodi, A.I., Maghsoodi, A.I., Poursoltan, P., Antucheviciene, J. and Turskis Z. (2019). Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering, 19 (4), pp. 1194-1210.
26. Malek, J. and Desai, T.N. (2019). Prioritization of sustainable manufacturing barriers using Best Worst Method. Journal of Cleaner Production, 226, pp. 589-600.
27. Mangla, S.K., Govindan, K. and Luthra, S. (2017). Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy Analytical Hierarchy Process, Journal of Cleaner Production, 151, pp. 509-525.
28. Maruthi, G.D. and Rashmi, R. (2015). Green Manufacturing: It’ s Tools and Techniques that can be implemented in Manufacturing Sectors. MaterialsToday Proceeding, 2, pp. 3350-3355.
29. Massoud, M.A., Fayad, R., Kamleh, R. and El-Fadel, M. (2010). Environmental management system (ISO 14001) certification in developing countries: challenges and implementation strategies, Environ. Sci. Technol., pp. 1884-1887.
30. Mont, O. and Leire, C. (2009). Socially responsible purchasing in supply chains: drivers and barriers in Sweden, Social Responsibility Journal, 5 (3), pp. 388-407.
31. Mueller, T.S. 8.2017). Consumer perceptions of electric utilities: insights from the Center for Analytics Research & Education Project in the United States. Energy Res. Soc. Sci., 26, pp. 34-39.
32. Niu, B., Mu, Z., Chen and Lee, L.C.K.M. (2019). Coordinate the economic and environmental sustainability via procurement outsourcing in a co-opetitive supply chain, Resour. Conserv. Recycl., 146, pp. 17-27.
33. Prajapati, H., Kant, R. and Shankar, R. (2019). Prioritizing the solutions of reverse logistics implementation to mitigate its barriers: a hybrid modified SWARA and WASPAS approach. Journal of Cleaner Production, 240, 118219.
34. Prakash, C. and Barua, M.K. (2015). Integration Of AHP-TOPSIS Method For Prioritizing The Solutions Of Reverse Logistics Adoption To Overcome İts Barriers Under Fuzzy Environment. Journal of Manufacturing Systems, 37(3), pp.599-615.
35. Samvedi, A., Jain, V. and Felix, T.S. (2013). Quantifying risks in a Supply chainthrough integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research, 51 (8), pp. 2433-2442.
36. Shannon C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, pp. 379-423.
37. Singh, R.K., Rastogi, S. and Aggarwal, M. (2016). Analyzing the factors for implementation of green supply chain management, Competitiveness Review, 26 (3), pp. 246-264.
38. Singh, S., Srivastava, S. and Jangirala, S. K. (2021). System dynamics analysis of sugarcane supply chain in Indian sugar industry, Global Business Review. https://doi.org/10.1177/0972150921999521.
39. Taslaman, C. (2006). ‘Din Felsefesi Açısından ENTROPİ Yasası, Marmara Üniversitesi İlahiyat Fakültesi Dergisi, (30), 89-111.
40. Wang, T.C. Ve Lee, H.D. (2009).’Developing A Fuzzy TOPSİS Approach Based On Subjective Weights And Objective Weights’. Expert Systems With Applications, 36(5), 8980-8995.
41. Wu, K., Lang, M., Lim, M.K. and Antoniy, S.F. (2019). Causal sustainable resource management model using a hierarchical structure and linguistic preferences, Journal of Cleaner Production., 229, pp. 640-651.
42. Wu, Z., Sun, J., Liang, L. & Zha, Y. (2011).’Determination Of Weights For Ultimate Cross Efficiency Using Shannon Entropy’, Expert Systems With Applications, 38, 5162-5165
43. Wu, K.J., Cui, L., Tseng, M.L., Hu, J., and Huy, P.M. (2017). Applying Big Data with Fuzzy DEMATEL to Discover the Critical Factors for Employee Engagement in Developing Sustainability for the Hospitality Industry under Uncertainty, In: Supply Chain Management in the Big Data Era, pp. 218–253.
44. Zailani, S., Govindan, K., Shaharudin, M.R. and Kuan, E.E. (2017). Barriers to product return management in automotive manufacturing firms in Malaysia. Journal of Cleaner Production, 141 (2017), pp. 22-40.
45. Zarte, M., Pechmann, A. and Nunes, I.L. (2019). Decision support systems for sustainable manufacturing surrounding the product and production life cycle – A literature review. Journal of Cleaner Production, 219, pp. 336-349.
46. Zolfani, S.H. and Saparauskas, J. (2013). New application of SWARA Method in prioritizing sustainability assessment indicators of energy system, Engineering Economics, 24 (5), pp. 408-414.
47. Zolfani, S.H., Salimi, J., Maknoon, R. & Kildienė, S. (2015). Technology Foresight about R&D Projects Selection: application of SWARA method at the policy making level. Engineering Economics, 26(5), pp. 571-580.