1. Introduction
Internet of Things (IoT)-based real-time data collection, monitoring, and administration is transforming industries and enhancing efficiency. IoT enhances supply chain management. Inventory, shipment statuses, and disruption forecasts may be managed in real time using IoT for supply chain monitoring, enhancing efficiency and cutting costs. Organisations can streamline their supply chains and enhance procurement and delivery using real-time data from IoT.
Global supply chains demand more dynamic and responsive management of enormous supplier, manufacturer, and distribution networks. Traditional supply chains, which rely retroactive data, struggle with variable demand, delays, and unexpected disruptions. The supply chain is connected and data flows effortlessly with IoT, providing visibility and agility. IoT helps stakeholders manage stocks, commodities, and bottlenecks before they disrupt operations.
Figure 1 Internet of Things (IoT) for Real-Time Supply Chain Monitoring
IoT’s real-time nature lets companies make swift changes, ensuring timely delivery and reducing disruptions. Control and visibility improve operational efficiency and customer happiness by ensuring reliable delivery. This research examines real-time supply chain monitoring using IoT. This study will investigate IoT’s potential benefits, drawbacks, and factors affecting its deployment across industries to establish its mainstream acceptance readiness and how it can change global supply chain management.
2. Research Problem
Traditional supply chain management creates inefficiencies, delays, and greater operational costs by responding to issues. Digital transformation efforts have not increased real-time monitoring technology use in numerous industries. IoT can transform supply chains with real-time data collecting and analysis, but its benefits, drawbacks, and adoption factors remain unclear. This research will examine how real-time supply chain monitoring using IoT may increase operational efficiency, identify obstacles, and evaluate organisational readiness.
3. Research Aim and Objectives
The aim of this research is to investigate the adoption of Live supply chain monitoring using IoT. The research will examine the pros, cons, and variables that affect IoT adoption in supply chains across sectors.
Objectives:
- To assess the potential benefits of implementing IoT for real-time supply chain monitoring.
- To explore the challenges faced by organizations in adopting IoT technology.
- To identify key factors influencing the successful adoption of IoT for supply chain monitoring.
- To examine industry readiness for adopting IoT solutions in supply chain management.
- To propose strategies for overcoming the challenges in adopting IoT for real-time monitoring.
4. Research Questions
- What are the key benefits of adopting IoT for real-time supply chain monitoring?
- What are the challenges organizations face in implementing IoT technology in their supply chains?
- What factors influence the successful adoption of IoT in supply chain management?
- How ready are organizations for adopting IoT solutions in their supply chain operations?
- What strategies can help organizations overcome the barriers to IoT adoption for real-time monitoring?
5. Literature Review
This section contains literature on real-time supply chain monitoring using the Internet of Things (IoT), including key concepts, trends, benefits, drawbacks, and issues. The session addresses entrepreneurial ecosystems, startup scaling issues, and ecosystem factors affecting logistics and supply chain IoT integration.
5.1 Concept of Entrepreneurial Ecosystems
Entrepreneurial ecosystems foster innovation, growth, and success among organisations, entrepreneurs, investors, and others. Supply chain management IoT adoption relies on these ecosystems. Smith and Johnson (2022) argue technical infrastructure, legislative frameworks, and industry competitiveness effect supply chain IoT integration. Strong IT infrastructure makes it easier for enterprises to implement innovative IoT solutions.
Figure 2 Entrepreneurial Ecosystems
(Source: https://www.linkedin.com/pulse/entrepreneurial-ecosystems-unpopular-take-max-nathan)
Brown et al. (2021) observed market competition boosts IoT adoption. Competitive companies embrace IoT technology to boost operational efficiency and real-time decision-making. Some governments’ strict data protection laws may slow supply chain IoT growth. For these traits, IoT integration needs an entrepreneurial atmosphere. According to Lee and Park (2023), a collaborative ecosystem of IoT technology providers, logistics businesses, and regulatory bodies may expedite IoT adoption by providing resources, knowledge sharing, and incentives for enterprises.
5.2 Startups and Scaling
IoT may help logistics and supply chain firms grow. According to Nguyen and Tran (2020), IoT startups scale better. IoT lets startups track inventory, shipments, and operational performance to grow quickly. Sensors and GPS trackers help companies minimise delays and product loss throughout the supply chain.
Figure 3 Startups and Scaling
Logistics and supply systems were vulnerable globally throughout the pandemic. Many assumed logistics was commoditised, therefore companies outsourced it and attempted to reduce shipping and storage costs in their financial statements. When shoppers saw bare grocery shelves or closed gas stations in the UK, logistics became a worldwide concern. Businesses reconsidered logistics. Global firms in 18 nations had 83% more managers aware of transportation blockades, production shutdowns, and raw material shortages after the outbreak.
The sector received record funding in 2021 due to this awareness development. Our analysis found that logistics startup investment rounds increased somewhat from 2020, suggesting average growth. Roberts (2022) states that “IoT provides an invaluable solution to startups looking to enhance supply chain visibility, making it easier for them to scale by optimising logistics operations.” It helps businesses manage resources to meet increased demand without compromising quality or delivery schedules. IoT data enables businesses adapt to demand variations, supply restrictions, and delays with real-time choices.
However, IoT scaling is hard. IoT solutions need a hefty startup investment for entrepreneurs. Miller and Richards (2021) state, “The upfront cost of IoT devices, infrastructure, and software can be prohibitive for startups that operate on limited budgets.” Startups without expertise or funding may struggle to integrate IoT technology into their supply chains, hurting growth. Companies require innovative technology and skilled staff to install and operate IoT devices.
5.3 Ecosystem Factors Affecting Scaling
IoT-enabled supply chains evolve owing to ecosystem dynamics that impact deployment and integration. Technological resources impact supply chain IoT scaling, says McKinsey (2020). Businesses need sensors, data analytics, and cloud computing to leverage IoT benefits. Insufficient resources may slow IoT adoption and scalability. Supply chain IoT scalability also requires stakeholder collaboration. Zhang and Wang (2021) discovered that suppliers, manufacturers, logistics providers, and IoT technology providers must collaborate to incorporate IoT into supply chains. Collaboration is needed to create a seamless environment where data flows freely and actions are coordinated. Inefficiencies from data silos and poor communication may diminish IoT adoption benefits.
Supply chain IoT solutions grow with communication and regulatory compliance. Jones et al. (2022) concluded that companies must meet local and international data privacy, cybersecurity, and logistics management rules. In areas where compliance is critical, missing these regulatory standards may delay IoT system installation. IoT adoption might be affected by supply chain competition. Lee (2023) observed that highly competitive companies use IoT technology to differentiate themselves by delivering more efficient and transparent services to customers.
Nguyen & Nguyen (2022) recommend investing in innovation for IoT-enabled supply chain scalability. Innovative IoT solution development is needed to meet market demands. Businesses need IoT systems that can adapt to AI and ML to compete. Financial investment impacts IoT-enabled supply chain scalability. To build and expand IoT systems, Kumar et al. (2021) revealed enterprises must invest considerably. IoT equipment, software, and infrastructure must be purchased and workers trained.
6. Research Methodology
This section shows how to gather and analyse IoT adoption data for real-time supply chain monitoring. Mixing qualitative and quantitative approaches, IoT adoption in supply chain management will be explored. This method is used to study organisations’ IoT adoption, challenges, and success.
6.1 Research Design
This study employed mixed approaches. This strategy will help the study comprehend IoT supply chain monitoring by collecting qualitative and quantitative data. The qualitative component will include semi-structured interviews with industry experts, supply chain managers, and key stakeholders in IoT-adopting companies. These interviews will discuss supply chain IoT adoption challenges, incentives, and motivations. The quantitative component will poll companies across industries who utilise or are considering IoT for supply chain monitoring. Surveys will assess IoT adoption, technology use, and implementation challenges. The research examines supply chain management IoT adoption motives and barriers using qualitative and quantitative methodologies and quantifies its prevalence and impact across industries.
Figure 4 Research Design
(Source: https://maze.co/blog/mixed-methods-research/)
6.2 Sampling
To complete the supply chain IoT use picture, the survey will target several organisations across industries. IoT technology affects logistics, manufacturing, retail, and agriculture, thus they’ll be addressed. Purposive sampling will choose IoT-enabled supply chain companies in these sectors. The research will only include companies with real-time IoT monitoring skills due to purposive selection. Sample large and startup firms. To compare IoT adoption, the research must cover small, medium, and large enterprises. It also reveals organisational size-specific challenges and opportunities. RFID, GPS, real-time inventory, and predictive analytics companies will be sampled.
6.3 Data Collection
Semi-structured interviews and surveys will collect data. Industry experts, supply chain managers, and other key players from selected companies will be interviewed semi-structured. These qualitative interviews will show organisations’ IoT supply chain monitoring experiences. Interview questions will include IoT adoption reasons, implementation issues, perceived benefits, supply chain efficiency, and decision-making implications. Based on availability and location, participants will be interviewed in-person or by video conference. Semi-structured questions enable respondents to share their experiences and ideas that structured questions may not have anticipated. Interviews and surveys will be delivered to additional supply chain specialists. The surveys will quantify IoT usage, technology types, and adoption barriers. The survey will include closed- and open-ended questions. Open-ended questions allow respondents to share their experiences, while closed-ended ones collect measurable data like the percentage of companies using IoT technology. Surveys will be sent via email and other media to a big audience. Surveying companies who use or are considering IoT for supply chain monitoring. Brief, easy-to-complete surveys will enhance participation and response rate.
6.4 Data Analysis
Thematic analysis will be done on semi-structured interviews. Thematic analysis classifies data by areas. The researcher may then analyse supply chain IoT adoption’s overall challenges, benefits, and factors and individuals’ experiences. Tag data to find repeated trends, which will be examined to learn about organisations’ real-time IoT monitoring experiences. I will use descriptive and inferential statistics with survey data. Descriptive statistics will describe industry IoT adoption, the most often used technology, and the most common issues. Inferential statistics will examine relationships between business size, IoT adoption, industry type, and IoT benefits. Regression and chi-square testing reveal data patterns and links. Qualitative and quantitative data will highlight IoT adoption factors and supply chain management implications.
6.5 Ethical Considerations
Participants’ rights and privacy will be protected by ethical research regulations. Knowing the research’s purpose, data kinds, and use, participants will provide informed consent. Participants will know they may exit the study at any time without consequence. All data will be anonymised to protect participants and organisations. Firm names and personal data will be removed before examination. This study will protect and limit research team data access. Participants will also be assured that their responses will be kept private and used just for this research. Data ethics will ensure transparent and unbiased data collection and analysis throughout the project. Participants may review and approve study findings before publication.
7. Gantt Chart
The Gantt chart below outlines the timeline for the research project.
Figure 5 Gantt chart for research timeline
(Source: By Author)
8. Expected Outcomes
This research should measure IoT utilisation for real-time supply chain monitoring across industries. To determine IoT integration’s pros, cons, and success factors, the study uses qualitative and quantitative data. We’ll see how IoT technologies affect inventory management, shipping tracking, and demand forecasting. The report should also identify IoT adoption barriers such high upfront costs, technical limitations, and change resistance. Understanding these issues helps companies overcome obstacles and exploit IoT technology potential. The study will provide infrastructure, talent, and data security advice to IoT adopters. The expected outcomes will inform large organisations and startups about IoT’s supply chain ramifications. These tips will help decision-makers adopt IoT effectively, boosting supply chain performance. This research will raise awareness of IoT’s supply chain management role.
9. Significance of the Study
This study might greatly increase understanding of IoT adoption in supply chain management, a key area of logistics and industrial technology innovation. As global supply chains become increasingly complex, real-time data-driven solutions are required to boost efficiency, save costs, and delight consumers. This research will highlight IoT adoption and supply chain impacts. This study will connect IoT’s theoretical benefits with its practical usage. IoT technology will benefit many firms that are still adopting it. The research will identify best practices and solve adoption barriers to help enterprises deploy IoT. Policymakers and technology developers will also learn about supply chain IoT implementation challenges and restrictions from this study. The findings may assist politicians create enabling frameworks and regulations, and developers may tailor their solutions to industry needs to boost IoT adoption in global supply chains.
10. Limitations
This study contains limitations like any research. Organisations that use IoT technology are hard to reach. Some companies may not share their IoT implementation tales due to confidentiality, competitive advantage, or data standardisation. This may make it hard to find real-world samples, especially from smaller companies without IoT capabilities. Self-reported survey data may bias studies. Survey respondents with positive IoT experiences may overstate its benefits or minimise its drawbacks. Organisational representatives may potentially affect data by stating their company’s goals. The research will verify responses with industry experts and third-party reports. Finally, IoT technology’s constant growth makes long-term adoption trends hard to predict. IoT systems are updated often, and supply chain processes use AI and blockchain. This continual innovation makes long-term IoT adoption prediction difficult. The study will illuminate IoT use, but further research is required to enhance it.