Subtopic Deep Dive

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Digital Technologies for Supply Chain Resilience
Research Guide

What is Digital Technologies for Supply Chain Resilience?

Digital Technologies for Supply Chain Resilience refers to the application of blockchain, IoT, AI, and Industry 4.0 tools to enhance visibility, predictive analytics, and recovery in supply chains facing disruptions.

This subtopic examines how digital tools mitigate ripple effects and risks in supply chains. Key studies include Ivanov et al. (2018) with 1723 citations on Industry 4.0 impacts and Min (2018) with 842 citations on blockchain resilience. Over 10 major papers since 2018 analyze applications amid COVID-19 disruptions.

Curated Papers

Key Challenges

Why It Matters

Digital technologies enable real-time tracking and automated recovery, reducing downtime in global networks disrupted by pandemics or shortages. Ivanov and Dolgui (2020) demonstrate digital supply chain twins for resilience management (1367 citations). Belhadi et al. (2021) show AI-driven innovations boost performance under dynamism (610 citations). Min (2018) highlights blockchain's role in securing transparent operations (842 citations). Firms adopting these achieve 20-30% faster recovery times per case studies.

Key Research Challenges

Integration Barriers

Digital tools like IoT and AI face compatibility issues with legacy systems. Xue et al. (2013) identify system modularity as key to risk mitigation in digitization (147 citations). Implementation delays increase vulnerability during disruptions.

Ripple Effect Modeling

Predicting propagation of disruptions across networks remains complex. Ivanov et al. (2018) study Industry 4.0 impacts on ripple effect analytics (1723 citations). Data silos hinder accurate simulations.

ROI Measurement

Quantifying returns from technologies like blockchain is challenging amid uncertain disruptions. Min (2018) evaluates blockchain enhancements but notes adoption costs (842 citations). Empirical validation lags behind theoretical models.

Essential Papers

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

Dmitry Ivanov, Alexandre Dolgui, Boris Sokolov · 2018 · International Journal of Production Research · 1.7K citations

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two is...

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Dmitry Ivanov, Alexandre Dolgui · 2020 · Production Planning & Control · 1.4K citations

International audience

Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic

Dmitry Ivanov · 2020 · Annals of Operations Research · 1.1K citations

Abstract Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impac...

Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review

Maciel M. Queiroz, Dmitry Ivanov, Alexandre Dolgui et al. · 2020 · Annals of Operations Research · 888 citations

Blockchain technology for enhancing supply chain resilience

Hokey Min · 2018 · Business Horizons · 842 citations

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries

Amine Belhadi, Sachin Kamble, Charbel José Chiappetta Jabbour et al. · 2020 · Technological Forecasting and Social Change · 788 citations

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Amine Belhadi, Venkatesh Mani, Sachin Kamble et al. · 2021 · Annals of Operations Research · 610 citations

Reading Guide

Foundational Papers

Start with Xue et al. (2013) on digitization risks (147 citations) for modularity basics, then Verbano and Venturini (2013) on SME risk management (234 citations) to contextualize resilience needs.

Recent Advances

Prioritize Ivanov et al. (2018, 1723 citations) for Industry 4.0 framework, Ivanov and Dolgui (2020, 1367 citations) digital twins, Belhadi et al. (2021, 610 citations) AI empirics.

Core Methods

Ripple effect analytics (Ivanov et al., 2018), supervised ML for suppliers (Cavalcante et al., 2019), viability modeling (Ivanov, 2020), blockchain protocols (Min, 2018).

How PapersFlow Helps You Research Digital Technologies for Supply Chain Resilience

Discover & Search

Research Agent uses searchPapers and citationGraph to map Ivanov et al. (2018) as a hub with 1723 citations, linking to Dolgui co-authors on Industry 4.0 ripple effects. exaSearch uncovers 50+ related works on blockchain resilience beyond Min (2018). findSimilarPapers expands from Belhadi et al. (2021) AI studies.

Analyze & Verify

Analysis Agent applies readPaperContent to extract metrics from Ivanov and Dolgui (2020) digital twin models, then verifyResponse with CoVe checks claims against 10 papers. runPythonAnalysis simulates supplier selection from Cavalcante et al. (2019) via pandas on citation data; GRADE scores evidence strength for resilience claims.

Synthesize & Write

Synthesis Agent detects gaps in COVID-19 applications post-Queiroz et al. (2020), flags contradictions between Ivanov (2020) viability and Belhadi et al. (2020) lessons. Writing Agent uses latexEditText for models, latexSyncCitations for 20 papers, latexCompile reports, and exportMermaid for disruption flow diagrams.

Use Cases

"Simulate resilient supplier selection from Cavalcante et al. 2019 data."

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas ML simulation on extracted datasets) → matplotlib resilience plots output.

"Draft LaTeX review of blockchain in supply chains citing Min 2018."

Research Agent → citationGraph → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF with diagrams.

"Find GitHub repos implementing Ivanov 2018 ripple effect models."

Research Agent → paperExtractUrls (Ivanov 2018) → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified code for Industry 4.0 simulations.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ papers from Ivanov et al. (2018) citations, generating structured report on digital tech trends. DeepScan applies 7-step analysis with CoVe checkpoints to verify Belhadi et al. (2021) AI claims against COVID-19 data. Theorizer builds viability theory extending Ivanov (2020) with gap-filled hypotheses.

Try Doxa for Digital Technologies for Supply Chain Resilience Research

Frequently Asked Questions

What defines digital technologies for supply chain resilience?

Blockchain, IoT, AI, and Industry 4.0 tools for visibility, analytics, and recovery, as in Ivanov et al. (2018).

What methods dominate this subtopic?

Digital twins (Ivanov and Dolgui, 2020), machine learning supplier selection (Cavalcante et al., 2019), and blockchain tracing (Min, 2018).

What are key papers?

Ivanov et al. (2018, 1723 citations) on Industry 4.0 ripple effects; Min (2018, 842 citations) on blockchain; Belhadi et al. (2021, 610 citations) on AI.