Business and Social Sciences

1. Introduction

Supply chains no longer sit inside a single company, or even a single country. A garment might pass through a cotton grower, a spinner, a dye house, a cut-and-sew factory, a freight forwarder, a customs office, and three or four retailers before it reaches a shelf — and each of those actors typically keeps its own records, in its own format, on its own system. This is not a design flaw so much as a natural consequence of specialization: dispersing tasks across firms lets each one focus on what it does best, and that division of labor is part of why global trade works at all (Papadopoulos et al., 2022). But it comes at a cost. When information about a single shipment lives in six different databases, verifying where a product actually originated, or figuring out who is responsible when something goes wrong, becomes a slow and often unreliable process. In sectors where provenance and safety genuinely matter — food, pharmaceuticals, electronics, aerospace — that slowness is not merely inconvenient; it can be dangerous (Rana et al., 2021).

Distributed Ledger Technology has emerged, over roughly the past decade, as one candidate response to this problem. The basic idea is not complicated: instead of every organization maintaining its own private version of events, permitted participants keep, or at least validate, a shared record together. Blockchain is the best-known variant, in which data is grouped into blocks and chained together cryptographically so that altering an earlier entry would be immediately detectable (Hellwig & Huchzermeier, 2022). What is appealing here, at least in principle, is the shift away from unilateral trust — no single company’s database is the arbiter of truth; the ledger itself, distributed across many nodes, plays that role. A reasonably substantial body of work now links blockchain-enabled supply chains to gains in traceability, transparency, and process automation (AL-Ashmori et al., 2022; Hellani et al., 2021).

That said, it would be a mistake to take these claims entirely at face value, and this is where the literature — to its credit — starts to get more careful. A ledger, however tamper-evident, can only record what it is told. If a sensor is faulty, or a supplier misreports a harvest date, the blockchain will faithfully preserve that error forever; immutability guarantees consistency, not correctness (Irannezhad, 2020; A. Kumar et al., 2020). Transparency, too, is not simply switched on. Commercially sensitive data — pricing, volumes, supplier identities — often needs to stay hidden from competitors even while remaining visible to auditors or regulators, which is precisely why most enterprise deployments favor permissioned networks with selective disclosure rather than fully open ledgers. And whether any of this succeeds seems to depend at least as much on organizational incentives, data-exchange standards, contractual arrangements, and cybersecurity practice as it does on the underlying cryptography (Hellwig & Huchzermeier, 2022; Liu et al., 2022).

Given this mixed picture — real promise, but real and under-discussed limitations — the present paper attempts something fairly specific. Rather than adding another isolated case study or another purely technical description of blockchain architecture, we propose an integrated framework, built from the existing literature, for evaluating DLT-based supply chain oversight along three outcomes that we think are too often discussed separately. The first is authenticity: the ability to verify a product’s origin and history. The second is accountability: the capacity to assign responsibility, support audits, and enforce agreed rules. The third is operational resilience: the ability to anticipate, absorb, respond to, and recover from disruption. We also examine how smart contracts, Internet of Things (IoT) sensing, artificial intelligence, and big-data analytics augment — and sometimes complicate — what a distributed ledger can do on its own. The sections that follow work through each of these outcomes in turn, before turning to the barriers that still stand between conceptual promise and dependable, everyday deployment.

1.2 Research Objectives

The principal objective of this study is to examine how Distributed Ledger Technology can improve transparency, traceability, and end-to-end visibility in contemporary, multi-tier supply chains. More specifically, the paper considers how blockchain-based mechanisms support product authentication and counterfeit reduction; how an immutable transaction record can strengthen accountability and audit readiness among stakeholders who do not fully trust one another (Figure 1); and how shared, real-time information exchange contributes to operational resilience — that is, to faster detection of disruption and more coordinated recovery. A further objective is to consider how smart contracts and IoT integration extend automation and real-time decision-making within these systems, and — perhaps most usefully for readers approaching this literature for the first time — to lay out the technical, organizational, and regulatory obstacles (scalability, interoperability, privacy, cost) that continue to constrain adoption, before closing with directions for future research.

1.3 Research Questions

How can Distributed Ledger Technology enhance transparency and end-to-end traceability across complex, multi-tier supply chain networks?

How can blockchain systems increase product authenticity and reduce counterfeiting in global supply chains?

What do immutable ledger records contribute to accountability and trust between supply chain stakeholders?

How can smart contracts automate supply chain processes to improve efficiency and coordination?

What opportunities arise from connecting IoT devices with blockchain systems for real-time monitoring and decision-making?

How does DLT contribute to operational resilience — specifically, faster response and recovery — during supply chain disruptions?

What technical, organizational, and regulatory obstacles constrain blockchain-based supply chain solutions?

How might scalability, interoperability, privacy, and cost barriers to global DLT adoption be addressed?

2. Methodology

2.1 Review design

This study adopts a structured narrative (critical) literature synthesis rather than a fully quantitative systematic review or meta-analysis. That choice reflects the state of the underlying evidence: empirical, comparable, quantitative studies of DLT performance in real supply chain deployments remain scarce, and much of what exists is conceptual, case-based, or simulation-driven. A structured narrative approach lets us draw connections across a heterogeneous evidence base while still applying an explicit, reportable protocol — search strategy, eligibility criteria, and a documented screening process — so that another researcher could, in principle, retrace our steps and arrive at a similar set of included sources.

2.2 Search strategy

Literature was identified through systematic searches of [Scopus, Web of Science, and Google Scholar] conducted between [insert start date] and [insert end date]. Search strings combined core concepts using Boolean operators, for example: (“distributed ledger technology” OR “blockchain”) AND (“supply chain” OR “logistics”) AND (“traceability” OR “transparency” OR “accountability” OR “resilience”). Reference lists of retrieved articles were also hand-searched (a “snowball” step) to capture relevant work not surfaced by the database queries.

2.3 Eligibility criteria

Studies were included if they (a) were published in a peer-reviewed journal or an edited academic volume between 2017 and 2022; (b) addressed DLT or blockchain application within a supply chain, logistics, or provenance-tracking context; and (c) were available in English with full-text access. Studies were excluded if they addressed blockchain purely in a financial-trading or cryptocurrency-speculation context unconnected to physical supply chains, or if they were non-peer-reviewed opinion pieces, preprints without subsequent publication, or duplicate reports of the same dataset.

2.4 Screening and selection

Records were screened in two stages: an initial title/abstract screen against the eligibility criteria above, followed by full-text review of the remaining articles. A PRISMA-style flow diagram summarizing this process is provided in Figure 1.

2.5 Data extraction and synthesis

For each included study, we extracted the application domain (e.g., food, pharmaceuticals, electronics), the DLT architecture described (public, permissioned, hybrid), the supporting technologies discussed (IoT, smart contracts, AI, big data), the outcome(s) addressed (authenticity, accountability, resilience), and the principal limitations or barriers reported. These extractions were then thematically grouped into the comparative tables presented in Sections 5–8 (Tables 1–3), following an iterative coding process in which categories were refined as new studies were added, rather than fixed in advance.

2.6 Quality appraisal

Because the included literature spans conceptual papers, case studies, and empirical evaluations, a single standardized quality-appraisal instrument (e.g., a Cochrane-style risk-of-bias tool) was not applicable. Instead, each study was assessed qualitatively for methodological transparency (whether the DLT architecture and evaluation context were clearly described) and for the strength of the underlying evidence (empirical deployment versus conceptual proposal versus simulation), and this distinction is reflected in how confidently claims are stated throughout the Results and Discussion.

3. Results and Discussion

3.1 DLT Architecture and the Traceability Framework

The proposed framework treats suppliers, carriers, and customers as peer nodes within a decentralized architecture, with no single central server mediating exchange (Figure 2). Custody events — a shipment changing hands, a temperature reading, a customs clearance — are hashed and written to the shared ledger,

Figure 1. PRISMA-informed flow of the literature identification, screening, eligibility assessment, and thematic synthesis process used to develop the authenticity–accountability–resilience framework. Bracketed values indicate counts to be completed by the authors from their actual search records.

which lets goods movement be tracked more or less in real time (Wu et al., 2017). Consensus mechanisms, in turn, are what keep these custody events trustworthy: no single node can unilaterally insert or alter a record, since the surrounding network must first validate it. It is worth pausing on why this matters practically, rather than just architecturally — it means a dispute over “who said the shipment left on Tuesday” no longer collapses into one party’s word against another’s, but can instead be checked against a jointly validated history.

3.2 Blockchain as a Subtype of DLT

Blockchain, specifically, tends to outperform more general DLT designs on structural robustness and auditability, largely because its data is chained sequentially and cryptographically linked (Table 1). Where a generic distributed ledger might use flexible structures — hash tables, directed acyclic graphs, and the like — blockchain’s linear chaining makes tampering with historical entries computationally expensive, which in turn strengthens both immutability and the accountability that depends on it (Kaur et al., 2022; Ramachandran et al., 2022). This is not a purely academic distinction: it is precisely this immutability property that underpins the authenticity claims discussed next.

3.3 Authenticity and Traceability

Where product authenticity is concerned, the evidence is fairly consistent: recording origin data — raw material source, production details — into a tamper-evident ledger at the point of creation gives downstream actors a considerably stronger basis for verifying legitimacy than paper trails or siloed databases allow (Khanna et al., 2022; Table 2). Continuous, time-stamped transaction records extend this into end-to-end traceability, while consensus validation means only verified events are appended to the chain in the first place (Yiu, 2021). Pairing the ledger with IoT sensing pushes this further still, enabling near-real-time shipment monitoring (Helo & Shamsuzzoha, 2020), and the resulting audit trail supports both compliance reporting and, more broadly, the kind of stakeholder trust that repeated, verifiable transactions tend to build over time (Joo & Han, 2021). None of this eliminates counterfeiting risk outright — a determined actor can still falsify data before it ever reaches a sensor — but it does raise the cost and difficulty of doing so considerably.

3.4 Accountability and Smart Contracts

Accountability, at its core, is really a question with four parts: who did something, under what authority, when, and on what evidence. Digital signatures and role-based identities let ledger events be tied to a specific organization or authorized individual, which means disputes over delivery timing or inspection results can be settled against one shared history rather than several conflicting ones (Cornelius, 2021) — a small operational detail, perhaps, but one that saves considerable reconciliation effort in practice.

Smart contracts extend this further by automating rule-based actions: releasing payment once delivery is verified, flagging a regulator when a recall condition is met, or rejecting a shipment where a temperature threshold has been breached (Hassanein et al., 2022). The efficiency gains here are real, but so is the risk — poorly written contract logic executes its errors just as faithfully and just as fast as it executes its successes. Robust governance, then, has to include verified identity, separation of duties, independent code review, and clearly assigned liability (Elverum & Whitman, 2020; Gurzawska, 2020), along with a working distinction between technical finality (the network has accepted the transaction) and legal finality (the transaction may still be contested under contract, consumer-protection, or data-protection law). Consortium members would do well to settle questions of onboarding, voting rights, and dispute resolution before deployment, rather than after — a ledger introduced without this groundwork risks surfacing conflicts rather than resolving them.

3.5 Operational Resilience

Operational resilience — maintaining essential functions through disruption and recovering quickly afterward — appears to benefit from DLT chiefly through shared visibility: when multiple firms draw on the same validated event history, disruptions tend to surface earlier and responses tend to be better coordinated (Al-Farsi et al., 2021; Bekrar et al., 2021; Tanwar, 2022). Smart contracts can trigger contingency actions automatically — rerouting shipments, prioritizing scarce stock, releasing contingency payments — while IoT feeds supply real-time evidence of location and condition, and AI models attempt to anticipate delays before they fully materialize (N. M. Kumar et al., 2020; Mandaroux et al., 2021; Sedhom et al., 2021).

It would be misleading, though, to present this as risk-free. A consortium platform can fail; keys can be compromised;

Figure 2. Distributed ledger-based supply chain architecture showing suppliers, customers, and carriers as peer nodes enabling decentralized data sharing, secure custody event recording, real-time monitoring, and consensus-driven validation for traceability, transparency, and operational integrity (Wu et al., 2017).

Table 1. The comparative analysis shows that blockchain, as a subset of Distributed Ledger Technology (DLT).

Table 2. Product origin information is recorded at the source and stored in a tamper-proof ledger, ensuring accurate identification of raw materials and production details.

Table 3. Major adoption challenges and practical responses.

a software bug can propagate across every member simultaneously. Resilience design therefore has to build in redundancy — failover procedures, geographically distributed hosting, secure key-recovery mechanisms — not to eliminate central points of control altogether, but to prevent any single one of them from becoming a single point of failure (Abdelmaboud et al., 2022; Ahamed Ahanger et al., 2022; Madaan et al., 2021). And resilience claims should be measured, not simply assumed: metrics such as time-to-exception-detection, recall accuracy, and cost per traced unit, benchmarked against a pre-implementation baseline, offer a more defensible basis for evaluating pilot outcomes than anecdote does.

3.6 Challenges and Limitations

The barriers to adoption are best understood as interdependent rather than isolated (Table 3): solving scalability without addressing privacy, for instance, or building interoperable standards without resolving the underlying incentive problem, tends to produce technically elegant solutions that organizations still decline to trust or use. Interoperability across platforms and identifier schemes, oracle risk (the possibility that a flawed physical-world input is faithfully recorded but simply wrong), regulatory ambiguity around tokens and cross-border data, and the practical costs of integration and skills training all recur across the literature we reviewed, and none of them yields to a purely technical fix.

3.7. Future Research Directions

Two broad trajectories seem worth prioritizing. The first is comparative, empirical evaluation — studies that place DLT-based and conventional centralized systems side by side, under matched operational conditions, so that efficiency and cost claims move beyond conceptual assertion (Dasaklis et al., 2022; Malik & Kushwah, 2022). The current absence of shared metrics across studies makes cross-study comparison, and therefore generalizable guidance, genuinely difficult. The second is infrastructural: more work on physical-to-digital binding methods resistant to tag cloning or substitution, on privacy-preserving traceability models that still satisfy regulatory disclosure requirements (Tozanlı et al., 2020; Turner et al., 2022), and on long-term field studies of multi-stakeholder governance in practice, rather than in simulation alone.

4. Conclusion

Distributed Ledger Technology can meaningfully strengthen supply chain oversight by giving participants a shared, tamper-evident, time-stamped record of transactions — and when paired with reliable digital identity, IoT-based data capture, and carefully designed smart contracts, it appears to genuinely improve provenance verification, auditability, and coordinated disruption response, particularly where no single actor is fully trusted to hold the system of record. That said, DLT is not a substitute for trustworthy data or sound governance: immutability preserves whatever is written, but it does not validate whether that entry was true to begin with. Realizing the technology’s benefits, in practice, seems to depend less on cryptographic sophistication than on the surrounding socio-technical scaffolding — standards, incentives, and institutional discipline — built deliberately around it.

Acknowledgements

The authors M.F.A.B. thank their respective institutions for their support during the preparation of this manuscript. No external funding was received for this work.

Author Contributions

M.F.A.B. conceived the study, conducted the literature review, and drafted the manuscript. M.N.H. contributed to conceptual framework development, critically revised the manuscript, and supervised the project. Both authors read and approved the final manuscript.

Competing Financial Interests

The authors M.F.A.B. declare no competing financial interests related to this work.

References