The Bright and Dark Sides of Digitalization for Supply Chain Resilience
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".