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Real-Time Supply Chain Visibility: Leveraging IoT and Data Analytics for Real-Time Monitoring and Insights
Latest   Machine Learning

Real-Time Supply Chain Visibility: Leveraging IoT and Data Analytics for Real-Time Monitoring and Insights

Last Updated on April 22, 2024 by Editorial Team

Author(s): Navruzbek Ibadullaev

Originally published on Towards AI.

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The current business climate, which is dynamic and global, requires visibility now across the whole supply chain, not a preferred option but mandatory. Buyers today require not only faster delivery options but also transparency and always tracking updates. The businesses, on top of it, should be responsive and flexible about disruption, ensure the right inventory, and go for evidence-based decision-making to have an edge over the competitors. This is the point in time where there are good results from the integration of IoT and data analytics when the strategy of the real-time supply chain becomes prominent.

The Necessity of Real-Time Visibility in Supply Chain Management

In the domain of supply chain management, previously data analysis has been carried out in piecemeal ways, such as those involving disconnected updates and frequent disruptions, which result in key points of congestion, low-efficiency levels, and a lack of supervision. Nevertheless, sometimes the most crucial discovery for a company is discovered through a study, and in this case, it was with KPMG in the year 2023, when there was a report on the significance of the “control tower” perspective. This site highlights the widespread transmission of data in all the links of the supply chain, a standpoint that is pretty much distinct lot from the old partial practice (KPMG, n.d.).

This paradigm shift towards real-time visibility brings forth a host of advantages for businesses, empowering them in various aspects of their operations:

Proactive Disruption Management: Real-time data becomes crew tools through which the crew can recognize the early warning of faults. If it is mind power disruption we are talking about, or unforeseen disruptions caused by port delays or outrage of crucial raw malfunction and materials the companies equipped with real-time insights are in a better position to mitigate this and ensure punctual delivery of their consumer goods (Dey, 2023).

Optimized Inventory Control: The ongoing visibility situation for all inventory in the supply chain indeed yields the ultimate privilege for businesses to maneuver the sway between out-of-stock or surplus in stocking. Through the fine line between those, companies can succeed at carrying cost reduction, increasing cash flow curving, and raising market satisfaction (Jones, 2023).

Increase Customer Satisfaction: Tracking systems are a way to track orders for companies in real-time, meaning that shoppers are getting specific and prompt updates concerning the status of their orders. This figure is especially important today when the tendency toward transparency is growing. This methodology gives an implication of trust and loyalty among customers, which is in line with the constant rise in customers’ demand for transparency and reliability (Jones, 2023).

Enhanced Partner Collaboration: Sharing real-time data in a supply chain will offer collaboration opportunities with various participants such as suppliers, distributors, and logistics service providers throughout the supply chain. These tighter connections, which improve the operation speed, flow, and general efficiency, underline the nature that is present in the supply chain, which is that its constituents exist in a mutual pursuit of the best possible result.

The Role of IoT and Data Analytics

The creativity of IoT rests behind the goal of accomplishing real-time supply chain visibility. Sensors and devices are embedded throughout the supply chain network, collecting data on various parameters such as:

  • Tracking of goods according to their location (which can be achieved using GPS or RFID tags)
  • Environmental factors (heat, humidity)
  • Inventory levels
  • Equipment performance

Then, this real-time data is sent to the central hub where it is collected, processed, and translated to business articulate information using data analytics. Advanced analytics techniques, including machine learning (ML) and artificial intelligence (AI), can further unlock valuable insights from the data, such as:

Predictive Maintenance: The companies can know beforehand when a particular equipment will start failing and maintenance can be carried out on time through the analysis of equipment sensor data. This curls down on time and production risk that in turn allows for the maximum resource allocation optimization, streamlining of operations efficiency, and equipment reliability (Owczarek, 2023).

Demand Forecasting: Using sophisticated data analytics businesses will be able to run through historical sales performance and extrapolate this behavior to define the demand for the future and resolve market complexities. It shortens information-based decision-making throughout planning procedures of production, inventory management, and allocation of resources which helps to fulfill consumer needs to a greater extent and, thus, improve competitiveness globally.

Route Optimization: Real-time traffic data and tracking of locations allow companies to find the best routes, contributing to cutting loss of time and costs for transportation. The way the operator adjusts routes on the base of traffic situations and other factors leads to an increase in reliability, lower costs, and ecosystem maintenance efforts (Owczarek, 2023).

Challenges and Considerations

While the benefits of real-time supply chain visibility are undeniable, there are challenges to consider:

Data Integration: A hybrid process of integrating different data streams through various sources within the network of suppliers brings together different aspects and can also be complex. It will demand a highly endowed IT infrastructure that can adequately cater to various forms of data and standards. Data standardized data formats simplify the integration process so that they all function smoothly together regardless of the platform or system they are built on.

Data Security: With the prevalence of interdependent network devices and systems, cyber threats have become a looming issue. Privacy of sensitive information is a crucial part. Secrecy against potential data leaks and misuse should be observed. Authentication mechanisms such as encryption protocols, access control levels, and conduct of regular security audits are necessary security measures not only to mitigate the risk but also to protect valuable digital assets (Sallam et al., 2023).

Investment Costs: Though the actual purchase of real-time visibility tools including IoT sensors, data analytics platforms, and systems integration might seem strenuous in the lead time, the benefits of such long-term applications overshadow the newly invested capital. Hence increased operational efficiency, lowered inventory holding costs, the elimination of disturbances, and the satisfaction of the client base as some of the most obvious financial returns are just a few of them.

Talent and Training: To fully appreciate the value of real-time data resources, employing an adequately capable and well-educated workforce becomes very crucial. The development of programs in training as well as investment in professional development initiatives for employees makes it possible for those employees to use timely data efficiently. Ranging from data analysts, who are highly skillful in fetching meaningful and actionable insights, to supply chain managers featured in the list who are proficient in using data-driven strategies, a well-trained workforce undeniably is the most significant factor driving an organization towards success and superiority.

Emerging Trends in Real-Time Visibility

Blockchain Technology: Blockchain technology transforms the supply chain world by offering a safe and immune publishing data platform that ensures transparent and traceable data exchange. With the use of distributed ledger technology, traced of the authenticity and intactness of the sales transaction can be verified by the stakeholders, so the trust level and accountability might be increased inside the network of the supply chain network (KPMG, n.d.).

Edge Computing: Edge computing defines a whole new idea about data processing, which makes the processing of such data to devices much more accessible and quicker. Through analyzing data right at “the edge” of the network, i.e. the nearest to IoT devices and sensors, latency gets reduced, and the potential for real-time analysis is increased. A capacity to change strategy quickly and agilely is made possible in supply chain management (Dey, 2023).

Artificial Intelligence (AI): AI-based technologies are transforming the vista of the supply chain seeker in real-time. While AI algorithms perform various functions like predictive analytics and prescriptive insights with a higher level of accuracy than humans, they analyze massive datasets and identify unusual patterns.


The Internet of Things and data analytics enabling real-time supply chain selection are no longer futuristic concepts; they are a current need for small, medium, and large businesses. Through these technologies, companies can enjoy an integrated view of their supply chains, intelligence that helps them preempt delays or hiccups, market discipline that ensures efficient management of inventory stocks, and customer delight through positive experiences. The technology is expected to advance, with the emergence of blockchain, fog computing, and deep learning, with its future built on new supply.


KMPG. (n.d.). The supply chain trends shaking up 2023.

Jones, Ch. (2023). The Role of Customer Engagement Technologies in Enhancing Deliveries.

Owczarek, D. (2023). Supply Chain Visibility: The Role of Real-Time Data in Logistics.

Dey, S. (2023). Surviving major disruptions: Building supply chain resilience and visibility through rapid information flow and real-time insights at the “edge”. Sustainable Manufacturing and Service Economics, 2, 100008.

Sallam, K., Mohamed, M., & Mohamed, A. W. (2023). Internet of Things (IoT) in supply chain management: challenges, opportunities, and best practices. Sustainable Machine Intelligence Journal, 2, 3–1.

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