# A Study in Analysing the Critical Determinants of Internet of Things (IoT) Based Smart Processing for Sustainable Supply Chain Management

S. Meena, T. Girija

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S. Meena, T. Girija. A Study in Analysing the Critical Determinants of Internet of Things (IoT) Based Smart Processing for Sustainable Supply Chain Management. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.286-295, 10.1007/978-3-031-39811-7_23. hal-05363226

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# A Study in Analysing the Critical Determinants of Internet of Things (IoT) Based Smart Processing for Sustainable Supply Chain Management

S. Meena<fnref n="1" /> and T. Girija<fnref n="2" />✉ ID

**Abstract.** An important part of a successful supply chain is the end user, as customer satisfaction is at the heart of every chain. Due to the interconnected nature of the processes, a change in one link will affect the others. Communication and information flow between processes is both dynamic and constantly changing. There is no one-size-fits-all supply chain model as supply chain architecture must be customized for each organization or industry. Every supply chain should be structured according to these two principles, but there is no such thing as a global supply chain. A comprehensive analysis should include all relevant features or elements. Integration requires the creation and processing of all necessary information for the coordination of all parties involved. The Internet of Things (IoT) and related advancements such as cloud computing, basic digital, smart workstations and artificial intelligence have seen unprecedented changes in recent years, especially within the enterprise. The benefits of integrating digital technology throughout the supply chain are easy to see: lower costs, operational flexibility, more accurate forecasting, customer satisfaction, etc. Due to these challenges, companies must plan to adopt digital strategies if they want to maintain their edge in the market. Solving the problems created by modern technological developments requires significant investments in time, money and resources. Before deciding to use advanced technology in a smart and connected supply chain, a company must first demonstrate its readiness by carefully analyzing the supply chain for each connected component. In fact, the commitment to continuous improvement has digitized the supply chain and evolved into a system of intelligent and integrated communication. While the technical barriers to automated monitoring are slowly disappearing, leading to significant cost reductions, the uniqueness of IoT lies in its widespread use. The concept of “Internet of Things” (IoT) involves connecting devices so that they can share data, learn from each other, analyze data and evaluate important decisions. Supply chain efficiency is a major issue in the manufacturing and logistics industry. The main objective of this study is to investigate the key factors affecting IoT-based smart processing for environmentally responsible supply chain. A survey part is conducted with closed questionnaires, this method

<footnote n="1">¹ Department of Commerce, MGR Educational and Research Institute, Chennai, India
meena.comm@drmgrdu.ac.in</footnote>

<footnote n="2">² Department of Management Studies, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
girijat@ssn.edu.in</footnote>
was used to obtain data from a group consisting of network administrators, senior managers, network security experts, system analysts, etc. We were able to collect data from 129 randomly selected participants.

**Keywords:** Sustainable supply chain · Internet of Things · Supply 4.0 · Correlation analysis

# 1 Introduction

Product creation, manufacturing, shipping, and arranging for storage are all parts of the supply chain. An integral part of every successful supply chain is its end user, since customer happiness is the fundamental goal of any given chain. Due to the interconnected nature of the processes, a shift in any one link will affect the others. Both the connections and the information flow between processes are dynamic and ever-changing. Since the supply chain architecture must be modified for each individual organisation or industry, there is no universal supply chain model. Every supply chain should be structured according to these two principles, but there is no such thing as a universal supply chain. A complete analysis has to include in all relevant processes or elements. Knowledge is a key factor in achieving success. Aligning all parties engaged in the integration requires the creation and processing of all necessary information. Due to the Internet of Things (IoT) and related breakthroughs such as cloud computing, digital twins, smart factories, and artificial intelligence, the industrial sector in particular has witnessed unprecedented transformations in recent years (Dolgui 2019).

It's easy to see the benefits of integrating digital technologies across the supply chain: lower costs, more operational flexibility, more accurate forecasts, happier customers, etc. Because of these challenges, firms must strictly regulate the introduction of digital enablers if they want to keep their advantage in the market. But to reap the benefits of digitalization, one must consider the financial outlay that will be necessary. Tackling the challenges brought on by today's technological advances calls for a significant investment of time, money, and resources. Before deciding whether or not to implement state-of-the-art technology into an intelligent and connected supply chain, a firm must first demonstrate its readiness to do so by producing in-depth research on each of the supply chain's integrating components (Kouhizadeh 2018).

Policies and processes must be revised, qualified staff members must be recruited, and the organization's structure must be reorganised to facilitate ongoing progress. After all, the supply chain has been digitalized and developed into a system of intelligent and integrated links due to a commitment to continuous improvement. Since technology barriers to automated monitoring have been progressively eroding, resulting in substantially reduced costs, IoT's uniqueness lies in its potential for widespread application (Alcácer 2019). The concept of the "Internet of Things" (IoT) calls for the interconnection of devices so that they may share information, learn from one another, analyse data, and weigh in on important decisions. The efficiency of the supply chain is a primary concern for manufacturing and logistics companies.

Industry 4.0, which is based on cyber-physical systems, has opened up new avenues for production, such as the use of computer-integrated manufacturing, lean management,
and cellular manufacturing (CPSs). Cyber systems are digital setups where computers and networks are integrated with actual physical procedures. These intelligent technology can provide quick solutions to issues like shifting product demands and line breakdowns. Industry 4.0 might be a game-changer for the region's manufacturing industry, according to recent advancements in SCs. The digitization-first mentality of Industry 4.0 encourages SCs to become more customer-centric in their approaches to service (Fragapane 2020).

A logistical basis for Supply 4.0 may become obvious when discussing visibility, despite the fact that it is a relatively new notion. The ability to keep tabs on participants within SC organisations was a crucial consideration. However, the study results demonstrate that there is no common understanding of what transparency actually means. Instead, SC visibility based on the Internet of Things (IoT) places a premium on an organization's data collecting and analysis skills in order to guarantee that its strategy and goals are compatible. Operations and inventory viewpoints explain how the SC's limited ability to progress successfully results from a lack of information across upstream and downstream activities. Furthermore, a lot of research on SC visibility has looked at how inventory, quality, lead time, sales, and costs may be affected by a company's financial and operational performance.

Several research projects have examined the effects of the Internet of Things on topics like sustainability, organisational structure, lean manufacturing, product development, and strategic management. Most of these studies have looked at the pros and cons of IoT in the context of Industry 4.0, analysing its potential for facilitating or hindering things like adaptability, openness, information sharing, connectivity, traceability, and tracking. Despite the extensive study of these topics, academics have concluded that the impact of the Internet of Things (IoT) on supply chain visibility (SCV) has received insufficient attention (Ivanov 2019).

Some empirical investigations on the effects of IoT on SCV do exist, however the great majority of the published literature on the topic is theoretical. The technologies that will propel SCV forward have yet to be determined. However, studies have attempted to identify SCV features by conducting a comprehensive literature review, rather than attempting to synthesise the current literature on the implications of IoT on SCV. Due to the study's limited scope, additional research into the interrelationships and patterns between IoT and SCV was necessary. Moreover, while Supply 4.0 has received a lot of attention from academics, a thorough understanding of the procedures required for effective transformation is still absent. The lack of agreement makes it simpler for practitioners to implement Supply 4.0 strategies for achieving SCV (Golan 2020). Furthermore, although the advantages and downsides of IoT have been explored at length theoretically, further study is needed in the domain of supply 4.0 to deliver a solid and trustworthy solution for the practical application of supply 4.0 in the context of reaching SCV.

In order to create an intelligent, interconnected whole, smart supply chains (SSC) will use cutting-edge technology, most notably developing ICT, to link the processes in a SC's numerous partners (Wu 2016). Researchers have been studying the effects of Industry 4.0's various new technologies on supply chains for some time now. Internet of Things (IoT) on SCM, IoT integrated sustainable SC, and big data analytics in SCM
were among the areas of inquiry (Nguyen et al. 2018). However, with a few notable exceptions, these publications rely almost exclusively on scholarly works. ICT for multimodal transportation, for example, has seen recent developments based on large EU framework projects, which have shown that national research projects represent the current major efforts and reflect the state of the art in research and development, in contrast to the rather dated and limited perspective of journal publications. If you browse through these sources, you’ll obtain a thorough picture of the present state of research, development, and application of cutting-edge new technologies, as well as a view into the future and a roadmap on how to get there. This is owing to the fact that multiple sources, such as scholarly literature, contemporary, cutting-edge national research initiatives, and national policies, all approach the subject from different directions and focus distinct aspects (Apiyo 2018).

Despite the extensive employment of the word “smart supply chain” in the academic literature, the notions linked with it are typically inadequately defined and inconsistently implemented. This phrase is often used to characterise the many stages and drivers in the supply chain, as well as the concept’s numerous applications, attributes, and underlying technologies.

Smart logistics, transportation, and storage are only a few examples of what drive the supply chain (Liu et al. 2018). In this category, you’ll find the vast bulk of books and scholarly publications.

The movement of information and the presence of digital properties: Streaming data and historical data repositories are both employed with various analytics.

Advanced Technology; Technological Features When SC is combined with current technology like the Internet of Things (IoT) and big data, an SSCM is created.

Characteristics: IBM advises that an SSC have several qualities such as flexibility, instrumentation, networking, intelligence, automation, integration, and innovation.

## 2 Review of Literature

Developing and producing manufacturing SC in today’s advanced manufacturing settings like Industry 4.0 calls for an awareness of and comfort with cutting-edge technology, as well as an appreciation for the diversity of available markets and the individual needs of consumers. The above-mentioned literature study reveals that most developing technologies have an impact on design, strategic sourcing, and network decision making. This includes 3D printing for sharing designs and outsourcing manufacture, RFID for optimising ERP system operations, and Industry 4.0 for a smart wood supply chain. Many current strategic initiatives revolve around data-driven phenomena like big data, blockchain for the food supply chain, and 3D printing for decentralised networks (Javaid 2020).

The Internet of Things has the potential to provide real-time visibility, which might improve efficiency, environmental impact, and data exchange. Through the use of Internet of Things (IoT) technology and data integration, the project (NSF-1822092 2018) intends to simplify the full “Farm to Fork” supply chain in the United States and improve food quality, safety, and traceability (Fernández-Caramés 2019).

A smart factory is one that increases automation, decreases labour costs, and responds quickly to shifts in demand or disruptions in production. When utilised to novel materials,
large-scale items, integrated and dispersed manufacturing, and supply chain networks, 3D printing's benefits—including lower prices and production times—will expand considerably. The IBM IoT platform may help businesses in many ways, including with demand, configuration, quality control, and corporate asset management through the use of connected devices, analytics, mobility, and the cloud. As ideas can be shared and production can be outsourced, the process of generating and tweaking products is sped up and simplified, while prices are decreased. 3D printing allows for all of this to be done.

Rapid reaction to consumer needs is achieved via decentralised supply chains. One NSF study is on establishing a realistic design set that enhances resilience and CPS incentive schemes, which would lead to a shorter lead time for refilling, which in turn would minimise cycle inventory. Through the implementation of GE's iFIX industrial automation software, Subaru's Indiana assembly facility has become the quickest automaker in the country (Li 2020).

Increased supply chain resilience and risk management may be feasible with the use of information and communication technology and adaptive supply network design. The introduction of Industry 4.0's smart technologies into SCs has the potential to improve their resilience. According to the report, if you want to establish a solid, cutting-edge manufacturing SC, you need to focus on a number of distinct areas. Effects of COVID-19 on SC are shown, and the relevance of Industry 4.0 and digitalization in disruption risk management are studied. Damage from the outbreak reveals just how vulnerable global supply networks (Ben-Daya 2019).

French railway operator SNCF utilises IBM Watson IoT to increase efficiency and protection on the rails. Cascades Tissue Group now has access to GE Digital's problem-detection capabilities, allowing them to decide swiftly if there are any difficulties. Using the course, organisations have a higher chance of eliminating unexpected downtime in production, optimising the efficiency of their operations, and making educated decisions based on their data. Amazon's fulfilment sites are more efficient and safe thanks to the employment of robot guards. The precision and efficiency of warehouse operations may be considerably improved by employing IoT technology.

## 3 Research Methodology

The main purpose of this research is to analyse the critical aspects that influence IoT-based smart processing for environmentally responsible supply chain management. Researchers will find it easier to get writers involved in the study by having them gather, record, and analyse numerical data using a quantitative research approach. Research is conducted in part via the use of closed-ended questionnaires, which are delivered to a representative sample of the population in order to collect information about their opinions. Data was collected using a convenience sampling strategy from a population consisting mostly of network administrators, upper-level management, cyber security professionals, system analysts, etc. We were able to collect data from 129 participants who were randomly chosen. The most common analyses were Chi-squared tests for associations. When analysing the data, the researchers employed SPSS. To further their understanding of the topic, the researchers also rely on secondary data sources, such as
articles from online libraries like EBSCO, Google Scholar, etc. that have been indexed
by Scopus. In the next sections of the study article, the analysis and interpretation will
be presented.

**3.1 Research Hypothesis**

Null hypothesis 1: There is no critical variances among better visibility through IoT and sustainable supply chain management.

Null hypothesis 2: There is no critical variances among fast responsiveness to market through IoT and sustainable supply chain management.

Null hypothesis 3: There is no critical variances among enhanced resilience through IoT and sustainable supply chain management.

# 4 Analysis and Discussion

In this section, we provide the results of our statistical analysis of the responses. Closed-ended questions were used to obtain data from about 129 participants. Percentage rate analysis is used to look at the respondent's demographic information.

**4.1 Percentage Analysis**

Table 1 shows that males made up 57.4% of the sample and females 42.6%. About a third of the respondents (28.5%) were young adults (25 years old), nearly a third (33.3%) were 26–35 years old, about 15 percent (16%) were 36–45 years old, and nearly a quarter (24.7%) were 46–55 years old.

In addition, 17.8% worked as assistant managers, and 34.9% were supply chain
executives. About half of the respondents were new to their current employers, while
another 25% were in entry-level positions, 16.3% were in middle management, and
13.2% were at the executive level. Only 38.8% of respondents were raised in blended
families, while 61.2% were from nuclear households. Finally, 27.1% had less than three
years of experience, 24.8% had three to five years, 18.6% had five to ten years, 20.9%
had ten to fifteen years, and 8.5% had more than fifteen years.

**4.2 Correlation Analysis**

The next part of the analysis is involved in understand the nature of association between
the independent variables viz., Confidentiality Impact; Data Storage and Authentica-
tion with the dependent variable: Blockchain based cybersecurity. The research mainly
focuses in understanding the main factors in using the blockchain technology in improv-
ing cybersecurity current computing ecosystem. For this purpose, Karl Pearson coeffi-
cient of correlation analyses is performed, the coefficient lies between -1 to +1, with
-1 being stated to be highly negative relation between the variable and vice versa.

From Table 2, it is noted that all the variables possess higher positive correlation
towards the dependent variable. It is noted that highest correlation is between fast respon-
siveness to the markets with the use of IoT and Sustainable supply chain management,
<table><caption>**Table 1.** Percentage rate analysis</caption><thead><tr><th></th><th>Frequency</th><th>Percent</th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td></tr><tr><td>Male</td><td>74</td><td>57.4</td></tr><tr><td>Female</td><td>55</td><td>42.6</td></tr><tr><td>Age Groups</td><td>Frequency</td><td>Percent</td></tr><tr><td>Less than 25 years</td><td>37</td><td>28.7</td></tr><tr><td>26 - 35 years</td><td>43</td><td>33.3</td></tr><tr><td>36 - 45 years</td><td>18</td><td>14</td></tr><tr><td>46 - 55 years</td><td>31</td><td>24</td></tr><tr><td>Current Role</td><td>Frequency</td><td>Percent</td></tr><tr><td>Supply chain executive</td><td>61</td><td>47.3</td></tr><tr><td>Senior Supervisor</td><td>45</td><td>34.9</td></tr><tr><td>Assistant Manager</td><td>23</td><td>17.8</td></tr><tr><td>Type of Family</td><td>Frequency</td><td>Percent</td></tr><tr><td>Nuclear Family</td><td>79</td><td>61.2</td></tr><tr><td>Joint Family</td><td>50</td><td>38.8</td></tr><tr><td>Level of Management</td><td>Frequency</td><td>Percent</td></tr><tr><td>Fresher</td><td>58</td><td>45</td></tr><tr><td>Lower level management</td><td>33</td><td>25.6</td></tr><tr><td>Middle level management</td><td>21</td><td>16.3</td></tr><tr><td>Top level management</td><td>17</td><td>13.2</td></tr><tr><td>Work experience</td><td>Frequency</td><td>Percent</td></tr><tr><td>Less than 3 years</td><td>35</td><td>27.1</td></tr><tr><td>3 - 5 years</td><td>32</td><td>24.8</td></tr><tr><td>5 - 10 years</td><td>24</td><td>18.6</td></tr><tr><td>10 - 15 years</td><td>27</td><td>20.9</td></tr><tr><td>Above 15 years</td><td>11</td><td>8.5</td></tr><tr><td>Total</td><td>129</td><td>100</td></tr></tbody></table>

with value of +0.870, on the other hand, the correlation between better visibility and sustainable supply chain management is at +0.869 and lastly the correlation between enhanced resilience and sustainable supply chain is at +0.838.

## 4.3 Chi Square Test

This portion of the analysis states the test of hypothesis.

1st hypothesis: There is no critical variances among better visibility through IoT and sustainable supply chain management.
<table><caption>**Table 2.** Coefficient of correlation</caption><thead><tr><th>Coefficient</th><th>Better visibility</th><th>Fast responsiveness</th><th>Enhanced resilience</th><th>Sustainable supply chain</th></tr></thead><tbody><tr><td>Better visibility</td><td>1</td><td>0.892</td><td>0.829</td><td>0.869</td></tr><tr><td>Fast responsiveness</td><td>0.892</td><td>1</td><td>0.857</td><td>0.870</td></tr><tr><td>Enhanced resilience</td><td>0.829</td><td>0.857</td><td>1</td><td>0.838</td></tr><tr><td>Sustainable supply chain</td><td>0.869</td><td>0.870</td><td>0.838</td><td>1</td></tr></tbody></table>

<table><caption>**Table 3.** Analysis between better visibility through IoT and sustainable supply chain management</caption><thead><tr><th>Better visibility</th><th>Data</th><th>P sig</th></tr></thead><tbody><tr><td>Chi-Square</td><td>207.07</td><td>0.00</td></tr><tr><td>LR</td><td>182.72</td><td>0.00</td></tr><tr><td>Linear-by-Linear</td><td>97.00</td><td>0.00</td></tr></tbody></table>

The findings presented in Table 3 indicate that the chi-squared value is 207.07, and the p-value is 0.00; hence, the p-value is lower than the threshold of 0.05 that denotes statistical significance. As a result, we conclude that the alternative is more plausible and hence reject the null hypothesis. Therefore, there is a major gap between environmentally responsible supply chain management and improved visibility achieved through the Internet of Things.

2nd hypothesis: There is no critical variances among fast responsiveness to market through IoT and sustainable supply chain management.

<table><caption>**Table 4.** Analysis between Fast responsiveness and sustainable supply chain management</caption><thead><tr><th>Fast responsiveness</th><th>Data</th><th>P sig</th></tr></thead><tbody><tr><td>Chi-Square</td><td>252.31</td><td>0.00</td></tr><tr><td>LR</td><td>163.32</td><td>0.00</td></tr><tr><td>Linear-by-Linear</td><td>85.42</td><td>0.00</td></tr></tbody></table>

The findings presented in Table 4 indicate that the chi-squared value is 252.31, and the p-value is 0.00; hence, the p-value is less than 0.05, which indicates that the data are statistically significant. Based on this information, we can deduce that the alternative hypothesis has a higher probability of being right than the null hypothesis does. Therefore, it is possible to assert that environmentally responsible supply chain
management and rapid market response made possible by IoT are essentially distinct from one another.

3rd hypothesis: There is no critical variances among enhanced resilience through IoT and sustainable supply chain management.

<table><caption>**Table 5.** Analysis between enhanced resilience and sustainable supply chain management</caption><thead><tr><td>Enhanced resilience</td><td>Data</td><td>P sig</td></tr></thead><tbody><tr><td>Chi-Square</td><td>251.00</td><td>0.00</td></tr><tr><td>LR</td><td>159.46</td><td>0.00</td></tr><tr><td>Linear-by-Linear</td><td>76.51</td><td>0.00</td></tr></tbody></table>

The findings presented in Table 5 indicate that the chi-squared value is 251, and the p-value is 0.00; hence, the p-value is lower than the threshold of 0.05 required for statistical significance. As a result, we conclude that the alternative is more plausible and hence reject the null hypothesis. Therefore, it is abundantly evident that there are substantial distinctions between environmentally responsible management of supply chains and enhanced resiliency achieved through the Internet of Things.

# 5 Conclusion

Accessibility 4.0 is a relatively new concept, but when it comes to visibility, the regulatory framework can be clear. Participatory monitoring capacity is a major problem in SC institutions. However, the study found that there is no agreed definition of transparency. In contrast, a SC identity based on the Internet of Things (IoT) focuses more on data collection and analytical capabilities of organizations to ensure that strategies and goals are aligned. An explanation of how a lack of understanding of upstream and downstream processes hinders SC's ability to continue successfully comes from a process and storage perspective. In addition, a large body of literature on SC identification examines how a firm's financial and operational performance can affect inventory, quality, lead time, sales, and costs (Birkel 2019).

Sustainability, organizational structure, green manufacturing, product development and strategic management are among the areas that many studies of IoT's impact have focused on. In the context of Industry 4.0, most of these studies have focused on the potential advantages and disadvantages of IoT in terms of scalability, mobility, data sharing, connectivity, detection and tracking. Despite extensive research in these areas, researchers have found that the impact of the Internet of Things (IoT) on supply chain visibility (SCV) has not been sufficiently investigated.

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