Comprehensive review:
Enhancing Supply Chain Transparency through IoT: A Systematic Review, Conceptual
Framework, and Case Study Insights
Ashutosh Umesh Jani
University Canada West
MBAR 661 Consulting / Research Project (SPRING 25-02)
Supervisor: Dr. Amit Kohli
April 2025

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Abstract
Purpose
The study evaluates the relevance of IoT technology in supply chain transparency,
operational accountability, and ESG (Environment, Social, and Governance) compliance in
various international sectors. The focus is on SMEs because of their structural and financial
limitations in complying with traceability and regulatory demands of the twenty-first century.
The paper ends with proposing a three-layered framework to operationalize scalable digital
transformation through IoT adoption.
Design/methodology/approach
The two-phase approach was used. In phase one, the systematic review of 36 peerreviewed articles from (2018 – 2025) was carried out to understand core enabling
technologies, adoption patterns, and barriers to implementation. The second phase comprises
of a comparative case rundown of five global leaders - Walmart, Maersk, Volvo, ZhongAn,
and the pharmaceutical cold chain of India - showcasing the use of RFID, smart contracts,
blockchain, BLE sensors, and analytics in practice. Despite such large scales, these cases
gave rise to concepts for a modular framework intended for SMEs. To associate the
framework with SMEs, a phased strategic roadmap was created for Canadian SMEs, with
prominent mention of Bothwell Cheese, Happy Planet, and Chudleigh's Limited. These SME
case examples showcase possible practical approaches ranked by cost that conform with ESG
standards in the public cold chain space and align with government stimulus such as the
Canadian Digital Adoption Program (CDAP).

Findings
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IoT fosters traceability, automates compliance throughout the supply chain, supports
the ESG disclosures, and develops trust among stakeholders. The framework proposed,
consisting of Technological Enablers, Operational Execution, and Transparency Outcomes,
would allow SMEs to adopt IoT by stages. The chief challenges to implementation are
interoperability, regulatory complexity, and cost. These can be addressed though, low-code
platforms, middleware layers like APIs (Application Program Interface), and well-structured
roadmap planning.
Practical implications
The study will offer relevant guidelines for SME policymakers, executives, and
supply chain managers for operational modernization in the foreseeable future. The
framework gives rise to a model that underpins digitally resilient, regulation-ready, and
ethically governed supply chains.

Originality/value
The study draws a scalable hybrid industry-agnostic IoT framework for SMEs by
combining academic insights with validated cases in practice. This framework serves to bring
the SME community to the global compliance regime and, in turn, propel digital innovation,
transparency, and sustainability.
Keywords
Internet of Things, Blockchain, Supply Chain Transparency, SMEs, ESG Compliance,
RFID, Traceability, Smart Contracts, Digital Supply Chains.

Table of Contents

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Abstract ..................................................................................................................................................2
Abbreviations .........................................................................................................................................4
Introduction............................................................................................................................................5
Research Questions ................................................................................................................................9
Phase 1 – Section B: Technological Enablers and Barriers (RQ2) .................................................19
A. Three-Layer Conceptual Framework .......................................................................................30
B. Case-Aligned Justification of Framework ................................................................................32
C. Visual Representation: Conceptual Framework Diagram .....................................................32
Case Studies Insights: Large-Scale Lessons for SME Implementation ..........................................36
1.Walmart and IBM Food Trust ....................................................................................................36
2. Case Study: Maersk and IBM TradeLens.................................................................................38
3. Case Study: Volvo & Circulor ....................................................................................................41
4. Case Study: ZhongAn Technology .............................................................................................44
5. Case Study: India’s Cold Chain Modernization .......................................................................46
Strategic Roadmap for SME IoT Implementation ...........................................................................51
Phase 1: Assessment – Diagnosing Digital Readiness and Traceability Gaps............................51
Phase 2: Integration Planning – Laying the Digital with Interoperable Systems......................53
Phase 3: Implementation & Testing – Edge Analytics for Real-Time Actionability. ................55
Phase 4: Monitoring & Scaling – Institutionalizing Blockchain and ESG Governance ...........56
Discussion .............................................................................................................................................59
Conclusion ............................................................................................................................................65
References.............................................................................................................................................69
Appendices............................................................................................................................................73

Abbreviations
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The incompatible blend of supply chains acts hyper sensitively along the lines of
traceability, so supply chains in the current hyperconnected global economy may be
described as vital yet fragile lifelines. Could disruption in traceability by way of theft, system
failure, or regulatory non-compliance cascade across markets, it may shake up stakeholder
trust evenly and bring about attracted on intense scrutiny from third parties? Supply chain
visibility was once a strategic competitive edge but in an ESG world this has now become an
operative and necessary consideration (Budler et al., 2024; Saxena & Arya, 2023).
The shift has hit hard among SMEs-the engines of global production and trade.
Despite their size and contribution, SMEs are oftentimes subjected to the ill effects of old
systems, limited data flows, and limited resource capacity in the financial and human sense.
While custom digital systems designed for their needs are well within the means of big
multinationals, SMEs seek modular, interoperable, and cost-sensitive solutions that enhance
transparency without monopolizing their operational capacity. At the same time, however,
newly emerging regulations such as the EU Corporate Sustainability Reporting Directive
(CSRD) and the U.S. Food Safety Modernization Act (FSMA) intensify the expectations,
requiring agencies and supply chain nodes at every level to state their status, audit results,
traceability, and accountability digitally (Dakhia et al., 2025; Ahmad et al., 2024).
In this scenario, the Internet of Things assumes the role of a transformative enabler.
By digitally wiring physical assets into intelligent networks, IoT technologies such as RFID
tags, GPS trackers, environmental sensors, and Bluetooth Low Energy (BLE) devices,
provide real-time location, condition, and movement visibility of a product. When merged
with blockchain for immutable record-keeping and AI analytics for dynamic decisionmaking, these technologies breed self-regulating ecosystems that afford automated
compliance, ethical sourcing, and predictive logistics (Malik et al., 2021; Zhu et al., 2021;
Yadav et al., 2022).

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At least, ecosystems implementing this vision are becoming a reality, not
metaphysical and hypothetical; for instance, Walmart's blockchain-empowered RFID system
imparts traceability at the ingredient level within 2.2 seconds as opposed to the FSMAmandated average of seven days-speeding radically both audit preparedness and consumer
trust (Billah et al., 2023). Through cooperation with Circulor, Volvo ensures ethical sourcing
of conflict minerals in accordance with ESG standards using IoT sensors and smart contracts
(Volvo Group, 2023). Globally spanning over 150 ports, TradeLens, a blockchain-powered
platform by Maersk, is enhancing the coordination between ports and customs (Bosco et al.,
2024).
Emerging economies have equally enthralling instances. In the pharmaceutical cold
chain in India, BLE sensors and distributed ledger systems reduced spoilage in rural areas by
38% and sped up compliance audits by 41% (Ahmad et al., 2024). For the agri-food sector,
smart labelling and IoT-enabled monitoring reduced food wastage by 27% and considerably
enhanced consumer confidence (Vitaskos et al., 2024).
However, while the tech promise is crystal clear, most studies target siloed
technologies their focus towards the abilities of large enterprises. There is a sharp scarcity of
fully integrated, interoperable framework proposals for addressing the unique needs and
constraints of SMEs. Only 20% of existing peer-reviewed literature engages with IoT multitechnology architectures, and far fewer survey the feasibility of these architectures in
resource-constrained environments (Wang et al., 2024; Wong You King et al., 2024).
This thesis intends to bridge that gap by delivering a multi-sectoral, SME-focused
study of how IoT can enable end-to-end supply chain transparency. With the research rooted
in both academic literature and industry praxis, a scalable, evidence-based framework will be
introduced that links operational performance to ethical governance and the realization of
digital compliance mandates.

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In the Phase 1 engages in the systematic review of literature to map present
knowledge, identify gaps, and analyse adoption trends, and in the Phase 2 builds on the
identified knowledge to design a sector-agnostic conceptual framework, which is then
validated through comparative case studies and an SME-focused strategic roadmap—putting
theory into actionable practice.

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Research Questions
Modern supply chains require transparency and accountability to meet the
expectations of stakeholders and conform to ever-changing standards of Environmental,
Social, and Governance (ESG). Despite the theoretical promise of an Internet of Things (IoT)
as a digital enabler, the implementation has not been non-standard across industries. SMEs
suffer from lack of infrastructure, expertise, and scalable implementation models. Against
this backdrop, most studies have also chosen to isolate IoT technologies from their industrial
contexts rather than adopting a comparative, cross-sectoral, and SME-inclusive lens.
To redress the situation, the present research has a rigorously structured two-phase
approach, comprising a Systematic Literature Review following the PRISMA 2020
framework, and a Comparative Case Study method. This, therefore, brings revelatory
academic depth and practical applicability. The grand intent is to obtain the highest quality
empirical knowledge in the ever-changing landscape of the IoT for transparency, while also
proposing a validated and scalable implementation framework for SMEs.
Phase 1: Foundations & Industry Insights
•

RQ1: How did the transparency standards in the IoT basis change in industries like
agriculture, pharmaceutical, and logistics from 2015-2025?

•

RQ2: What are the primary technological and organizational aspects that fulfill the
necessity of effectively implementing IoT systems in compliance with industry
regulation?

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3. Screening and Selection Process
Initially, 214 publications were gathered. 117 articles were considered for full-text
screening after duplicate removal and title and abstract screening. Out of which, 36 were
retained based on relevance and quality.
4. PRISMA Flow Diagram
The screening and selection workflow is represented in Appendix E, following the
structure of PRISMA 2020 across the four stages: Identification, Screening, Eligibility, and
Inclusion.
5. Quality Appraisal
The CASP (Critical Appraisal Skills Programme) checklist was applied to each
article. The quality appraisal included aspects about being clear in its research focus,
appropriate research design, implementation of the study by data, consideration of
transferability of the findings. Studies with scores below 8/10 were rejected. The summary of
the CASP appraisal is shown in Appendix F.
6. Data Extraction and Thematic Coding
Using a structured data extraction matrix, each study was categorized by:
•

Author(s), Year

•

Country/Region and Industry

•

IoT Technologies Used (e.g., RFID, Blockchain, BLE)

•

ESG/Traceability Focus and Outcomes

•

Identified Enablers and Barriers

Manual reviewing helped identify key themes and common concerns arising in those
selected studies. Often recurring words, phrases, and ideas were marked and grouped into
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categories such as system compatibility issues; needs for flexible and low-cost technologies;
special needs of SMEs; and alignment of existing ESG policies and government regulations.
The result is a clear map of the most important factors driving or hindering the uptake of IoT
in different supply chains.
7. Justification for Timeline (2018–2025)
This era sees at least three trends converging: (i) rapid maturation of IoT technologies
since 2018, (ii) ESG legislation on a global scale such as EU CSRD and FSMA, and (iii)
Starting a surge in post-COVID digital transparency initiatives; a list of cited key regulatory
frameworks is detailed in Appendix G. Hence the range 2018–2025 is meant to include both
foundational and frontier research.
The supplementary materials including the PRISMA flow diagram and CASP
assessment score are stored in Appendices E and Appendices F.
Phase 2: Comparative Case Studies – Framework Development & Realization
This second phase leverages directly the findings emanating from the Systematic
Literature Revie w and embarks on applied, real-world contextualization. The comparative
case study approach has been chosen to explore how different sectors use IoT tools to achieve
transparency and ESG alignment. Importantly for this phase are SMEs; this phase pays
special attention to SMEs because of the role they play in national economies and the
difficulties they face while adopting complex technologies like IoT.
Research Questions:
•

RQ3: How can the concept framework link IoT enablers to operational processes for
results in traceability and transparency?

•

RQ4: How can the framework be tailored via the validation of case studies for SME
applications across industries in addressing visibility, ESG, and regulation concerns?

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Methodological Design:
This comparative case study design was adopted for the purpose of validation of the
conceptual framework developed from the SLR. Sectoral leaders were chosen for the premise
of diversity and documented maturity in IoT implementation:
1. Retail–Walmart (IBM Food Trust Blockchain)
2. Logistics–Maersk (TradeLens Platform)
3. Automotive–Volvo (Circularity with Circulor Blockchain)
4. Agri-Tech–ZhongAn (IoT-based Smart Agriculture)
5. Pharmaceutical–India Cold Chain (IoT Temperature Control Networks)
The cases were then mapped into the Three-Layer IoT Framework, which consists of:
1. Enabling Technologies Layer–IoT devices, Smart Tag, Blockchain, Smart Contract
2. Operational Integration Layer–Middleware, Data Hubs, Cloud ERP,
Interoperability Protocols
3. Transparency Outcome Layer–ESG Metrics, Real-Time Dashboard, Compliance
Report
For SME readability, three Canadian SMEs were conceptually modelled: Bothwell
Cheese, Happy Planet, and Chudleigh's. These companies were chosen because they are
relevant to agri-food, retail, and sustainability-track operations supply chains in Canada.
Their inclusion gives practical meaning to the framework by showcasing how small,
resource-constrained companies can afford modular layers of IoT to comply with evolving
ESG obligations and get plugged into a digital ecosystem at a reasonable cost. The SMEfocused findings will also pave a useful pathway for other small enterprises looking to fasttrack programs such as the Canadian Digital Adoption Program (CDAP). The implementation
pathways were further adapted to both the CDAP and local ESG standards.
Phase 1 – Section A: Sectoral Insights (RQ1)
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Section Introduction.
Firms are now more accountable in their supply chain, as visibility in this area has
become required by law rather than a benefit over others. This type of transition is visible in
sectors such as public health, where maintaining safety is extremely important. Being open
and honest has become essential for food, pharmaceutical and healthcare companies. An
example is that controlling the source of what we eat keeps it safe and ensures its quality,
while maintaining vaccine cold storage safeguards health. Likewise, better tracking of goods
in real time allows businesses to spot any issues or concerns related to quality and safety
(Budler et al., 2024; Gao et al., 2023).
Many industries now use Internet of Things (IoT) to track processes and automate
activities to verify everything is done in accordance with guidelines. If smart contracts and
blockchain are used in the cloud with IoT, they can help create safe and constantly visible
supply chains (Baig et al., 2022; Malik et al., 2021).
The standards in transparency based on IoT in agriculture, pharmaceuticals and logistics
are carefully considered for the period 2015 to 2025. For every topic, we consider it in terms
of:
•

An overview of the issues associated with traceability in various industries.

•

A listing of the main tools utilized for IoT.

•

They include case studies and precise findings.

1. Agriculture Sector
It enables the agriculture and food industry to handle fraud, missed economic transactions
and concerns regarding the public’s health. Each perishable or organic product, as well as any
animal-based commodities items, should be safety-tested at every point in the supply chain.
Without traceability, the business may deal with unsafe, incorrectly labelled products and
possible recalls that can hurt its customers. (Bosona & Gebresenbet, 2023).
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Right now, RFID (Radio-Frequency Identification) tags, GPS (Global Positioning
System) devices, sensors and blockchain technology are part of the IoT and help producers to
keep track of their harvests as they are moved and stored. They allow producers to log and
share data on harvest timing, storage conditions, and shipment routes watches over the
business, ensures it meets all regulations and includes ESG standards (Saxena & Arya, 2023).
In the south of Europe, there is an Olive Oil Blockchain Traceability Platform. The use of
RFID barrels, blockchain harvest logs and consumer-friendly screens makes tracing any stage
of the supply chain possible. While fraudulent labelling went down by 21%, the percentage of
people who trusted and were loyal to the food system increased by 15% (Vitaskos et al.,
2024).
Essentially, indicate that with RFID and sensors, 27% of produce spoilage was prevented
under given temperature conditions, whereas it is widely accepted and believe that consumer
cared more about where the food was sourced because of the blockchain Especially whether
It is ethically source or not (Wang et al. 2024).
Altogether, using IoT in farming has assisted with food quality, decreased carbon
emissions and better managed the supply chain to ensure products can be traced more easily
everywhere.

2. Pharmaceutical Sector
In Pharmaceutical supply it is important to maintain their temperatures according to
safety standards so that counterfeiting, poor quality and unlawful handling don’t happen. The
pharmaceutical industry relies on IoT to keep an eye on the cold chain, adds QR-coded
serialization and uses current data during shipping to ensure drugs stay safe and adhere to the
Good Distribution Practice standards (Ahmad et al., 2024).

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It is now possible to carry out instant monitoring and auditing, Because use of
temperature sensors, anonymized blockchain and smart packaging. these systems effective
because the records are digital and cannot be edited (Araujo et al. 2022).
As an example, the Medi Ledger Project uses IoT and blockchain in North America to
check that they meet the Drug Supply Chain Security Act (DSCSA) standards. By using this
system, enterprises in the pharmaceutical industry can check their orders, notice any
difficulties and automate the steps required to get their goods cleared by customs.
Mention that Medi Ledger’s applications have led to a 25% decrease in counterfeits,
Increase satisfactory by 38% with government regulations and stronger WHO links. Also,
mention that using blockchain in traceability shortens the time needed for gate checking
exports and improves India’s global reputation (Volvo Group 2023; Ahmad et al. 2024;
Enayati et al. 2024).
Overall, IoT has enabled the pharmaceutical sector to transition toward a resilient,
digitally verified, and regulation-aligned ecosystem, strengthening public health and
compliance.

3. Logistics Sector
To ensure all global trade moves as it should, logistics and transportation rely on
visibility, compliant actions and forecast tools. There are many uncertainties caused by
handling multi-modal shipments, customs rules and the final mile. IoT has successfully
addressed these challenges (Ivanov & Dolgui, 2021).
The main tools for this are smart containers, GPS trackers, simulations of different
scenarios and AI on fleet management. Via these systems, the conditions of vibration,
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temperature, humidity and where the cargo is located may be monitored and updated instantly
for all involved stakeholder parties (Tao et al., 2018; Dakhia et al., 2025).
For example, of Maersk TradeLens, uses the TradeLens Platform by integrating
blockchain and IoT to supervise data about each container. By using GPS, temperature
monitors and smart contracts, all movements of our shipments can be watched over 150 areas
around the globe.
The system helped reduce docking time by 20%, speed up the verification of documents
and prevent fraud through smart contracts as well as predictive analytics powered by IoT
reduced delivery errors by 23%, while ESG improvements included lower idling emissions
and route optimization, This means that using IoT in logistics raises the quality of forecasts,
improves efficiency and helps meet ESG standards while encouraging trust during operations
between different countries (Li, 2024 ; Kareem et al., 2024 ; Wu et al. 2024 ; Almelhem et
al., 2023).

Table 2:
Sector-Wise Impact of IoT Tools on Supply Chain Transparency
Sector

Key IoT

Benefits

Quantitative

Tools
Agriculture

Reference

Impact

RFID, GPS,

Perishability

27% spoilage

Bosona &

remote

control, fraud

reduction, 21%

Gebresenbet

prevention

less fraud

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sensors,

(2023); Vitaskos

blockchain

et al. (2024)

Pharmaceuticals Temp

Logistics

GDP

38% spoilage

Ahmad et al.

sensors, QR

compliance,

reduction, 25%

(2024); Volvo

codes,

anti-counterfeit

fewer

Group (2023)

blockchain

traceability

counterfeits

GPS, digital

Customs speed,

20% port delay

Ivanov & Dolgui

twins, smart

predictive

reduction, 23%

(2021); Wu et al.

containers,

delivery, ESG

better forecasts

(2024);

AI

gains

Almelhem et al.
(2023)

See Appendix A for extended quantitative data on IoT benefits across agriculture,
pharma, and logistics.

Transition & Synthesis.
Despite differing operational contexts, the agriculture, pharmaceutical, and logistics
sectors share a common digital transformation trajectory through IoT adoption. Crosssectoral themes include:
1. Real-time traceability via RFID, sensors, and GPS tracking.
2. Automated compliance using blockchain and smart contracts.

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3. Transparency-led trust and ESG reporting through cloud dashboards and
regulatory alignment tools.
These shared patterns establish a strong foundation for analysing the technological
enablers and barriers that influence IoT integration at scale. The next section (Phase 1,
Section B) directly addresses RQ2 by exploring the underlying digital infrastructure, security
risks, and organizational readiness that shape success across industries and SME ecosystems.

Phase 1 – Section B: Technological Enablers and Barriers (RQ2)
Introduction.
Influences on the success in IoT-based supply chain systems (SCS) come from the
technology and from the company’s structure, rules and the way they function. Since there
are problems with interoperability, different regulations, managing data and varying progress
in digital technology, these advancements in reliability, complying and traceability are
possible but it also has a complex Internal operations issues, regulatory fragmentation and
data governance challenge and Digital infrastructure availability across industry is required
for full acceptance.
Here, RQ2 is considered, examining the elements within technology and organizations
that help IoT systems comply with rules set by the industry. It relies on seven important
areas: monitoring operations in real time, applying blockchain, overseeing operations,
managing business operations, paying attention to ESG points, winning consumers’ trust and
adhering to ethical and legal rules.
Through real-time traceability, the supply chain monitors and follows each product and
step. Many of these tools are made up of sensors, RFID tags and trackers using GPS
technology. They are crucial for IoT-based SCS, particularly when used in fields managing

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goods that should be kept in controlled environments, for example agriculture and logistics
for food and medication (Ahmad et al., 2024).
Overall, adopting RFID and environmental sensors in the agri-food industry leads to 27%
decrease in wasted food and enhances the accuracy of checking how fresh products are. The
authors found that including temperature and humidity sensors in pharmaceutical SCS
packaging improved the trustworthy management of temperature during distribution. They
regularly share information on their status with the cloud, so we can monitor both the quality
and location of the products at any time (Wang et al., 2024).
Regardless of the variety of technical resources, companies still encounter issues. One
issue might be that technical experts are not available; it costs money to adopt new solutions
and people in SMEs are not trained to work with data. Also, if connecting sensors to
enterprise resource planning (ERP) is too difficult, the implementation project won’t be
finished successfully (Budler et al., 2024).

Blockchain Integration
Blockchain is used to securely keep track of transactions in a way that cannot be changed
on multiple computers. Using blockchain in IoT-based supply chains improves auditability,
prevents counterfeiting and ensures better compliance with documents (Said et al., 2023).
Smart contracts that handle compliance tasks can be created using the co-operation of IoT
and blockchain an outcome of that has led to more automation and smooth workflow.
Blockchain technology used in the pharmaceutical sector has decreased certification fraud by
40% (Araújo et al., 2022; Kareem et al. 2024).
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Even so, getting this technology up and running is still expensive and difficult for most
SMEs. Since blockchain is decentralized, it introduces difficulties related to speed, decision
making and trust within its network. The lack of global standards often leads to conflicts
when deploying blockchain in different areas.
Operational Resilience
Supply chains with IoT are strong if they can deal with surprises and adjust appropriately.
These technologies like Machine learning, digital twins, adoptive logistics platforms are
currently assisting supply chains in smoothly working around changes and requests.
Basically, digital twins, this refers to things and processes that get transferred into the
digital arena. Having this capability means one can quickly develop numerous time-sensitive
models for purposes of disaster response. They suggest that the analysis of different scenarios
through further validate that digital twins are foundational in smart manufacturing systems.
(Ivanov and Das,2020; Tao et al. 2018).
IoT with AI-based analytics in SCSs reduces the recovery time from disruptive events by
30%. Even in case of disruptions, the system cannot fail in gathering information as its
backups the data through multiple duplications at the time of collection (Enayati et al. 2024).
Governance and Interoperability
One of the possible barriers to scalable IoT deployments is interoperability, particularly
multi-vendor platforms and cross-country regulations. Disjointed protocols, legacy systems,
and therefore the lack of middleware integration limit real-time data consolidation (Budler et
al., 2024).
Moreover, it has been argued that fragmented governance structures serve to undermine
the true trust within the system, mainly when the IoT applications extend customs,

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warehousing, and logistics partners. Some have tried, but big names have yet to implement
widely: protocol standards such as MQTT and ISO/IEC 3014. (Bosco et al. 2024).
A key pain for organizations is that many of them still use HTTP (Hypertext Transfer
Protocol) and FTP (File Transfer Protocol) for communication, whereas, for IoT, lightweight
message protocols work best. Such delays and data loss increase the risk of audit failure and
product spoilage.
Understanding Figure 1: Where Do Supply Chain Systems Fail
The bar chart (Figure 1) informs us about the places where majority of technical
failures take place in Internet of Things-based supply-chain systems. Think of supply chain as
one long highway marching from farms, through factories and warehouses, ending in stores.
Along this highway, ambient digital technologies attempt to keep track and manage goods
using smart devices.
Now, just like sometimes we face road traffic jams, there can be communication
breakdowns among those technologies. When systems fail to communicate with each other
correctly, it means they stand in a condition of interoperability failure. This chart, therefore,
tells us where most interoperability failures occur.
Figure 1:
Protocol Failure Chart

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(Figure 1: showing % of failures caused by protocol mismatches: transport layer = 42%,
app layer = 35%, identity/authentication = 23%)
What Are the “Protocol Layers”?
A protocol is basically a rule telling one device how to communicate with another. These
rules are layered atop another, like floors in a building. Here's what each one means:
•

Transport Layer (42%).
In the technical world, this layer transmits the data between hosts, any disruption here
being a failure. This layer, at 42%, fairly compares to the highest failure rate in that
many issues happen just getting the data from A to B.

•

Application Layer (35%).
That's the layer in which software or applications do something with the data. Imagine
a chef reading a delivery list-if the list is all mixed up, the chef can't cook! On 35% of
cases, the problem will be in here, when the data is received but no understood right.

•

Identity/Authentication Layer (23%).
This is the checkpoint where a prospective traveler verifies if they will be allowed
within the entry. About 23% of the problems arise when devices or users are
improperly identified or verified and cause difficulties for granting access.
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ESG Alignment
Thus, IoT systems are ever-more being used as enablers for Environmental, Social,
and Governance (ESG) compliance. They gather real-time emissions data, check on questions
of ethical labor, and manage resource utilization (Gao et al., 2023).
IoT-based route optimization algorithms cut down fuel consumption by 18% in urban
logistics. Smart farming goes further in the ESG compliance arena, where irrigation and soil
monitoring systems prevent water wastage and over-application of chemicals (Almelhem et
al. 2023),
Governance dashboards now integrate live ESG data streams, including those for
Scope 1-3 emissions, energy intensity, and social impact scores, which are instrumental in
SDG reporting, carbon disclosure, and investor alignment.
Consumer Trust
In this age of transparency, consumer trust acts as a competitive advantage. Traceability
platforms that use IoT allow consumers to check on ethical sourcing, sustainability practices,
and product authenticity (Duong, 2024).
Research indicates that the usage of QR-enabled smart labels, combined with verified
blockchain data, witnessed a 15% uplift in consumer retention and brand perception.
Examples pertinent to foods and luxury goods illustrate the use of digital provenance to
further marketing claims (Khan et al. 2023).
To further cement this trust, consumer-facing dashboards display data from farms
concerning methods of production and environmental data. These platforms often convert
complex compliance data into engaging visuals for the general mass.
Ethical and Legal Considerations.

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In the expanse of IoT networks affecting supply chains, the ethical and legal
disappointments thereof burgeon, especially concerning SMEs that do not provide mitigation
mechanisms through a dedicated compliance infrastructure. Continuous data flow via smart
sensors poses critical problems to organizations: cyber breaches, misuse of information, and
lack of clarity in ownership rights (Korkmaz & Erkayman, 2023).
Current-day IoT applications generally lack well-established mechanisms for seeking
informed consent, which creates ambiguity concerning the collection, storage, and sharing of
user data. Such situations grow increasingly problematic in view of the strict regulatory
landscapes emerging through different parts of the world. As an example, the European
General Data Protection Regulation (GDPR), Canada's Personal Information Protection, and
Electronic Documents Act (PIPEDA), and similar laws present in the Asia-Pacific Region
mandate strong protective safeguards for personal data, such as explicit user consent and data
storage in regional locations of sensitive records (Houge, 2024).
To balance these conditions without overwhelming SMEs, the following practical,
inexpensive remedies are proposed, which find a balance between security and scale:
•

Data Minimization and Consent by Design: SMEs should limit themselves to
collecting trough data and ensuring that they develop consent processes in all digital
touchpoints. Open-source Consent Management Systems (CMS) can be automated to
process user permissions, so the SMEs remain compliant with the regulations at the
lowest possible technical overhead.

•

Localization of Stored Data: Cloud services are thus chosen in a way that data is hosted
within the respective geographical location such as CDAP-certified Canadian providers
to satisfy any local residency laws while retaining secure information access from
outside the region.

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This heatmap (Figure 2) visually emphasizes the strength of correlation across different IoT
technologies:
•

High Correlation: Digital Twins (0.90), Blockchain (0.88), Sensors (0.85)

•

Moderate Correlation: Smart Labels (0.60)

•

Low Correlation: Legacy Databases (0.30)

What Does "Correlation" Mean Here?
In short, correlation is simply the presence of a relationship between technology and
enhanced supply chain resilience. The higher the value of correlation (tending to 1), the
stronger will be its positive impact. For instance, a value of 0.90 means that the technology
will really aid companies in avoiding or managing dislocations. Conversely, a value of 0.30
means that technology contributes minorly to resilience.
Phase 2 – Section A: Framework Construction (RQ3)

28

Persistent challenges concerning visibility, accountability, and compliance in global
supply chains call for the construction of a scalable conceptual framework spanning several
sectors. The framework must be able to systematically align IoT technical feasibility with
relevant operational workflows to yield traceable and transparent results. Thus, this segment
deals with RQ3: How would a conceptual framework be effective to align IoT enablers with
operational processes in generating traceability and transparency outcomes?
Theoretically, drawing from the presented cases and proven operational concepts, the
model proposes this three-layer framework, namely (1) Technological Enablers (2)
Operational Execution, and (3) Transparency Outcomes. Each layer is built on the other layer
above it by integrating IoT-related functionalities such as RFID, blockchain, edge computing,
API, and smart contracts to strategically pass outputs at a regulatory-compliance level, ESG
reporting, and consumer-trusting levels (Baig et al., 2022; Malik et al., 2021; Bosco et al.,
2024).

29

A. Three-Layer Conceptual Framework
Layer 1: Enabling Technologies.
The bottom layer consists of the core IoT components for collecting data, encrypting
it, and sending it into the supply chain. These include:
•

RFID sensors: Track the location of goods and the state of the goods in real-time
(Baig et al., 2022).

•

Environmental sensors: Measure humidity, temperature, and vibrations while being
transported (Billah et al., 2023).

•

Blockchain: Creates permanent and tamper-proof transactional records (Malik et al.,
2021).

•

Edge Computing: Data processing happens on-site hence any latency or bandwidth
usage is collaterally reduced (Bosco et al., 2024).

•

APIs: Allow communication among heterogeneous systems and platforms (Korkmaz
& Erkayman, 2023).

This digital infrastructure layer is vital for crafting integrity into the data, persistence in
surveillance, and interoperability. The amalgamation of these technologies into ZhongAn's
Agri-tech system for rapid health surveillance for livestock and environmental control was
accomplished via RFID and sensor arrays (Wang et al., 2024).
Layer 2: Execution Operations.
The middle layer transforms technological input into a dynamism-oriented rule-based
execution supply chain. System elements lie in richness, among which include:
•

Smart contracts: Enforcement of compliance, inspection, or shipment protocols
based on sensor input (Kareem et al., 2024).

30

•

Interoperability protocols: For smooth integration between ERP systems and
partner platforms (Budler et al., 2024).

•

Workflow automation: For routing, inventory management, and quality assurance
alerts (Bosco et al., 2024).

•

Compliance engines: Checker engines in real-time for FSMA, GDP, and ISO 27001
compliance (Tao et al., 2018).
In practice, the Circulor platform of Volvo, which is IoT-powered, traces the ethical

sourcing of cobalt via smart contracts and compliance logic that automatically check for
compliance with the EU mineral law (Volvo Group, 2023).
Layer 3: Transparency Impacts.
This layer describes the strategic benefit being delivered on top of IoT-integrated
workflow technologies:
•

ESG dashboards: Aggregate emission data, fair labor data, and supply chain
sustainability data (Gao et al., 2023).

•

Regulatory readiness: Audits are embedded within the system and audit rules are
baked into the technology (Araújo et al., 2022).

•

Consumer trust: Real-time transparent verification of ethical sourcing enabled
through consumer-facing QR code interfaces (Duong, 2024; Khan et al., 2023).

•

Auditability: Immutable blockchain records can foster fraud detection and antigreenwashing protocols (Budler et al., 2024).
An example for this layer would be the IBM-Walmart Food Trust. By integrating

blockchain with RFID technologies, they managed to reduce traceability lag from seven days
to just 2.2 seconds, thus greatly contributing to FSMA compliance and brand loyalty
(Vitaskos et al., 2024).

31

conversion of raw data into strategic value. This highly scalable framework bridges
technological and governance aspects, providing a platform from which organizationsspecially SMEs-can chart a path to traceability, sustainability, and regulatory compliance
across sectors.
Figure 3:
IoT-Enabled Supply Chain Transparency Framework.

By linking operational enactment to technological enablers and, eventually,
transparency outcomes, this framework operationalizes the promise of IoT-based traceability.

33

Phase 2 – Section B: Practical Applicability for SMEs (RQ4)
This phase addresses research question 4 (RQ4): How can this framework, supported
by case study validation, be tailored for SME usage across industries in solving visibility
concerns while simultaneously supporting ESG and regulatory objectives? SMEs form the
backbone of global supply chains but have either neglected or lacked technologies and
infrastructures conducive to large-scale IoT adoption. Drawing from validated use cases in
five key industries—retail, logistics, automotive, agriculture/insurance, and
pharmaceuticals—this section will translate the higher-level frameworks into models that
work around SME constraints.
Synthesizing lessons from Walmart, Maersk, Volvo, ZhongAn, and India Pharma's
cold chain transformation, we identify transferable best practices and build an SME-friendly
roadmap housing key enabler such as Application Programming Interfaces (APIs), RFID,
edge computing, blockchain, and smart labelling, alongside deployment models that speak
the SME language of cost efficiency, such as plug-and-play sensors and Software-as-aService (SaaS) dashboards.

35

Case Studies Insights: Large-Scale Lessons for SME Implementation
1.Walmart and IBM Food Trust
Integration of IoT technologies like RFID tags, smart labels, cloud-based dashboards,
and blockchain via the IBM Food Trust platform into the Walmart supply chain appears to be
among the most sophisticated digital traceability systems that exist within a retail food supply
chain. Walmart sought to overcome latency in traceability and unintended food-borne illness
risks, confronted by public health challenges and attendant governmental interventions such
as the Food Safety Modernization Act (FSMA). For instance, before the implementation of
these solutions, it could take as long as seven days to trace the source of a single produce
item like mangoes. It was, therefore, a manual, disjointed process, difficult to comply with
and exposing the system at large to pressure during product recalls (Vitaskos et al., 2024).
The Food Trust initiative started with a 2016 pilot, in which RFID sensors were
installed on mango shipments to capture real-time data on temperature, humidity, and
handling events. These sensors were in turn linked to blockchain ledgers that immutably
recorded and verified transactions from farm to shelf (Bosco et al., 2024). Within one year,
Walmart cut that traceability period of seven days to 2.2 seconds, setting an industry standard
for recall efficiency and FSMA compliance. Following the success of the mango pilot, the
platform was then rolled out to more than 50 Stock Keeping Units (SKUs) by 2020 across
fresh produce, dairy, and poultry (Billah et al., 2023).
One of the crucial elements of scalability involved the use of a modular IoT
architecture, which enabled Walmart to plug new products into an existing traceability
system without disrupting upstream or downstream supply chain workflows. This was
facilitated by Application Programming Interfaces (APIs), which allowed integration with
any third-party logistics provider, supplier, or compliance dashboard. These APIs

36

synchronized the sensor data streams with the ERP Systems at Walmart, thereby providing
interoperable phenomena throughout the digital layers (Baig et al., 2022).
From an operational standpoint, several measurable benefits accrued from this
transformation. With the enhanced environmental monitoring, shelf life of inventory
decreased while automatic alerts ensured that products spotted to be compromised were
quickly taken off the shelves. Predictive analytics built into the platform managed
replenishment cycles and minimized Overstock of arrivals. According to internal estimates,
an improvement in inventory efficiency to the extent of 15-20% and a significant
enhancement in visibility across all the global sourcing networks was reported by Walmart
(Vitaskos et al., 2024; Bosco et al., 2024).
In contrast to operational metrics, the initiative produced strategic results to support
ESG goals. Automated ESG dashboards leveraged by the IoT data were used by Walmart to
prove compliance with the FSMA, cut carbon emissions through strategic routing, and
develop consumer confidence via transparent labelling schemes. By scanning these smart
labels, customers access provenance data, including farm location, handling history, and
sustainability ratings, which reinforces loyalty to the brand and buyer confidence (Duong,
2024; Khan et al., 2023).
Implications for SMEs are both practical and transformational. Walmart's approach
demonstrates that beginning with a small pilot, focused on a single product line, can bring
great insights with little upfront risk. SMEs can follow such a method by employing cheap
open-source RFID kits, cloud-based dashboards, and lightweight blockchain services. With
regulatory sandbox environments already being piloted elsewhere, including in India, they
present a safe space where SMEs can experiment with compliance and data interoperability
without carrying legal liabilities (Ahmad et al., 2024; Enayati et al., 2024).
Table 5:
37

A.P. Moller–Maersk had to deal with increasing pitfalls when attempting to retain
trade documentation across disparate and opaque supply chain systems. To tackle issues such
as customs coordination, manual paperwork, and missing traceability, Maersk teamed up with
IBM to co-develop TradeLens platform —a blockchain-based application that would fulfil
the promising goal of digitally transforming the global shipping landscape to improve
opportunities for real-time visibility, regulatory compliance, and stakeholder trust (Li, 2024;
Bosco et al., 2024).
TradeLens exploits a robust technology stack combining IoT-enabled smart
containers with decentralized ledger technology through blockchain and API (Application
Programming Interface) based interoperability. Smart containers loaded with IoT sensors
collect temperature, humidity, vibration, and GPS location data. These inputs are
automatically ingested into a permissioned blockchain ledger that timestamps and irrevocably
stores the data, accessible to actors in the ecosystem such as customs officials, terminal
operators, port authorities, and shipping clients (Kareem et al., 2024; Said et al., 2023).
TradeLens has had both operationally and strategically operative impacts. Key quantitative
results are:
•

20% reduction in port dwell time through facilitation of customs pre-clearance
(Li, 2024)

•

15% reduction in document processing errors through smart contract
validations (Bosco et al., 2024)

•

Over 150 stakeholders involved across more than 600 ports worldwide

•

Improved audit-readiness and ESG traceability with real-time visibility into the
ledger

The collaboration between Maersk and IBM contributed to interoperability standards that
allow data sharing between legacy Enterprise Resource Planning (ERP) systems and
39

TradeLens using modular APIs. Modularity in architecture enables adoption by users at their
own pace, including smaller carriers and terminals who pick and choose which blockchain
nodes to participate on, IoT modules to deploy, or dashboard interfaces to consume. This
makes TradeLens a path-breaking reference for SMEs that are trying to put transparency
solutions together without sinking huge investments into full enterprise infrastructure
(Enayati et al., 2024).
A few lessons for SMEs include:
•

Phased deployment: Start with digitizing documents like invoices, Bills of
Lading, etc., employing low-cost IoT tags or RFID

•

Sandbox pilots: Try out blockchain integrations in a controlled environment
before full deployment

•

Consortium-based models: Work with regional logistics hubs in co-sharing
infrastructure and compliance responsibilities

•

Modular selection: Pick scalable components such as dashboards or APIs
depending on digital maturity

The challenges with adoption remain, however. SMEs often mention cybersecurity
concerns, initial setup costs for installing IoT sensors, and a lack of expertise on blockchain
as barriers. These can be alleviated by using plug-and-play APIs, bringing in third-party
integrators, or taking advantage of collaborative training programs to build digital capabilities
(Korkmaz & Erkayman, 2023; Kareem et al., 2024).
In essence, TradeLens is a good example of a scalable approach for supply chain
transparency that combines permissioned blockchain, real-time sensor data, and collaborative
governance approaches. While obviously geared toward truly big companies, the architecture
is modular enough that SMEs, especially in cross-border logistics, Agri-export, or pharma
distribution, can explore applying it as a low-cost traceability-as-a-Service option.
40

3. Case Study: Volvo & Circulor
Volvo Cars, the major European automotive manufacturer, has taken the lead in the
ethical sourcing of critical minerals needed for EV batteries, particularly cobalt and lithium.
Aligning with the growing ESG expectations and the resultant regulatory mandates, including
the European Union Battery Regulation and the Conflict Minerals Directive, Volvo decided
to partner with Circulor, a blockchain-based traceability platform, to grant an unprecedented
level of transparency to its battery supply chain (Volvo Group, 2023). Two strong
imperatives thus led to its inception: one, to avoid reputational and regulatory risk associated
with conflict mineral sourcing; and two, to form verifiable end-to-end audit trails in line with
ESG performance indicators (Gao et al., 2023).
The heart of this deployment is an integrated technology stack featuring blockchain,
IoT sensors, and GPS-enabled smart logistics. The system of Circulor uses DLT to create an
incorruptible and immutable audit log for mineral movements from mines in the Democratic
Republic of Congo to assembly plants in Europe (Yavaprabhasa et al., 2024). Each shipment
is tagged with GPS-enabled IoT sensors to track geolocation changes, custody handovers, and
environmental conditions during transit. At every node, blockchain smart contracts execute
automated compliance triggers attesting that the materials being transported have remained
true to accepted sourcing conditions and sustainability criteria (Baig et al., 2022).
Transparency resulted quite dramatically from this initiative. The company now traces
cobalt and lithium supplies with over 98% coverage, and CTRs and supplier accountability
are verified at every step. Manual compliance auditing hours have been cut by about 35%, as
the blockchain automated validation procedures now serve to replace the fragmented
document checks. Real-time dashboards have facilitated timely investigations and investor
disclosures by internal compliance officers and independent ESG auditors (Volvo Group,
2023; Bosco et al., 2024).
41

Further scaling insights are provided for SMEs in manufacturing. While the full-stack
Circulor solution is likely financially or technologically beyond the reach of most SMEs,
modular alternatives exist, including mobile-enabled QR audits, open-source blockchain
frameworks like Hyperledger or Ethereum, as well as lightweight IoT-sensor solutions
integrated with APIs. SMEs can follow a phased rollout beginning with just one high-risk
raw material and later expanding across product lines after validation of the compliance
value. This traceability-as-a-service concept can allow SMEs to join responsible sourcing
consortia, supported without upfront financial pressure (Gao et al., 2023; Baig et al., 2022).
The Volvo and EU Battery Regulation relationship also brands DLT as an
international compliance strategy. As the regulation moves toward mandatory digital product
passports for battery life cycle transparency, early adopters such as Volvo are already gaining
a distinct commercial advantage by stake-holding product integrity and market access. SMEs
willing to enter or expand the EU automotive value chain will be forced to increasingly
demonstrate traceability practices aligned to ESG. Scaled-down blockchain-integrated
models may provide the means to enable firms to comfortably meet export and ethical
sourcing audits (Bosco et al., 2024; Yavaprabhasa et al., 2024).
Overall, the partnership between Volvo and Circulor offers a replicable playbook for
digitized ESG traceability. Although originally developed for high-risk global manufacturing,
this architecture may be adapted for SME operations through scalable components, publicprivate consortia, and sector-specific compliance toolkits. It provides an excellent example
showing how traceability protects brand reputation while opening access to capital,
certification, and sustainability-conscious markets.
Table 7:
Sidebar Box: SME Ethical Traceability Readiness Checklist.

42

4. Case Study: ZhongAn Technology
Background and Challenge
Coming to grips with the huge outbreak of African Swine Fever (ASF) in 2018,
following which over 200 million pigs were culled in China, it put an extraordinary test on
the insurance sector, plagued by its credibility and financial solvency. Paper-based systems
that were traditional in nature were useless for real-time monitoring. This delayed the
response to outbreaks, and parametric insurance claims were prone to fraud. It became clear
that there was an urgent need for transparent and tamper-proof solutions that could attest to
the health status of livestock, events of mortality, and claim eligibility (Wang et al., 2024;
Billah et al., 2023).
Technological Deployment
ZhongAn Technology, China’s top-line digital insurer, had come up with a solution
by perfecting the first-ever platform that merged RFID ear tags; Low-Power Wide-Area
Networks (LPWAN); and real-time environmental sensors like temperature, humidity,
movement into a Distributed Ledger Technology -building block. The IoT infrastructure
recorded unique ID and biometric data for each animal and transmitted them securely to a
cloud-based blockchain network for verification (Wong You King et al., 2024; Baig et al.,
2022).
This architecture gave rise to tamper-proof data trails, which meant parametric
insurance payout (for instance, insurance cover for pig mortality) was able to be backed with
immutable digital proofs. Smart contracts based on pre-established criteria (abnormal
temperature for longer than 24 hours, for example) raised alarms and approved claims. This
immensely reduced human intervention in the claims process, thereby lessening opportunities
for fraud and allowing for real-time veterinary intervention (Supino et al., 2024).

44

Blockchain's Role in Auditability and Trust.
While blockchain promised and delivered traceability from end to end, it also
provided a measure of assurance to all the stakeholders—farmers, insurers, government
agencies, and consumers. Distributed Ledger Technology audit logs of sensor data,
intervention history, and the location shifts throughout a pig's lifecycle remain immutable,
underpinning ESG expectations on transparency and humane governances (Gao et al., 2023;
Kareem et al., 2024).
Outcomes and Measurable Impacts.
The ZhongAn solution recorded a 17% jump in consumer trust in pork safety, with
surveys indicating a higher willingness to buy verified livestock (Wang et al., 2024). The
average claim resolution time dropped by 32%, while false mortality report instances dropped
by 28%. Regional implementation across the three provinces of Jiangxi, Henan, and Sichuan
too manifested a strong impact, with early warning time for diseases getting improved by
48%, allowing faster quarantine decisions and resource allocations (Wong You King et al.,
2024).
Scalability Lessons for SMEs
For SMEs operating within Agri-tech or in a rural farming setup, ZhongAn’s hightech model might appear very pricey. However, modularization is possible. LoRaWAN
(Long Range Wide Area Network) -enabled sensors, mobile apps for reporting on animal
health + open-source blockchain frameworks such as Hyperledger Sawtooth can provide
possible alternatives (Baig et al., 2022; Kareem et al., 2024). At an even lower scale, SMEs
could use local telecoms for LPWAN (Low Power Wide Area Network) coverage, adopting
QR code tracking for livestock herds of low volume.

45

ESG and Food Safety Alignment
The system developed by ZhongAn tackles bigger questions like food safety,
biosecurity, and supply resilience. It offers a blueprint model for ethical IoT monitoring
aligned with local food safety standards and global ESG criteria. Combining real-time
traceability with insurance verification, this creates an open ecosystem of digital livestock
governance that fuels confidence from consumers and investors alike (Billah et al., 2023; Gao
et al., 2023).
5. Case Study: India’s Cold Chain Modernization
Context and Challenge
The enormous pharmaceutical supply chain in India is believed to be a very big
logistical nucleus for the global distribution of vaccines and temperature-sensitive drugs. This
was before the digital dawn, the then set of cold storage systems in India were fraught with
inefficiencies, irregular temperature control, fragmented reporting, and non-adherence to
regulations, especially in the rural districts. Over 25 % of vaccines are thought to be spoilt
due to improper real-time monitoring and handling in certain areas (Ahmad et al., 2024). It
was quite difficult to meet the GDP (Good Distribution Practise) requirements laid down by
the World Health Organization (WHO) because the whole system worked on manual
documentation, paper trails, and delayed audits for end-point deliveries (Araujo et al., 2022).
Technology Deployment
With this, a program for the overall modernization of cold storage facilities was
launched on a pan-India scale in selected public-private health corridors. The design of this
deployment thus contained IoT-enabled temperature sensors, RFID tags, and blockchainbased dashboards, creating a single and tamper-proof traceability infrastructure (Baig et al.,
2022). Temperature monitors capable of capturing thermal data in real time were installed in
46

cold stores and refrigerated trucks. These temperature information devices communicated
data to cloud platforms by means of Bluetooth Low Energy (BLE) services and mobile
Application Programming Interfaces (APIs); any deviation from proper temperature would
cause alerts to pop up. All of this was integrated with India's National Digital Health Mission
(NDHM), achieving smooth interoperability with electronic health records and vaccine batch
IDs.
Field officers received a push notification on the mobile interface when something
went wrong on-site and were further permitted to view chain-of-custody logs on their mobile
devices, putting an end to time-consuming administrative paperwork and improving decisionmaking efficiency. Data were captured, time-stamped, and validated for compliance using the
permissioned blockchain ledger at all critical control points—starting from the manufacturing
plants to the community health centres.
Role of Blockchain in Compliance and Traceability
In Distributed Ledger Technology installation bestowed the supply chain with an
additional layer of guaranteed data integrity, non-repudiation, and transparency: each thermal
data point and handling record was hashed and logged in an immutable fashion, whilst smart
contracts configured for compliance thresholds (2–8°C for vaccine stability) would
automatically escalate warnings and risk flags when real-time readings violated their preset
safety-area parameters. With this concept, whenever a possible opportunity to lose is
recognized, manual interaction is minimized or avoided completely so that proactive action
can be taken well in time for averting the losses (Araújo et al., 2022; Ahmad et al., 2024).
Due to blockchain consensus mechanisms and audit trails that were timestamped,
regulators and stakeholders could scrutinize the transactional records without losing data to
tampering risk. Besides guaranteeing Good Distribution Practice audit preparedness, the

47

system supported digital documentation to comply with WHO and Indian Central Drugs
Standard Control Organization (CDSCO) frameworks of regulations.
Outcomes and Measurable Metrics
Significant quantitative results were achieved. Indeed, there was a 38% reduction in
vaccine spoilage within the pilot implementation areas (Ahmad et al., 2024). Manual audit
hours were reduced by 41%, and cold chain compliance rates soared by 36%, notably in the
rural hubs such as Odisha and Bihar. Using blockchain-validated handling data, health
officials were able to prolong the window of vaccine distribution by verifying the integrity of
the cold chain on multi-leg shipments safely.
Operational costs related to cold chain disruptions dropped due to faster anomaly
detection and subsequent resolution. Apart from detection, the data was fed to the integrated
dashboard that aided the logistics managers in maintaining the oversight of Key Performance
Indicator such as route average temperature, alert resolution time, and successful delivery rate
by way of a customizable analytics module (Billah et al., 2023).
Lessons for SMEs
Although it was government sponsored, the technology blueprint holds scalable lessons for
SMEs engaged in regional pharma logistics. SMEs can replicate this layered system using
Bluetooth Low Energy (BLE) -enabled temperature loggers, QR code scanning, and opensource blockchain platforms like Ethereum Test net or Hyperledger Fabric for inexpensive
deployment (Baig et al., 2022). Modular rollouts could start at one or two control points, such
as last-mile health clinics, before scaling up to the warehouse.
The following is a suggested implementation roadmap for SMEs:
•

Phase 1: Digitization of assets (vaccine crate RFID tagging)

•

Phase 2: Installation of sensors and linking to cloud with mobile app interface
48

Strategic Roadmap for SME IoT Implementation
The subject of this chapter is a practical, phase-wise roadmap for implementing IoT
technologies in Canadian agri-food SMEs with examples from Bothwell Cheese, Happy
Planet, and Chudleigh’s Limited. Each phase specifies SME issues, technical interventions
(such as BLE sensors, blockchain, edge analytics), and cost-tiered adoption strategies (Lean,
Balanced, Progressive). The expected outcomes would reduce cold chain spoilage, increase
alignment for ESG audit purposes, and promote cross-functional traceability. This roadmap
complements FSMA/SFCR-based compliance, along with government programs like CDAP,
to provide agri-food SMEs with the building blocks for resilient supply chains. It is also
broadly scalable across industries. It is built around four broadly defined progressive phases,
so the entire roadmap offers a modularized approach for digital transformation grounded in
IoT maturity, traceability tools, compliance automation, API orchestration, and low-code
integrations. Each phase is supplemented with SME-specific actions, measurable results, and
cost-tiered implementation strategies.
Phase 1: Assessment – Diagnosing Digital Readiness and Traceability Gaps
The assessment phase is a critical starting point in the IoT adoption process, aimed at
establishing a clear baseline of a firm’s digital maturity, traceability systems, and regulatory
alignment. This phase often reveals that many SMEs continue to rely heavily on manual or
semi-digital systems that fail to provide real-time visibility, particularly in high-risk domains
like cold chain logistics. Tools such as RFID sensors, BLE (Bluetooth Low Energy) tags, and
structured digital maturity audits are essential to assess readiness and uncover gaps in
traceability, especially under evolving regulatory frameworks like the FSMA and SFCR.
Research emphasizes the importance of initiating these diagnostics early to reduce the risk of
misallocated investment and ensure downstream interoperability with digital platforms
(Ahmad et al., 2024).
51

Bothwell Cheese – Cold Chain Digital Maturity Bothwell Cheese, a mid-sized
dairy producer based in Manitoba, makes more than 2,500 tonnes of cheese annually, and its
operations are hampered in parts by older temperature-logging systems. A preliminary audit
found that more than 40% of cold chain incidents were registered only through manual postdelivery reviews. Installation of 50+ RFID tags in critical storage units and 3 wireless
temperature probes accommodated a 60% reduction in inspection time alongside compliance
enhancement at batch-tracking levels. The tools stream real-time metrics into a centralized
dashboard, increasing compliance accuracy and early anomaly detection by over 30%. The
audit checklist scores also improved significantly from an initial 65% to 87% in mock FSMA
audits within just three months.
•

Tools: RFID sensors, wireless temperature probes, FSMA-aligned maturity checklists

•

Pitfall: Manual logs delay response to thermal anomalies, impacting regulatory
compliance

•

Cost (CAD): Lean: $500 | Balanced: $1,200 | Progressive: $2,500
o Lean Tier ($500 CAD)
▪

10-15 RFID tags, basic cloud dashboard

▪

Manual data upload, basic FSMA checklist

▪

Traceability pilot with minimal system change

o Balanced Tier ($1,200 CAD)
▪

30-50 RFID tags with 1 or 2 wireless temperature probes

▪

Real-time monitoring from the 3rd party dashboard

▪

60% reduction in manual inspection hours

o Progressive Tier ($2,500 CAD)
▪

More than 50 RFID tags with 3 or more sensors on a centralized
dashboard

52

▪

Automated alerting and simulated FSMA audits on batch-level
tracking

▪
•

FSMA audit readiness has been improved from 65% → 87%

Reference: Ahmad et al. (2024) found that SMEs deploying RFID in cold chains
experienced measurable improvements in spoilage reduction and regulatory reporting
efficiency.
Phase 2: Integration Planning – Laying the Digital with Interoperable Systems
This phase attempts to build an interoperable digital infrastructure, by somehow

interconnecting hardware, for instance, sensors, and software systems, such as dashboards,
via low-code integration platforms for API orchestration. Most SMEs simply fail to connect
the systems, which means that reporting is delayed; data quality can be questionable, and
there is risk of audits. Tools such as Node-RED and RESTful APIs enable SME compliance
automation workflows and standardization of data exchange. Previous studies have indicated
that integrating middleware at earlier phases of the roadmap aids in furthering data
consistency and traceability (Manupati et al., 2020).
Happy Planet – Digital Twin + ESG Integration Happy Planet, a British Columbiabased organic food and beverage company, sought to increase supply chain traceability while
integrating ESG performance indicators. The company used BLE sensors and GIS-mapped
digital twins to simulate sourcing pathways and monitor environmental situations. Around 60
BLE loggers were deployed at 15 supplier locations feeding into a cloud-based ESG
dashboard. This system provided real-time sustainability scoring, which helped the managers
reduce emissions-related discrepancies in batch sourcing by 45%. Pre-audit integration also
contributed to an increase in compliance alignment from 72% to 91% within the very first
quarter.

53

•

Tools Used: BLE sensors, GIS-enabled digital twin platform, ESG compliance
dashboard

•

Pitfall: Ignoring ESG compliance markers early in the planning phase may lead to
certification gaps downstream.

•

Cost (CAD): Lean: $1,000 | Balanced: $2,800 | Progressive: $6,500

•

Lean Tier ($1,000 CAD)
o 10–15 BLE (Bluetooth Low Energy) sensors at critical supplier nodes
o Basic ESG (Environmental, Social, and Governance) dashboard setup using
free/open-source tools
o Manual sustainability scoring based on basic inputs

•

Balanced Tier ($2,800 CAD)
o 30–40 BLE sensors + GIS (Geographic Information System)-linked digital
twin
o Cloud-based ESG dashboard with semi-automated data feeds
o 30% drop in emissions-related discrepancies, ESG compliance improved to
~85%

•

Progressive Tier ($6,500 CAD)
o 60+ BLE sensors across 15 supplier locations
o Real-time ESG scoring through fully integrated GIS-based digital twin
o 45% emissions discrepancy reduction, compliance alignment increased from
72% → 91%

•

Reference: According to Vitaskos et al. (2024), early integration of ESG dashboards
in food traceability systems aids improved audit preparedness and sustainability
disclosures.

54

Phase 3: Implementation & Testing – Edge Analytics for Real-Time Actionability.
This phase is sought to assure IoT technologies can be implemented in a live
operational environment. SMEs began the pilot installation of edge analytics facilities and
real-time sensor-cloud dashboard for data gathering and interpretation at critical points of
control. The purpose was to move from manual sampling to continuous monitoring of
processes, speeding up response and promoting decision-making. This must be met with solid
KPIs, secure data streams, and readiness of the workforce to interpret those alerts. Empirical
studies emphasize that small-scale piloting before full deployment increases adaptability of
the system with fewer disruptions (Enayati et al., 2024).
Chudleigh’s Limited-Integrated Pilot along Food & Visitor Streams Chudleigh’s
in Ontario, combining food manufacturing with agritourism, provides an occasion for unique
testing. For Phase 3 deployment, it installed 25 Bluetooth Low Energy sensors throughout
freeze tunnels and orchards, connected with a mobile edge analytics dashboard. Temperature
changes, visitor flows, and humidity trends were monitored by this system. Within 90 days,
they reduced inaccuracies in food safety logs by 38%, increasing recognition speed of
refrigeration failures by 46%, while the dashboard also doubled cross-functional insight on
visitor information into traceability logs
•

Tools: BLE sensors, mobile edge devices, cloud-integrated analytical dashboard

•

Pitfall: Attempting to pilot too many variables at once leads to inconsistent data
interpretation.

•

Cost (CAD): Lean: $1,600 | Balanced: $3,600 | Progressive: $7,200

•

Lean Tier ($1,600 CAD)
o Installation of 10–15 BLE (Bluetooth Low Energy) sensors in high-risk zones
(e.g., freeze tunnels)
o Manual syncing with mobile edge dashboard for basic anomaly detection
o Limited real-time visibility with periodic manual checks
55

•

Balanced Tier ($3,600 CAD)
o 20–25 BLE sensors across food production + visitor areas
o Mobile edge analytics dashboard with automated thermal + humidity alerts
o 38% reduction in food log errors, 30% faster incident recognition

•

Progressive Tier ($7,200 CAD)
o 25+ BLE sensors, real-time data streaming from freeze zones + orchards
o Cross-functional traceability integration for food + visitor data
o 46% faster refrigeration fault detection, full dashboard analytics with double
data insight

•

Reference: Enayati et al. (2024) have reported that phase-wise IoT piloting base on
edge analytics helped increase data reliability and operational adaptability in SME
manufacturing and service settings.

Phase 4: Monitoring & Scaling – Institutionalizing Blockchain and ESG Governance
Upon reaching the final phase, the central theme changes from experimentation to
full-fledged deployment, stressing aspects of data governance, control across various
platforms, and longevity. Successful SMEs at this stage utilize blockchain-based audit trails,
ESG dashboards, and smart labeling to ensure stockholding-level traceability. These tools
could be deployed alongside the current IoT infrastructure with Hyperledger, Power BI, and
QR smart contracts in place. It has been highlighted that the adoption of blockchain makes
food safety compliant and ESG metrics trustable in real time for investors and consumers
alike (Billah et al., 2023).
Happy Planet – Blockchain-Backed ESG Expansion To institutionalize its ESG
gains, Happy Planet integrated Hyperledger Fabric with its sourcing and traceability systems.
Deployment included 20 smart contract nodes and QR-linked batch codes for 100 product
lines, enabling direct consumer access to emission and origin data. Real-time ESG
dashboards tracked waste reduction, energy consumption, and carbon emissions. As
56

traceability queries went from over 12 hours down to under 20 minutes, there was a 34%
improvement in the accuracy of compliance reporting.
•

Tools: Hyperledger nodes, ESG dashboard extensions, QR-code smart labels.

•

Pitfall: Scaling without trusted IoT baselines could result in unverified reporting or
reputational risks.

•

Cost (CAD): Lean: $2,000 | Balanced: $4,500 | Progressive: $8,000

•

Lean Tier ($2,000 CAD)
o Setup of 5–8 Hyperledger nodes with basic ESG dashboard extension
o Manual QR code printing for 1–2 pilot product lines
o Foundational audit trail creation and limited batch-level traceability

•

Balanced Tier ($4,500 CAD)
o 10–15 Hyperledger nodes + cloud-linked ESG dashboard
o Automated QR smart labels for up to 50 SKUs (product lines)
o Real-time data integration with improved reporting accuracy (~25–30%)

•

Progressive Tier ($8,000 CAD)
o 20+ Hyperledger nodes, full ESG dashboard suite
o QR-coded traceability for 100+ product lines with blockchain-backed
consumer access
o 34% improvement in audit accuracy, traceability query time cut from 12 hours
to <20 minutes

•

Reference: Referring to Billah et al. (2023), the use of blockchain for ESG
governance serves to augment supply chain transparency, regulatory credibility, and
consumer trust.

Comparative Summary Table
Table 9:
SME IoT Implementation Phases, Technologies, and Strategic Outcomes

57

(Note: BLE = Bluetooth Low Energy; FSMA = Food Safety Modernization Act)
Cross-Sector Lessons from IoT Roadmaps
This roadmap proves that scalable IoT transformation is achievable across diverse
SME sectors—not just agri-food. Modular adoption, low-code tools, and grant-backed
funding [e.g., CDAP (Canadian Digital Adoption Program)] enable stepwise digital maturity.
For traceability improvement across supply chains, core principles like API orchestration,
traceability tools, and compliance automation offer transferrable templates. Through sensorenabled infrastructure, edge analytics, and blockchain verification, SMEs can transform
operations into digitally resilient ecosystems (Khan et al., 2023; Kareem et al., 2024).
Even though the chapter centers on agri-food firms, the phased methodology, costtiered architecture, and associated tools, such as BLE sensors, digital twins, and Hyperledger,
apply to many other domains, including logistics, healthcare, and retail. These sectors, just
like agri-food, confront increasing pressure to ensure transparency in supply chains, automate
compliance, and integrate ESG considerations into those supply chains. Policymakers,
industry stakeholders, and SME networks can scale up this roadmap for application toward
responsible digital innovation.

Discussion
1. Thematic Synthesis Across RQs
The original purpose of this study was to explore how Internet of Things (IoT) could
improve transparency within Supply Chain Systems through a structured two-phase
framework. The four RQs studied the sectoral evolution, technological enablers and barriers,
framework design, and applicability for SMEs.
This RQ1 on sectoral insight showed that: great divergence prevails on the adoption
and standardization of IoT solutions on agriculture, pharmaceutical, and logistics sectors. For
59

example, agriculture focused on sensor-based perishability monitoring (Bosona &
Gebresenbet, 2023; Saxena & Arya, 2023) while the pharmaceutical industries focused on
compliance enforcement and cold chain integrity (Ahmad et al., 2024). Logistics needed
multi-point visibility and customs integration, putting forth even higher infrastructural
demands and concerns of global interoperability (Budler et al., 2024).
RQ2 highlighted technological enablers such as Blockchain, Digital Twins, RadioFrequency Identification (RFID), and modular Application Programming Interfaces (APIs)
that enable the integration of legacy systems (Ivanov & Dolgui, 2021; Baig et al., 2022). The
phase further explored how interoperability remains among the critical barriers, especially for
SMEs with limited IT infrastructure. Nonetheless, blockchain, as an immutable source of
compliance logs, has been trusted, whereas RFID has been commoditized for high-frequency,
real-time traceability (Billah et al., 2023).
RQ3 set the ground for building a conceptual framework with three layers: (1)
Technological Enablers, (2) Operational Execution, and (3) Transparency Outcomes. These
three layers align infrastructure for data-gathering with execution in the form of workflow
automation and smart contracts for outcomes like Environmental, Social, Governance (ESG)
alignment, regulatory preparedness, and enhanced stakeholder trust (Kareem et al., 2024;
Duong, 2024).
RQ4 upheld the framework's empirical validity with five varied case studies:
Walmart, Maersk, Volvo, ZhongAn, and India's pharmaceutical cold chain modernization.
Each for demonstrate scalable SME use cases thus validating the modular adoption model
that allows plug and play integration of sensors and dashboards in a resource-constrained
environment (Volvo Group, 2023; Manupati et al., 2020).
Drawing from all four RQs, two important notions can be derived: Firstly, IoTenabled traceability is at the heart of growing stakeholder trust as it allows enabled visibility
60

across production and distribution nodes (Duong, 2024). Secondly, the full realization of
compliance and ESG integration is achieved when IoT is complemented by blockchain
(Enayati et al., 2024; Almelhem et al., 2023). These two notions go toward legitimizing the
utility of the framework not only as a conceptual model but in the practical deployment of
SMEs for digital transparency.
2. Interlinkages: Technology, Governance, and Trust
The effective implementation of IoT in supply chain ecosystems is predicated on the
intricate interplay of technological tools, governance frameworks, and trust formation. This
end-to-end transparency of data is what helps regulators and industry auditors to fulfil their
requirements (Srivastava & Dashora, 2022). IoT equipment, from temperature sensors,
Global Positioning System (GPS) trackers to smart labels, serves as a real-time data source,
the information from which is captured and verified within an ecosystem enabled by
blockchain ledgers (Gao et al., 2023).
And that is the greatest way to build trust: transparency. Consumers and investors
have increasingly started demanding transparency on whether products are ethically sourced
and manufactured. Transparent data governance, defined by how IoT data is handled
securely, ethically, and with privacy concerns in mind, has increasingly become a brand
badge of honour. The implementation of blockchain by companies with some ethical consent
management will increase both regulatory and consumer trust (Kareem et al., 2024;
Yavaprabhasa et al., 2024).
Trust in the supply chain also largely depends on predictive analytics and Digital
Twins, enabling the modelling of scenarios and proactive disruption management. They
facilitate governance agility by simulating disruptions and rerouting logistics instantly to
avoid stockouts and reputational losses (Ivanov & Das, 2020; Tao et al., 2018).

61

caters to visibility-and-accountability requirements as well as regulatory needs of all
concerned supply chain actors, mostly SMEs.
Success depends on not just the availability of such technological solutions but also
on their suitability for accepted governance protocols and trust-building mechanisms peculiar
to a sector. These findings will, in turn, be instrumental in pushing the digital maturity of
supply chains operating at a global or regional scale, laying the foundation for trade systems
that are more resilient, sustainable, and ethically governed.

64

Conclusion
This research was thus truly driven by the pressing need to regard supply chain
opacity, traceability gaps, and increasing demands about regulatory compliance and
Environmental, Social, and Governance (ESG) accountability. Through a two-phase process
framework, the study tackled sectors in which the Internet of Things could raise visibility and
stakeholder trust. Industries involved in Phase 1 included agriculture, pharmaceuticals, and
logistics. It uncovered opposing maturity levels and systemic challenges facing adoption. The
validated framework contributed by Phase 2 brings cross-sector support towards real
implementations, mainly toward Small and Medium-sized Enterprises (SMEs) working on
scalable digital transformation.
The foregoing Three-Layer Conceptual Framework of Technological Enablers,
Operational Execution, and Transparency Outcomes ultimately offers a structured but
working roadmap for IoT. Bottom layer technologies like Radio-Frequency Identification
(RFID), blockchain, and edge computing allow real-time capture and secure integration of
data. This means data powers workflow automation, smart contracts, and so forth, which
means being able to measure the result, i.e., traceability, ESG alignment, audit readiness, a
consumer trust result. Because of the modular, interoperable architecture, the framework can
adapt to high-volume enterprises as well as SMEs with limited digital capacity.
These findings provide useful directions for supply chain strategists, digital
transformation leaders, and public-sector policymakers. An organization can use the model
and framework to mitigate some gap issues-but technical, regulatory, and ethical-in their
supply chains. Whether it is perishables, pharmaceuticals, or logistics, the model provides
actionable insights for deploying ethical, transparent, and resilient IoT. As industries become
digitized amidst higher scrutiny from stakeholders, this research provides a timely, grounded,
and scalable entryway toward inclusive future-ready supply chain ecosystems.
65

Future Research Directions
With digitization of supply chains worldwide, the IoT technologies ensure
opportunities to transform transparency, governance, and ESG accountability. The present
study has developed a three-layer framework, with Technological Enablers → Operational
Execution → Transparency Outcomes. Nonetheless, there remains a need to examine themes
looking toward the future that tie into emerging challenges, technological convergence, and
sector-specific constraints. The following line of research, although based upon empirical
findings of this paper and purposed to generate new multidisciplinary and applied
investigations, will then guide future researchers and industry leaders in establishing
transparent, data-driven, and ethically governed supply chain systems.
Theme

Opportunity &

Suggested Research

Illustrative Use

Rationale

Question

Case /
Technology

Generative AI +

The fusion of generative

How can generative

Happy Planet's

IoT for Predictive AI and IoT unlocks

AI models integrated

ESG platform

Compliance and

automated compliance

with IoT platforms

augmented with

ESG Automation

reporting and dynamic

automate ESG

AI-generated

ESG storytelling. This

reporting,

regulatory briefs

synergy empowers SMEs

compliance audits,

and predictive

to extract predictive

and predictive

alerts

insights from sensor data,

maintenance in

enhancing traceability,

resource-constrained

reducing audit effort, and

SMEs?

translating raw input into

66

decision-ready ESG
dashboards.
Regulatory

Regulatory sandboxes

What governance

India’s pharma

Sandboxes for

offer a secure testing

and technical

traceability

Modular IoT &

ground for SMEs to

structures make

trials; Bothwell

Blockchain Pilots

explore modular IoT and

regulatory sandboxes

Cheese’s

blockchain solutions

effective for testing

FSMA-aligned

without legal

blockchain-IoT

sandbox

repercussions. These

traceability in SME

diagnostics

environments accelerate

supply chains?

innovation, encourage
iterative prototyping, and
lower compliance
barriers—especially in
high-risk or lowinfrastructure domains.
Longitudinal

A multi-year comparative

What are the long-

Comparative

Comparative

lens reveals how digital

term organizational,

study of

Studies: SMEs vs. maturity, sectoral scale,

financial, and ESG

Chudleigh’s and

Enterprises

and resource allocation

impacts of IoT

a Tier-1 food

shape the ROI and ESG

adoption in SMEs

manufacturer

outcomes of IoT adoption.

compared to large

tracking

SMEs often evolve

enterprises across

transparency

through modular gains,

varying national

ROI over 3 years

while large enterprises

67

benefit from integrated

digital readiness

scale—this divergence

levels?

deserves focused study.
Cross-Sector

IoT adoption success

How do IoT-enabled

Synthesizing

Benchmarking of

hinges on sector-specific

transparency ROI

metrics from

Transparency

returns. Standardizing

metrics differ by

Walmart,

ROI Models

ROI frameworks across

sector, and what

Maersk, Volvo

food, pharma, logistics,

benchmarking

for SME-tailored

and automotive sectors

models can

transparency

allows SMEs to assess

standardize adoption

calculators

feasibility, justify

business cases for

investment, and replicate

SMEs?

high-yield patterns of
transparency innovation.
Interoperability

IoT system breakdowns

What federated

Maersk’s

Protocols and

often trace back to

protocols or open

TradeLens APIs

Federated

protocol mismatches.

standards can ensure

adopted by SME

Standards

Federated open

secure, scalable IoT

logistics clusters

standards—like MQTT,

interoperability

in regional co-

ISO/IEC 30141—can

across SME and

operatives

serve as digital bridges

enterprise

across fragmented

ecosystems?

platforms, enhancing
system stability, cross-

68

border compliance, and
scalable trust.
Digital Twin

Federated digital twins

How can shared

Happy Planet's

Ecosystems for

allow SMEs to simulate

digital twin

ESG-linked

Adaptive Risk

disruptions, assess ESG

ecosystems improve

digital twin

Modeling

exposure, and collaborate

risk mitigation, ESG

federated with

on proactive mitigation.

scenario modeling,

local agri-food

These systems transform

and real-time

networks to co-

static supply chains into

decision-making in

simulate drought

dynamic, self-correcting

SME supply chains?

impacts

networks grounded in
shared intelligence.
Ethical IoT

Trust is the bedrock of

What ethical design

Volvo-Circulor’s

Design for Data

transparency. Ethical-by-

models can support

ethical sourcing

Governance and

design IoT architectures— user-centric data

layered with

Consent

featuring consent layers,

governance in IoT-

tiered access

decentralized identifiers,

based supply chains

rights and

and privacy-preserving

while maintaining

decentralized

logs—enable responsible

regulatory

supplier

data sharing and

auditability?

verification

compliance with global
privacy mandates.

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Appendices
Appendix A: Sectoral Impact Tables
The following table lists quantified benefits accruing from IoT implementations in the
three sectors that are deemed the most essential: Agriculture, Pharmaceuticals, and Logistics.
The figures pertain to the recognized enhancements in operational performance, traceability,

73

Database Sources Used: This research draws on three renowned academic databases of the
world: Scopus, Web of Science, and IEEE Xplore, which significantly building up the
academic shelter with which the studies selected for review may feel comfortable:
•

Scopus: A multidisciplinary research database, serving well for publications in
science and business.

•

Web of Science: Provides high-impact articles curated from global journals in all
varieties of academic fields.

•

IEEE Xplore: Being a technical repository, it retains the top-notch technical research
in engineering and IoT innovation.

Database Sources Used:
This study leveraged three globally recognized academic databases—Scopus is a large
research database that collects articles from science, business, and technology fields. Web of
Science indexes the world’s most important academic journals across disciplines, ensuring
high research quality. IEEE Xplore is a specialized database focused on engineering and
computing research, ideal for studies involving IoT and smart technologies. These databases
were chosen to provide a well-rounded and credible foundation of knowledge. The use of
multiple databases minimized bias and ensured comprehensive inclusion of articles spanning
business, engineering, sustainability, and information systems literature.
An initial gathering of 214 articles had been there thanks to Boolean searches of
keywords ("IoT AND Supply Chain AND Transparency", for instance) across Scopus, Web
of Science, and IEEE Xplore to make sure there were interdisciplinary bases. The list was
then weeded down through a formal four-step procedure: removing duplicates, screening by
title and abstract, evaluating against full text, and appraising with the CASP checklist. 36
peer-reviewed articles ended up being considered as final selections, recognized to the study
because of utmost relevance and reliability.
80