PANTONE PANTONE PANTONE3015C PANTONE3015C 1 0.3 0 0.2 PANTONE Date of
publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2017.DOI

†These authors contributed equally to this work and share the first
authorship. This work was supported by the Competitive Research Fund of The
University of Aizu, Japan.

Corresponding author: Jungpil Shin (e-mail: jpshin@u-aizu.ac.jp).

# Exploring Internet of Things Adoption Challenges in Manufacturing Firms: A
Fuzzy Analytical Hierarchy Process Approach

HASAN SHAHRIAR1†, MD. SAIFUL ISLAM1†, MD ABRAR JAHIN*  — 1†, ISTIYAQUE AHMED
RIDOY*  — 3, TARO SUZUKI2, JUNGPIL SHIN*  — 2  Department of Industrial
Engineering and Management, Khulna University of Engineering and Technology
(KUET), Khulna 9203, Bangladesh  Department of Computer Science and
Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan  Institute
of Business Administration, University of Dhaka, Dhaka, Bangladesh

###### Abstract

Innovation is the key to gaining a sustainable edge in an increasingly
competitive global manufacturing landscape. For Bangladesh’s manufacturing
sector to survive and thrive in today’s cutthroat business environment,
adopting transformative technologies like the Internet of Things (IoT) is not
a luxury but a necessity. This article tackles the formidable task of
identifying and comprehensively evaluating the impediments to IoT adoption
within the Bangladeshi manufacturing industry. Drawing upon a synthesis of
expert insights and a meticulously selected body of contemporary literature,
we delve deep into the complex terrain of IoT adoption challenges. We employ a
robust methodology combining the Delphi method with the Fuzzy Analytical
Hierarchy Process to analyze and prioritize these challenges systematically.
Through this approach, we harness the collective wisdom of experts in the
field and then apply the power of fuzzy logic to navigate the inherent
uncertainties. Our findings illuminate a clear path forward. They reveal that
among the myriad barriers, ”Lack of top management commitment to implementing
new technology” (B10), ”High initial implementation investment costs” (B9),
and ”Risks associated with switching to a new business model” (B7) loom most
extensive, demanding immediate attention. These insights are not confined to
the realm of academia but serve as a pragmatic guide for industrial managers.
Armed with the knowledge gleaned from this study, managers can craft tailored
strategies, set well-informed priorities, and embark on a transformational
journey toward harnessing the vast potential of IoT in the Bangladeshi
industrial sector. This article provides a comprehensive understanding of IoT
adoption challenges and equips industry leaders with the tools necessary to
navigate these challenges effectively. This strategic navigation, in turn,
contributes significantly to enhancing the competitiveness and sustainability
of the Bangladeshi manufacturing sector in the IoT era.

###### Index Terms:

IoT adoption challenges, Bangladeshi manufacturing industry, Fuzzy Analytical
Hierarchy Process, Industrial sector obstacles, Internet of Things barriers,
Technology adoption strategies

=-15pt

##  I Introduction

###  I-A Background and Motivation

In the modern world, alongside essential needs like food, water, clothing, and
shelter, the internet has emerged as a transformative force, revolutionizing
various industries. The Internet of Things (IoT) is one such revolutionary
concept that has gained substantial traction since its inception in 1999 when
Kevin Ashton coined the term [25]. IoT refers to a fast-expanding network of
interconnected physical objects, including machines, automobiles, home
appliances, and other items, enabled to exchange data through connectivity,
electronics, software, and sensors. With its potential to reshape how we live,
work, and interact with the world, IoT stands out as a game-changer in today’s
technologically advanced era.

The impact of IoT is already evident across multiple industries, from
manufacturing and healthcare to transportation and agriculture. IoT plays a
significant role in manufacturing, enhancing operations, and driving
competitiveness. Through IoT-enabled devices and real-time data exchange,
manufacturing companies can implement predictive maintenance strategies,
identify potential issues in advance, and optimize equipment performance,
resulting in reduced downtime, increased reliability, and prolonged equipment
lifespan.

Moreover, IoT empowers manufacturers to streamline their supply chains,
tracking the movement of components, goods, and raw materials to improve
inventory management and reduce wastage. It also fosters energy efficiency by
monitoring energy consumption and automating operations, leading to more
sustainable and eco-friendly manufacturing practices. Additionally, IoT
enhances workplace safety by continuously monitoring environmental factors and
alerting workers to potential hazards. IoT drives process automation, enabling
the industrial sector to lower labor costs and boost productivity. With the
limitless possibilities of IoT, from simple temperature sensors to complex
smart systems controlling entire factories, it has the potential to
revolutionize the manufacturing industry by making companies more efficient,
competitive, and responsive to market demands.

However, as the number of connected devices grows, so does the importance of
ensuring their security and safeguarding the data they generate. Cyber threats
pose a significant concern, requiring manufacturers to prioritize robust
security measures to protect sensitive information.

###  I-B The Role of IoT in the Manufacturing Industry

IoT plays a significant role in the manufacturing industry, helping companies
to improve their operations and increase their competitiveness. Here are a few
ways in which IoT is being used in the manufacturing industry:

  1. 1.

Predictive Maintenance: Manufacturing equipment performance and health can be
tracked with IoT devices, which also provide real-time data that can be
utilized to spot possible issues before they arise. This can save downtime,
increase equipment dependability, and increase the equipment’s lifespan.

  2. 2.

Quality Control: Manufacturing equipment performance and health can be tracked
with IoT devices, which also provide real-time data that can be utilized to
spot possible issues before they arise. This can save downtime, increase
equipment dependability, and increase the equipment’s lifespan.

  3. 3.

Supply Chain Optimization: IoT may track the movement of components, completed
goods, and raw materials along the supply chain, helping businesses manage
their inventories more effectively and reduce waste.

  4. 4.

Energy Efficiency: By tracking energy use, finding areas for improvement, and
automating operations to cut energy use, IoT can assist manufacturing
organizations in increasing their energy efficiency.

  5. 5.

Safety: By monitoring factors like temperature and humidity and warning
workers of potential hazards, IoT can increase workplace safety.

  6. 6.

Process Automation: IoT automation streamlines manufacturing processes,
lowering labor costs and increasing productivity.

Implementing IoT in manufacturing, however, presents challenges. Manufacturers
need to understand the benefits of IoT and integrate it effectively into their
operations. Compatibility issues with existing systems and the associated
expenses, especially for smaller organizations, can hinder adoption.
Additionally, securing IoT devices and data against cyber threats remains
crucial.

To address these challenges, this research aims to identify barriers to IoT
adoption in the manufacturing sector, evaluate and prioritize them using the
Fuzzy Analytical Hierarchy Process (FAHP), and provide practical
recommendations to overcome these obstacles and foster successful industry
integration.

###  I-C Historical Background of IoT

The inception of the IoT can be traced back to the early 1990s when the idea
of interconnected devices began to take shape at MIT’s Auto-ID Centre. Kevin
Ashton, the Centre’s director, introduced the term ”IoT” in 1999 [34]. The
concept revolved around using radio-frequency identification (RFID) tags to
track products along the supply chain of companies like Procter and Gamble. As
early as 1997, Ashton envisioned the potential of RFID tags to scan and
identify items, with data transmitted wirelessly across a network. Notably,
the industry had started implementing RFID tags as early as 1980 [9].

Subsequently, the idea of a wireless sensor network (WSN) emerged, finding
applications in traffic management and healthcare. This new concept
incorporated sensors and actuators to detect, track, and monitor various
objects [9]. Over time, this vision evolved to encompass other technologies,
such as GPS devices, cell phones, social networks, cloud computing, and data
analytics, forming the foundation for the modern IoT concept.

IoT has emerged as a pivotal technology driving the development of Industry
4.0, particularly in the European manufacturing industry, with Germany leading
the charge. Industry 4.0 represents the fourth industrial revolution, building
upon its predecessors, Industry 1.0, Industry 2.0, and the Digital Revolution
(Industry 3.0). According to [38], Industry 4.0 integrates information and
communications technologies seamlessly with industrial processes.

To realize the vision of Industry 4.0, IoT serves as a critical enabler
alongside cloud manufacturing and cyber-physical systems (CPS). Cyber-physical
systems consist of intelligent equipment, storage systems, and production
facilities capable of autonomous communication, action, and monitoring [41].
CPS involves the integration of analog/digital hardware, bridging
manufacturing entities’ physical and virtual components [28]. IoT provides the
necessary framework to connect CPS via a network of devices, sensors, and
actuators.

In this context, cloud manufacturing (CM) has emerged, leveraging cloud
computing resources hosted in external data centers to complement Industry 4.0
initiatives. Together, these technological advancements promise to
revolutionize manufacturing, enabling enhanced connectivity, data-driven
decision-making, and increased efficiency in industrial processes. As IoT
continues to advance and intertwine with other technologies, it is expected to
play an even more significant role in reshaping the manufacturing landscape,
unlocking further possibilities for innovation and growth.

###  I-D Barriers for IoT adoption

IoT implementation in the manufacturing sector presents several challenges
that hinder its seamless integration and utilization. These obstacles must be
carefully addressed to harness IoT’s advantages in industrial settings fully.
The following are key barriers manufacturers may encounter:

  1. 1.

Technical Complexity: Implementing IoT technology in manufacturing requires
high technical expertise and understanding. The complexity of IoT systems and
their interconnections can be daunting for manufacturers, making it
challenging for them to grasp IoT’s full potential and benefits. To overcome
this hurdle, educational initiatives and training programs can be employed to
empower manufacturers with the necessary technical knowledge to embrace and
leverage IoT effectively.

  2. 2.

Integration Challenges: Integrating IoT technology into existing manufacturing
systems can be arduous, particularly if these systems are outdated or not
designed with IoT in mind. The lack of compatibility may lead to issues with
seamless communication and operation between different components, impeding
the overall efficiency and performance of the IoT implementation.
Manufacturers should thoroughly assess their current systems and strategize
for a smooth transition, which may involve gradual upgrades or replacements to
ensure harmonious integration.

  3. 3.

Cost Considerations: The adoption of IoT technology often comes with
significant expenses, which may pose a particular challenge for small and
medium-sized organizations with limited financial resources. The costs
associated with IoT include investments in hardware, software, installation,
and ongoing maintenance. To mitigate this barrier, manufacturers can explore
cost-effective IoT solutions, consider partnerships, or seek government
incentives and support to facilitate IoT adoption.

  4. 4.

Data Security and Privacy: As IoT devices generate and transfer vast amounts
of sensitive data in manufacturing, ensuring this information’s security and
privacy becomes critical. The potential for cyberattacks and unauthorized
access necessitates robust security measures, encryption protocols, and data
management practices. Manufacturers must prioritize data protection and
establish comprehensive cybersecurity strategies to safeguard their IoT
systems and the valuable data they collect.

  5. 5.

Lack of Universal Standardization: There is no universal standard for IoT
systems and devices, leading to a fragmented market with numerous solutions
and technologies. This lack of standardization can make it challenging for
manufacturers to select the most suitable IoT solutions for their specific
needs and to ensure seamless interoperability between various devices and
platforms. The industry can address this barrier by fostering collaboration
and developing common standards to facilitate a more cohesive and integrated
IoT ecosystem.

  6. 6.

Reluctance to Adopt New Processes: Some manufacturers may hesitate to adapt
their established procedures and infrastructure to accommodate IoT
technologies. This reluctance can impede the widespread adoption of IoT,
preventing companies from fully realizing its advantages. Overcoming this
barrier requires effective change management strategies, clear communication
of the benefits, and showcasing successful case studies to demonstrate the
positive impact of IoT implementation.

The successful integration of IoT in the manufacturing sector depends on
overcoming these barriers. By addressing technical complexities, facilitating
seamless integration, managing costs, prioritizing data security, fostering
standardization, and promoting a positive shift in mindset, manufacturers can
embrace IoT’s potential and achieve enhanced efficiency, innovation, and
competitiveness in their operations.

###  I-E Research Objectives

The primary aim of this research is to gain comprehensive insights into the
barriers hindering the adoption of IoT in the manufacturing sector. To achieve
this overarching goal, the specific research objectives are as follows:

  1. 1.

To identify barriers to IoT adoption in the manufacturing sector: The first
objective is to conduct a thorough investigation and analysis to identify the
key obstacles and challenges manufacturers face when adopting IoT technology.
By delving into the perspectives of various stakeholders, expert opinions, and
relevant literature, this research will pinpoint the critical factors
inhibiting the successful integration of IoT in manufacturing processes in the
context of Bangladesh.

  2. 2.

To evaluate and Prioritize IoT adoption barriers using the FAHP: This research
will employ the FAHP technique to comprehensively and systematically
understand the identified barriers. FAHP is a powerful decision-making tool
that can handle complex and uncertain data, allowing for a more nuanced
evaluation of the barriers’ relative importance. Through this process, the
research will rank the barriers based on their significance, providing
valuable insights into which obstacles require immediate attention and
mitigation strategies.

  3. 3.

To provide practical recommendations for overcoming IoT adoption barriers in
the industry: The final objective of this research is to offer pragmatic and
actionable recommendations for manufacturing companies in Bangladesh to
overcome the identified barriers and facilitate successful IoT adoption. These
recommendations will be based on the research findings, expert insights, and
best practices in the field. The aim is to equip manufacturing managers and
decision-makers with feasible strategies to address the challenges
effectively, fostering a more conducive environment for embracing IoT
technology in their operations.

The novelty of this study lies in its comprehensive approach, combining the
Delphi method and FAHP. While the Delphi method harnesses the collective
wisdom of experts to identify and select the most relevant barriers to IoT
adoption, FAHP takes these selected barriers and rigorously assesses their
relative importance, accounting for the inherent uncertainty in decision-
making. This dual-method approach provides a robust and nuanced understanding
of the obstacles to IoT adoption in the Bangladeshi manufacturing sector,
enhancing the accuracy and practicality of the findings. The outcomes will
empower companies to make informed decisions, develop effective implementation
plans, and ultimately thrive in the increasingly competitive global business
landscape driven by IoT innovations.

In the subsequent sections, we delve into the specifics of our research
methodology, starting with the “Methodology” section. Here, we outline our
approach, which includes three key subsections: ”Identifying Barriers for IoT
Adoption,” ”Fuzzy Delphi Method” for selecting relevant barriers, and the
”FAHP Methodology” for ranking these barriers. Following this, we present our
findings and engage in comprehensive discussions in the “Results and
Discussions” section. We then draw key insights and implications in the
“Conclusions and Future Works” section, shedding light on the critical
barriers to IoT adoption in the manufacturing sector in Bangladesh. Finally,
we outline avenues for future research, providing a holistic view of our
study’s structure and objectives.

##  II Methodology

###  II-A Identifying Barriers for IoT Adoption

This study identified barriers to IoT adoption in the manufacturing sector
based on a comprehensive review of relevant research papers. The search
process employed three significant keywords: (“Internet of Things” OR “IoT”)
AND “adoption” AND “manufacturing” AND (“industry” OR “sector)” to ensure a
targeted and focused approach. A rigorous search was conducted using the
Google Scholar database to gather the research articles, encompassing a
diverse range of peer-reviewed journals. It is important to note that white
papers, theses, book chapters, conference proceedings, and websites were
excluded from this evaluation, ensuring the inclusion of only high-quality,
peer-reviewed literature. Additionally, for consistency and accessibility,
only articles published in English were considered in this study. This
decision ensured that the language barrier did not affect the comprehensive
analysis and interpretation of the chosen articles. Furthermore, a meticulous
Title and Abstract review process was undertaken to select articles that
directly pertained to the objectives of this study. Those articles that did
not directly align with the research focus were excluded from the final set of
chosen papers. Moreover, additional papers were included after thoroughly
examining the selected articles’ reference lists to enhance the
comprehensiveness of the identified barriers. This approach helped capture
potential barriers that might have been explored in related studies but not
explicitly mentioned in the initial search results.

The culmination of this systematic process resulted in the identification of
sixteen significant barriers in the context of IoT adoption in the
manufacturing sector. As outlined in the research objectives, these barriers
will serve as the foundation for the subsequent evaluation and prioritization
using the FAHP). Table I presents a concise overview of the identified
barriers along with a brief description of each, setting the stage for further
analysis and discussion in this study.

TABLE I: IoT adoption barriers in Bangladeshi manufacturing industry

Serial No |  Barriers for IoT Adoption |  Description |  Authors
---|---|---|---
B1 |  Absence of training initiatives |  After implementing IoT, staff members will no longer be only operators; instead, they will be real problem solvers who analyze the data IoT has collected. In order to grasp IoT technology, training programs will be required. |  [27, 13, 1]
B2 |  Lack of knowledge of return on investment (ROI) |  Adoption of IoT technology is hampered by the lack of a standardized method for calculating ROI, particularly when a significant initial investment is required. Evaluating and producing financial returns involves high risk. The largest obstacle to IoT adoption is an unknown payback period. |  [5, 24, 26]
B3 |  Interoperability issues |  IoT devices have trouble communicating with one another since they are made by many vendors and adhere to various standards. The use of IoT devices may be constrained, and this lack of interoperability may slow adoption. |  [27, 17]
B4 |  Unawareness of the advantages of IoT |  Despite the fact that there are many articles discussing the benefits of using IoT technology, many IoT applications are still in their infancy, and the benefits of the technology for businesses are not well understood. |  [6, 10, 5, 40]
B5 |  Employees’ resistance to embracing new technologies |  The fear of losing their jobs prevents workers from embracing IoT technologies. |  [35, 10]
B6 |  Limited connectivity |  A dependable and steady internet connection is necessary for IoT devices to operate correctly. Adoption may be hampered in some places by low or sporadic connectivity. |  [35, 1]
B7 |  Power and battery life |  Many IoT gadgets run on batteries and need to be charged frequently, which can be annoying for consumers. In some circumstances, it may be difficult to use particular devices because they need a steady power source. |  [5, 24, 26]
B8 |  Regulatory challenges |  IoT devices may be governed by several laws, which can differ by sector, nation, or location. Businesses may find it difficult to comply with these rules, which could prevent adoption. |  [35], [21]
B9 |  Security and privacy concerns |  Data security is a serious worry because the IoT is constantly sharing massive amounts of data. To prevent risks to data security, authentication, authorization, and access control procedures are necessary. Data privacy is another key problem because these linked gadgets are exchanging a lot of personal or industry-specific information. |  [2], [24], [5], [17], [37], [33], [27], [12], [10], [35]
B10 |  Unskilled or inexperienced Manpower |  Handling IoT technologies requires skilled personnel. IoT technology installation requires technical expertise and IT skills due to the massive amount of generated data. |  [36], [21]
B11 |  Risks associated with switching to a new business model |  Any technological paradigm shift necessitates modifying the business model. The implementation of IoT technology requires a new business model framework. Organizations are reluctant to assume the risk associated with implementing a new business model for IoT technology because they are unsure of the ROI. |  [10],[27],[11]
B12 |  Data overload |  IoT devices can produce a lot of data, which can be daunting for both individuals and enterprises. This data can be difficult to process and analyze, especially for individuals lacking the knowledge or skills to do so. |  [42], [39], [5], [17]
B13 |  Inadequate technology |  Needs for IoT applications real-time analysis of data produced by IoT devices requires high-speed internet and IT gear to implement, support, and manage IoT devices. |  [18], [23], [17], [33], [36], [2], [42], [39]
B14 |  High initial implementation investment costs |  IoT implementation requires a significant upfront investment, which includes the cost of sensors, networks, software, maintenance, training, and other expenses. Indian organizations lack the necessary financial resources. Additionally, given that sensor costs are falling daily, Indian businesses prefer to wait for lower prices. |  [26, 2, 1, 32, 5, 17, 33, 10, 40, 35]
B15 |  Lack of top management’s commitment to implementing new technology |  The acceptance of new technology by a company is strongly influenced by top management backing. IoT adoption and implementation demands senior management support as well as time and financial commitment. |  [26],[2],[42],[5],[23],[33],[37],[35]
B16 |  Absence of Standardization |  IoT implementation is difficult. Still, the greatest obstacle to this technology’s acceptance is the lack of standardized interoperability interfaces between various IoT devices made by various suppliers. There are various businesses that have created versions of standards that are incompatible with one another. |  [1, 26, 24, 18, 17, 10]

###  II-B Fuzzy Delphi Method to select the relevant barriers

The Fuzzy Delphi Method is a methodical technique for combining expert
thoughts and opinions to reach a consensus on complex and individualized
situations. It blends the conventional Delphi technique with fuzzy logic and
set theory to deal with ambiguity and imprecision in expert opinions.

The Fuzzy Delphi approach entails several rounds of anonymous questionnaires
and comments, where the experts are invited to express their thoughts using
language variables (e.g., very high, high, moderate, low, very low) rather
than numerical values. Fuzzy set operations are used to aggregate the expert
opinions once these linguistic variables are converted into fuzzy sets using
fuzzy membership algorithms.

####  II-B1 The linguistic variables’ triangular fuzzy number setup

A fuzzy set with a triangle membership function is referred to as a triangular
fuzzy set. The triangular fuzzy set’s formula is provided by:

$\mu_{A}(x)=\begin{cases}0,&\text{if }x<a\\\ \frac{x-a}{b-a},&\text{if }a\leq
x\leq b\\\ \frac{c-x}{c-b},&\text{if }b\leq x\leq c\\\ 0,&\text{if
}x>c\end{cases}$ (1)

A triangular fuzzy number $a_{ij}$ is defined as
$a_{ij}=(a_{ij},b_{ij},c_{ij})$, in which $i\in\\{1,2,\ldots,n\\}$ denotes the
experts and $j\in\\{1,2,\ldots,m\\}$ denotes the barriers, where $n$ and $m$
are the numbers of experts and barriers, respectively. The linguistic scale
and its fuzzy equivalent,

TABLE II: Setting up Fuzzy number set for Linguistic variables Linguistic Variables | Rating | Triangular Number |
---|---|---|---
Very low | 1 | (0,0,1) |
Low | 2 | (1,2,3) |
Low medium | 3 | (2,3,4) |
Medium | 4 | (3,4,5) |
Medium High | 5 | (4,5,6) |
High | 6 | (5,6,7) |
High-very High | 7 | (6,7,8) |
Very High | 8 | (7,8,9) |
Very High-Extreme | 9 | (8,9,10) |
Extreme | 10 | (10,10,10) |

####  II-B2 Obtaining the opinions of experts on the significance of the
barriers

At this step, comments on the applicability of the barriers were gathered from
four manufacturing industry experts.

TABLE III: Gathered information from the experts on identifying relevant barriers Barriers | Expert Opinion 1 | Expert Opinion 2 | Expert Opinion 3 | Expert Opinion 4
---|---|---|---|---
B1 | (6,7,8) | (6,7,8) | (6,7,8) | (7,8,9)
B2 | (7,8,9) | (6,7,8) | (8,9,10) | (8,9,10)
B3 | (6,7,8) | (5,6,7) | (4,5,6) | (5,6,7)
B4 | (7,8,9) | (7,8,9) | (7,8,9) | (8,9,10)
B5 | (5,6,7) | (10,10,10) | (7,8,9) | (7,8,9)
B6 | (5,6,7) | (4,5,6) | (5,6,7) | (3,4,5)
B7 | (6,7,8) | (4,5,6) | 2,3,4 | (3,4,5)
B8 | (4,5,6) | (2,3,4) | (2,3,4) | (3,4,5)
B9 | (10,10,10) | (8,9,10) | (8,9,10) | (10,10,10)
B10 | (8,9,10) | (6,7,8) | (7,8,9) | (6,7,8)
B11 | (6,7,8) | (9,10,10) | (7,8,9) | (7,8,9)
B12 | (1,2,3) | (2,3,4) | (5,6,7) | (4,5,6)
B13 | (10,10,10) | (10,10,10) | (7,8,9) | (6,7,8)
B14 | (8,9,10) | (8,9,10) | (6,7,8) | (7,8,9)
B15 | (10,10,10) | (8,9,10) | (10,10,10) | (10,10,10)
B16 | (8,9,10) | (8,9,10) | (8,9,10) | (6,7,8)

####  II-B3 Defuzzification

Defuzzification, often referred to as definite value creation, reduces a fuzzy
set to a single crisp value. It transforms the result of the fuzzy inference
process into a single value that can be utilized as a decision or an action,
which makes it a critical step in fuzzy logic systems. There are several
techniques to defuzzify a fuzzy integer, but the centroid method was employed
in this stage. Using the geometric mean, this method determines the fuzzy
set’s center of mass and produces the associated crisp value.

The following are the fuzzy weights for the barriers:

$a_{-}j=(a_{j},b_{j},c_{j})$ (2)

where $a_{j}=\min\\{a_{ij}\\},$ $b_{j}=\sqrt[n]{\prod_{i=1}^{n}b_{ij}},$ and
$c_{j}=\max\\{c_{ij}\\}.$

In this study, the group choice of the experts is ascertained using a
geometric mean approach.

To obtain a crisp value ($S_{j}$), which is determined by the following
formula, the fuzzy weights are defuzzified using the straightforward center of
gravity approach:

$S_{j}=\frac{a_{j}+b_{j}+c_{j}}{3},\quad j\in\\{1,2,\ldots,m\\}$ (3)

####  II-B4 Barrier Selection Based on Significance

In the process of barrier selection, we aim to prioritize barriers according
to their significance. To achieve this, we calculate a threshold value denoted
as $S$ using the formula:

$S=\frac{\sum_{i=1}^{m}S_{i}}{n}$ (4)

Where $i\in\\{1,2,\ldots,n\\}$, and $n$ represents the total number of
barriers under consideration.

The following principles guide the selection of barriers:

  * •

If $S_{i}\geq S$, then barrier $i$ is chosen for further consideration.

  * •

If $S_{i}<S$, then barrier $i$ is not selected for further analysis.

Our analysis shows the calculated value of ’S’ in Table IV.

TABLE IV: Selection of Final Barriers Based on Significance Barriers | Defuzzification Weighted Value | Decision
---|---|---
B1 | 7.41 | Selected
B2 | 8.07 | Selected
B3 | 5.99 | Rejected
B4 | 8.41 | Selected
B5 | 7.62 | Selected
B6 | 5.06 | Rejected
B7 | 4.84 | Rejected
B8 | 3.89 | Rejected
B9 | 9.16 | Selected
B10 | 7.9 | Selected
B11 | 8.06 | Selected
B12 | 3.89 | Rejected
B13 | 8.22 | Selected
B14 | 8.07 | Selected
B15 | 9.25 | Selected
B16 | 8.15 | Selected

###  II-C FAHP methodology

The FAHP’s pair-wise assessments of both criteria and alternatives are carried
out through the linguistic variables represented by triangular numbers. While
FAHP offers a range of calculation methods, we opt for Buckley’s FAHP method
in this study due to its elegant simplicity and effectiveness in handling the
complexity of our research scope. Buckley’s FAHP is a modification of Saaty’s
AHP approach, designed to incorporate uncertainty into the mathematical model
while maintaining its representation. The FAHP process unfolds systematically
through the following consecutive steps:

Hierarchy construction: We commence by meticulously constructing a hierarchy
that encapsulates the objectives of the various barriers under consideration,
subsequently organizing them into a structured list of options. Each barrier
is thoughtfully outlined and defined within the context of our research goal,
aiming to discern the most crucial barriers within the selected set. This
hierarchical representation provides a clear and organized framework for our
subsequent evaluations, ensuring that every aspect is considered in the
ranking process.

Pairwise comparison matrices between barriers: To rank the barriers in the
hierarchy according to their relative importance to one another, fuzzy
triangular scales are utilized. As stated in Table V below, the barriers are
contrasted using language phrases.

TABLE V: Linguistic Terms and Corresponding Triangular Fuzzy Scale for Barrier Significance Assessment Saaty Scale | Linguistic Terms | Corresponding Triangular Fuzzy Number
---|---|---
1 |  | Equally important
---
(Eq. Imp)
(1,1,1)
3 | Weakly important (W. Imp) | (2,3,4)
5 | Fairly important (F. Imp) | (4,5,6)
7 | Strongly important (S.Imp) | (6,7,8)
9 |  | Absolutely important
---
(A. Imp)
(9,9,9)
2 | The intermittent values between two adjacent scales | (1,2,3)
4 | 4.84 | (3,4,5)
6 | 3.89 | (5,6,7)
8 | 9.16 | (7,8,9)

Leveraging insights gleaned from sets of expert questionnaires, we conducted
pairwise comparisons among all barriers. To consolidate the expert opinions,
we calculated the average of the collected data. Subsequently, the data
obtained from the pairwise comparisons, as presented in Table II, were
aggregated on a triangular scale to form the matrix of contributions. Table VI
shows the pairwise comparison matrix of barriers’ significance. The elements
in each row represent the relative significance of the corresponding barriers
concerning one another, as assessed by experts using a triangular scale. Each
value reflects the degree of significance, with larger values indicating
higher significance. The table assists in prioritizing and understanding the
relative importance of barriers in the study context.

TABLE VI: Pairwise Comparison Matrix of Barriers’ Significance

| B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | B9 | B10 | B11
---|---|---|---|---|---|---|---|---|---|---|---
B1 | 1 | 1 | 1 | 0.25 | 0.33 | 0.5 | 0.17 | 0.2 | 0.25 | 2 | 3 | 4 | 0.125 | 0.147 | 0.17 | 4 | 5 | 6 | 1 | 1 | 1 | 2 | 3 | 4 | 0.17 | 0.2 | 0.25 | 0.111 | 0.111 | 0.111 | 0.25 | 0.33 | 0.5
B2 | 2 | 3 | 4 | 1 | 1 | 1 | 0.17 | 0.2 | 0.25 | 1 | 1 | 1 | 9 | 9 | 9 | 2 | 3 | 4 | 0.17 | 0.2 | 0.25 | 1 | 1 | 1 | 0.125 | 0.147 | 0.17 | 0.25 | 0.33 | 0.5 | 6 | 7 | 8
B3 | 4 | 5 | 6 | 4 | 5 | 6 | 1 | 1 | 1 | 2 | 3 | 4 | 0.17 | 0.2 | 0.25 | 2 | 3 | 4 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 0.25 | 0.33 | 0.5 | 0.17 | 0.2 | 0.25 | 4 | 5 | 6
B4 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 0.17 | 0.2 | 0.25 | 2 | 3 | 4 | 0.25 | 0.33 | 0.5 | 4 | 5 | 6 | 9 | 9 | 9 | 0.25 | 0.33 | 0.5 | 0.125 | 0.147 | 0.17
B5 | 6 | 7 | 8 | 0.11 | 0.11 | 0.11 | 4 | 5 | 6 | 4 | 5 | 6 | 1 | 1 | 1 | 2 | 3 | 4 | 0.17 | 0.2 | 0.25 | 4 | 5 | 6 | 0.17 | 0.2 | 0.25 | 2 | 3 | 4 | 6 | 7 | 8
B6 | 0.17 | 0.2 | 0.25 | 0.25 | 0.33 | 0.5 | 0.25 | 0.33 | 0.5 | 0.25 | 0.33 | 0.5 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 0.25 | 0.33 | 0.5 | 2 | 3 | 4 | 0.25 | 0.33 | 0.5 | 0.125 | 0.142 | 0.17 | 2 | 3 | 4
B7 | 1 | 1 | 1 | 4 | 5 | 6 | 2 | 3 | 4 | 2 | 3 | 4 | 4 | 5 | 6 | 2 | 3 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 0.17 | 0.2 | 0.25 | 1 | 1 | 1 | 4 | 5 | 6
B8 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 0.17 | 0.2 | 0.17 | 0.17 | 0.2 | 0.25 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 0.25 | 0.33 | 0.5 | 0.17 | 0.2 | 0.25 | 4 | 5 | 6
B9 | 4 | 5 | 6 | 6 | 7 | 8 | 2 | 3 | 4 | 0.11 | 0.11 | 0.11 | 4 | 5 | 6 | 2 | 3 | 4 | 4 | 5 | 6 | 2 | 3 | 4 | 1 | 1 | 1 | 0.125 | 0.147 | 0.17 | 9 | 9 | 9
B10 | 9 | 9 | 9 | 2 | 3 | 4 | 4 | 5 | 6 | 2 | 3 | 4 | 0.25 | 0.33 | 0.5 | 6 | 7 | 8 | 1 | 1 | 1 | 4 | 5 | 6 | 6 | 7 | 8 | 1 | 1 | 1 | 2 | 3 | 4
B11 | 2 | 3 | 4 | 0.125 | 0.147 | 0.17 | 0.17 | 0.2 | 0.25 | 6 | 7 | 8 | 0.125 | 0.147 | 0.17 | 0.25 | 0.33 | 0.5 | 0.25 | 0.33 | 0.5 | 0.17 | 0.2 | 0.25 | 0.11 | 0.11 | 0.11 | 0.25 | 0.33 | 0.5 | 1 | 1 | 1

Normalized Relative Weights of Barriers: In this stage, we calculate the
normalized relative weights of barriers using the geometric mean of fuzzy
comparison values. An example calculation for ”Criteria 1” is presented in
Equation 5:

$\tilde{r}_{l}=\left(\prod_{j=1}^{n}\tilde{d}_{ij}\right)^{\frac{1}{n}}$ (5)

For ”Criteria 1,” the calculation is as follows:

$\tilde{r}_{l}$ $=[(1\times 0.25\times 0.17\times 2\times 0.125\times 4\times
1\times 2\times 0.17\times 0.111\times 0.25)^{\frac{1}{11}};(1\times
0.33\times 0.2\times 3\times 0.147\times 5\times 1\times 3\times 0.2\times
0.111\times 0.33)^{\frac{1}{11}};(1\times 0.5\times 0.25\times 4\times
0.17\times 6\times 1\times 4\times 0.25\times 0.111\times
0.5)^{\frac{1}{11}}]$

This results in the following normalized relative weights for ”Criteria 1”:

$\tilde{r}_{l}=[0.4911,0.5932,0.7232]$

These values represent the normalized weights for ”Criteria 1” concerning the
given fuzzy comparison values. Table VII presents the geometric means of fuzzy
comparison values (ri) for various criteria labeled from B1 to B11. These
values indicate the normalized relative weights for each criterion.
Additionally, the table includes a ”Total” row summarizing the cumulative
values for all criteria and two additional rows denoted as ”P(-1)” and ”INCR,”
which represent specific parameters or calculations related to the analysis.
These values play a crucial role in assessing the relative importance of the
criteria within the study context.

TABLE VII: Geometric Means of Fuzzy Comparison Values ($r_{i}$) for Criteria
B1 to B11 (Geometric mean of fuzzy)
---
| $r_{i}$
B1 | 0.4911269 | 0.593166 | 0.723203
B2 | 0.8619317 | 1.008719 | 1.179025
B3 | 0.9322815 | 1.155445 | 1.437111
B4 | 0.6278548 | 0.757425 | 0.950749
B5 | 1.3594037 | 1.647404 | 1.946593
B6 | 0.3752132 | 0.482518 | 0.661569
B7 | 1.5008527 | 1.808046 | 2.097435
B8 | 0.4793547 | 0.551569 | 0.644492
B9 | 1.7160993 | 2.096448 | 2.444355
B10 | 2.3177318 | 2.843423 | 3.382683
B11 | 0.3488563 | 0.420009 | 0.52264
Total | 11.010707 | 13.36417 | 15.98986
P(-1) | 0.0908207 | 0.074827 | 0.06254
INCR | 0.0625397 | 0.111 | 0.090821

In Table VIII, the relative fuzzy weights for each barrier are calculated
using a method that combines geometric means of fuzzy values with the sum of
the geometric means of reverse fuzzy values in ascending order, as described
by the following calculation: $\tilde{w}_{l}=[(0.4911269\times
0.0625397),(0.593166\times 0.111),(0.723203\times
0.090821)]=[0.0307,0.0658,0.0657]$. The resulting values represent the
relative importance of each barrier in the analysis.

TABLE VIII: Relative fuzzy weight of each barrier (Fuzzy weight)
---
| $W_{i}$
B1 | 0.0307 | 0.0658 | 0.0657
B2 | 0.0539 | 0.112 | 0.1071
B3 | 0.0583 | 0.1283 | 0.1305
B4 | 0.0393 | 0.0841 | 0.0863
B5 | 0.085 | 0.1829 | 0.1768
B6 | 0.0235 | 0.0536 | 0.0601
B7 | 0.0939 | 0.2007 | 0.1905
B8 | 0.03 | 0.0612 | 0.0585
B9 | 0.1073 | 0.2327 | 0.222
B10 | 0.145 | 0.3156 | 0.3072
B11 | 0.0218 | 0.0466 | 0.0475

The fuzzy numbers associated with each barrier were aggregated to calculate
the relative non-fuzzy weights for each barrier. Normalized weights for each
barrier were determined by dividing the relative fuzzy weight of each barrier
by the sum of the fuzzy weights for all the barriers. Consequently, Table IX
presents the averaged and normalized weights for these barriers.

TABLE IX: Averaged ($M_{i}$) and Normalized ($N_{i}$) Relative Weights of Criteria | $M_{i}$ | $N_{i}$
---|---|---
B1 | 0.054 | 0.04476
B2 | 0.091 | 0.07531
B3 | 0.106 | 0.08749
B4 | 0.07 | 0.05786
B5 | 0.148 | 0.12269
B6 | 0.046 | 0.03783
B7 | 0.162 | 0.13383
B8 | 0.05 | 0.04132
B9 | 0.187 | 0.15507
B10 | 0.256 | 0.21185
B11 | 0.039 | 0.03198
Total | 1.208 | 1

##  III Results and Discussion

Table X presents the priority weights for barriers to IoT adoption in the
Bangladesh manufacturing industry, as calculated using FAHP.

TABLE X: Relative Weight and Ranking of IoT Adoption Barriers in the Bangladeshi Industrial Sector Barrier No | Barriers to IoT Adoption | Weights | Ranking
---|---|---|---
B1 | Absence of training initiatives | 0.04476 | 8
B2 | Lack of knowledge of ROI | 0.07531 | 6
B3 | Unawareness of IoT advantages | 0.08749 | 5
B4 | Employees’ resistance to new tech | 0.05786 | 7
B5 | Security and privacy concerns | 0.12269 | 4
B6 | Unskilled or inexperienced Manpower | 0.03783 | 10
B7 | Risks with new business model | 0.13383 | 3
B8 | Inadequate technology | 0.04132 | 9
B9 | High initial implementation costs | 0.15507 | 2
B10 | Lack of top management commitment | 0.21185 | 1
B11 | Absence of Standardization | 0.03198 | 11

The most critical barrier identified through our research is the ”Lack of top
management commitment to implementing new technology” (B10), with a weight of
0.21185. Other significant barriers include ”High initial implementation
investment costs” (B9) at 0.15507 and ”Risks associated with switching to a
new business model” (B7) at 0.13383. These three factors demand immediate
attention for more effective IoT adoption in the Bangladesh manufacturing
industry. Top management must understand the adoption of new technology
despite the significant initial investment cost. Furthermore, adopting new
technology necessitates changes to the business strategy and a well-planned
action plan. Organizations may be reluctant to embrace such changes due to the
high risk and potential financial losses.

Ranking from fourth to seventh are barriers of intermediate importance.
”Security and privacy concerns” (B5) rank fourth at 0.12269, ”Unawareness of
the advantages of IoT” (B3) is fifth at 0.08749, ”Lack of knowledge of return
on investment (ROI)” (B2) is sixth at 0.07531, and ”Employees’ resistance to
embracing new technologies” (B4) is seventh at 0.05786. While these barriers
are significant, they are relatively less substantial than the top-tier
obstacles. Privacy and security issues are well-documented in existing
literature. A comprehensive understanding of IoT benefits and the conventional
payback model is essential to maximize returns on IoT investments.
Organizations should also help employees understand how their roles will
change with new technology adoption to reduce resistance.

In contrast, the remaining barriers have lower rankings and less impact on IoT
adoption decisions in the manufacturing sector. Ranging from eighth to
eleventh in relative weights, the least essential barriers are: ”Absence of
training initiatives” (B1) with a weight of 0.04476, ”Inadequate technology”
(B8) at 0.04132, ”Unskilled or inexperienced Manpower” (B6) at 0.03783, and
”Absence of Standardization” (B11) at 0.03198. There’s a clear need to develop
the necessary technology for IoT adoption in the Bangladesh manufacturing
sector. Government agencies and policymakers should focus on creating this
essential technology infrastructure. Additionally, specialized training
programs are necessary to equip personnel with the skills required for IoT
adoption.

##  IV Conclusions and Future Works

In this study, we have undertaken a comprehensive examination of the hurdles
manufacturing firms face in embracing IoT technologies. Leveraging the
combined Delphi and FAHP methodology, we have identified these challenges and
ranked them in terms of their relative significance. Our findings have
illuminated the landscape of IoT adoption within the context of the
Bangladeshi manufacturing industry. Our research has yielded several key
insights. Firstly, we have identified and ranked eleven prominent barriers to
IoT adoption in Bangladeshi manufacturing. These findings serve as a valuable
resource for industry practitioners and decision-makers, providing them with a
clear understanding of the challenges they must address. Our study highlights
that ”Lack of top management commitment to implementing new technology” (B10),
”High initial implementation investment costs” (B9), and ”Risks associated
with switching to a new business model” (B7) are the top three hurdles that
require immediate attention. Furthermore, our research contributes to the
growing body of knowledge on IoT adoption in the manufacturing sector,
shedding light on the nuanced challenges specific to the Bangladeshi context.
It underscores the need for tailored strategies and initiatives to facilitate
a smoother transition to IoT technologies.

However, it’s important to acknowledge the study’s limitations. It primarily
focused on barriers, potentially overlooking opportunities and enablers. The
findings are specific to Bangladeshi manufacturing, and generalizability to
other regions and sectors may require further investigation. Additionally, the
FAHP approach, while effective, relies on expert opinions and predefined
criteria, introducing subjectivity.

Future research should concentrate on developing practical strategies to
address these identified barriers, involving in-depth case studies and real-
world implementations. Expanding the scope to different regions and sectors
would offer a more comprehensive view while investigating enablers and success
factors could provide valuable insights. Additionally, ongoing research is
crucial to understand emerging challenges and opportunities with the ever-
evolving IoT landscape.

Our study lays the foundation for comprehending and addressing IoT adoption
challenges in manufacturing firms. As technology continues to advance, our
understanding and strategies must evolve to ensure successful IoT integration
in the manufacturing sector.

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|  HASAN SHAHRIAR was born in Bogura District, Rajshahi Division, Bangladesh
in 2000. He received the B.Sc. degree in industrial and production engineering
from Khulna University of Engineering & Technology in 2023. His academic
journey and experiences at Khulna University Of Engineering And Technology
have equipped him with a solid foundation in Industrial and Production
Engineering principles, a knack for innovation, and a commitment to continuous
learning. His research interest include Integration of Advanced Technologies
in Manufacturing. In an era of rapid technological evolution, he recognizes
the immense potential of technologies such as Industry 4.0, Internet of Things
(IoT), and Artificial Intelligence (AI) in revolutionizing manufacturing
landscapes. Furthermore, he is captivated by the field of Supply Chain
Management and Logistics. With a focus on optimizing logistics networks,
mitigating risks, and enhancing the flow of goods, he envisions contributing
to the development of resilient and agile supply chain strategies.
---|---
|  Md. Saiful Islam has been working as an Assistant Professor in the
Department of Industrial Engineering and Management at Khulna University of
Engineering & Technology (KUET), Bangladesh, since 2017. He received his B.Sc.
Eng. in industrial & production engineering degree in 2013 and M.Sc. Eng. in
industrial engineering and management degree from the Department of Industrial
Engineering and Management, KUET, in 2018. His main research areas are
warehouse, inventory, and supply chain management. He has published more than
10 research articles in the corresponding fields.
---|---
|  Md Abrar Jahin, CSCA™ (Certified Supply Chain Analyst), is an ambitious and
dedicated undergraduate student currently pursuing a B.Sc. in industrial and
production engineering at Khulna University of Engineering & Technology (KUET)
in Bangladesh. With a commitment to academic excellence, Abrar has
consistently demonstrated his passion for learning and achieving remarkable
milestones. His exceptional leadership skills and innovative thinking led him
to win the ”Smart Roads – Winter Road Maintenance Hackathon 2021” and
subsequently earn a three-week research internship at UiT - The Arctic
University of Norway. During this internship, he actively contributed to the
DIT4BEARs Smart Roads project and conducted research in the field of Machine
Learning. In 2021, he received a fully funded scholarship as a ’Research
Intern’ at Okinawa Institute of Science and Technology Graduate University
(OIST) in Japan. Under the supervision of Prof. Miller in the Physics and
Biology Unit, he focused on investigating perfectly conserved sequences in
humans and other species. This valuable experience further enhanced his
research skills and deepened his understanding of comparative genomics.
Building on his previous achievements, he was offered another opportunity in
2023 as a ’Visiting Research Student’ at OIST. He is actively collaborating
with Prof. Miller, Dr. Biller, Dr. Zifcakova, Mr. Parker, and Dr. Pnini,
delving into the evolutionary dynamics of strongly conserved sequences in
vertebrates and insects. Mr. Jahin’s research interests span a wide range of
disciplines, including Sustainable Supply Chain Management (SCM), Human-
Computer Interaction (HCI), Quantum Computing, Deep Learning (DL), Comparative
Genomics, Explainable AI (XAI), AI for Business, Natural Language Processing
(NLP), Image Processing, Reinforcement Learning (RL), and Business Analytics.
With his exceptional academic background, international research experiences,
and diverse research interests, Abrar is poised to make significant
contributions to the fields of engineering, genomics, and technology-driven
solutions for sustainable supply chains.
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|  Istiyaque Ahmed Ridoy, CSCA™ (Certified Supply Chain Analyst), was born in
Dhaka, Bangladesh in 2000. He completed his BBA from the Institute of Business
Administration, University of Dhaka, in 2023. Additionally, he is completing
his Master of Data Science and Analytics from East West University. Mr. Ridoy
worked as a BI Analyst at GoZayaan from 2021 to 2022. He is currently working
in the same company as a Data Analyst. His research interest includes Supply
Chain, Data Analytics, and Statistics.
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|  TARO SUZUKI (Member, ACM) received the B.S. degree from the Science
University of Tokyo in 1987, the M.E. degree from the University of Tsukuba in
1993, and the D.S. degree from the Tokyo University in 1998. He was an
engineer at Mitsubishi Electric Toubu Computer Company during 1987-1991. He
was a Research Associate at the Institute of Electronics and Information
Science and TARA center, University of Tsukuba during 1995-1998, at School of
Information Science, JAIST until 2000, and at Research Institute of Electric
Communication, Tohoku University until 2001, respectively. He was an Assistant
Professor at the School of Computer Science and Engineering, the University of
Aizu until 2006, where he was an Associate Professor until 2012, where he has
been a Senior Associate Professor since 2013. His research interests include
language theory with automata, functional programming, formal methods, and
foundation of deep learning and reinforcement learning.
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|  JUNGPIL SHIN (Senior Member, IEEE) received the B.Sc. degree in computer
science and statistics and the M.Sc. degree in computer science from Pusan
National University, South Korea, in 1990 and 1994, respectively, and the
Ph.D. degree in computer science and communication engineering from Kyushu
University, Japan, in 1999. He was an Associate Professor, a Senior Associate
Professor, and a Full Professor at the School of Computer Science and
Engineering, The University of Aizu, Japan, in 1999, 2004, and 2019,
respectively. He has co-authored more than 300 published papers for widely
cited journals and conferences. His research interests include pattern
recognition, image processing, computer vision, machine learning, human-
computer interaction, non-touch interfaces, human gesture recognition,
automatic control, Parkinson’s disease diagnosis, ADHD diagnosis, user
authentication, machine intelligence, handwriting analysis, recognition, and
synthesis. Prof. Shin is a member of ACM, IEICE, IPSJ, KISS, and KIPS. He
received a scholarship from the Japanese Government (MEXT). He served as a
program chair and as a program committee member for numerous international
conferences. He serves as an Editor for IEEE journals, Sensors (MDPI),
Electronics (MDPI), and Tech Science. He serves as a reviewer for several
major IEEE and SCI journals.
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