Edge Computing Applications in Supply Chain Management

Introduction

We shall introduce the edge computing concept in this chapter.

Definition and overview of edge computing

A disruptive computing paradigm known as edge computing has gained a lot of steam in recent years, mostly due to the quick spread of Internet of Things (IoT) devices and the growing need for real-time data. Fundamentally, edge computing is the process of processing data closer to the location where it is generated, as opposed to depending exclusively on cloud data centers that are centralized. This localized technique decreases latency, reduces bandwidth usage, and enables quick decision-making (Shi et al., 2016). Organizations can improve their capacity to extract relevant insights from data in real time and increase operational efficiency by running computations at the edge of the network. The explosion of data produced by networked devices has led to the emergence of edge computing: by 2025, there will be more than 75 billion IoT devices globally, all of which will be generating enormous amounts of data (Statista, 2021). Conventional cloud computing architectures frequently find it difficult to efficiently handle such enormous volumes of data since they depend on centralized data centers for processing. Latency issues, bandwidth constraints, and the costs associated with transferring large datasets to cloud servers can significantly hinder performance and increase operational costs (Mahmood, 2019). Edge computing addresses these challenges by enabling local data processing, allowing organizations to analyze and act upon data almost instantaneously. An example is provided in Figure 1.

Figure 1 Example of edge computing data flow¹

One of the primary advantages of edge computing is its ability to facilitate real-time analytics. In applications where timing is critical, such as autonomous vehicles and smart manufacturing, the immediate processing of data is essential for ensuring safety and operational efficiency. For example, in autonomous vehicles, data from various sensors must be processed in real time to facilitate safe navigation and decision-making (Zhang et al., 2018). In manufacturing environments, edge computing allows for immediate responses to equipment performance issues, thereby significantly reducing downtime and optimizing operational efficiency. By processing data locally, organizations can minimize the time it takes to derive insights and act on them, which is vital for maintaining a competitive edge in today's market.

Edge computing is often discussed in conjunction with related concepts such as fog computing and mist computing. Fog computing extends the capabilities of cloud computing to the edge of the network, creating a distributed computing environment that allows for data processing, storage, and analysis across a network of devices (Bertino et al., 2018). This design makes it easier to handle large-scale IoT deployments by offering a more flexible and responsive data handling framework. Mist computing, on the other hand, extends the notion by putting computation closer to end devices, resulting in lower latency and greater data privacy (Zhang et al., 2018). Collectively, these frameworks represent a range of edge computing options that can be adapted to individual application requirements.

One difficulty is the lack of experienced workers capable of managing and maintaining edge computing infrastructure. The technology landscape is continuously changing, and companies must ensure that they have the necessary skills to build, run, and troubleshoot edge computing solutions. This may necessitate a considerable investment in staff training or expert hire, which can be prohibitively expensive for smaller firms or those with limited resources (Gupta et al., 2020).

Furthermore, because edge computing devices frequently work in diverse and occasionally severe environments, assuring their dependability and security becomes critical. Edge devices are susceptible to physical dangers, climatic conditions, and cyber-attacks, necessitating strong security measures and maintenance plans (Mahmood, 2019). Organizations must establish end-to-end security measures that safeguard data in transit and at rest, especially since edge devices might serve as possible entry points for cyber threats. This is especially important in supply chain management, where data integrity and confidentiality are key to building confidence with partners and stakeholders.

Still, standardization remains a significant issue in edge computing. The lack of universally accepted standards for edge device communication and data formats can complicate integration efforts, making it challenging for organizations to adopt edge computing solutions that work seamlessly across their existing systems (Bertino et al., 2018). Efforts to establish common standards and protocols will be essential for facilitating broader adoption and interoperability of edge computing technologies.

To sum up, edge computing signifies a paradigm change in the way businesses handle data processing and analysis, especially when it comes to supply chain management. Edge computing lowers latency, improves operational efficiency, and allows real-time analytics by moving computation closer to the data source. In addition to facilitating prompt decision-making, local data processing lowers bandwidth expenses and enhances data security. However, thorough consideration of the difficulties involved—such as technology integration, staff training, reliability, and standardization—is necessary for the successful implementation of edge computing solutions. Supply chain management will probably see a rise in the use of edge computing as sectors continue to change and adjust to the demands of the digital era. Businesses that successfully use edge computing will be in a better position to increase customer happiness, boost operational efficiency, and hold onto a competitive edge in a market that is changing quickly. Therefore, for businesses hoping to prosper in the age of digital transformation, investigating edge computing technologies in supply chain management is not only appropriate but also crucial.

Basic concepts of supply chain management

Supply chain management (SCM) is a comprehensive strategic approach to managing the flow of goods, information, and finances as they move from the initial supplier to the final customer. At its core, SCM involves the coordination and integration of these flows to optimize efficiency, reduce costs, and enhance customer satisfaction (Chopra & Meindl, 2016). The concept encompasses a range of activities, including procurement, production, inventory management, transportation, and distribution. The ultimate goal of SCM is to deliver the right product to the right place at the right time, all while minimizing costs and maximizing value for all stakeholders involved (Mentzer et al., 2001). The supply chain, which is a network of businesses, individuals, activities, data, and resources used in the development and provision of goods and services, is one of the fundamental elements of SCM. Suppliers, manufacturers, distribution hubs, warehouses, retailers, and customers are usually included in this network. According to the Council of Supply Chain Management Professionals (CSCMP), a supply chain encompasses all activities associated with the flow and transformation of goods from the raw materials stage through to the end user (CSCMP, 2021). This broad definition underlines the complexity and interconnectedness of supply chain activities, highlighting the need for effective management to ensure smooth operations. An example of a supply chain is provided in Figure 2.

Figure 2 Example of supply chain network²

Demand forecasting, inventory control, and logistics are some of the fundamental ideas of SCM. Estimating future consumer demand for a good or service is known as demand forecasting, and it is a crucial step in efficient planning and inventory control. Organizations can reduce stockouts and excess inventory by matching production and inventory levels to projected market