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Big Data Analytics in Supply Chain Management
Research Guide

What is Big Data Analytics in Supply Chain Management?

Big Data Analytics in Supply Chain Management applies large-scale data processing techniques to enhance visibility, forecasting, and risk mitigation in logistics networks.

Researchers integrate IoT streams and predictive models for resilient supply chains in Industry 4.0. Studies examine organizational factors influencing big data adoption (Brock and Khan, 2017, 119 citations). Over 10 papers from 2017-2024 link big data to business value in operations.

Curated Papers

Key Challenges

Why It Matters

Big data analytics optimizes global supply chains amid disruptions, reducing costs and improving sustainability. Enholm et al. (2021, 767 citations) show AI and big data drive business value through efficiency gains in logistics. Sima et al. (2020, 727 citations) highlight Industry 4.0 transformations in operations, enabling real-time demand forecasting. Brock and Khan (2017) demonstrate organizational factors boost technology acceptance for supply chain resilience.

Key Research Challenges

Data Integration from IoT

Merging heterogeneous IoT streams with supply chain data creates silos and quality issues. Guergov and Radwan (2021, 157 citations) analyze convergence challenges of IoT, AI, and blockchain in logistics. Real-time processing demands scalable architectures.

Organizational Adoption Barriers

Firms face resistance due to cultural and structural factors in big data implementation. Brock and Khan (2017, 119 citations) find organizational factors significantly impact technology acceptance in analytics. Training gaps hinder supply chain teams.

Scalability for Predictive Forecasting

Handling volume and velocity of big data strains forecasting models for volatile demand. Chen et al. (2022, 141 citations) apply resource-based views to AI-driven e-commerce performance, noting scalability limits. Volatility in global chains amplifies errors.

Essential Papers

Artificial Intelligence and Business Value: a Literature Review

Ida Merete Enholm, Emmanouil Papagiannidis, Patrick Mikalef et al. · 2021 · Information Systems Frontiers · 767 citations

Abstract Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizati...

Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review

Violeta Sima, Ileana Georgiana Gheorghe, J. Subić et al. · 2020 · Sustainability · 727 citations

Automation and digitalization, as long-term evolutionary processes, cause significant effects, such as the transformation of occupations and job profiles, changes to employment forms, and a more si...

E-commerce and consumer behavior: A review of AI-powered personalization and market trends

Mustafa Ayobami Raji, Hameedat Bukola Olodo, Timothy Tolulope Oke et al. · 2024 · GSC Advanced Research and Reviews · 181 citations

In the dynamic landscape of electronic commerce (e-commerce), understanding and adapting to evolving consumer behavior is critical for the sustained success of online businesses. This review delves...

Blockchain Convergence: Analysis of Issues Affecting IoT, AI and Blockchain

Sasho Guergov, Neyara Radwan · 2021 · International Journal of Computations Information and Manufacturing (IJCIM) · 157 citations

The purpose of this study is to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the fi...

Blockchain Technology and Smart Contracts in Decentralized Governance Systems

Adam P. Balcerzak, Elvira Nica, Elżbieta Rogalska et al. · 2022 · Administrative Sciences · 150 citations

The aim of our systematic review was to inspect the recently published literature on decentralized governance systems and integrate the insights it articulates on blockchain technology and smart co...

Artificial intelligence focus and firm performance

Sagarika Mishra, Michael T. Ewing, Holly B. Cooper · 2022 · Journal of the Academy of Marketing Science · 143 citations

Abstract Artificial Intelligence is poised to transform all facets of marketing. In this study, we examine the link between firms’ focus on AI in their 10-K reports and their gross and net operatin...

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

Donghua Chen, José Paulo Esperança, Shaofeng Wang · 2022 · Frontiers in Psychology · 141 citations

The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial inte...

Reading Guide

Foundational Papers

Start with Brock and Khan (2017) for organizational adoption basics in big data analytics, as it grounds technology acceptance in supply chain contexts.

Recent Advances

Study Enholm et al. (2021, 767 citations) for AI-driven business value and Sima et al. (2020, 727 citations) for Industry 4.0 supply chain transformations.

Core Methods

Core techniques: predictive modeling from IoT data (Guergov and Radwan, 2021), resource-based AI applications (Chen et al., 2022), and TAM extensions (Brock and Khan, 2017).

How PapersFlow Helps You Research Big Data Analytics in Supply Chain Management

Discover & Search

Research Agent uses searchPapers and exaSearch to find papers like 'Big data analytics: does organizational factor matters impact technology acceptance?' by Brock and Khan (2017), then citationGraph reveals 119 downstream works on supply chain adoption, while findSimilarPapers uncovers IoT-big data integrations.

Analyze & Verify

Analysis Agent employs readPaperContent on Enholm et al. (2021) to extract business value metrics, verifyResponse with CoVe checks claims against Sima et al. (2020), and runPythonAnalysis runs pandas scripts on IoT dataset simulations for forecasting accuracy, with GRADE scoring evidence strength on adoption factors.

Synthesize & Write

Synthesis Agent detects gaps in Industry 4.0 resilience via contradiction flagging across papers, while Writing Agent uses latexEditText for supply chain model revisions, latexSyncCitations for 10+ references, and latexCompile to generate polished reports with exportMermaid for logistics network diagrams.

Use Cases

"Analyze demand forecasting accuracy using big data in volatile supply chains"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas on IoT simulation data from papers) → matplotlib forecast plots and error metrics output.

"Draft a literature review on big data adoption barriers in SCM"

Research Agent → citationGraph (Brock 2017) → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → LaTeX PDF review with citations.

"Find GitHub repos implementing big data SCM analytics from papers"

Research Agent → paperExtractUrls (Guergov 2021) → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified code examples for IoT-blockchain supply chain prototypes.

Automated Workflows

Deep Research workflow conducts systematic reviews by chaining searchPapers on 50+ papers like Enholm (2021) and Sima (2020), producing structured SCM analytics reports. DeepScan applies 7-step verification with CoVe checkpoints on IoT-big data claims. Theorizer generates theories on organizational adoption from Brock and Khan (2017) literature.

Try Doxa for Big Data Analytics in Supply Chain Management Research

Frequently Asked Questions

What defines Big Data Analytics in Supply Chain Management?

It applies large-scale data processing to optimize visibility, forecasting, and risk in logistics, integrating IoT with predictive models.

What methods are used in this subtopic?

Methods include predictive analytics on IoT streams, technology acceptance models (Brock and Khan, 2017), and AI-blockchain convergence (Guergov and Radwan, 2021).

What are key papers?

Enholm et al. (2021, 767 citations) on AI business value; Sima et al. (2020, 727 citations) on Industry 4.0; Brock and Khan (2017, 119 citations) on organizational factors.

What open problems exist?

Challenges include IoT data scalability, organizational barriers, and real-time forecasting amid volatility, as noted in Chen et al. (2022) and Guergov and Radwan (2021).