Application of Machine Learning in Supply Chain Management in the Context of Transaction Costs
DOI:
https://doi.org/10.29327/2384439.2.4-3Keywords:
Machine Learning, Transaction Cost Economics Theory, egic Supply Chain Management Orientation, Supply Chain Performance, Firm Performance, Value AdditionAbstract
This paper aimed to identify the impact of supply chain performance on organizational performance through the application of machine learning in the relationships between constructs, in a context of transaction cost economics. The research had three stages: bibliographical; exploratory and qualitative research, with 11 supply chain professionals. Subsequently, a questionnaire with 72 statements was administered to 121 professionals who work with machine learning. Through exploratory factor analysis, significant variables were identified. The findings confirmed that the use of machine learning has a positive impact on the relationships between supply chain strategic orientation and transaction cost reduction and between transaction cost reduction and supply chain performance. It can be concluded that the use of machine learning led to improved supply chain performance, which was passed on to the company's performance. As contributions the study brought: the application of machine learning expands the understanding of the relationship between constructs; obtained subsidies to discover patterns in the data involved in supply chain processes through factor analysis that identified influential factors for the success of the supply chain.
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ABUBAKER, H.; DESOUZA, A.; KHARE, A.; LEE, H. Examining potential benefits and challenges associated with the Internet of Things integration in supply chain. Journal of Manufacturing Technology Management. Vol. 28, nº 8. 2017.
ARYAL, A.; LIAO, Y.; NATTUTHURAI, P.; LI, B. The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management: An International Journal 25/2, 141–156. 2020.
AUGUSTO, C. A.; SOUZA, J. P.; DELLAGNELO, E. H. L.; CARIO, S. A. F. Pesquisa qualitative: rigor metodológico no tratamento da teoria dos custos de transação em artigos apresentados nos congressos da Sober (2007-2011). RESR. Vol. 51, p. 745-764. Out/Dez 2013.
BARYANNIS, G.; DANI, S.; ANTONIU, G. Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems Volume 101, Pages 993-1004. December 2019.
BESANKO, D.; DRANOVE, D.; SHANLEY, M.; SCHAEFER, S. A economia da estratégia. 3. ed. Bookman. Porto Alegre. 2006.
BURGESS, K.; SINGH, P. J.; KOROGLU, R. Supply chain management: a structured literature review and implications for future research. International Journal of Operations & Production Management, Vol, 26, nº 7. 2006
CARBONNEAU, R.; LAFROMBOISE, K.; VAHIDOV, R. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research 184, p. 1140–1154. 2008.
CARVALHO, H.; DUARTE, S.; MACHADO, V. C. Lean, agile, resilient and green: divergencies and synergies. International Journal of Lean Six Sigma Vol. 2 No. 2, 2011.
COASE, R. H. The nature of the firm. Economica. V. 4, issue 6. Willey. November. 1937
DOMINGOS, P. A Few Useful Things to Know About Machine Learning. Disponível em: https://prod-edxapp.edx-cdn.org/assets/courseware/v1/9020f1ea5293f47aec1c5cd03cf0d1e0/asset-v1:ColumbiaX+DS102X+2T2019+type@asset+block/A_Few_Useful_Things_MachineLearning_Domingos.pdf . Acessado em 13/01/2020. Communications of the ACM. Vol. 55 no. 10. October 2012.
DYER, J. H. Effective interfirm collaboration: how firms minimize transaction costs and maximize transaction value. Strategic Management Journal, Vol. 18:7, p. 535–556. 1997
ELLINGER, A.; SHIN, H.; NORTHINGTON, W. M.; ADAMS, F. G. The influence of supply chain management competency on customer satisfaction and shareholder value. Supply Chain Management: An International Journal. 17/3. 2012.
ESPER, T. L.; DEFEE, C. C.; MENTZER, J. T. A framework of supply chain orientation. The International Journal of Logistics Management, Vol. 21, nº 2. 2010.
FACELI, K.; LORENA, A. C.; GAMA, J.; ALMEIDA, T. A.; CARVALHO, A. C. P. L. F. Inteligência artificial: uma abordagem de aprendizado de máquina. 2. ed. Rio de Janeiro: GEN. 2022.
FARINA, E. M. M.Q.; AZEVEDO, P. F.; SAES, M. S. M. Competitividade: Mercado, estado e organizações. Ed. Singular. São Paulo. 1997.
FINLAY, S. Artificial intelligence and machine learning for business: a no-nonsense guide to data driven technologies. Relativistic Books. UK. 2017.
IANSITI, M.; LAKHANI, K. R. A competição na era da IA: a inteligência de máquina mudou as regras dos negócios. Harvard Business Review Brasil. Fevereiro de 2020.
JÜTTNER, U.; CHRISTOPHER, M. The role of marketing in creating a supply chain orientation within the firm. International Journal of Logistics: Research and Applications Vol. 16 nº 2, pp. 99-113. 2013.
KARAMI, M.; MALEKIFAR, S.; NASIRI, A. B.; NASIRI, M. B.; FEILI, H.; KHAN, S. U. R. Retracted: A conceptual model of the relationship between market orientation and supply chain performance. Global Business and Organizational Excellence, January/February. 2015.
leanness level of supply chains. Supply Chain Management: An International Journal. 2021
KIRCHOFF, J. F; TATE, W. L; MOLLENKOPF, D. A. The impact of strategic organizational orientations on green supply chain management and firm performance. International Journal of Physical Distribution & Logistics Management; Bradford Vol. 46, Ed. 3. 2016
LEE, T.; NAM, H. An empirical study on the impact of individual and organizacional supply chain orientation on supply chain management. The Asian Journal of Shipping and Logistics, 32. 2016.
LIU, S.; EWEJE, G.; HE, Q.; LIN. Z. Turning motivation into action: a strategic orientation model for green supply chain management. Business Strategic Environment, 29, p. 2908-2918. 2020.
MOHRI, M.; ROSTAMIZADEH, A.; TALWALKAR, A. Foundations of machine learning. 2nd ed. MIT Press. 2018.
QI, Y.; BOYER, K. K.; ZHAO, X. Supply Chain Strategy, Product Characteristics, and Performance Impact: Evidence from Chinese Manufacturers. Decision Sciences, Volume 40 Number 4. November 2009.
RAHIMI, A.; RAAD, A.; TABRIZ, A. A.; MOTAMENI, A. Providing an interpretive structural model of agile supply chain practices. Journal of Modelling in Management Vol. 15 No. 2, 2020
RUSSELL, S.; NORVIG, P. Inteligência artificial: uma abordagem moderna. 4. ed. Rio de Janeiro: GEN. 2022.
SANTOS, L. C.; REUL, L. M. A.; GOHR, C. F. A graph-theoretic approach for assessing the
SAWANGWONG, A.; CHAOPAIRSARN, P. The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance. Kybernetes. Emerald Publishing Limited. 2021.
SCHWAB, K. A quarta revolução industrial. Edipro. São Paulo. 2016.
SIMCHI-LEVI, D.; KAMINSKY, P.; SIMCHI-LEVI, E. Cadeia de suprimentos: projeto e gestão. 3. ed. Porto Alegre: Bookman. 2010.
SIFFERT FILHO, N. F. A economia dos custos de transação. Revista do BNDES. V. 2 n. 4, p. 103-128. Dez. 1995.
SMITH, H. Machine learning: the absolute beginner’s guide to learn and understand machine learning effectively. CPSIA. USA. 2018.
SRIYAKUL, T.; PRIANTO, A. L.; JERMSITTIPARSERT, K. Is the supply chain orientation in an agile supply chain determining the supply chain performance? Humanities & Social Sciences Reviews. Vol. 7, n. 3. Pp 695-702. 2019.
STONEBRAKER, P. W.; LIAO, J. Environmental turbulence, strategic orientation: Modeling supply chain integration. International Journal of Operations & Production Management. Volume 24 Issue 10. 2004.
WHITTEN, G. D.; GREEN JR, K. W.; ZELBST, P. J. Triple-A supply chain performance. International Journal of Operations & Production Management Vol. 32 nº1, pp. 28-48. Emerald Group Publishing Limited. 2012
WILLIANSON, O. E. The economic institutions of capitalism. The Free Press. USA. 1985.
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