Barriers to Reverse Logistics in the Computer Supply Chain Using Interpretive Structural Model
Researchers and practitioners are paying attention to reverse logistics (RL) issues due to growing environmental concerns, competitive advantage, promising financial potential, legislative reasons and social responsibility. This study aims to examine the contextual relationship and interactions among barriers to implement RL practices in the computer supply chain of Bangladesh. We applied Interpretive Structural Modeling (ISM) technique to diagnose significant barriers and proposed a hierarchical framework for investigating the relationships among them. We also used MICMAC (Matriced’ Impacts Croisés Multiplication Appliquée á unClassement) analysis to classify the barriers based on the driving power and dependence among them. Seven barriers were finalized in the Bangladesh context based on the previous literature and professional feedback. The findings reveal that financial constraints along with the lack of interest from top management are the most influential barriers to RL for the computer supply chains of Bangladesh. The ISM-based analysis can provide managers with insights for developing strategies for implementing RL practices in the computer supply chain of Bangladesh.
KeywordsBarrier analysis Bangladesh Computer supply chain ISM Reverse logistics
Compliance with Ethical Standards
Conflict of interest
No potential conflict of interest was reported by the authors.
- Attri, R., Dev, N., & Sharma, V. (2013a). Interpretive structural modelling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3–8.Google Scholar
- Attri, R., Grover, S., Dev, N., & Kumar, D. (2013b). An ISM approach for modelling the enablers in the implementation of total productive maintenance (TPM). International Journal of Systems Assurance Engineering and Management, 4(4), 313–326. https://doi.org/10.1007/s13198-012-0088-7.CrossRefGoogle Scholar
- Bouzon, M., Govindan, K., & Rodriguez, C. M. T. (2016a). Evaluating barriers for reverse logistics implementation under a multiple stakeholders’ perspective analysis using grey decision making approach. Resources, Conservation and Recycling. https://doi.org/10.1016/j.resconrec.2016.11.022.Google Scholar
- Dhanda, K. K., & Peters, A. A. (2005). Reverse logistics in the computer industry. International Journal of Computers Systems and Signals, 6(2), 57–67.Google Scholar
- Dubey, R., Gunasekaran, A., Sushil, & Singh, T. (2015). Building theory of sustainable manufacturing using total interpretive structural modelling. International Journal of Systems Science: Operations & Logistics, 2(4), 231–247.Google Scholar
- Fleischmann, M., van Nunen, J., Gräve, B., & Gapp, R. (2005). Reverse logistics—capturing value in the extended supply chain. In Supply chain management on demand: Strategies, technologies, applications (pp. 167–86).Google Scholar
- Garg, D., Luthra, S., & Haleem, A. (2016). An evaluation of barriers to implement reverse logistics: A case study of Indian fastener industry. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 10(8), 1484–1489.Google Scholar
- Ginter, P. M., & Starling, J. M. (1978). Reverse distribution channels for recycling. California Management Review, 20(3), 72–82. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=6412891&site=ehost-live&scope=site.
- Govindan, K., Kannan, D., Mathiyazhagan, K., Jabbour, A. B. L. D. S., & Jabbour, C. J. C. (2013). Analysing green supply chain management practices in Brazil’s electrical/electronics industry using interpretive structural modelling. International Journal of Environmental Studies, 70(4), 477–493. https://doi.org/10.1080/00207233.2013.798494.CrossRefGoogle Scholar
- Govindan, K., Palaniappan, M., Zhu, Q., & Kannan, D. (2012). Production economics analysis of third party reverse logistics provider using interpretive structural modeling. International Journal of Production Economics, 140(1), 204–211. https://doi.org/10.1016/j.ijpe.2012.01.043.CrossRefGoogle Scholar
- Grenchus, E., & Johnson, S. (2001). Improving environmental performance through reverse logistics at IBM, IEEE International Symposium on Electronics and the Environment, 236–240.http://www.scopus.com/inward/record.url?eid=2-s2.0-0034827687&partnerID=40.
- Hossain S., Sultan S., Shahnaz F., Akram A. B., Nesa M., & Happell, J. (2010). Study on E-waste: Bangladesh Situation. (2010), Environment and Social Development Organization-ESDO, 1–36. https://www.env.go.jp/recycle/circul/venous_industry/pdf/env/h27/02_4.pdf.
- Jindal, A., & Sangwan, K. S. (2011). Development of an interpretive structural model of barriers to reverse logistics implementation in Indian industry. In Glocalized solutions for sustainability in manufacturing (pp. 448–453). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19692-8.
- Kara, S. S., & Onut, S. (2010). Expert systems with applications a two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling. Expert Systems with Applications, 37(9), 6129–6137. https://doi.org/10.1016/j.eswa.2010.02.116.CrossRefGoogle Scholar
- Khalid, Z. B., Mufti, N. A., & Ahmad, Y. (2016). Identifying and modeling barriers to collaboration among auto-parts manufacturing SMEs. Pakistan Business Review, 18(2), 487–507.Google Scholar
- Kokkinaki, A., Dekker, R., & Koster, M. (2001). From e-trash to e-treasure: How value can be created by the new e-business models for reverse logistics (No. EI 2000-32/A). Retrieved from http://repub.eur.nl/res/pub/1662/.
- Laribi, L., & Dhouib, D. (2016). Barriers analysis for reverse logistics adoption in Tunisian enterprises. In 3rd International Conference on Logistics operations management (GOL), May 2016 (pp. 1–8). IEEE.Google Scholar
- Ngadiman, N. I. B., Moeinaddini, M., Ghazali, J. B., & Roslan, N. F. B. (2016). Reverse logistics in food industries: A case study in Malaysia. International Journal of Supply Chain Management, 5(3), 91–95.Google Scholar
- Rameezdeen, R., Chileshe, N., Hosseini, M. R., & Lehmann, S. (2016). A qualitative examination of major barriers in implementation of reverse logistics within the South Australian construction sector. International Journal of Construction Management, 16(3), 185–196. https://doi.org/10.1016/j.rser.2016.09.067.CrossRefGoogle Scholar
- Rick, G., & Liu, N. (2007). Using interpretive structural modeling to identify and quantify interactive risks. In Orlando–USA: ASTIN Colloquium, (pp. 1–11).Google Scholar
- Sorker, F., & Shukla, V. (2009). Reverse logistics of passenger cars in the UK—an examination, 1–8.Google Scholar
- Starostka-Patyk, M., Zawada, M., Pabian, A., & Abed, M. (2013). Barriers to reverse logistics implementation in enterprises. In 2013 International conference on advanced logistics and transport (ICALT), (pp. 506–511). IEEE.Google Scholar
- Stock, J.R. (1992). Reverse logistics: White paper. Council of Logistics Management.Google Scholar
- Venkatesh, V. G., Rathi, S., & Patwa, S. (2015). Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling. Journal of Retailing and Consumer Services, 26, 153–167. https://doi.org/10.1016/j.jretconser.2015.06.001.CrossRefGoogle Scholar
- Verma, R. K. (2014). Implementation of interpretive structural model and topsis in manufacturing industries for supplier selection. Industrial Engineering Letters, 4(5), 1–9.Google Scholar