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Assessing the Social Impacts of Additive Manufacturing Using Hierarchical Evidential Reasoning Approach

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Abstract

In recent times, the wider adoption and development of additive manufacturing is prominent in society, but the information regarding the social impacts of this technology is very limited. Due to this, assessing the social impacts of additive manufacturing technology is crucial. The assessment process to determine the social impacts of additive manufacturing information from factors, which are qualitative, incomplete, and uncertain in nature, is observed. The evidential reasoning (ER) approach is a method that can handle subjective, uncertain, and incomplete data. In this paper, the ER approach along with the analytical hierarchy process (AHP) is incorporated for the first time to build up a model for assessing the social impacts of additive manufacturing technology. Based on the experts’ opinion, AHP is applied to the relevant attributes of social impacts to rate and structure the attributes. In this research, the model will be tested using subjective judgmental belief structure data. The data will be aggregated using the ER approach and the attributes will be illustrated in a distributed manner. In the proposed model, Yager’s combination method is applied to compare the output of the D–S approach. The model output is comprised of the average state of social impact from additive manufacturing along with a level of uncertainty for each attribute. The proposed model is now available for utilization by the decision maker to assess the social impacts of additive manufacturing technology. Furthermore, the model could be used as a baseline for planning mitigation of impacts to or improvement to a current state of social impacts.

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References

  • Ahmadzadeh, F., & Bengtsson, M. (2017). Using evidential reasoning approach for prioritization of maintenance-related waste caused by human factors—A case study. International Journal of Advanced Manufacturing Technology, 90(9–12), 2761–2775. https://doi.org/10.1007/s00170-016-9377-7

    Article  Google Scholar 

  • Attaran, M. (2017). The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing. Business Horizons, 60(5), 677–688. https://doi.org/10.1016/j.bushor.2017.05.011

    Article  Google Scholar 

  • Bappy, M. M., Ali, S. M., Kabir, G., & Paul, S. K. (2019). Supply chain sustainability assessment with Dempster–Shafer evidence theory: Implications in cleaner production. Journal of Cleaner Production, 237, 117771. https://doi.org/10.1016/j.jclepro.2019.117771

    Article  Google Scholar 

  • Birtchnell, T., & Hoyle, W. (2014). 3D printing for development in the global south: The 3D4D challenge. In 3D printing for development in the global south: The 3D4D challenge. https://doi.org/10.1057/9781137365668

  • Bonilla-Alicea, R. J., & Fu, K. (2019). Systematic map of the social impact assessment field. Sustainability (Switzerland). https://doi.org/10.3390/su11154106

    Article  Google Scholar 

  • Campbell, I., Bourell, D., & Gibson, I. (2012). Additive manufacturing: Rapid prototyping comes of age. Rapid Prototyping Journal, 18(4), 255–258. https://doi.org/10.1108/13552541211231563

    Article  Google Scholar 

  • Chen, D., Heyer, S., Ibbotson, S., Salonitis, K., Steingrímsson, J. G., & Thiede, S. (2015). Direct digital manufacturing: Definition, evolution, and sustainability implications. Journal of Cleaner Production, 107, 615–625. https://doi.org/10.1016/j.jclepro.2015.05.009

    Article  Google Scholar 

  • Contador, J. C., Satyro, W. C., Contador, J. L., & Spinola, M. D. M. (2020). Flexibility in the Brazilian industry 4.0: Challenges and opportunities. Global Journal of Flexible Systems Management, 21, 15–31.

    Article  Google Scholar 

  • Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued Mapping. The Annals of Mathematical Statistics, 38(2), 325–339.

    Article  Google Scholar 

  • Diegel, O., Singamneni, S., Reay, S., & Withell, A. (2010). Tools for sustainable product design: Additive manufacturing. Journal of Sustainable Development. https://doi.org/10.5539/jsd.v3n3p68

    Article  Google Scholar 

  • Dwivedi, A., Agrawal, D., Jha, A., Gastaldi, M., Paul, S. K., & D’Adamo, I. (2021). Addressing the challenges to sustainable initiatives in value chain flexibility: Implications for sustainable development goals. Global Journal of Flexible Systems Management, 22(2), 179–197.

  • Elkington, J. (1997). Cannibals with Forks: the Triple Bottom Line of 21st Century Business. Capstone, Oxford.

  • Esteves, A. M., Franks, D., & Vanclay, F. (2012). Social impact assessment: The state of the art. Impact Assessment and Project Appraisal, 30(1), 34–42. https://doi.org/10.1080/14615517.2012.660356

    Article  Google Scholar 

  • El Amrani, S., Hossain, N. U. I., Karam, S., Jaradat, R., Nur, F., Hamilton, M. A., & Ma, J. (2021). Modelling and assessing sustainability of a supply chain network leveraging multi Echelon Bayesian Network. Journal of Cleaner Production, 302, 126855.

    Article  Google Scholar 

  • Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573–1587. https://doi.org/10.1016/j.jclepro.2016.04.150

    Article  Google Scholar 

  • Frazier, W. E. (2014). Metal additive manufacturing: A review. Journal of Materials Engineering and Performance, 23(6), 1917–1928. https://doi.org/10.1007/s11665-014-0958-z

    Article  Google Scholar 

  • Gebler, M., Schoot Uiterkamp, A. J. M., & Visser, C. (2014). A global sustainability perspective on 3D printing technologies. Energy Policy, 74(2014), 158–167. https://doi.org/10.1016/j.enpol.2014.08.033.

    Article  Google Scholar 

  • Gershenfeld, N., & Gershenfeld, N. (2015). How to make almost anything machine! SIGGRAPH 2015: Studio. SIGGRAPH, 2015,. https://doi.org/10.1145/2775280.2792721

    Article  Google Scholar 

  • Godina, R., Ribeiro, I., Matos, F., Ferreira, B. T., Carvalho, H., & Peças, P. (2020). Impact assessment of additive manufacturing on sustainable business models in industry 4.0 context. Sustainability (Switzerland), 12(17), 1–21. https://doi.org/10.3390/su12177066

    Article  Google Scholar 

  • Grover, P., & Kar, A. K. (2017). Big data analytics: A review on theoretical contributions and tools used in literature. Global Journal of Flexible Systems Management, 18(3), 203–229.

    Article  Google Scholar 

  • Huang, S. H., Liu, P., Mokasdar, A., & Hou, L. (2013). Additive manufacturing and its societal impact: A literature review. International Journal of Advanced Manufacturing Technology, 67(5–8), 1191–1203. https://doi.org/10.1007/s00170-012-4558-5

    Article  Google Scholar 

  • Huang, Y., Leu, M. C., Mazumder, J., & Donmez, A. (2015). Additive manufacturing: Current state, future potential, gaps and needs, and recommendations. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 137(1), 1–11. https://doi.org/10.1115/1.4028725

    Article  Google Scholar 

  • Jiang, R., Kleer, R., & Piller, F. T. (2017). Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030. Technological Forecasting and Social Change, 117, 84–97. https://doi.org/10.1016/j.techfore.2017.01.006

    Article  Google Scholar 

  • Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., & Hill, C. (2021). Impact of big data and artificial intelligence on industry: Developing a workforce roadmap for a data driven economy. Global Journal of Flexible Systems Management, 22(3), 197–217.

  • Kong, G., Xu, D. L., Yang, J. B., & Ma, X. (2015). Combined medical quality assessment using the evidential reasoning approach. Expert Systems with Applications, 42(13), 5522–5530. https://doi.org/10.1016/j.eswa.2015.03.009

    Article  Google Scholar 

  • Kurfess, T., & Cass, W. J. (2014). Rethinking additive manufacturing and intellectual property protection. Research Technology Management, 57(5), 35–42. https://doi.org/10.5437/08956308X5705256

    Article  Google Scholar 

  • Kwon, H., Kim, J., & Park, Y. (2017). Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology. Technovation, 60, 15-28

  • Ma, J., Harstvedt, J. D., Dunaway, D., Bian, L., & Jaradat, R. (2018). An exploratory investigation of additively manufactured product life cycle sustainability assessment. Journal of Cleaner Production, 192, 55–70. https://doi.org/10.1016/j.jclepro.2018.04.249

    Article  Google Scholar 

  • Malshe, H., Nagarajan, H., Pan, Y., & Haapala, K. (2015). Profile of sustainability in additive manufacturing and environmental assessment of a novel stereolithography process. In: International Manufacturing Science and Engineering Conference (Vol. 56833, p. V002T05A012). American Society of Mechanical Engineers.

  • Mani, V., Agrawal, R., & Sharma, V. (2016). Impediments to social sustainability adoption in the supply chain: An ISM and MICMAC analysis in Indian manufacturing industries. Global Journal of Flexible Systems Management, 17(2), 135–156. https://doi.org/10.1007/s40171-015-0106-0

    Article  Google Scholar 

  • Matos, F., Godina, R., Jacinto, C., Carvalho, H., Ribeiro, I., & Peças, P. (2019). Additive manufacturing: Exploring the social changes and impacts. Sustainability (Switzerland). https://doi.org/10.3390/su11143757

    Article  Google Scholar 

  • Matos, F., & Jacinto, C. (2019). Additive manufacturing technology: Mapping social impacts. Journal of Manufacturing Technology Management, 30(1), 70–97. https://doi.org/10.1108/JMTM-12-2017-0263

    Article  Google Scholar 

  • Mokline, B., & Ben Abdallah, M. A. (2022). The mechanisms of collective resilience in a crisis context: The case of the ‘COVID-19’ crisis. Global Journal of Flexible Systems Management, 23(1), 151–163.

  • Naghshineh, B., Lourenço, F., Godina, R., Jacinto, C., & Carvalho, H. (2020). A social life cycle assessment framework for additive manufacturing products. Applied Sciences (Switzerland). https://doi.org/10.3390/app10134459

    Article  Google Scholar 

  • Naghshineh, B., Ribeiro, A., Jacinto, C., & Carvalho, H. (2021). Social impacts of additive manufacturing: A stakeholder-driven framework. Technological Forecasting and Social Change, 164, 120368. https://doi.org/10.1016/j.techfore.2020.120368

    Article  Google Scholar 

  • Nair, S., Walkinshaw, N., Kelly, T., & De La Vara, J.L. (2015). An evidential reasoning approach for assessing confidence in safety evidence. In: 2015 IEEE 26th International Symposium on Software Reliability Engineering, ISSRE 2015, pp. 541e552. https://doi.org/10.1109/ISSRE.2015.7381846

  • Niaki, M. K., Torabi, S. A., & Nonino, F. (2019). Why manufacturers adopt additive manufacturing technologies: The role of sustainability. Journal of Cleaner Production, 222, 381–392. https://doi.org/10.1016/j.jclepro.2019.03.019

    Article  Google Scholar 

  • Oberoi, J. S., Khamba, J. S., & Kiran, R. (2007). Impact of new technology and sourcing practices in managing tactical and strategic manufacturing flexibilities—An empirical study. Global Journal of Flexible Systems Management, 8(3), 1–14. https://doi.org/10.1007/BF03396523

    Article  Google Scholar 

  • Ortiz, G., Domínguez-Gómez, J. A., Aledo, A., & Urgeghe, A. M. (2018). Participatory multi-criteria decision analysis for prioritizing impacts in environmental and social impact assessments. Sustainability: Science, Practice, and Policy, 14(1), 6–21. https://doi.org/10.1080/15487733.2018.1510237

    Article  Google Scholar 

  • Richter, J. S., Mendis, G. P., Nies, L., & Sutherland, J. W. (2019). A method for economic input-output social impact analysis with application to U.S. advanced manufacturing. Journal of Cleaner Production, 212, 302–312. https://doi.org/10.1016/j.jclepro.2018.12.032

    Article  Google Scholar 

  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-i

    Article  Google Scholar 

  • Sethi, A. P. S., Khamba, J. S., & Kiran, R. (2007). Linkages of technology adoption and adaptation with technological capability, flexibility and success of AMT implementation in Indian manufacturing industry: An empirical study. Global Journal of Flexible Systems Management, 8(3), 25–38. https://doi.org/10.1007/BF03396525

    Article  Google Scholar 

  • Settembre-Blundo, D., González-Sánchez, R., Medina-Salgado, S., & García-Muiña, F. E. (2021). Flexibility and resilience in corporate decision making: A new sustainability-based risk management system in uncertain times. Global Journal of Flexible Systems Management, 22(2), 107–132.

  • Shafer, G. (1976). A mathematical theory of evidence (Vol. 42). Princeton: Princeton University Press.

    Book  Google Scholar 

  • Shan, Y. (2015). Decision making study: methods and applications of evidential reasoning and judgment analysis. Retrieved from https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/17330/3/Thesis-2015-Shan.pdf.

  • Singh, R. K., & Acharya, P. (2013). Supply chain flexibility: A frame work of research dimensions. Global Journal of Flexible Systems Management, 14(3), 157–166.

  • Soares, B., Ribeiro, I., Cardeal, G., Leite, M., Carvalho, H., & Peças, P. (2021). Social life cycle performance of additive manufacturing in the healthcare industry: The orthosis and prosthesis cases. International Journal of Computer Integrated Manufacturing, 34, 327–340. https://doi.org/10.1080/0951192X.2021.1872100

    Article  Google Scholar 

  • Sushil. (2017). Multi-criteria valuation of flexibility initiatives using integrated TISM–IRP with a big data framework. Production Planning & Control, 28(11–12), 999–1010.

    Article  Google Scholar 

  • Tesfamariam, S., Sadiq, R., & Najjaran, H. (2010). Decision making under uncertainty—An example for seismic risk management. Risk Analysis. https://doi.org/10.1111/j.1539-6924.2009.01331.x

    Article  Google Scholar 

  • Tracey, M., Vonderembse, M. A., & Lim, J. S. (1999). Manufacturing technology and strategy formulation: Keys to enhancing competitiveness and improving performance. Journal of Operations Management, 17(4), 411–428. https://doi.org/10.1016/S0272-6963(98)00045-X

    Article  Google Scholar 

  • Tumpa, T. J., Ali, S. M., Rahman, M. H., Paul, S. K., Chowdhury, P., & Rehman Khan, S. A. (2019). Barriers to green supply chain management: An emerging economy context. Journal of Cleaner Production,. https://doi.org/10.1016/j.jclepro.2019.117617

    Article  Google Scholar 

  • Uzoka, E., & Seleka, G. (2005). MCDA decision support model for measuring performance of professional organizations. The International Journal of Applied Management and Technology, 3(2), 125–140.

    Google Scholar 

  • Vanclay, F. (2002). Conceptualising social impacts. Environmental Impact Assessment Review, 22(3), 183–211. https://doi.org/10.1016/S0195-9255(01)00105-6

    Article  Google Scholar 

  • Wadhwa, S., & Rao, K. S. (2004). A unified framework for manufacturing and supply chain flexibility. Global Journal of Flexible Systems Management, 5(1), 29–36.

  • Yang, J., & Xu, D. (2002). On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transactions on Systems Man and Cybernetics—Part A: Systems and Humans, 32(3), 289–304. https://doi.org/10.1109/tsmca.2002.802746

    Article  Google Scholar 

  • Zhou, X., & Xu, Z. (2018). An integrated sustainable supplier selection approach based on hybrid information aggregation. Sustainability, 10, 2543.

    Article  Google Scholar 

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Correspondence to Niamat Ullah Ibne Hossain.

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Bappy, M.M., Key, J., Hossain, N.U.I. et al. Assessing the Social Impacts of Additive Manufacturing Using Hierarchical Evidential Reasoning Approach. Glob J Flex Syst Manag 23, 201–220 (2022). https://doi.org/10.1007/s40171-021-00295-5

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