A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing

By Ian M. Cavalcante, Enzo Morosini Frazzon, Fernando Antonio Forcellini, Dmitry A. Ivanov. International Journal of Information Management, 2019

464 citations recorded by the source corpus.

Abstract There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. We conceptualize an entirely different approach to analyzing the risk profiles of supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing. Digital manufacturing peculiarly challenge the supplier selection by the dynamic order allocations, and opens new opportunities to exploit the digital data to improve sourcing decisions. We develop a hybrid technique, combining simulation and machine learning and examine its applications to data-driven decision-making support in resilient supplier selection. We consider on-time delivery as an indicator for supplier reliability, and explore the conditions surrounding the formation of resilient supply performance profiles. We theorize the notions of risk profile of supplier performance and resilient supply chain performance. We show that the associations of the deviations from the resilient supply chain performance profile with the risk profiles of supplier performance can be efficiently deciphered by our approach. The results suggest that a combination of supervised machine learning and simulation, if utilized properly, improves the delivery reliability. Our approach can also be of value when analyzing the supplier base and uncovering the critical suppliers, or combinations of suppliers the disruption of which result in the adverse performance decreases. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, re-designing the supplier base or investing in most important and risky suppliers.

Subfields: Management Information Systems, Safety, Risk, Reliability and Quality, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/ddf4008d6e57306de30f6050b7a1bf1bf0cdd50f

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Citations in the corpus

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, background): “Recent research pointed to the new opportunities for managing SC disruption risks by data-driven approaches (Subramanian and Abdulrahman 2017; Cavalcante et al. 2019; Dubey, Gunasekaran, Childe, Wamba, et al. 2019; Ivanov, Dolgui, and Sokolov 2019).”
Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, methodology): “Additionally, a data-driven culture in firms may give rise to makeshift supplier-customer relationships thus affecting the bargaining power of firms represented by supplier selection predictive models based smart contracts (Cavalcante et al., 2019).”
Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management (2020, background): “2016), improving forecastingmodels usingBigData (Johnson, Lee, and SimchiLevi 2016), combining optimisation, machine learning algorithms and agent-based modelling for supply chain resilience (Ivanov 2018; Cavalcantea et al. 2019; Zhao, Zuo, and Blackhurst 2019).”
Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain (2019, background): “literature calls for new methodical approaches to real-time capabilities in the SC (Cavalcantea et al. 2019; Queiroz and Wamba 2018; Oliveira and Handfield 2019; Dubey, Gunasekaran, Childe, Blome, et al. 2019; Dubey, Gunasekaran, Childe, Wamba, et al. 2019). The smart contract monitors the operation”
The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives (2022, background): “…which, in their integrity secure the provision of society and markets with goods and services’ (Ivanov and Dolgui 2020); a digital supply chain (Cavalcante et al. 2019, Ivanov and Dolgui 2021a, Frazzon et al. 2021); and a human-centric ecosystem responsible for securing society’s needs in…”
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020, background): “Hence, the applications in the management field of big data analytics (BDA), machine learning (ML), and artificial intelligence (AI) have received increased attention (Waller and Fawcett, 2013; Chen and Zhang, 2014; Sivarajah et al., 2017; Delen and Zolbanin, 2018; Cavalcante et al., 2019; Dwivedi e”
Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† (2019, background): “…and Li 2012; Torabi, Baghersad, and Mansouri 2015; Yildiz et al. 2016; Fattahi, Govindan, and Keyvanshokooh 2017; Rezapour, Farahani, and Pourakbar 2017; He et al. 2018; Ivanov et al. 2016; Lücker, Seifert, and Biçer 2019; Pavlov et al. 2019; Cavalcantea et al. 2019; Hosseini et al. 2019).”
Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, unknown): “D) can be used for selecting new suppliers and monitoring the current supply base’s SCRES (Cavalcante et al., 2019; Kahiluoto, Mäkinen, & Kaseva, 2020; Sanders, 2016).”
Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model (2023, background): “At the same time, incorporating digital technologies such as artificial intelligence, machine learning, and big data into the supply chain process can better monitor and analyze supplier operations, eliminate poorly operated suppliers, and build a robust supply chain cooperation network, which will ”