The Bright and Dark Sides of Digitalization for Supply Chain Resilience
Notice bibliographique
Résumé
The Bright and Dark Sides of Digitalization for Supply Chain Resilience TREO Talk Paper Atiyeh Kazeroonimonfared Toronto Metropolitan University akazeroonimonfared@torontomu.ca Ravi Vatrapu Toronto Metropolitan University vatrapu@torontomu.ca Abstract The growing reliance on digital technologies and innovations to enhance supply chain resilience (SCRES) in the aftermath of the COVID-19 pandemic has brought this topic to the forefront of scholarly attention. Several researchers recognized digitalization as a key approach for developing resilience capabilities such as visibility, agility, and collaboration in supply chains (Spieske & Birkel, 2021; Yuan et al., 2024). Moreover, practitioners are increasingly investing in digital technologies with the expectation of improving SCRES. Despite these trends, research on this topic, which lies at the intersection of information systems (IS) and operations management (OM) fields, is limited (Zouari et al., 2021), and many aspects of this phenomenon are still unknown (Huang et al., 2023). Resilience is “the adaptive capability of a supply chain to prepare for and/or respond to disruptions, to make a timely and cost-effective recovery, and therefore progress to a post-disruption state of operations -ideally, a better state than prior to disruption” (Tukamuhabwa et al., 2015, p.5599). SCRES consists of two main components: vulnerabilities, referring to factors that increase SC’s susceptibility to disruptions, and capabilities, denoting attributes that allow a supply chain to foresee and endure disruptions (Pettit et al., 2013). While implementing digital technologies creates numerous SCRES capabilities, it also introduces new challenges, such as cybersecurity threats and unforeseen technology outages, which might result in unintended vulnerabilities. As such, digitalization can simultaneously hinder and enhance SCRES (Ivanov & Dolgui, 2021). However, prior research has primarily focused on the positive impacts of digitalization on resilience, leaving its potential drawbacks underexplored. Motivated by the current SC digitalization and resilience trends and acknowledging the dual role of digital technologies, this study conducts a systematic literature review to answer the following research question: What SCRES capabilities and vulnerabilities are impacted by SC digitalization? We found that among the digitally driven SCRES capabilities, visibility, recovery, and collaboration are the most frequently examined. In contrast, capabilities such as social capital, dispersion, organization, innovativeness, and market position remain overlooked in the context of digital supply chains. Moreover, our study highlights the scarcity of research on the dark side of SC digitalization. Cybersecurity concerns emerged as the most critical challenge associated with digital technology adoption in supply chains, alongside other vulnerabilities such as complexity and connectivity issues, legacy equipment constraints, financial burdens, and human capability loss. By highlighting both bright and dark sides, we aim to initiate a more nuanced conversation on digital transformation strategies in supply chain management, emphasizing the need for balanced, risk-aware approaches. References Spieske, A., and Birkel, H. (2021). Improving Supply Chain Resilience through Industry 4.0: A Systematic Literature Review under the Impression of the COVID-19 Pandemic. Computers & Industrial Engineering, (158), p. 107452. Yuan, Y., Tan, H., and Liu, L. (2024). The Effects of Digital Transformation on Supply Chain Resilience: A Moderated and Mediated Model. Journal of Enterprise Information Management (37:2), pp. 488–510. https://doi.org/10.1108/JEIM-09-2022-0333 Ivanov, D., and Dolgui, A. (2021). A Digital Supply Chain Twin for Managing the Disruption Risks and Resilience in the Era of Industry 4.0. Production Planning & Control (32:9), pp. 775–788. https://doi.org/10.1080/09537287.2020.1768450
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,012 |
| Communication savante | 0,015 | 0,021 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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