Sustainable Supply Chain Finances implementation model and Artificial Intelligence for innovative omnichannel logistics
 
 
 
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Ph.D., Faculty of Logistics of Poznań University Of Economics And Business, Poland
 
2
Ph.D Student, Faculty of Logistics of Poznań University Of Economics And Business, Poznań, Poland
 
 
Online publication date: 2022-04-18
 
 
Management 2022;26(1):19-35
 
KEYWORDS
ABSTRACT
Whilst there is significant research on supply chain finance, there is little information about its application to the omnichannel logistics. Hence, the primary adopted goal is to identify the ways of supporting the implementation and development of SSCM with use of Artificial Intelligence and developed SSCF implementation model. Potential paths to improve supply chain’s sustainability based on SSCF and AI are presented on the example of two internationally operating companies from the clothing industry using omnichannel. An exploratory case study has been conducted. Three methods were used to gather data: document/reports analysis, direct and participative observation and unstructured interviews. By implementing AI, supply chain leaders can more easily improve all key dimensions of sustainability, especially in the strategic field, based on strengthening partnership and cooperation with suppliers offering value-added materials that guarantee a competitive advantage. The paper contributes to the limited existing literature on SSCF and AI and disseminates this information to provide impetus, guidance and support toward increasing the productivity, efficiency, consistency and quality of service.
 
REFERENCES (35)
1.
Adams, N. M. (2010). Perspectives on data mining. International Journal of Market Research, 52(1), 11-19.
 
2.
Alibhai, S., Bell, S., Conner, G. (2019). What’s happening in the missing middle?: lessons from financing SMEs. World Bank, Washington, DC.
 
3.
Chalmeta, R., Barqueros-Muñoz, J. E. (2021). Using Big Data for Sustainability in Supply Chain Management. Sustainability, 13(13), 7004. https://doi.org/10.3390/su1313....
 
4.
Czakon, W., 2013, Podstawy metodologii badań w naukach o zarządzaniu, Wolters Kluwer Polska, Warszawa.
 
5.
de Boer, R., Bals, L., Tate, W., Gelsomino, L., Steeman, M., Bals, C. (2017). Exploring the Financial Flows in Sustainable Supply Chains. Paper Presented to the 24th International Conference on Production Research. Poznan Poland, August.
 
6.
Fritz, M. M. (2019). Sustainable supply chain management. Responsible Consumption and Production. Encyclopedia of the UN Sustainable Development Goals, Springer, Cham.
 
7.
Gao, Z. (2020). The application of artificial intelligence in stock investment. Journal of Physics: Conference Series, Vol. 1453, No. 1, p. 012069, IOP Publishing.
 
8.
Han, Y., & Zhu, X. (2017). Research on Optimization of Production Process and Warehouse Management System. Revista de la Facultad de Ingenieraí UCV, 32(15), 36-41.
 
9.
Karaosman, H., Brun, A., Morales-Alonso, G. (2017). Vogue or vague: sustainability performance appraisal in luxury fashion supply chains. Sustainable management of luxury, pp. 301-330, Springer, Singapore.
 
10.
Kayikci, Y. (2018). Sustainability impact of digitization in logistics. Procedia manufacturing, 21, 782-789.
 
11.
Kraljic, P. (1983). Purchasing must become supply management. Harv. Bus. Rev., vol. 61, no. 5, pp. 109–117.
 
12.
Kumar, V., Rajan, B., Venkatesan, R., Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.
 
13.
Lee, C. K., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2018). Design and application of Internet of things-based warehouse management system for smart logistics. International Journal of Production Research, 56(8), 2753-2768.
 
14.
Li, R., Dong, Q., Jin, C., Kang, R. (2017). A new resilience measure for supply chain networks. Sustainability, 9(1), 144, doi:10.3390/su9010144.
 
15.
Mahroof, K. (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45, 176-190.
 
16.
Milder, B. (2008). Closing the gap: Reaching the missing middle and rural poor through value chain finance. Enterprise development & microfinance, 19(4), 301.
 
17.
Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13-39.
 
18.
Noor, K. B. M. (2008). Case study: A strategic research methodology. American journal of applied sciences, 5(11), 1602-1604.
 
19.
Olan, F., Liu, S., Suklan, J., Jayawickrama, U., & Arakpogun, E. O. (2021). The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry. International Journal of Production Research, 1-16.
 
20.
Patnaik, D. (2015). Theorizing change in artificial intelligence: inductivising philosophy from economic cognition processes. AI & SOCIETY, 30(2), 173-181, doi.org/10.1007/s00146-013-0524-5.
 
21.
Pettigrew, A.M. (1997), “What is a processual analysis?”, Scandinavian Journal of Management, Vol. 13 No. 4, pp. 337-348.
 
22.
Rewolucja AI. Jak sztuczna inteligencja zmieni biznes w Polsce (2017). Raport McKinsey & Company, Forbes Polska.
 
23.
Serrador, P., Pinto, J. K. (2015). Does Agile work? A quantitative analysis of agile project success. International journal of project management, 33(5), doi.org/10.1016/j.ijproman.2015.01.006.
 
24.
Someh, I., Wixom, B., Zutavern, A. (2020). Overcoming organizational obstacles to artificial intelligence value creation: propositions for research. Proceedings of the 53rd Hawaii International Conference on System Sciences, doi: 10.24251/HICSS.2020.712.
 
25.
Stawiarska, E. (2016). Logistyczne systemy informatyczne wykorzystujące sztuczną inteligencję w branży motoryzacyjnej. Organizacja i Zarządzanie: kwartalnik naukowy, vol. 4, p. 101-119.
 
26.
Steeman, M. (2014). The Power of Supply Chain Finance: How companies can apply collaborative finance models in their supply chain to mitigate risks and reduce costs. Hogeschool Windesheim.
 
27.
Syam, N., Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146.
 
28.
Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., Mangla, S. (2019). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research, 283(1), 1517-1550.
 
29.
Vipul, J., (2009). Editorial Note for the Special Issue on ‘Artificial Intelligence Techniques for Supply Chain Management’. Engineering Applications of Artificial Intelligence, 22 (6), p. 829–831. doi:10.1016/j. engappai.2009.01.009.
 
30.
Voss, C., Sikriktsis, N. and Frohlic, M. (2002), Case research in operations management, International Journal of Operations and Production Management, Vol. 22 No. 2, pp. 195-219.
 
31.
Wildemann, H. (1999). Das Konzept der Einkaufspotentialanalyse. In: Handbuch Industrielles Beschaffungsmanagement (pp. 435-452). Gabler Verlag.
 
32.
Wyskwarski, M. (2015). Metody sztucznej inteligencji w organizacji inteligentnej. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska.
 
33.
Yina, R.K., 2014, Case Study Research: Design and Methods, SAGE Publications.
 
34.
Yu, G., Li, F., & Yang, Y. (2017). Robust supply chain networks design and ambiguous risk preferences. International Journal of Production Research, 55(4), 1168-1182, doi.org/10.1080/00207543.2016.1232499.
 
35.
Zahraee, S. M., Assadi, M. K., & Saidur, R. (2016). Application of artificial intelligence methods for hybrid energy system optimization. Renewable and Sustainable Energy Reviews, 66, 617-630, doi.org/10.1016/j.rser.2016.08.028.
 
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