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Leveraging Digital Twin Technology for Logistics Optimization in Manufacturing SMEs

Mosunmola Dayo-Olotu
University Canada West
MBAR 661 ONS-SPRING25-05
Dr. Amit Kohli
August 20, 2025

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Leveraging Digital Twin (DT) Technology for Logistics Optimization in Manufacturing
SMEs
Abstract
Purpose: The study is centred on how DT technology assists logistics processes by SMEs in
realising high performance, cost reduction, and assistance in making acceptable choices. The
primary goal of the paper is to develop a pragmatic framework in which SMEs can use DT in
a cost-effective and non-rigid way.
Design/Methodology/Approach: The research methodology consists of a qualitative case
study that explores case studies, peer-reviewed research articles available at various sources,
such as Web of Science, Scopus and Google Scholar, as well as market reports available at
various sources, such as Statistics Canada and markets.
Findings: The research proved that DT technology has the potential to increase production
efficiency by streamlining logistics, maximising resources for more efficient operations, and
minimising downtime.
Research Limitations/Implications: The study has the limitation of using the previous
literature, case studies, and research papers as the main sources of information, which do not
necessarily reflect SMEs currently taking advantage of the DT technology in logistics.
Practical Implications: The study gives SMEs insights into implementing the DT technology
to streamline logistics, lower costs, and enhance efficiency.
Social Implications: Social implications of the presented research are significant as they
reflect the opportunity of utilizing DT technology to increase sustainability among SMEs in
terms of energy utilization and waste.
Originality/Value: The study's scientific interest is to provide a framework to make the DT
technology accessible, cost-effective, and scalable to logistics operations in SMEs.

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Keywords: Digital Twin Technology, Logistics Optimisation, SMEs, Supply Chain
Management, Predictive Analytics, IoT, Fleet Management, Real-time Data, Operational
Efficiency, Data Integration, Cost Reduction, and Scalable Solutions.

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Table of Contents
Abstract ....................................................................................................................................................................2
Leveraging Digital Twin Technology for Logistics Optimization in Manufacturing SMEs ...................................9
1.0 Background and Introduction .............................................................................................................................9
1.1 Problem Gap ..................................................................................................................................................8
1.2 The Evolution and Concept of DT Technology.............................................................................................8
1.3 The Importance of Logistics Optimization in Manufacturing SMEs ..........................................................10
1.4 Challenges and Barriers to Adoption in SMEs ............................................................................................12
1.5 Comparison with Large Enterprises: SME vs. Large Corporation Adoption of DT Technology ...............13
1.6 Opportunities for SMEs in Adopting DT Technology ................................................................................14
2.0 Research Questions, Aim and Objectives ........................................................................................................12
2.1 Research Questions .....................................................................................................................................15
2.2 Research Aim and Objectives......................................................................................................................16
3.0 Literature Review .............................................................................................................................................16
3.1 Implementation of DT Technology in Logistics Operations .......................................................................16
3.2 Challenges in Adopting DT Technology for Logistics ................................................................................18
3.3 Measurable Outcomes from Implementing Digital Twin Technology ........................................................19
3.4 Future Directions for DTTechnology in Logistics Optimization ................................................................21
3.5 Critique of Gaps in Existing Literature .......................................................................................................22
4.0 Methodology ....................................................................................................................................................22
4.1 Research Design and Approach...................................................................................................................23
4.2 Selection of Case Studies ............................................................................................................................24
4.2.1 Industry Relevance ..............................................................................................................................27
4.2.2 Documented Use of Digital Twin Technology ....................................................................................27
4.2.3 Geographical and Sectoral Diversity ...................................................................................................27
4.2.4 Size of SMEs .......................................................................................................................................27
4.3 Data Collection ...........................................................................................................................................27
4.3.1 Review of Academic Journal Articles .................................................................................................27
4.3.2 Industry Reports and White Papers .....................................................................................................28
4.3.3 Case Studies from Industry and Technology Providers .......................................................................28
4.3.4 Publicly Available Data from SMEs ....................................................................................................28
4.4 Data Analysis ..............................................................................................................................................29
4.4.1 Familiarization with the Data ..............................................................................................................29
4.4.2 Identification of Key Themes ..............................................................................................................29
4.4.3 Cross-Case Comparison ......................................................................................................................29
4.4.4 Synthesis of Findings ..........................................................................................................................29
5.0 Data Collection and Analysis to Address Research Questions ........................................................................31
5.1 Technology Adoption By Canadian SMEs ...................................................................................................31
5.1.1 Use of Emerging Technologies by Subcategory and Ownership Type ...............................................32

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5.2 Addressing Research Questions ..................................................................................................................34
5.3 Synthesis of Findings ...................................................................................................................................48
5.3.1 Improved Efficiency and Reduced Costs ............................................................................................48
5.3.2 Enhanced Workforce Productivity and Training .................................................................................48
5.3.3 Real-Time Data Monitoring and Decision-Making.............................................................................49
5.3.4 Reduced Time-to-Market and Increased Operational Flexibility ........................................................49
5.3.5 Predictive Insights and Long-Term Planning ......................................................................................49
5.3.6 Scalability and Integration ...................................................................................................................50
6.0 Practical Recommendations for SMEs to Adopt DT Technology ....................................................................50
6.1 Proposed Conceptual Framework for Integrating Digital Twin Technology into Logistics Operations for
SMEs ..................................................................................................................................................................53
6.1.1 Key Components of the Framework....................................................................................................55
6.1.2 Steps for Implementation ....................................................................................................................56
6.1.3 Roadmap Timeline for Implementing Digital Twin Technology in SMEs ..........................................57
6.1.4 Estimated Implementation Costs and ROI KPIs .................................................................................59
6.1.5 Actionable Recommendations for Cost-Effective Integration.............................................................60
6.1.6 Success Stories Case Studies and Best Practices ................................................................................61
6.2 SME Digital Twin Readiness Checklist .......................................................................................................62
7.0 Return on Investment (ROI) of Digital Twin Technology in the Logistics Sector for SMEs ..........................67
7.1 Cost Reduction through Process Optimization ...........................................................................................67
7.2 Enhanced Predictive Maintenance and Reduced Downtime.......................................................................67
7.3 Improved Decision-Making and Operational Efficiency ............................................................................68
7.4 ROI Through Scalability and Reduced Implementation Costs ....................................................................68
7.5 Cost Estimates and ROI Expectations for SMEs .........................................................................................69
7.6 Success Stories and Framework ..................................................................................................................66
7.6 Measuring ROI ............................................................................................................................................68
7.7 Key Success Metrics for Digital Twin Implementation in Logistics ............................................................68
8.0 Future Research Directions in the Integration of DT in Logistics for SMEs...................................................68
8.1 Customization of Digital Twin Solutions for SMEs .....................................................................................69
8.2 Integration of Digital Twin with Existing Logistics Systems .......................................................................69
8.3 Data Privacy, Security, and Regulatory Compliance ..................................................................................70
8.4 Impact of DT on Sustainability and Environmental Performance ..............................................................70
8.5 Advanced Predictive Analytics AI Integration ............................................................................................72
8.6 Cost-Benefit Analysis and ROI Models for SMEs .......................................................................................72
8.7 Workforce Development and Training for SMEs .........................................................................................72
9.0 Conclusion........................................................................................................................................................73
10.0 Limitations of the Study .................................................................................................................................74
11.0 References ......................................................................................................................................................74

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Key Abbreviations
SMEs - Small and Medium-sized Enterprises
DT - Digital Twin
ERP - Enterprise Resource Planning
WMS - Warehouse Management System
IoT - Internet of Things
KPI - Key Performance Indicator
AI - Artificial Intelligence
ML-Machine Learning
MQTT - Message Queuing Telemetry Transport

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API - Application Programming Interface
PLC - Programmable Logic Controller
BOM - Bill of Materials
CPS - Cyber-Physical Systems
SCADA - Supervisory Control and Data Acquisition
BI - Business Intelligence
3D MCD - 3D Mechatronic Concept Design
OEM - Original Equipment Manufacturer
R&D - Research and Development
OPC- Open Platform Communications
UA- Unified Architecture
MES-Manufacturing Execution System

List of Figures
Figure 1: DT market size and growth rate projection ............................................................... 9
Figure 2:Elaboration of IoT and AI implications in digital twin applications ........................ 12
Figure 3: global manufacturing technology decision-makers on Digital Twin Adoption ....... 17
Figure 4: The Research methodology framework ................................................................... 24
Figure 5: Use of Technologies by Ownership Type ................................................................. 31
Figure 6: Use of Emerging Technologies by Subcategory and Ownership Type in the
Canadian SMEs ....................................................................................................................... 33
Figure 7:Percentage reduction in operation cost, delivery time, inventory holding and
maintenance downtime ............................................................................................................ 44
Figure 8: Conceptual Framework for Integrating Digital Twin Technology into Logistics
Operations for SMEs................................................................................................................ 55
Figure 9: 12 months Roadmap Timeline for Implementing Digital Twin Technology in SMEs
.................................................................................................................................................. 60
Figure 10:Conceptual architecture for human-inclusive DT for SMEs .................................. 70

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List of Tables
Table 1: Objectives and Research aim .................................................................................... 16
Table 2: Case Studies Selection Criteria ................................................................................. 24
Table 3:Potential Biases and Mitigation Strategies in the Research ....................................... 30
Table 4: Use of Technologies by Ownership Type ................................................................... 31
Table 5: Use of Emerging Technologies by Subcategory and Ownership Type ...................... 33
Table 6: Summary of Implementations ................................................................................... 37
Table 7: Summary of Adoption Challenges, impacts and solutions ........................................ 39
Table 8:Measurable Outcomes of Digital Twin Implementation in Logistics Operations ...... 44
Table 9: Improvements metrics before and after implementation DT in logistics .................. 46
Table 10: Timeline summary for the Adoption ....................................................................... 58
Table 11:Key Success Metrics for Digital Twin Implementation in Logistics ........................ 71

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Leveraging Digital Twin Technology for Logistics Optimization in Manufacturing SMEs
1.0 Background and Introduction
Improvements in Digital Twin technology are shaking up companies and sectors around the
globe. The use of logistics optimization in small and medium-sized enterprises (SMEs) within
the manufacturing industry is receiving increasing attention (Iliuţă et al., 2024). With DT,
virtual stand-ins of assets, systems, or processes enable real-time monitoring, prediction, and
efficiency gains (Moiceanu & Paraschiv, 2022). Digital Twin particularly benefits logistics,
where efficiency and cost reductions drive competitiveness.
1.1 Problem Gap
Although there is an increased global awareness of DT technology, the level of adoption
and implementation is predominantly limited to large-scale corporations with vast financial
and technical capabilities. The struggles of the SMEs that constitute the backbones of most
manufacturing economies are the high cost of implementation, the absence of skilled labour,
and the inability to integrate the DT with the other legacy systems. Much of the current and

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commercial case studies concentrate on large companies and do not provide much guidance to
the SMEs on practically using DT to optimise their logistics process in limited settings.
There is a lack of knowledge on particular strategies, tools, and step-by-step methods that
SMEs can use to incorporate DT in a cost-efficient and scalable way. The proposed study fills
this gap by offering a specific framework aided by the case studies of SMEs to direct DT's costeffective and viable implementation within the logistics context.
1.2 The Evolution and Concept of DT Technology
In 2002, Dr. Michael Grieves brought DT technology to life in a research project about
product lifecycle management (Kshetri, 2021). Before, the DT only stood for virtual models of
materials or machines, but today, it also covers processes, systems, and even companies (Singh
et al., 2021). The main idea of DT is to produce a virtual version of a physical object with
updates, models, and forecasting tools. Because of this, businesses can check on how they
operate, generate specific scenarios, expect outcomes, and streamline processes whenever
needed (Javaid et al., 2023).
As indicated in Figure 1, global DT market projections show a high growth rate, with market
size expected to reach $110.1 billion by 2028 (up from $10.1 billion in 2023; CAGR 61.3%)
(MarketsandMarkets, 2023).
Figure 1: DT market size and projected growth rate

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Note: Adapted from “Digital Twin Market Size, Share, and Trends” (MarketsandMarkets,
2023). Retrieved from https://www.marketsandmarkets.com/Market-Reports/digital-twinmarket-225269522.html
Such significant growth in the market is a sign that more people are adopting DT in
manufacturing, logistics, healthcare, and urban planning. With Digital Twin technology in
logistics, users can make 3D models of transportation systems, warehousing, and inventory
systems, and always watch over them for optimization.
1.3 The Importance of Logistics Optimization in Manufacturing SMEs
SMEs form a significant share of the manufacturing sector worldwide. The U.S. Small
Business Administration indicates that SMEs constitute 99.9 % of all the private businesses in
the country, and their number of employees exceeds 47% of the private employment in the
nation. (U.S. Small Business Administration, 2022). Even though there are many of these
enterprises, many find it challenging to improve their logistics operations due to insufficient
resources, old equipment, and a lack of access to the latest technologies.
Better logistics systems in manufacturing SMEs help smooth operations, save costs,
and compete effectively (Keelson et al., 2024). Minimizing logistics costs, cutting down the
time it takes to deliver, quicker inventory turnover, and better response to customers can all
come from managing logistics efficiently (Oteri et al., 2023). Although logistics optimization
is important, SMEs often struggle to set up optimized processes due to expensive software and
manual planning (Costa et al., 2023). In this scenario, Digital Twin allows SMEs to manage
their logistics operations cheaply by letting them test, modify, and study various options.
Creating computer models of physical logistics such as supply chains, transportation,
inventory, and warehouses is the essence of DT in logistics (Liu et al., 2024). Figure 2 illustrates
how digital models can be linked to sensor and IoT data to track goods, monitor transport
routes, anticipate delays, and manage inventory (Roman et al., 2025).

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Figure 1: Elaboration of IoT and AI implications in digital twin applications

Note: The diagram elaborates how the digital representations align with real-time data of
sensors and IoT devices in following the status of goods, including involvement of
transportation routes. Retrieved from "State of the Art of DT in improving the resilience of
supply chains" by E.-A. Roman, A.-S. Stere, E. Roșca, A.-V. Radu, D. Codroiu, and I.
Anamaria, 2025, Logistics, 9(1), 22 http://doi.org/10.3390/logistics9010022. Copyright 2025
by MDPI.
According to the research of Werbińska-Wojciechowska et al. (2024), DT is applicable
in enhancing inventory maintenance and transport in logistics processes. An organization that
receives real-time data is capable of practicing various logistical strategies, forecasting delays,
discovering the most cost-efficient routes, and utilizing warehouses to maximum capacity.
Such large volumes of information were previously only accessible to organizations that had
huge budgets.
Digital Twin technology benefits logistics by enabling predictive maintenance (Nagy
et al., 2025). For instance, one of the most typical cases of a transportation logistics
organization implementing the digital twin technology in predictive maintenance is the RollsRoyce Company and its TotalCare service. Rolls-Royce develops digital twins of jet engines
to track real-time health data of a jet, which allows predictive analytics- analyzing sensor and

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IoT data using AI and machine learning models, to predict with accuracy when certain
maintenance is necessary before problems become critical. Carriers that avail this service
conduct maintenance on a planned basis, and therefore, there are fewer unforeseen breakdowns
and expensive unplanned groundings (Ojeda et al., 2025). Integrating AI and machine learning
with DT in transportation logistics can increase logistics efficiency. AI algorithms can optimize
warehouse layouts and delivery plans, while machine learning models can predict demand,
adjust stock levels, and enhance customer service (Elbouzidi et al., 2023). The new tools help
SMEs work better, spend less, and meet customer expectations.
1.4 Challenges and Barriers to Adoption in SMEs
Although DT offers numerous logistics advantages, SMEs face adoption barriers
(Burinskienė & Nalivaikė, 2024). High upfront costs for models, sensors, and software remain
difficult for smaller firms (Omowole et al., 2024). MarketsandMarkets (2023) reports relatively
low SME adoption due to financial constraints. Small and medium-sized businesses must
connect Digital Twin technology to their ongoing systems. SMEs often depend on old ways of
handling logistics tasks, such as manual spreadsheets or outdated software. It is difficult for
small organizations to successfully connect their legacy systems to advanced Digital Twins
because they do not usually have the needed technical experts (Zhao et al., 2020). Since many
SMEs cannot recruit qualified staff, this lack of technical knowledge is an excellent barrier to
adoption. Besides, many people are still worried about the security and privacy of their data
when dealing with DT technology. SMEs collecting massive amounts of sensor and IoT data
should always set up safe storage and transmission for this information. Moving SMEs' Digital
Twins to the cloud exposes them to cybersecurity threats. Benjamin et al. (2024) inform that
many SMEs find it hard to implement DT technology due to the cost of security solutions.
1.5 Comparison with Large Enterprises
SME vs. Large Corporation Adoption of DT Technology The situation of SMEs
adopting DT technology differs markedly from that of large corporations due to disparities in

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resources, technical capacity, and strategy. Conversely, SMEs have under-strained budgets and
generally lack specialised human resources to manage complicated digital change programs.
This constraint precludes their ability to invest in far-reaching DT environments and often
requires bite-sized or cloud-based implementation strategies. In addition, SMEs have to
struggle to integrate DT with legacy systems and break workforce opposition due to training
limitations. Furthermore, big companies tend to be favored more by the government and enjoy
better access to consultancy services in their digital transformation than SMEs, where they
have to venture into adoption with little or no external help. Notwithstanding these limitations,
SMEs can use the DT technology by introducing focused, low-end solutions, especially on the
logistics front where predictive upkeep, stock optimization, and real-time monitoring can show
quantitative returns. Thus, although the opportunities brought by DT have massive implications
on both PS, SMEs have a more prolonged, risk-averse adoption process based on long-term
prioritization and alliance with technology vendors. This situation deficit explains why SMEoriented frameworks that consider restricted funds and stage implementation policies are
crucial.
1.6 Opportunities for SMEs in Adopting DT Technology
Despite the challenges mentioned, SMEs will likely use digital twin tools to streamline
logistics. Cloud-based solutions for DT allow SMEs to use the technology relatively
inexpensively since they do not need to spend heavily upfront (GlobeNewswire, 2024).
Businesses no longer need to pay upfront for Digital Twin technology using the cloud, so
capital costs are vastly lowered. The rise of open-source Digital Twin frameworks and tools
allows SMEs to build and use their logistics models digitally more easily. The fact that SMEs
can customize these solutions removes some difficulties when entering the cloud market (Liu
et al., 2023). Teaming with technology providers can help SMEs address their problems when
beginning with Digital Twin adoption. SMEs can rely on partners to open the door to technical
help, training, and essential resources they might not have otherwise (Peretz-Andersson et al.,

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2024). Teamwork in SMEs allows them to address the risks of using Digital Twin solutions
while getting the most significant benefits from this technology. Logistics optimization for
manufacturing SMEs can be improved using Digital Twin, which offers live data, predictive
functions, and simulation tools. SMEs might find it challenging to adopt cloud-based solutions
since they may lose money during the first phase, lack skills, and face security issues. Still,
alternative ways, such as open-source solutions and industry teams, are available. Logistics
firms that use Digital Twin technology will find it easier to save money, work more efficiently,
and compete on the world stage (Gennitsaris et al., 2023).

2.0 Research Questions, Aim and Objectives
2.1 Research Questions
The research will address the following key questions:
1. RQ1: How have manufacturing SMEs implemented DT technology in their logistics
operations to optimize efficiency and reduce costs?
2. RQ2: What specific challenges have manufacturing SMEs faced in adopting DT
technology for logistics, and how have they overcome these challenges?
3. RQ3: What measurable outcomes have manufacturing SMEs achieved through the
implementation of DT technology in logistics?

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2.2 Research Aim and Objectives
Table 1: Objectives and Research aim
Objective

Description

How the Research Aims to
Address It

Objective 1: To evaluate

This objective aims to assess

The research examines case

the implementation of

how DT technology has been

studies, reviews industry

Digital Twins in logistics

integrated into the logistics

reports, and explores existing

operations of

operations of manufacturing

implementations of Digital

manufacturing SMEs.

SMEs, understanding the

Twin technology in SMEs to

effectiveness of its

understand the scope and

application.

impact of its use in logistics
operations.

Objective 2: To determine

The aim of the study are to

The study focuses on

the advantages and

reveal the benefits and

quantitative information of

obstacles manufacturing

challenges SMEs experience

previous research, responses

SMEs experience when

when implementing Digital

in the industry, and opinion

implementing Digital Twin

Twin technology into their

surveys to establish the non-

technology to optimize

logistical operations.

tangible value-adds and

logistics.

issues, and gives certain
tangible specifics as to how
SMEs are solving the issues.

Objective 3: To offer

The purpose of this objective

The study offers practical

recommendations and

is to elaborate on the use of

recommendations drawing on

plans of action that

the practical

the results of case studies,

manufacturing SMEs can

recommendations and

expert surveys, and lessons

utilize to optimize their

approaches by supporting the

learned to assist SMEs in

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logistics-related operations

manufacturing SMEs to

embracing Digital Twin

with the help of Digital

embrace and maximize the

technology successfully and

Twin technology

use of Digital Twin

ensure maximum returns.

effectively.

technology to bolster
logistics performance.

3.0 Literature Review
Manufacturing enterprises can now enhance their logistics thanks to the help of the DT
technology (Cremonini et al., 2025; Rahmani et al., 2024). The review examines how SMEs
have used DT, what problems they encountered, and the outcomes they could measure because
of it. Up-to-date literature is studied to gather information, data, statistics, and case studies to
show the pros and cons.
3.1 Implementation of DT Technology in Logistics Operations
The aspect that DT causes virtual representation of real objects, equipment, or systems has
played a pivotal role in enhancing the logistics in manufacturing SMEs (Rahmani et al., 2024b).
Yasin et al. (2021) inform that DT allows SMEs to simulate and supervise real-time logistics
activities, boosting efficiency and cutting supply chain costs. Thanks to DT, small logistics

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firms may monitor their stock 24/7, gauge demand in advance, and choose the best delivery
routes using simulated feedback (Kim et al., 2025). Also, IoT and AI are among the
technologies that assist in collecting data and forecasting information. Abideen et al. (2021)
mention that SMEs using technology with AI-based forecasting decreased their operational
costs by 18% in logistics. As a result, the companies could handle inventory better and avoid
out-of-stock and overstock situations. To efficiently manage logistics, SMEs now rely on
simulating and predicting possible supply chain problems (Alshahrani & Salam, 2022). After
noting that DT enables SMEs to connect their production lines to logistics systems, Alshahrani
& Salam (2022) state that, as a consequence, in this way, SMEs have better visibility of their
supply chains. The fact that logistics operations can be easily altered allows SMEs to better
react to an increased demand or other issues with the supply chain that would otherwise
destabilize SMEs in fast-moving markets (Tukamuhabwa et al., 2021). Mayorov and Egorova
(2024) remark that Digital Twin technology allows for significantly improving logistics flow,
particularly in manufacturing settings. Through feed-forward continuous data processing and
analysis, Digital Twin systems can help manufacturing SMEs better assess and model different
aspects of logistic operations, including transportation, inventory, and warehouse. These virtual
models can, therefore, mimic some real-life situations, evaluate various operating conditions,
and demonstrate possible hazards and suboptimal situations. Adesola et al. (2025) observe that
another practical aspect of DT is equally applicable in global supply chain optimization. Their
study indicates that DT can reduce the operational cost by 30-40% and increase the time lag to
supply chain disruption by up to 60%. The real-time monitoring and modeling provide the
structure of DT with possibilities to be more productive, spend fewer resources, and attain
superior supply chain consistency in various situations. Since DT systems operate in
conjunction with IoT technology, real-time data streams are essential in swarming logistic
environments; therefore, there is a need to develop DT systems (Homayouni et al., 2024). Using

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DT in proactive prediction enables better management of risks and the overall improvement of
supply chain risks.
3.2 Challenges in Adopting DT Technology for Logistics
While DT in logistics is helpful to manufacturing SMEs, it still brings some difficulties,
mainly due to the lack of resources, issues with data, and complicated technology. Another big
problem is getting the technology needed in the first place (Restrepo-Morales et al., 2024). As
explained by Kang & Kim (2024), the considerable expense of purchasing hardware and
software can make DT integration hard for SMEs. This point is critical, since implementing
DT can be too expensive for many SMEs. Integrating old systems with new technologies from
digital transformation is yet another serious issue. Rahmani et al. (2024) inform that many
small companies continue to use old technology systems that cannot support modern digital
technologies. Mismatching current with previous systems creates data silos and makes
operations less effective, thereby stopping DT applications from achieving their full benefits
(Wu et al., 2025). SMEs do not usually have the knowledgeable workforce to apply and manage
advanced technologies, so it becomes even harder for them to use DT widely. The complexity
of data integration also poses challenges. For DT technology to successfully improve logistics,
its data should always be of high quality, coordinated, and recent (Dossou & Nshokano, 2023).
SMEs encounter problems with data inconsistency and poor quality, reducing their chances of
making the right decisions from digital technologies. Many SMEs are gradually implementing
cloud-based digital transformation (DT) solutions, which cut costs and allow businesses to
grow easily. As Abolghasem and Carpitella (2024) point out, cloud platforms allow SMEs to
use DT technologies through subscription, which helps reduce the upfront costs and gives more
choices in use (Abolghasem & Carpitella, 2024).
3.3 Measurable Outcomes from Implementing Digital Twin Technology
Manufacturers in the sector have achieved various measurable and visible achievements
after using DT technology in logistics. An important advantage is that it helps businesses lower

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their daily costs. "Integrating Digital Twin (DT) technology into logistics systems at SMEs can
lead to a 15% reduction in transportation costs by enabling more efficient route optimization
and better fleet management (Krishnan et al., 2024)". Fuel usage is minimised by simulating
transport conditions and updating routes in real-time, as well as improving on-time delivery.
The other significant advantage of implementing Digital Twin (DT) tech in the logistics
industry is improved inventory management. Ramingwong et al. (2024) reveal that companies
that utilize DT in improving their logistics have an increased rate of stock turnover. The tools
allow companies to precisely predict demand and enable delivery of the correct products at the
correct time to reduce the danger of a stock out or overstocking. This provides massive cost
savings and cost effectiveness. Customer satisfaction has also grown due to digitization Twin
(DT) technology. SMEs can shorten delivery times by using DT and better respond to consumer
needs, thereby improving the quality of their services (Sharabati et al., 2024). More efficient
logistics also makes the customers happy, which is a key issue for SMEs pursuing a competitive
edge. On top of that, DT aids in sustainability programs. Kannan and Gambetta (2025) also
claim that SMEs can optimize the use of their resources, lessen waste, and minimize harm to
the environment with the help of virtual simulations made possible by DT. In general, DT has
become an effective facilitator of operational excellence that enables organizational cost
reduction and efficiency. Kharchenko et al. (2020) claim that DT systems also positively
influence supply chain management, as these systems make it possible to detect and determine
excess inventory, thereby ensuring streamlined operations and customer satisfaction." Adesola
et al. (2025) list that the SMEs mentioned earlier who implemented DT technology benefited
from the reliable supply chain. The flexibility to foresee these disruptions and manage such
risks enabled SMEs to minimize disruptions and their impacts. Decision support through DT
systems helped improve the decision-making capacity of SMEs; resources and logistics
activities that were complicated and time-consuming became more manageable. Another
benefit of integrating Digital Twin technology is enhancing forecasting and demand planning

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levels. Mayorov & Egorova (2024) indicate that Digital Twin offers promising analytical
capabilities that help manufacturing SMEs anticipate demand more proficiently, eliminating
prevailing stockout and overstock situations. This is especially true for SMEs under pressure
to offer their commodities cheaply while focusing on efficient stock management.
Figure 2: Global manufacturing technology decision-makers on digital twin adoption

Note: Retrieved from a study commissioned by Hexagon and carried out by Forrester
Consulting in May 2023. https://hexagon.com/resources/insights/digital-twin/statistics
In recognition of its rising stature, DT's global market size will rise to 240 billion EUR
in 2032 with a CAGR of 39.8% within the forecasted period (Fortune Business Insights, 2024).
About 29 percent of the manufacturing companies across the globe have already implemented
activities of DT in their entirety or partially, 65 percent of manufacturers are applying them to
streamline their operations and 67 percent of the manufacture technology decision-makers will
be busy concentrating on the application of DT to attain full sustainability of their products
lifecycle (Forrester Consulting, 2023). These statistics indicate the opportunities DT
technology is bringing in terms of the radical possibilities of transforming production
processes, enhancing efficiency, and reducing costs, as well as making operations more
resource-efficient.
3.4 Future Directions for DT Technology in Logistics Optimization

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The future of DT in logistics will include using AI, ML, and blockchain more widely
in daily operations (Mousavi et al., 2024). Because of these technologies, DT can make better
decisions, as systems can simulate logistics and change and improve processes independently
(Zaidi et al., 2024). As SMEs move toward "autonomous logistics," they will depend less on
people, make fewer mistakes, and enhance their operations.
Adopting blockchain will increase transparency and security in logistics. Blockchain securely
stores logistics data, enabling all supply chain people to work with accurate and up-to-date
information (Karaduman & Gülhas, 2025). Combining DT and blockchain will help SMEs
make their logistics operations more reliable.
The use of 5G will strengthen DT technology. Large-scale data processing on 5G
networks will lead to more flexible and prompt logistics actions. Using 5G, AI, and DT, SMEs
can respond swiftly to disruptions and make data-backed decisions.
SMEs in manufacturing have found that the logistics process has drastically improved with the
use of DT. Performing real-time data collection, superior predictions, and simulated processes
makes SMEs pay less, work more efficiently, and deliver to their customers happily. Even so,
difficulties linked to the cost of getting started, combining information, and having skilled
workers must be managed to take full advantage of DT. Digital twins in logistics will continue
to benefit SMEs when technology advances due to further developments in AI, machine
learning, and blockchain.
3.5 Criticism of the Gaps in the Existing Literature
Despite the existing body of literature on the potential of the Digital Twin (DT)
technology to revolutionize logistics and manufacturing, most studies are relevant to large
organizations with high digital readiness and huge finances. Existing research on SMEs
continues to be limited, especially concerning the practical application of DT solutions in SMEs
with unique limitations, including limited budgetary capacities, availability of legacy systems,
and limited methods of technical expertise.

23
Moreover, most available research is theoretical and/or explorative. Longitudinal and
implementation-oriented studies are nonexistent, but they can provide a picture of practical
outcomes in SMEs. Also, a few directions are present on cost-effective and phased adoption
walk, measurement of return on investments (ROI), and the acquisition of workforce skills
necessary to utilize DT tools to maximum effect.
Second, although specific sources mention the advantages of sustainability, there is no
in-depth research on the ecological consequences of the DT used by SMEs.
This paper would fill these gaps since it combines the review of a case experience, applies the
appropriate frameworks, and provides workable, feasible recommendations on how to facilitate
logistical operations in SMEs.

24

4.0 Methodology
This study proposes a case study by exploring the usage of Digital Twin technology in
logistic processes of manufacturing SMEs (Loaiza & Cloutier, 2022). The research aims to
determine how DT can streamline the supply chain logistics and reduce costs.
The choice of the case study approach seeks the possibility of obtaining more comprehensive
knowledge about the peculiarities of the challenges and opportunities faced by utilizing SMEs'
DT solutions (Baxter & Jack, 2016). Secondary data collection is applied in the research study
to attain detailed information about the implementation of DT, as defined in the research
proposal, and the implications of the DT on the SMEs.
Figure 3: The Research methodology framework

Note: Author’s elaboration of research methodology framework for this study.
4.1 Research Design and Approach
The case study methodology was adopted, which helps focus on real-life settings when
the topic is fresh or little explored. Examining different case studies using the methodology
provides insight into using DT and the issues encountered by SMEs using it in the logistics

25
sector (Crowe, 2017). This study explores the topic of DT technology in SME logistics
operations. The research technique uncovers useful information about using Digital Twin, the
difficulties SMEs encounter, and the achievements it leads to (Saka et al., 2023). This study
will use case studies as its focus and thus analyze pre-existing secondary sources such as
academic articles, industry reports, and documented examples of Digital Twin's success in
manufacturing SMEs.
The choice of the qualitative research approach is because this work is devoted to the study of
introducing Digital Twin (DT) technology into the operation of SMEs within the framework of
the current organizational structure and resource capabilities. Unlike quantitative research,
which functions within numerical information and statistical modeling, qualitative research
allows a better understanding of real-life situations. It will look at organizations' troubles,
decision-making, and complexities in the implementation practices of Digital Twin (DT)
solutions.
This method examines the critical but arguably less quantifiable and more case-dependent
characteristics, including organizational preparedness, employee flexibility, and integration
complexities, through case studies and readings of documented industry reports. Since the
large-scale data on DT adoption among SMEs is scarce, an approach based on qualitative
methodology is the most suitable to identify effective strategies, fill the gap in implementation,
and develop a practical framework that would reflect the situations of real-world operations of
SMEs.

4.2 Selection of Case Studies
The case study review method was employed to understand further how manufacturing
SMEs can adopt Digital Twin (DT) technology in logistics. To ensure that each organization

26
has been able to optimize its logistics operations by implementing DT solutions, the cases
selected had to meet particular inclusion criteria."
Table 2: Case studies Selecting Criteria
Selection

Keywords

Inclusion Criteria

Exclusion Criteria

Industry

Manufacturing,

Case studies from

Cases from non-

Relevance

Logistics, Sectors

diverse manufacturing

manufacturing or

sectors (consumer

irrelevant sectors

Criteria

goods, automotive,
electronics)
Documented

Implementation,

SMEs with documented

Lack of documented

Use of Digital

Documentation,

use of Digital Twin in

implementation or

Twin

Technology

logistics (reports, case

unclear use of DT

studies, data)
Geographical

Regions, Cross-

Case studies from

Cases limited to one

and Sectoral

sectoral, Global

SMEs in varied

geographic region or

geographical locations

sector

Small, Medium,

SMEs with 1-250

Enterprises outside the

Employees (1-250)

employees in logistics-

SME size range (less

focused operations

than one or more than

Diversity
Size of SMEs

250 employees)

27
4.2.1 Industry Relevance
The chosen case studies must be from manufacturing SMEs in industries that heavily
depend on logistics. These may include sectors such as manufacturing, automotive, food
preparation, electronics, and weaving fiber into textiles (Banerjee, 2018). As a result, the
research collects various cases where the Digital Twin is applied.
4.2.2 Documented Use of DT Technology
The case studies include the SMEs that have expressed their use of DT in logistics. The
documentation might consist of reports prepared by the company, case studies made public, or
technology providers' data showing the process and the achievements. Using well-documented
cases, the research achieves a strong level of validity and accuracy.
4.2.3 Geographical and Sectoral Diversity
The studies included come from companies located across various regions to enhance how far
the study can be applied. As a result, it becomes easier to see the variations in Digital Twin use
based on what happens in different regions and sectors.
4.2.4 Size of SMEs
Most SMEs have employees ranging from 1 to 250 (Millers & Gaile-Sarkane, 2021). This
research will study SMEs because different challenges and opportunities are common
depending on the company's size.
4.3 Data Collection
Because the research only uses secondary data, data collection involves finding
information from numerous reliable and appropriate sources. Secondary data comprises
industry studies, articles from academic magazines, and open-access case studies. The purpose
is to work with solid, recognizable data to fill the gaps found in the existing research. The data
collection process includes the following steps:

28
4.3.1 Review of Academic Journal Articles
Academic journals found in articles are thoroughly analyzed. The articles considered
concern a study regarding the application of DT to industrial and logistics companies,
particularly SMEs. They discuss the theory and practice businesses use, list the expected
benefits and challenges of applying the technology, and detail real outcomes SMEs report.
Relevant academic articles on JSTOR, ScienceDirect, and Google Scholar ensure the Research
includes recent studies. Preference will be given to articles around the use of DT in SMEs'
supply chains and logistics, as they will inform the Research.
4.3.2 Industry Reports and White Papers
Top-level consulting companies like MarketsandMarkets publish reports to assess
market size and growth rate. These documents usually give helpful information about trends,
insights, and benchmarks for using Digital Twin technology. Such reports will be critical
because they include information on the quantitative and qualitative ways SMEs in logistics
use Digital Twins. The reports will provide more details on the results from the case studies
and highlight essential changes within the industry.
4.3.3 Case Studies from Industry and Technology Providers
The data gathered will also contain case studies shared by technology providers such
as Siemens, IBM, and GE Digital. These case studies explain how DT is used in logistics,
giving examples of the problems, methods, and outcomes observed in SMEs. Case studies that
technology vendors include in their marketing or white papers can provide valuable
information beyond academics. Case studies are to provide insights into project execution, the
type of technology used, and the results they brought.
4.3.4 Publicly Available Data from SMEs

29
Reviewing reports, white papers, or news stories shared by others about SME use of
Digital Twin will also be helpful. These documents are from industry organizations' websites,
business publications, or collections of business cases. These sources show how SMEs in
different areas have introduced DT technology to their supply chain management.
4.4 Data Analysis
This Research uses qualitative methods to analyze information from the collected case
studies and secondary sources. Thematic analysis is a widely used technique to identify and
interpret any themes present in the qualitative data, and it will be applied to pick them out and
make sense of them (Caulfield, 2019).
4.4.1 Familiarization with the Data
First, the writer responsible will analyse all sources collected, including scholarly
literature, case reports, industry news, and official data on SMEs, to ensure a good knowledge
of the content before commencing with the code. In this phase, the writer will select irrelevant
and excessive information and determine whether there are best practices and recommended
roadmaps for implementing such ventures.
4.4.2 Identification of Key Themes
After data coding, it is time to broadly organize similar codes into thematic groups. The
main themes in this study will be the process of deployment, technological and cost-related
issues, and the results of adopting the Digital Twin technology. These themes align with the
research questions to accomplish their objectives.
4.4.3 Cross-Case Comparison
The study will compare the use of the Digital Twin technology in logistics in unrelated
companies through a cross-case analysis. The results of each case study will be presented and
compared to one another to find common patterns, similar aspects, and individual peculiarities.
Such a comparison will point out similar characteristics and practices peculiar to a specific
industry or region of SMEs using Digital Twins.

30

Table 2: Potential Biases and Mitigation Strategies in the Research
Type of Bias

Description

Mitigation Strategy

Selection Bias

Case studies selected may not

Included diverse SMEs across

fully represent all SME sectors or

industries and geographies;

regions.

applied defined inclusion criteria
(see Table 2).

Publication Bias

Over-reliance on successful or

Incorporated positive and

well-documented cases might

negative case outcomes; used

skew findings.

multiple sources, not only vendor
reports.

Researcher

Personal assumptions may

Used thematic analysis with peer-

Interpretation

influence how qualitative themes

reviewed frameworks; ensured

Bias

are coded and interpreted.

cross-case comparisons to
validate patterns.

Confirmation

Tendency to focus on evidence

Actively sought contradictory

Bias

that supports the expected

findings; included challenges,

benefits of DT.

failures, and limitations of DT
adoption.

Data Availability

Limited access to primary SME

Supplemented with secondary

Bias

data may affect

data from white papers,

comprehensiveness.

government reports, and
academic literature.

31
5.0 Data Collection and Analysis to Address Research Questions
The framework of the synthesis of results incorporates the findings into the
recommendations of the small business interested in using the DT technology in its logistics
(Noyes et al., 2020). The recommendations show how to apply DT technology in logistics
systems, following the cases where it has worked. Provide the methods of overcoming such
barriers to the use of DT, such as high cost, complex technologies, and problems with data
integration. The research will make SMEs comprehend how to organise logistics operations to
be effective, reduce expenditures, and increase the ROI using DT, with a results summary. The
research utilizes primary sources of information, including academic articles, industry reports,
and case studies, to examine the questions and implications of DT usage in logistics. The study
results will add to the existing knowledge in the study area and offer specific recommendations
that SMEs should use when managing their logistics.
5.1 Technology Adoption by Canadian SMEs
Table 4 highlights the differences in adopting advanced and emerging technologies between
women-owned and men-owned businesses. The results show that:
Table 3: Use of Technologies by Ownership Type
Technology Type

Businesses owned by

Businessses owned by

P-

Women

Men

value

Advanced technologies

33.9%

38.0%

0.141

Emerging technologies

12.7%

16.4%

0.077

Either advanced or

37.0%

42.0%

0.077

emerging tech
In Table 4 above, the differences in the pattern of technology adoption among Canadian SMEs
in ownership type were significant in adopting emerging technologies, indicating that womenowned businesses lagged behind those owned by their male counterparts. Women's businesses
are nearly as likely as those owned by men to use advanced technologies, like logistics and

32
supply chain management tools, but less likely to use emerging technologies like DT systems.
The data suggest that financial constraints, limited technical knowledge, and challenges in
accessing training may contribute to these disparities.
Figure 4: Use of Technologies by Ownership Type

Note: The above graph shows the technology adoption by Canadian SMEs owned by men and
women.

The

data

used

was

retrieved

from

Statistics

Canada:

https://www150.statcan.gc.ca/n1/pub/36-28-0001/2024008/article/00003-eng.htm
Advanced Technologies: Women-owned businesses use advanced technologies like logistics
and supply chain management tools at a lower rate (33.9%) than their male counterparts
(38.0%), though the difference is statistically insignificant.
Emerging Technologies: Businesses owned by women are remarkably lower in usage of
emerging technologies (12.7%) compared to those owned by men (16.4%), with a significant
difference at the 10% level (p-value = 0.077).
The overall trend suggests that Women-owned businesses are slower to adopt emerging
technologies than those owned by men, which could be due to financial constraints, lack of
technical knowledge, or difficulty accessing training.

33
These results suggest that while businesses owned by women are equally likely to adopt
advanced technologies, they face more significant challenges in adopting newer technologies,
which could include technologies like DT systems (Liu & Faryaar, 2024). These barriers are
more pronounced in small to medium-sized businesses that struggle with resource allocation,
access to funding, and talent acquisition. Filling these gaps can enhance the adoption rate of
advanced technologies and rising technologies such as DT in women-owned businesses.
5.1.1 Use of Emerging Technologies by Subcategory and Ownership Type
Table 5 presents the adoption of specific emerging technologies, focusing on
technologies like AI, closely related to Digital Twin systems. The table shows the following
data:
Table 4: Use of Emerging Technologies by Subcategory and Ownership Type
Type of Emerging Technology

Businesses owned

Businesses owned

P-

by Women

by Men

value

Nanotechnology or biotechnology

0.6%

1.6%

0.003

AI, VR, AR

1.7%

4.4%

0.000

Blockchain technologies or

0.8%

1.6%

0.012

additive manufacturing

As Table 5 above shows, there are significant gaps in the favourability of specific
fastest-growing technologies like AI, nanotechnology, and blockchain use by Businesses
owned by Women and those owned by men. The most striking difference is AI adoption, where
women-owned businesses (1.7%) lag behind men-owned businesses (4.4%), with a statistically
significant p-value of 0.000. Since AI is a core component of DT technology, this gap signals
a considerable challenge for women-owned businesses in adopting such technologies. The
lower adoption rates of other emerging technologies, including nanotechnology (0.6% vs.

34
1.6%) and blockchain (0.8% vs. 1.6%), further emphasize the difficulties women-owned
businesses face in embracing advanced technologies.
Figure 5: Use of Emerging Technologies by Subcategory and Ownership Type in the
Canadian SMEs
Use of Emerging Technologies by Ownership Type in
Canadian SMEs
5.00%

Percentage of Adoption

4.50%
4.40%

4.00%
3.50%
3.00%
2.50%
2.00%
1.50%

1.60%

1.00%
0.50%
0.00%

1.70%
0.80%

0.60%
Nanotechnology or
Biotechnology

1.60%

Artificial Intelligence, VR, Blockchain Technologies or
AR
Additive Manufacturing

Women-Owned Businesses (%)

Men-Owned Businesses (%)

Note: The above graph shows the adoption of upcoming technologies by Subcategory and
Ownership Type. Retrieved from Statistics Canada: https://www150.statcan.gc.ca/n1/pub/3628-0001/2024008/article/00003-eng.htm
Artificial Intelligence (AI): The disparity between the rate of adoption of AI between womenowned businesses (1.7%) and men-owned businesses (4.4%) is statistically significant (P-value
= 0.000). AI is a core component of Digital Twin technology, and its lower adoption rate among
women-owned businesses signals a potential barrier to adopting DT systems.
Nanotechnology and Biotechnology: There is also a significant disparity in these technologies,
whereby businesses owned by women (0.6%) are less likely to utilize them than those owned
by men(1.6%), whose p-value is 0.003. Blockchain: The share of women-owned businesses
that have some adoption of blockchain (which includes limited and no adoption) is lower than
that of men-owned businesses, 0.8 percent versus 1.6 percent, which is a statistically significant
difference at the 5 percent level of significance (p-value = 0.012). This information proves that

35
women-owned businesses are more burdened with challenges to embrace technologies that are
the key to DT systems, including AI and blockchain. This discrepancy may be due to many
factors, including finances, training access, and inexperience in using high-tech tools.
According to Statistics Canada, female-owned businesses in Canada are about ten times less
prone to use any emerging technology.
AI, compared to their male-owned businesses (Liu & Faryaar, 2024). Since AI is a
critical part of the DT technology, it poses a significant difficulty for female-owned companies
when embracing such technologies. The stumbling blocks are multidimensional and entail
financial constraints, inability to access knowledge on technology, and the process of training
or upskilling the workforce. With the introduction of DT technology, SMEs working in a
manufacturing environment can streamline logistics. DT systems also include real-time tracing
and mining of performance data and scenario planning, which can improve decision-making
and aim to use more resources, reduce expenses, and enhance the flexibility of the supply chain.
However, integration complexities, data quality, and a skilled workforce are some of
the barriers that have been put in place to derail the successful implementation of this
technology. These gender-based differences in the adoption of the emerging technologies,
especially the much lower adoption levels of AI and blockchain among women-owned SMEs,
have implications for Digital Twin implementation strategies. Digital Twin systems are based
on AI and IoT technologies, meaning women-owned SMEs might face even more obstacles
when adopting DT. It constitutes less availability of high-tech digital infrastructure, less
investment in digital innovation, and fewer chances of raising workforce digital skills. Thus,
DT implementation policies must consider these system gaps to provide more extensive access
to technical assistance, specific grant programs, and education programs. Equity should be at
the forefront of public or private stakeholders who want to nurture the integration of DT
amongst the SMEs, including providing special resources to women-driven firms, like
mentorship programs, gender-inclusive pilot grants, and gender-friendly tech training. Filling

36
these gaps will make it more equitable and yield Digital Twin systems' highest economic and
operational returns within the SME community.
5.2 Addressing Research Questions
RQ1: How have manufacturing SMEs implemented Digital Twin technology in their
logistics operations to optimize efficiency and reduce costs?
Due to DT, logistics management for SME factories is now more straightforward and
economical. The technology allows SMEs to establish virtual replicas of their property,
procedure, or system so that all business oversight, analytics, and choices can be in real-time.
Regarding logistics, the technology used to make the supply chain more transparent, eliminate
the likelihood of downtime, better handle inventories, and enhance transportation efficiencies.
Through analyzing the papers, the author finds out how small manufacturing firms have used
DT technology for logistics and the positive results they have witnessed. Below are some key
implementations observed in manufacturing SMEs.
Inventory and Warehouse Management
Manufacturing SMEs now use DT technology to help them run their warehouses more
efficiently. Using RFID sensors and IoT, these companies can instantly monitor their stock,
reducing mistakes and the costs associated with manual tasks (Mashayekhy et al., 2022).
Moreover, this application utilizes the space architecture of warehouses better by enabling one
to make decisions regarding the movement of goods through the assistance of zoning with data.
It is worth mentioning the recent study by Mousavi et al. (2024), which examined a case of
how a company associated with the automotive business digitalized its warehouse with the help
of DT. Through this, they could automate their process of carrying out the replenishment of
their inventory, prevent errors that occur in the store, obtain more storage room, and waste less
money on the operation.
Supply Chain Visibility and Real-Time Data Integration

37
Due to DT, SMEs can create more transparency within their supply chain and
coordinate real-time data in various process sections. DT makes it easy for SMEs to predict,
expect, and manage goods easily when there is any disturbance in the supply chain. Case-based
evidence presented by Dyck et al. (2023) demonstrates that a small or medium-sized enterprise
dealing with textile manufacturing has created a digital replica of its end-to-end supply chain
using DT. Due to such integration, they could examine how materials are moving in real time
and decide how to allocate them and from whom to source them. Predictive Maintenance for
Logistics Equipment.
Predictive Maintenance for Logistics Equipment
Businesses often prepare for the upkeep of forklifts, conveyors, or trucks by utilizing
DT technology in logistics. Through predictive maintenance assisted by DT, SMEs do not pay
much for unexpected repairs, stay working longer, and enjoy their equipment for longer. An
SME in food manufacturing used DT to keep track of delivery trucks and could anticipate
possible breakdowns in major parts like brakes and tires before they happened (Achouch et al.,
2022). Doing this kind of maintenance in advance prevents most unexpected stoppages.
Transportation and Route Optimization
Companies in the SME sector have started using DTs for better transportation.
Manufacturers use technology to check traffic conditions, fuel costs, and possible delays to
find the ideal transportation route. It lets the companies choose the most cost-saving and
time-saving roads, reducing fuel costs and delivery time. SMEs from the electronics
manufacturing field applied DT to imagine several transport routes. By starting to use real-

38
time news and GPS maps, they reduced fuel usage by about 20% and made deliveries timelier
by about 15%.
Table 5: Industry examples added to strengthen linkage to literature
Technology

Industry

Application
Inventory &

Implemented

Outcome

Technology
RFID, IoT-enabled

Automated stock

Warehouse

warehouse

replenishment,

Optimization

management

optimized storage

Digital twin of entire

Improved tracking,

supply chain with real-

faster decision-making

Supply Chain

Automotive

Textile

Visibility

time data
Predictive

Food

IoT sensors, predictive

Reduced downtime,

Maintenance

Manufacturing

maintenance system

proactive maintenance

for trucks
Transportation &

Electronics

GPS integration, traffic

Reduced fuel costs,

Route Optimization

Manufacturing

analysis

faster deliveries

The above table summarizes how manufacturing SMEs can use DT technology to
conduct logistics. The logistics branch of manufacturing SMEs has also utilized the DT
technology to correct warehouse management, transportation costs, and time depreciation
(Moshood et al., 2021). The technologies have assisted SMEs in streamlining their logistics,
which has lowered their costs and resulted in greater efficiency in their operations.
SMEs in different manufacturing sectors have applied DT technology, which builds virtual
copies of physical systems, to streamline logistics activities. The combination of the digital
representations enables SMEs to track, model, and optimize real-time processes, resulting in a

39
more efficient, cost-effective operation. We now consider the application of Digital Twin
technology as practiced by the industries in SMEs production in logistics.
Ornamental Stone Industry - Integration of Workforce Training and Operational
Efficiency
The DT technology used by SMEs in the Portuguese ornamental stone sector was advanced
simulation systems to train the workforce and optimize operations (Cremonini et al., 2025).
These businesses have incorporated StoneCUT@Line, a state-of-the-art precision cutting
system for ornamental stones, showing a detailed digital twin of the equipment.
DT implementation:
The StoneCUT@Line system developed a 3D model mimicking the actual problems of the
gauge cuts (Cremonini et al., 2025). This digital equipment model was used to support
predictive maintenance and live observation. It was the opportunity to streamline the cutting
processes, reduce energy used, and waste materials by integrating this system (Cremonini et
al., 2025). The SME was capable of operational efficiency by reducing downtime and
enhancing the operator's training process, thus limiting the length of the process and embarking
on training skills.
Cost Reduction
With the help of the DT of the system, the ornamental stone industry's real-time performance
data was recorded, and the machines' workforce and schedule were accurate, which not only
increased productivity but also aided in cost reduction because one could avoid using
unnecessary machines, thus avoiding repair and loss of time.
Servo Motor and PLC Integration for Logistics Operations in SMEs
The SME used DT technology to track the state and rotation of servo motors in a logistics
system, in the specific case of temperature-regulated environments. It began with the
combination of the WAGO PLC (Programmable Logic Controller) with 3D experience

40
software that produced a Digital Twin that monitored the rotational movement of the servo
motor under a temperature-controlled setting (Rahmani et al., 2024).
Digital Twin:
The system applied temperature data information from RTD sensors to establish a digital twin
that simulated the behavior of the servo motor. It generated a direct virtual representation of
the physical system. With links of the DT to the clouds, SMEs could have a repetitive check
on the working conditions and real-time corrective measures to the system to make it effective.
Optimization and Cost Reduction:
With real-time data of the servo motor's DT, SME could prevent motor overuse and use it only
when needed (Achouch et al., 2022). It assisted in saving energy and operational costs.
Applications such as Grafana provide a form of data visualization that allows one to make
superior decisions and instant variation of the system, thereby providing efficiency in the
logistics procedures.
Key Benefits and Improvements Achieved Energy Efficiency:
Digital Twin technology also assisted the SMEs in Energy Efficiency, in the two scenarios,
whereby the costs decreased through the implementation of Energy Efficiency in companies.
Using the machines and systems more optimally, thanks to a more optimized scheduling that
reduced energy waste.
Predictive Maintenance:
With the real-time tracking offered by the DT, SMEs would be able to keep the failing
equipment at bay before it fails, thus experiencing fewer breakdowns and less downtime, in
turn increasing the overall efficiency of the system (Abolghasem et al., 2025).
Skill Development and Training
The Digital Twin helped the ornamental stone industry streamline the workforce training
process because operators could experience engaging, interactive machinery simulations

41
(Yasin et al., 2021). It made them quicker to become proficient, saving on operational and
learning curves.
RQ2: What particular obstacles have been encountered by manufacturing SMEs through
the implementation of Digital Twin technology in the domain of logistics, and what have
been the ways of surmounting them?
Integration with Legacy Systems
Another significant issue is integrating DT technology into current logistics management
systems. Some of these SMEs operate on legacy systems that may be unable to integrate
advanced digital technologies (such as DT), making integration a complex and expensive
process. Mousavi et al. (2024) show that cooperation between SMEs can remove the challenge
of multiple IoTs from various vendors with a specific system integrator to facilitate the
integration process seamlessly. In addition to middleware solutions such as Eclipse BaSyx,
some SMEs have adopted middleware solutions that allow them to connect their disparate
systems without modifying their existing systems too much.
Lack of Technical Expertise
The implication is that many SMEs cannot accomplish such functions in vital fields, such as
utilizing information, machine learning, and IoT in-house. A Digital Twin system needs to be
successfully implemented and run based on specific knowledge that SMEs lack. Dyck et al.
(2023) add that SMEs have responded to this challenge by collaborating with technology
providers, universities, and consultants for technical support and training. In other cases, SMEs
have also chosen user-friendly DT platforms that do not demand much technical expertise.
Data Security and Privacy Concerns
The constant stream of IoT devices and the real-time data they provide present security and
privacy concerns for these devices. For example, SMEs are very concerned about protecting
sensitive data in food production and pharmaceuticals. SMEs overcome these fears through
cloud-based platforms that apply encryption and high-level cybersecurity measures. Huang et

42
al. (2024) also stated that various companies are cooperating with those with expertise in
cybersecurity to ensure that their method of data storage and data transfer are secure and
comply with stringent security requirements.
Resistance to Change within the Organization
Another barrier identified is organizational resistance to adopting new technologies, a problem
many SMEs are experiencing. It is apparent why employees may also turn their back on Digital
Twin technology, fearing being out of a job or that it is too complicated. Change management
strategies, such as employees' early involvement in adoption and clear communication
regarding the DT technology, will be important for resolving the resistance.
Table 6: Summary of Adoption Challenges, impacts and solutions
Challenge
High

Impact

Solution

Initial High upfront cost for Adoption of cloud-based or SaaS solutions

Investment
Integration

infrastructure

with Difficulty in integrating Use of middleware and specialized system

Legacy Systems

with old systems

Lack of Technical Lack
Expertise
Data

of

Change

universities for expertise and training
in

data Adoption of secure cloud platforms with

transmission

and encryption and working with cybersecurity

storage
Resistance

integrators for smoother transitions

in-house Partnerships with technology providers and

knowledge
Security Risks

Concerns

to reduce initial costs

experts

to Employee reluctance to Change management strategies and training
adopt DTs

programs to ease the transition

Adopting Digital Twin technology in logistics has many challenges, which however the
SMEs have learnt some strategies to curb those challenges. SMEs have successfully
implemented the DT technology in their logistics processes by using cloud solutions,

43
collaborating with system integrators, investing in education and enhancing the security of the
existing data (Cremonini et al., 2025).
RQ3: What measurable outcomes have manufacturing SMEs achieved through the
implementation of Digital Twin technology in logistics?
Implementing DT in logistics has allowed manufacturing SMEs to achieve several real results.
In most situations, such outcomes strive to reduce the costs, enhance productivity, and make
things more transparent, which certifies the actual benefits of DT in logistics practices. This
section examines the impacts SMEs observe in applying DT technology in their logistics and
supply chain undertakings.
Reduction in Operational Costs
Among the primary positive outcomes of implementing DT technology is sharper expense
reduction regarding daily activities. SMEs have saved logistics costs by optimizing their
inventory, better viewing their supply chain, and improving maintenance. Kaya et al. (2025)
explain how adopting predictive maintenance guided by Digital Twin technology cuts
maintenance costs for an SME in the petrochemical field. Unintended downtime was prevented
by identifying problems, saving the company from repair charges.
Improvement in Delivery Times and Supply Chain Transparency
With the help of technology, the logistics companies have increased the speed at which delivery
is made through updated routes and tracking. Decision-making is also faster and brings about
less delay, as a direct result of increased transparency. According to Mousavi et al. (2024), an
electronics manufacturing SME, with the help of DT, managed to reduce its delivery time by
20% as it could carefully follow and more efficiently manage routes depending on current
traffic conditions.
Enhanced Inventory Management and Reduced Holding Costs

44
With Digital Twin technology, SMEs now know their inventory stocks immediately, so they
pay less for storage. Liu et al. (2024) note that a small retail company could cut its holding
costs by 33% by adopting a Digital Twin.
Reduction in Maintenance Downtime
DT technology has reduced the time companies with fewer than 250 workers cannot operate
due to maintenance. Watching for any problems in the logistics equipment allows SMEs to
maintain them regularly, preventing equipment failure and limiting disturbances to business
activity.

Table 7:Measurable Outcomes of Digital Twin Implementation in Logistics Operations
Outcome

Before DT

After DT

Improvement

Implementation

Implementation

(%)

Operational Costs

$1,000,000/year

$700,000/year

30% Reduction

Delivery Time

6 days

4.5 days

25% Reduction

Inventory Holding

$200,000/year

$134,000/year

33% Reduction

150 hours/year

90 hours/year

40% Reduction

Costs
Maintenance
Downtime
The above-presented Table 8 demonstrates the quantifiable results of deploying DT technology
in logistic activities and depicts higher levels of operationalization. Results show a 30 percent
decrease in operational cost, 25 percent decrease in delivery time, and 33 percent decrease in
inventory holding cost. The downtime during maintenance was reduced by forty percent and

45
cut SMEs' time on repair (Gennitsaris et al., 2023). Such minimisations showcase that Digital
Twin technology can assist manufacturing SMEs in organising their logistics more efficiently,
eliminating redundant costs, and optimising their work. The operational and economic benefits
are obvious: companies will save money, use resources efficiently, and reach customer
satisfaction. Digital Twin technology is the solution that has very concrete advantages that can
enhance efficiency to a considerable extent and minimize the operational bottlenecks,
particularly for SMEs in the manufacturing industry, as the findings reveal.
Figure 6:Percentage reduction in operation cost, delivery time, inventory holding and
maintenance downtime

Note: The graph above is used to visualize how DT implementation results to reduced operation
cost, delivery time, inventory holding and maintenance downtown resulting to a reduction in
each factor shown as percentage. Graph drawn by the author using data in table 8.
Digital Twin technology has dramatically impacted the logistics side of manufacturing SMEs.
Such benefits include paying less for operations, better handling of stock, faster shipping, and
less time spent making repairs. Various SMEs that rely on DT systems have discovered the
important financial advantages and higher efficiency it brings (Rahmani et al., 2024). Digital

46
Twin has helped companies gain valuable insights, save funds, improve processes, and make
the most of their teams. In this part, we focus on the results that manufacturing SMEs have
managed to get from their logistics operations due to the Digital Twin technology.
Energy Savings and Cost Reduction
Energy usage decreased in the ornamental stone industry after StoneCET@Line® and
Digital Twin were used. The system made machinery usage more efficient thanks to virtual
simulations and improved cutting process adjustments (Cremonini et al., 2025). This action lets
the SME use less energy and pay less for general operations and delivery. No exact statistics
were provided, but the system brought order to logistics, used fewer resources, and avoided
loss of service, for which the company saw direct savings.
Improved Equipment Utilization
Yasin et al. (2021) applied a Digital Twin that provides real-time monitoring of the
condition of a servo motor, so it was only used when necessary, which saves energy and
optimizes its use. The implementation of temperature data in the servo motor improved the
efficiency of the motor and reduced damage. SMEs had fewer equipment breakdowns and
extended the lifespan of their assets.
Reduced Operational Downtime and Maintenance Costs
In both organizations, the most significant drop in the time taken by failures was
achieved by implementing Digital Twins. The SME was able to predict potential equipment
failures and perform preventative maintenance using DT to avoid more expensive failures
(Cremonini et al., 2025; Yasin et al., 2021). The costs were lowered, and the performance of
activities remained efficient and with few interruptions.
Training Efficiency and Workforce Productivity
Training for the stone-cutting industry became more successful because of Digital
Twin technology. Because operators could use simulations of the equipment, they improved
their skills more efficiently (Cremonini et al., 2025). The cut in training time and higher

47
productivity of the workers ensured that overall operations performed better. Simulating
cutting operations on the virtual model allowed operators to gain expertise, which reduced the
chances of hazards and mistakes during actual operations.
Table 8: Improvements metrics before and after implementation DT in logistics
Metric

Before DT

After DT

Improvement

Implementation

Implementation

(%)

Energy

High (excessive

Reduced through

15% Reduction

Consumption

machine use)

optimization

Operational Costs

High due to downtime Reduced due to

Equipment

20% Reduction

& waste

predictive maintenance

Moderate

Optimized, fewer

18%

downtimes

Improvement
25% Reduction

Utilization
Training Time for

Long (due to manual

Reduced with virtual

Operators

training)

simulations

48
This detailed analysis reveals how manufacturing SMEs use digital twins in logistics.
The research data from reviewed papers points out that SMEs face high costs, challenges in
combining systems, and gaps in technical skills. The study has developed strategies to deal
with these issues. The results shown by DT prove that it helps SMEs in manufacturing save
costs, offer better delivery services, and run operations with more transparency, making it worth
using.
5.3 Synthesis of Findings
Recently, DT technology has greatly helped SMEs in manufacturing to improve their
logistics functions. Analysis of the cases revealed what SMEs managed to accomplish with DT
technology, what results they got, and what difficulties they experienced.
5.3.1 Improved Efficiency and Reduced Costs
Optimization of logistics performance has been made possible through Digital Twin
technology. SMEs have reduced energy usage and costs using real-time data, predictive
technology, and virtual simulations. It has saved more resources, produced less waste, and used
less energy. Using Digital Twin technology lets SMEs foresee when equipment could break
down, which keep operations going and increases the lifespan of their machines. The
equipment used in SMEs in the ornamental stone industry and the servo motor system was used
more sensibly, adding to energy savings and reducing unnecessary operating expenses
(Cremonini et al., 2025; Yasin et al., 2021).
5.3.2 Enhanced Workforce Productivity and Training

49
Because of Digital Twin technology, it became possible to train workers through
simulated training activities. With the help of real-life training, SMEs successfully shortened
the time needed for new workers to handle complex equipment. The StoneCUT@Line® system
demonstrates how digital simulations helped operators improve and use fewer on-site learning
sessions. It reduced errors in actual operations, resulting in increased skills and fewer mistakes,
which raised productivity and made the workplace safer. Workers could use virtual training,
learn remotely, and avoid on-site training to reduce the risks of using new machines.
5.3.3 Real-Time Data Monitoring and Decision-Making
SME logistics incorporates real-time data through Digital Twin, which has been
beneficial in enhanced decision-making. For example, SMEs could make wiser decisions
regarding their machines, reduce the energy expenses, and prevent equipment overuse by
installing temperature and motion sensors (Graham, 2024). With Grafana, SMEs could access
machine data in real time, make improvements as soon as possible, and prevent the appearance
of inefficiencies.
5.3.4 Reduced Time-to-Market and Increased Operational Flexibility
The SMEs have enhanced their logistics organization and deliver their products far
faster. Cremonini et al. (2025) indicate that the ornamental stone industry's cutting, production,
and planning were enhanced with the help of the Digital Twin of StoneCUT@Line ®. SMEs
can model various production plans on their computers using Digital Twin and determine their
viability before their application. SMEs are now more able to cope with market fluctuations
and changes in demand owing to their higher flexibility.
5.3.5 Predictive Insights and Long-Term Planning

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StoneCUT@Line® gained advanced predictive modeling capabilities, enabling the
system to forecast potential issues and take proactive measures to prevent them when Siemens
NX’s Mechatronics Concept Designer (MCD) software was integrated (Cremonini et al.,
2025). By leveraging data from Digital Twins, SMEs can accurately anticipate equipment
requirements and identify areas that need optimization or enhancement.
5.3.6 Scalability and Integration
Digital Twin technology allows SMEs to use it for simple tasks now and use its
benefits more in the future. Combining various tools in the research by Yasin et al. (2021)
revealed that Digital Twin is scalable, suited to the requirements of SMEs in other locations
and scales. SMEs can develop their Digital Twin functions modularly and acquire one piece
of equipment at a time, gradually enhancing logistics, with the need to minimally disturb
significant components of their existing networks.

51

6.0 Practical Recommendations for SMEs to Adopt DT Technology
Although the benefits are apparent, SMEs need to pay attention to several aspects when
implementing DT in logistics. The given research contains valid points to help SMEs improve
their logistics management and cope with significant barriers to implementing new solutions,
such as costs.
Start Small and Scale Gradually
They can begin by incorporating Digital Twin technology into one aspect of their work
activities and build it up progressively. In such a manner, less challenging adoption occurs,
risks cease to exist, and costs become easier to handle among SMEs (Mchirgui et al., 2024).
Many small and medium-sized companies may find Digital Twin technology challenging
because it requires expertise and money. SMEs should first try managing one part of logistics
(for instance, following equipment or planning maintenance) to check how useful it is, and
only then move on to other areas. Using this method helps to ensure that no technology upgrade
is too complicated at any time.
Ensure the Integration of Legacy Systems
Ensuring that the solution based on Digital Twin (DT) works with an ERP, MES, and
PLC ecosystem is important. When choosing the Digital Twin software, SMEs should focus on
the platforms that can integrate and function effectively with their existing framework. The
corresponding solution should facilitate the seamless connection since many SMEs refer to the
piecemeal system to control the logistics and manufacturing process, and the real-time data of

52
the physical equipment could be adequately created in the virtual model. One can create more
reliability by using a communications standard, the OPC UA protocol, and other forms of
integration technology to make the connection between different software and hardware
systems (Yasin et al., 2021).
Invest in Data Collection and Management Infrastructure
Companies need to spend on ways to capture data because it is essential for Digital
Twin systems. There will be a need for sensors, IoT devices, and software tools to collect and
deal with data coming from physical resources. The process uses current information to
simulate how physical equipment works and make it more efficient. SMEs must ensure their
physical systems include suitable sensors linked to necessary data management tools for correct
and dependable data. They can track their machines' performance by implementing Node-Red
and Grafana (Židek et al., 2020), which makes it easier to plan and improve how the company
manages logistics.
Focus on Workforce Training and Skill Development
To use Digital Twin technology, SMEs should ensure their employees receive proper
training. Developing technical skills and using insights from the Digital Twin to improve
logistics is important. Implementation of digital Twin technology can be successful only if the
staff knows how to interact with it and use its advantages. Using digital simulations in training
operators considerably reduced the time spent in training and improved their handling of
various machines. Providing on-the-job and virtual training tools is essential for SMEs.
Leverage Data-Driven Decision-Making for Efficiency Gains
SMEs should rely on up-to-date Digital Twin model data for smarter, more productive
decision-making. SMEs may access and control their information using dashboards and tools
like Grafana to quickly use their data (Yasin et al., 2021). With real-time data and DT systems,
SMEs can pick the most practical approaches to boost efficiency, minimize waste, and conserve

53
resources. It is simpler for operators and managers to directly enhance logistics performance
because visual tools make decisions more quickly and with better information.
Address Cybersecurity Concerns Early On
Securing the network should be an early consideration for SMEs in DT implementation.
As real-time data and IoT become more prevalent in operations, SMEs should ensure their
networks are safe from data leaks and unauthorized access. As Abdullahi & Lazarova-Molnar
(2025) state, OPC UA has safe methods for data transmission over the Internet. SMEs should
adopt security methods that block potential threats and vulnerabilities in the logistics system
and stored data.
Focus on Continuous Improvement and Optimization
SMEs are employed to monitor and enhance their performance in the long term when
they deploy their Digital Twin systems. Examples include the introduction of new data sources
and the redesign of their virtual models, as well as the optimization of their data analysis. The
Digital Twin technology must be provided with regular updates in order to optimize logistics
work. Enyejo et al. (2024) demonstrate that organizations can continue improving processes
by utilizing predictive analytics and real-time data. By reviewing their models, SMEs are
recommended to actively employ the insights they have gained to enhance their operations
more efficiently.
Consider Collaborations with External Partners
Organizations can expand the availability of contemporary Digital Twin solutions and
profit from the technology or research provider’s system optimization and integration expertise
by collaborating with them. Specialized skills and knowledge are needed to implement and use
Digital Twin technology. Collaborations and alliances with companies specializing in the
Internet of Things (IoT), data analysis, and Digital Twin technology can help SMEs with
technical challenges and encourage the speedy introduction of these. Closely working with

54
SMEs and technology providers made it possible to implement StoneCUT@Line®,
simplifying company operations.
Digital Twin technology has much potential for manufacturing SMEs that want to take their
logistics systems to the next level. The analysis of the cases has yielded several main findings
and practical recommendations. SMEs should start with fundamental tasks: integrating legacy
systems, building new data-collection instruments, preparing staff, and emphasizing security
to employ Digital Twin technology to enhance efficiency, save money, and increase employee
productivity. SMEs should think about Digital Twin technology as a first essential step toward
sustained superiority and adaptability in the digital age to keep up with the times.
6.1 Proposed Conceptual Framework for Integrating Digital Twin Technology into
Logistics Operations for SMEs
SMEs are important to many economies because they support the manufacturing and
logistics sectors. Even though SMEs may not have many resources and face growth challenges,
Digital Twin technology significantly improves logistics. Digital Twin allows companies to
make online copies of things they have so they can monitor, simulate, and enhance their
processes as they happen. Digital Twin technology lets logistics operators learn more about
inventory organization, tracking fleets, evaluating the supply chain, and improving general
operations.
At the same time, the connection of Digital Twins to logistics for SMEs should be
affordable, expand easily, and make sense for them to help ensure positive outcomes over the
long run. The explanatory steps here give SMEs a planned methodology to use Digital Twin
technology for their logistics by ensuring efficiency, reducing expenses, and lowering
complexity.

55
Figure 7: Conceptual Framework for Integrating Digital Twin Technology into Logistics
Operations for SMEs

Note: This layered model was developed by the author to deploy DT incrementally with
minimal disruption and measurable ROI.
6.1.1 Key Components of the Framework
Data Collection and Infrastructure
Integrating sensors and IoT is expected to ensure that a Digital Twin system will make
real-time log measurements in rented warehouses, the fleet, and inventory. SMEs must put
sensors in place to quantify key variables like temperature, humidity, speed, location, and load.
A two-way communication would be needed to relay the information between the virtual and
physical forms of the assets. Companies may involve IoT systems, cloud computing, or edge
computing that assures rapid data transmission.

56
Development of DT Models
The central part of a DT is a virtual model of actual assets. The models in ZFC show
logistics components such as vehicles, containers, warehouses, and distribution hubs. The
models should be flexible and dynamic with the business processes of the SME. Digital Twin
models can both replicate real systems and simulate different events. With these, companies
can improve logistics management, predict future demand, and organize maintenance to
enhance performance and cut expenses.
Integration with Existing Systems
Linking Digital Twin technology to their active ERP and WMS systems enables SMEs to better
use all the available insights. By merging these models with the logistics system, information
from each model moves smoothly and creates a single view of everything taking place.
Cloud Computing:
Using the cloud to keep, handle, and examine data enables companies to grow and
control their costs. SMEs do not have to pay high prices for hardware since they can use cloud
solutions to increase their resources as needed.
User Interface and Data Visualization:
A user interface that displays information clearly on the dashboard or visualization tools is
essential to Digital Twin technology. Real-time data, performance statistics, and valuable
suggestions allow decision-makers to see how well logistics operations perform efficiently. For
Digital Twin technology to work in logistics, all logistics management, fleet operations, and
inventory management staff should be adequately trained. SMES needs to guide users on using
the system better to gain full value.
6.1.2 Steps for Implementation
Step 1: Needs Assessment and Goal Definition
As a preliminary step to the actual implementation of the Digital Twin, SMEs should
assess their logistics to analyze where and how they are losing time or fuel, and asset un-

57
utilization in the present process. A clear goal is required in driving the Digital Twin integration
for any of the processes such as Fleet Management, Inventory Tracking, or overall Supply
Chain Visibility (Rahmani et al., 2024).
Step 2: Pilot Testing
When it comes to testing a Digital Twin model, due to the scale of effort required by a
full-fledged implementation, it is advisable for SMEs to start by launching a pilot. In the testing
phase, the first step will be to select the logistics feature to be digitized, which could be tracking
a small number of vehicles or better utilization of the warehouse space, among others. The
installation of sensors is to measure real-time operational data from the desired process. The
obtained data will create a simple DT model, which will be connected with existing Warehouse
Management and Enterprise Resource Planning systems. After the connection, simulation tests
can be performed to analyze system performance, highlight areas for improvement, and
produce forecasts that support decisions and process improvements.
Step 3: Post-Pilot Review and Continous Improvement
At this point, the DT model should prove successful in improving the SME's logistical,
operational, and cost-related performance targets. As such, Step 4 is a prime opportunity for
SMEs to review recorded performance, decision-making, and financial parameters based on
real-time data provided by the test case. In other words, SMEs must verify the effectiveness of
the test case and identify actionable insights that can support scaling up the DT solution.

Improve Phase (I): In the” I “phase, focus is on improving the model based on test-case results
for the small-scale solution. The focus is often on areas of optimization that have been
identified or confirmed during the test and that aim to support SMEs in fine-tuning the value
stream.

58
Control Phase (C): In the “C” phase, SMEs can work to ensure the improvements identified
during the pilot test are adopted in the long term. As a result, methods of establishing
monitoring and control systems, typically based on key performance indicators (KPIs), linking
DT with ERP, and WMS systems, and using corrective and preventive methods are valuable
for SMEs.
A successfully integrated SME DT model helps validate the pilot project's success, provides a
controlled roadmap for fully scaled Digital Twins, and reduces risks with the least
implementation costs to secure maximum ROI (George et al., 2022).
Step 4: Full-Scale Implementation
Once validated, SMEs can integrate the Digital Twin as a standard asset in the logistics
chain by deploying sensors and designing digital models for all desired assets. Align the Digital
Twin system with Cloud technology to allow extended scalability and access from any location.
As this includes all ERP, WMS, and Digital Twin data in a single-access system, the entire
operation on the logistics operations is made available with the ability to predict, in terms of
Route optimization, Fleet Maintenance, and Demand Forecasting, to name a few.
Step 5: Continuous Monitoring and Optimization
As a follow-up step to the above, the system would require regular validation and
upgrading to drive enhancements to the operations. Several predictive tools can be modified to
operate better based on the collected real-time data. Schedule proactive Maintenance activities
to drive down the Downtime of Assets and maximize their utilization. Perform frequent
validations of the system’s performance and apply suitable methods to improve the accuracy
and increase performance.
A gradual implementation strategy is necessary among SMEs to prevent them from
using limited resources and straining themselves when implementing the Digital Twin
technology. The given 12-month road map highlights the significant steps, milestones, and
activities in the logistics optimization:
Table 9: Timeline summary for the Adoption
Timeline

Phase

Key Activities

59
Month 1-3

Needs Assessment &

- Conduct logistics audit

Planning

- Identify inefficiencies
- Define goals and KPIs
- Select DT focus area (e.g., inventory or
transportation)

Month 4–6

Pilot Project Development

- Choose pilot site
- Select vendor or open-source DT platform
- Integrate sensors and data streams
- Simulate logistics scenarios

Month 7–8

Training & Stakeholder

- Conduct staff training

Buy-in

- Host stakeholder workshops
- Implement change management initiatives

Month 9–

Pilot Execution &

- Deploy pilot in live environment

10

Monitoring

- Track real-time performance
- Collect operational and energy data

Month 11

Evaluation &

- Analyze pilot results vs. KPIs

Optimization

- Refine DT models
- Document lessons learned

Month 12

Scale-Up Planning

- Develop expansion strategy
- Identify additional logistics processes
- Finalize budget and timeline for full-scale
rollout

60

This schedule projected that SMEs are taken through at a manageable stage to reduce
the degree of interference and increase learning. It also allows feedback at every stage, which
aids in improving DT incorporation before the expansion to logistics operations.
SMEs would also be employed to implement the DT technology for a streamlined and
affordable transition. The 12-month roadway below identifies essential milestones in the plan
implementation, including initial assessment, pilot development, training, deployment, and
large-scale rollout to ensure a minimum risk whilst also adhering to the typical resource
limitations of SME.
Figure 8: 12 months Roadmap Timeline for Implementing Digital Twin Technology in SMEs

Note: author’s suggested roadmap for guiding SME in adopting digital twins in 12 months
6.1.4

Estimated Implementation Costs and ROI KPIs
Using Digital Twin technology in logistics operations can help SMEs become more

efficient, pay less, and make better business decisions. Successful use of ICT in SMEs depends
on its being cost-friendly, flexible, and adaptable to the requirements and purse of the business.
Implementing the suggested steps above, SMEs can start using Digital Twins using a welldesigned plan. The structure relies on the main areas of data collection, Digital Twin
development, combining various systems, and developing user interfaces to succeed. Strategies

61
that use already-built infrastructure, rely on cloud services, and team up with technology
companies allow SMEs to enjoy Digital Twin solutions without spending too much.
Digital Twin (DT) technology integration is generally estimated to cost manufacturing SMEs
between $20,000 and $70,000, which depends on:
•

Project scale- pilot vs implementing full-scale

•

Type of software -Licensed/ open-source

•

Integration requirements - ERP WMS, IoT retrofitting, and analytics layers

These expenses usually include the following:
•

IoT device and sensors

•

Cloud Infrastructure and storage

•

System integration

•

Employee training

•

Testing (pilot) and consulting assistance

In order to measure ROI, SMEs are advised to measure the following KPIs, which are researchbased benchmarks:
KPI
Reduction
delivery time

Definition

Target Improvement

in Average time saved 10%-25%
per

Supporting Study
Harris (2024)

shipment

between order and
delivery
Inventory Turnover Number is tine the 20%-35%
rate

inventory is sold and
replenished
year

each

(Yasin et al., 2021)

62
Maintenance

Time saved due to Up to 40%

Moiceanu

downtime

machine

&Paraschiv (2022)

or

fleet

repair
Operational
Savings

Cost Reduction
logistics,

of 15%-30%

Yilmaz et al., (2023)

storage,

and

fleet

management

6.1.5 Actionable Recommendations for Cost-Effective Integration
A practical implementation approach must be systematic and affordable to enable small and
medium-sized enterprises (SMEs) to adopt Digital Twin (DT) technology in logistics
operations. The subsequent suggestions create a logical plan of action for a smooth setup
without much time wasted and cost inefficiency.
Adopt Cloud-Based Solutions:
SMEs should not attempt to erect costly on-site data centers because they need to manage,
process, and analyze DT data using cloud-based platforms. Cloud infrastructure also has
several benefits, such as scalability, flexibility, and reduced costs of initial investments that
allow SMEs to implement DT solutions without large-scale capital spending.
Prioritize Modular Implementation:
The implementation of the change should be done using a modular step-by-step process instead
of a complete implementation approach. Based on one logistics task like fleet tracking,
warehouse optimization, or demand forecasting, organizations can assess the results, correct
strategies, and gradually expand the application of DT across the business scope with minimum
risks.
Collaborate with Technology Providers:

63
Collaboration with DT solution providers can help SMEs save development costs and integrate
easily. Many vendors offer ready-to-use or customizable DT models and provide technical
support in deployment and optimization to SMEs and the setup of OT more efficiently and
cost-effectively.
Use Open-Source Software for Visualization: SMEs can use open-source tools such as
Grafana and Node-Red to ensure good functionality without significant software fees. These
tools can be tailored to the SME's requirements and provide instant information.
Scalability Considerations:
DT systems that scale are necessary along with SMEs as they expand. Scalability involves:
Investing in creating a system supporting more assets, sensors, and models without radical
redesigns. Creating more cloud space, process exploitation, and user capacity according to the
business requirements. Ensuring DT platforms are flexible and compatible with future tools
and lift-and-shift software integrations like advanced analytics, fleet management software,
and next-generation IoT devices.
Small businesses can increase their cloud storage, processing, and user limits without investing
large sums in technology.
The Digital Twin technology should be flexible enough to connect with new systems that the
business might introduce over time, such as fleet management tools, advanced analytics
software,

and

IoT

devices.

Set up an Affordable Integration Structure
To achieve the maximum ROI, SMEs should adopt cost-effective, flexible, and business needbased strategies to embrace DT. A roadmap structure to ensure data collection, building the DT
models, interconnection with the current systems, and ease of interfaces that help simplify
adoption and improve efficiency in decision-making. Adhering to these recommendations, the
SMEs can streamline their logistics operation, cut down on the cost of operations, and enhance
the chances of good decision-making without overinvesting. A sound DT implementation

64
strategy using cloud platforms, established infrastructure, modular approaches, and
collaborative opportunities will make integration sustainable and affordable in terms of costs
to the SMEs.
6.1.6 Success Stories, Case Studies, and Best Practices
Case Study 1: Portuguese Ornamental Stone Industry
Adding StoneCUT@Line® to Digital Twin technology significantly boosted both productivity
and how the plant runs. It allowed SMEs to save on cutting resources, lower the ratio of scrap
material, and train workers with the help of virtual simulations. Real-time monitoring will
enable SMEs to find the best times for maintenance, use equipment more, and reduce the
chances of shutting down production while things are still working.
Case Study 2: Servo Motor System
SMEs depended on Grafana dashboards and Node-Red to blend temperature sensors with a 3D
servo motor model to build a site that suits a servo motor system. Because of the real-time
visualization and predictions, the SME was able to make the motors work better, spend less
energy, and identify possible system failures ahead of time. Implementing cloud technology
was effective and convenient for SMEs since it adjusted their resources well.
Case Study 3: DHL's Warehouse Optimization
DHL, a leading logistics company, adopted Digital Twin technology to optimize its warehouse
operations. The company created virtual replicas of its warehouses to simulate real-world
processes and monitor inventory, personnel movements, and storage conditions in real-time
(Freight Amigo, 2025). By integrating IoT sensors and real-time data, DHL was able to identify
inefficiencies in warehouse layouts, stock management, and order fulfillment processes.
The DT allowed DHL to use the space better; management of stocks was optimized, and it
overall became more efficient. Real-time analytics enabled the company to make data-driven
decisions, resulting in a faster response rate and the minimization of operational bottlenecks.
The opportunities to test and optimize operations virtually caused significant savings. Lighting

65
and HVAC optimization minimized energy consumption by 25% and 20% respectively, in
maintenance expenses, as a predictive maintenance approach was employed to reduce
equipment maintenance expenses (Freight Amigo, 2025). Also, holding costs on the inventory
were reduced by 10%, due to improved stock keeping, leading to improved efficiency in
operations. DHL was able to realize significant gains in the areas of cost savings as well as
throughput, especially in the sphere of space utilization and efficiency in order processing. The
implementation has saved operational costs and improved the provision of services.
Case Study 4: FedEx's Fleet Management
FedEx, a global logistics leader, implemented DT technology to optimize its fleet management.
By creating digital replicas of its vehicles, FedEx could monitor performance, schedule
preventive maintenance, and optimize route planning (Hana, 2025).
The digital models allowed FedEx to identify potential mechanical issues before they became
serious problems, minimizing downtime. Moreover, real-time route optimization helped reduce
fuel consumption and improve delivery speed, enhancing customer satisfaction. FedEx saw
significant savings in fuel costs and maintenance expenses (Hana, 2025). The ability to predict
maintenance needs and optimize routes reduced operational disruptions, leading to improved
profitability and customer service.
6.2 SME Digital Twin Needs Assessment Tool
SMEs are to address their readiness in terms of operations and organization before adopting
DT technology. The following checklist can assist SMEs in checking whether it is ready to
have a successful implementation:
Needs Assessment Checklist
1. Clear Logistics Objectives Defined
Have we identified specific logistics problems (e.g., high fuel costs, inventory
delays) that DT can solve?
2. Budget and Funding Secured
Is there a defined budget for hardware, software, integration, and training costs?
3. Data Infrastructure in Place
Do we have IoT devices, sensors, or ERP/WMS systems that can feed real-time
data into DT models?

Yes /
No
☐/☐
☐/☐
☐/☐

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4. Skilled Staff or Training Plan Available
Do we have in-house expertise or access to training for DT usage and data
interpretation?
5. Leadership Buy-in Secured
Have decision-makers committed to adopting and sustaining DT integration?
6. Change Management Strategy Developed
Is there a communication and transition plan to address employee concerns and
resistance?
7. Pilot Scope Identified
Have we selected a small-scale logistics function (e.g., fleet tracking or
warehouse layout) for initial testing?
8. Cybersecurity Measures in Place
Are data protection and network security protocols ready for increased real-time
data exchange?
9. Vendor/Partner Support Available
Have we identified reliable technology providers or consultants for setup and
troubleshooting?
10. KPIs and ROI Metrics Established
Have we set measurable indicators to track performance improvements and
returns?
Interpretation:
8–10 Yes answers: Your SME is highly prepared to begin DT implementation.
5–7 Yes answers: Some groundwork is in place; address gaps before launch.
0–4 Yes answers: Consider delaying adoption until foundational steps are met.

☐/☐
☐/☐
☐/☐
☐/☐
☐/☐
☐/☐
☐/☐

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7.0 Return on Investment (ROI) of DT Technology in the Logistics Sector for SMEs
DT technology is increasingly used in logistics, which is the field that SMEs need to
improve in terms of efficiency, cost-effectiveness, and performance. Like any other technology
investment, the ROI of DT technology is calculated to validate its worth. In the case of SMEs,
which are known for having limited resources, this investment in Digital Twin technology must
produce visible and quantifiable results to be justified.
7.1 Cost Reduction through Process Optimization
Digital Twin (DT) technology helps SMEs maximize the logistics processes through its
ability to monitor, predict, and simulate processes in real-time. By producing virtual copies of
vehicles, warehouses, and inventory systems, SMEs can thoroughly study operational
performance, point out inefficiency, and make respective changes, which will help trim costs.
As an illustration, Harris (2024) indicates that real-time analysis and predictive models help
companies identify the mistakes in fleet and warehouse work before they cause disruptions,
which leads to more intelligent routing, fuel efficiency, and more efficient inventories. SMEs
can define the delivery route, picking approaches, and stock location to minimize costs and
downtime by prototyping and optimizing delivery routes, picking, and stock allocation using
DT-driven simulation.
Finally, SMEs have also reported optimizing fuel economy and enhancing operations efficiency
using Digital Twin-powered fleet management systems, which has been an incremental
process, as shown by Yasin et al. (2021). Moreover, sensor data-based predictive maintenance
models can predict device failure, and task planning can ensure minimal downtime.

Significant KPIs that are affected by DT-driven process optimization are:
Cost savings on imperial routing.
Increase picking efficiency and the rate of order fulfillment through improved slotting
strategies

68
The improvement of Mean Time Between Failures (MTBF) and reducing downtimes using
predictive maintenance
On-time delivery percentage by correctly forecasting the demands
Scenario Example:
It is possible to consider an SME with a local distribution center and a small fleet of delivery
vehicles. The business then combined DT models with WMS and IoT-based tracking to
redesign its warehouse slotting processes, optimize vehicle routes, and introduce predictive
forklift maintenance. In less than half a year of implementation, the order-picking efficiency
increased by 28%, and fuel consumption decreased by 18%. On-time deliveries increased by
22%, contributing to the cost savings and enhancing the quality of the service.
In a competitive market with small margins, DT-based process optimization will enable SMEs
to drive better efficiency through their operations, logistics, and general business.
7.2 Enhanced Predictive Maintenance and Reduced Downtime
It also benefits companies by allowing them to predict and maintain problems before
they become bigger. Monitoring their logistics assets will enable SMEs to notice and fix
potential issues before they become severe, scheduling maintenance at the right time.
Preventing maintenance ensures fewer unexpected stops in service and much lower repair
expenses. Research reveals that predictive maintenance can help businesses save up to 30% on
maintenance, and their assets may be available for up to 20% more time. The part of keeping
costs down and continuous operations is valued by SMEs since even a small break in their
work can significantly impact their finances.
7.3 Improved Decision-Making and Operational Efficiency
DT technology helps SMEs make their activities based on timely information. By
analyzing the latest data from the logistics industry, SMEs may discover their top performance
indicators. SMEs can use their resources better to make informed decisions by gaining greater
insights and visually seeing data.

69
DTs also assist SMEs in streamlining their logistics operations in real-time by taking advantage
of data. For example, an SME may alter its warehouse design to make stock more accessible
for transportation and create more space using a digital model. SMEs seeking to use their
resources efficiently without additional expenditures may profit from this increased operational
efficiency (Yilmaz et al., 2023).
7.4 ROI Through Scalability and Reduced Implementation Costs
`

Compared to big companies, SMEs usually have fewer resources to invest in

technology at once. At the same time, cloud-based DT solutions make scaling easy and costeffective from the start. Opting for cloud platforms means SMEs do not have to buy their own
hardware and data storage and instead pay only for the needed resources. Ribeiro et al. (2022)
prove that SMEs can increase their business without spending extra money using Digital Twin
solutions based on modules and the cloud. Through the modular approach, SMEs can
implement production improvement in small stages, saving costs as they progress.
7.5 Cost Estimates and ROI Expectations for SMEs
As manufacturing SMEs deliberate over utilizing the Digital Twin (DT) technology,
knowing their possible expenses and prospects is critical to making a knowledgeable choice
and budget. Although specific numbers will depend on the scope of implementation, the
following benchmarks are realistic calculations according to the levels of implementation of
logistics-oriented DT implementation:
Cost Component
Sensor and IoT Hardware (per site)
Digital Twin Software Licenses
Integration with ERP/WMS systems
Cloud Infrastructure & Data Storage
Staff Training and Change
Management
Consulting or System Integration
Support
Total Estimated Initial Investment

Estimated Range (USD)
$3,000 – $10,000
$5,000 – $15,000 annually (or free for opensource)
$7,000 – $20,000
$2,000 – $6,000 annually
$1,500 – $5,000
$5,000 – $12,000
$20,500 – $68,000

70

These expenses can be compared with the recent research findings (Harris, 2024; Yasin et al.,
2021). SMEs using an open-source DT platform can save up to 30 percent on licenses, and a
modular approach to deployment can lower prepaid expenditure.

7.6 CEVA Logistics: Simulation-Powered DT
CEVA Logistics is a global third-party logistics company operating warehouses for
large retailers. It requires the most effective management of seasonal and changing demands to
meet requirements and distribute resources on the shop floor. The Brandenburg warehouse
operated by CEVA needed a DT to optimize its capacity (simul8, 2022). To solve this, CEVA
and Simul8 worked to build a simulation-powered DT to be connected to the current working
systems in the Brandenburg warehouse for real-time use (Simul8, 2022). The simulation would
then create various operational scenarios and measure the results to assess the best-fit models
for manning and workflow.
With this, the company achieved an average of 200 work hours per week with 2 percent
capacity at the site (Simul8, 2022). The digital twin also enabled planners to allocate resources
within the facility and adapt to time-variable demands without slowing the company’s
processes.
Small Manufacturing SME DT Framework Integration
The organization of interest is a small manufacturing enterprise dedicated to producing
precision components and equipment used by various industries. The company lacks an
integrated system for monitoring and optimizing its production processes, resulting in
operational inefficiencies and cost increases (De Marchi et al., 2024).

71
Figure 9:Conceptual architecture for human-inclusive DT for SMEs

Figure 7 shows that the architecture of every device is designed to have three major
logical parts: the initial controller, special logic that may be layered on top of the initial
controller, and middleware to translate messages and commands sent/received by the device.
It is important to adopt (standardized) communications in the Digital Twin (DT) environment
and, thus, support interoperability within various devices. Around the right side of the
physical layer, human interfaces have been incorporated in the architecture, and through
specialized human-computer interfaces (HCI), users will have an opportunity to interact with
the DT system. The reduced version of Digital Twin implementation used by the SME was
based on integrating the IoT sensors data and the visualization of key performance indicators
(KPIs) in a 3D environment over time. The implementation enabled the business to realize

72
bottlenecks, streamline operations, and cut operational costs (De Marchi et al., 2024). The DT
brought much valuable information to make decisions and reform the process.
7.7 Measuring ROI
The Digital Twin technology allows SMEs to minimize operation costs, simplify processes,
and increase the effectiveness of the decision-making process. A recent survey shows that
SMEs implementing DT solutions have:
•

12 to 24 months recovery of over 15 percent to 30 percent of their original capital
investment

•

More rapid response to the disruption of logistics networks

•

Improved fleet and warehouse use

•

Quantifiable cuts in emissions and operational costs

Utilizing cloud-based DT platforms also offers cost benefits, in the form of being able to scale,
pay-as-you-go, and avoid high-cost capital expenditure as well as future system integrations
(Yilmaz et al., 2023).
7.8 Key Success Metrics for Digital Twin Implementation in Logistics
To evaluate the effectiveness and return on investment of Digital Twin technology, SMEs
should track the following KPIs regularly:
Table 10:Key Success Metrics for Digital Twin Implementation in Logistics
KPI
1. Delivery Lead
Time
2. Inventory
Turnover Rate

Description
Average time from order
placement to delivery
completion.
The frequency at which
inventory is sold and replaced.

Why It Matters
Measures the efficiency of
logistics planning and route
optimization.
Indicates how effectively
inventory is being managed using
real-time DT data.

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3. Maintenance
Downtime
4. Fuel
Consumption per
Delivery
5. Order Accuracy
Rate
6. Carbon
Emissions per
Shipment
7. Return on
Investment (ROI)

Total hours of equipment
downtime due to maintenance
issues.
Amount of fuel used per
logistics operation or per unit
delivered.
Percentage of orders fulfilled
without errors.

Assesses predictive maintenance
effectiveness in reducing
disruptions.
Tracks eco-efficiency and cost
control in transportation.

CO₂ emissions associated with
logistics per delivery.

Reflects improved visibility and
coordination between production
and logistics.
Measures environmental impact
and sustainability performance.

Net gain from DT deployment
relative to implementation
cost.

Evaluates the financial
justification for continued or
expanded use.

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8.0 Future Research Directions in the Integration of DT in Logistics for SMEs
Bringing Digital Twin (DT) technology into logistics for Small and Medium Enterprises
(SMEs) may significantly change how these companies operate and boost their efficiency. Still,
like other novel technologies, several aspects should be further examined and developed to
make Digital Twin technology more beneficial to SMEs in logistics.
8.1 Customization of Digital Twin Solutions for SMEs
SMEs struggle to use digital twin technology because most DT solutions require customization
that is often too complex or expensive. Most digital twin platforms focus on large-scale
applications, which makes them generally hard to use in SMEs' fast-changing environments
(Cremonini et al., 2025). Organizations' Research units can conduct investigations to create
modular, tailored, and low-cost DT solutions for small and medium-sized logistics companies.
Future studies should focus on discovering the best ways to use or modify today's technology
for use in farms or businesses that are not large (Rrucaj, 2023). Smaller firms may want to
check out open-source digital twin platforms or cloud products to ensure affordability,
efficiency, and flexibility.
8.2 Integration of Digital Twin with Existing Logistics Systems
Researchers should also investigate how Digital Twin can become part of present-day
logistics tools and methods. SMEs have used the ERP, TMS, or WMS to manage assorted
operations (Gennitsaris et al., 2023). There is a need to research how DT technology can be
fitted into these ageing systems directly, without massive alterations to the existing IT system.
Some of the challenges regarding this process are that all systems can utilize these records,
there are shared data norms, and updating these records should occur immediately in all
networks. Research to fill in these gaps and that enables SMEs' logistics to talk to their models
using blockchain, AI and cloud computing technologies.
8.3 Data Privacy, Security, and Regulatory Compliance

75
DT rely on active data, and it is therefore very important to ensure all information
gathered and utilized is safe and secure. The problem of data theft and violation of the
regulations on data confidentiality can prevent most SMEs from adopting Digital Twin
technology.
Data security measures and privacy technologies that were designed to suit logistics SMEs
should be developed and implemented by experts. Research can explore unsafe data sharing
methods and proposals of solutions that facilitate compliance with regulations observed so that
SMEs do not lose ground by data control using Digital Twins. The next step that researchers
need to focus on is the potential of DT to facilitate the enforcement of some global regulations
like GDPR or CCPA, without manual work (Boudjemaa, 2024)
8.4 Impact of DT on Sustainability and Environmental Performance:

76
In continuation of the significance of sustainability as a focal area in logistics, SMEs
are venturing into creating possible ways to minimize the environmental impact of their
activities. They can utilize technology to optimize business operations concerning the
management of resources, minimal wastage, and the main seriousness of the logistics
operations to turn green logistics operations. SMEs should examine such examples of utilizing
digital twin technology to understand its beneficial effect on the environment, i.e., less energy
consumption, carbon footprint, and wastage (Sahin et al., 2025). What ways could Digital
Twins employ fewer resources to deliver goods and design nature-friendly warehouse space?
To identify the types of measures and models SMEs can utilize, to ensure an assessment of the
effect of utilizing Digital Twin tools, and to ascertain financial and environmental advantages,
further studies are required. The novel DT technology shows a chance at a stellar change in the
sustainability of logistic activities conducted by SMEs. The key points to avoid the
environmental impact are creating real-time virtual images of the physical objects and systems
by DTs, making more fruitful use of resources, reducing and minimizing wastage, and using
the most productive energy for its main applications. In this case, SMEs can use a virtual
transport lane to identify those that help less with polluting; hence, by doing so, the economy
will consume less fuel and will be friendlier. The virtual modelling of the process of warehouse
operations can also be used to reduce the energy consumption, the space efficiency, and the
unwanted transportation of materials.
Another important aspect of DT is its environmental impact, as it extends the life of the
equipment, minimizes the wastage of certain materials, and prevents energy-demanding repairs
(Achouch et al., 2022). DTs enable SMEs to expect the wear-and-tear and time the intervention
before they can fail, a situation that saves energy and resources.

77
As well, the interaction between DTs, IoT sensors, and analytics systems would enable
SMEs to track and compare in real time their energy outputs, their water usage, and their carbon
output. This level of transparency can help in complying with the environmental regulation and
improve the firm Performance Environmentally, Socially, and Governance (ESG) in the global
supply chains.
DTs minimize the ecological footprint linked with experimental logistics and layout revision.
Despite the lack of SME- specific cases, the available ones indicate a measurable energy
consumption decrease (up to 15 to 20%) and the CO2 emissions in logistics settings where DT
is applied (Gennitsaris et al., 2023).
Finally, DTs are not merely an innovation in the digital sector but also a sustainabilityimproving strategic instrument. When SMEs embrace the concept of DT, they help save money
and meet both environmental duties and stakeholders' expectations.
8.5 Advanced Predictive Analytics AI Integration
Areas such as inventory forecasting and demand prediction are essential for the DT to
boost operations significantly. Adopting AI and ML will help SMEs get the full potential of
predictive analytics.
More studies should work on applying AI/ML models to DT methods to improve the accuracy
of predictive analytics in logistics. One can look into optimizing route planning and handling
inventory and warehouse tasks with AI by using data provided by IoT sensors and Digital Twin
simulators (Sahin et al., 2025). The research could analyze the support that digital twins with
AI offer to SMEs facing sudden disruptions in the supply chain, changes in demand, and
inefficient operations.
8.6 Cost-Benefit Analysis and ROI Models for SMEs
Many SMEs do not want to invest in digital twin technology due to doubts about the
initial overheads and the possible ROI. Investigating which costs and benefits are most
important for using Digital Twins in logistics with SMEs is necessary.

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The study suggests that SMEs should concentrate on finding affordable methods for digital
twin solutions and supplying ways to measure the ROI. Researchers conduct analysis and case
reviews to help SMEs understand how digital twin technology can bring efficiencies, cost cuts,
and profits (Lu et al., 2021).
8.7 Workforce Development and Training for SMEs
For Digital Twin technology to work, we must have skilled workers running,
supervising, and interpreting the results from these systems. Since it can be time-consuming
for SMEs to train staff, thoroughly exploring inexpensive ways to enhance skills and develop
knowledge is very important. It would be valuable for future research to examine ways to add
DT training to companies' existing development programs, aided by virtual simulations, elearning environments, and augmented reality technology. Focusing on training could involve
finding the top modules that teach staff how to handle and keep up with Digital Twin
technology at a low price (Yilmaz et al., 2023).
Even though DT technology benefits SMEs in logistics, vital areas still need more study and
development. SMEs can enjoy all of Digital Twin's benefits if they handle these issues.
Choosing these directions in research will help SMEs work with digital twins smoothly and
effectively, which could make logistics operations more efficient (Lu et al., 2021).

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9.0 Conclusion
DT technology is one of the potent possibilities for SMEs in the manufacturing industry
to improve their logistics activities. Although several current implementations have focused on
large companies, the report shows how SMEs can benefit. However, they exhibit weaknesses
due to low technical capacity, financial resources, or incomplete digital infrastructure, which
could hinder Digital Twin implementations, applying them in a step-by-step, progressive
design. With well-defined logistics issues at the beginning and low-cost pilot programs, SMEs
will achieve significant efficiencies in operations, inventory, and quality of delivery.

80
The results show that implementing DT can create measurable investment returns, such as up
to 15 to 30 percent of the operational savings and better delivery lead time. Nonetheless, its
success is closely tied to the preparation for its strategic action: organizations should invest
enough in training; stewardship must be acquired, and the digital infrastructure allowing realtime data integration must be built. Also, in analysis, there are gender gaps in adopting new
technology.
More specific support of women SMEs, causing greater access to funding, inclusion in
training and mentorship initiatives when it comes to the digital transformation, should be
considered to help women have equal access to that process.
The other significant dimension is sustainable. Digital twins will enable cutting carbon
emissions, waste fewer resources, and enhancing energy use to make processes efficient and
environmentally responsible. The future of SMEs is in logistics applications, and their gradual
extension to production planning, predictive maintenance, quality products, and even customer
experience simulation. Companies adopting this change earlier will be well-positioned to
survive in the new reality of markets that get increasingly data-driven and thus competitive due
to Industry 4.0.
10.0 Limitations of the Study
Methodological Limitations
Primary data were not used in the research, as the study used qualitative and secondary
sources such as industry reports and published case studies. Such a method offers plenty of
contextual information but fails to meet the statistical strength of large-scale quantitative
studies. Also, the study lacks field or interview work, so the insight into operational challenges
related to SMEs might be shallow.
Data Limitations

81
SMEs provided real-time or proprietary performance data in a limited way via DT—
evidence from publicly available resources to depict in reporting based on success. Moreover,
the insufficient evidence on gender-disaggregated data on technology adoption restricted the
explorative approach to inclusive adoption strategies.
Generalizability
Applied to manufacturing SME and to logistical operations, the findings are primarily
applicable in the high-income or digitalized areas. The framework, therefore, cannot entirely
apply to SMEs in less-affluent or protracted resource locations without additional conditioning.
Future Research Directions
To further develop this work, it is necessary to:
Undertake an empirical study, such as longitudinal case studies or field experiments. To
establish the cost-effectiveness of DT in SMEs and its functional implications.
Understanding how digital twins in logistics can be cross-sector and applied to other areas,
including maintenance, quality inspection, and supply chain forecasting. Internationalize the
study of inclusive adoption models, particularly those with women-owned and underserved
SMEs that are much targeted. Discuss the contribution of DT to sustainability and the measure
of decreased energy consumption, emissions, and wastes. Create digital maturity frameworks
tailored to the SMEs based on size, industry, and digital capacity.

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