Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments. Despite discussions in industry and academia over several years, ASCs lack well-established theoretical foundations. This paper addresses this research gap by presenting a formal definition of ASC along with its defining characteristics and auxiliary concepts. We propose a layered conceptual framework called the MIISI model. An illustrative case study focusing on the meat supply chain demonstrates an initial ASC implementation based on this conceptual model. Additionally, we introduce a seven-level supply chain autonomy reference model, delineating a trajectory towards achieving a full supply chain autonomy. Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain. We anticipate that this work will stimulate further research, both theoretical and technical, and contribute to the continual evolution of ASCs.

Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts has significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by predictive and self-decision-making capabilities, has recently emerged as promising solution. However, research on ASCs is relatively limited, with no existing studies on their implementations. This paper aims to address this gap by presenting an implementation of ASC using a multi-agent approach. It proposes a methodology for the analysis and design of such an agent-based ASC system (A2SC). This paper provides a concrete case study, the autonomous meat supply chain, which showcases the practical implementation of the A2SC system using the proposed methodology. Additionally, a system architecture and a toolkit for developing A2SC systems are presented. Despite with limitations, this paper demonstrates a promising approach for implementing an effective ASC system.

Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, there is only sparse work to facilitate generalisable technical implementation. We address this gap by investigating multi-agent system approaches for implementing autonomous supply chains, presenting an autonomous economic agent-based technical framework. We illustrate this framework with a prototype, studied in a perishable food supply chain scenario, and discuss possible extensions.

Autonomous technology continues to make an impact on the supply chain. The autonomous supply chain, applies to moving goods without human intervention (to some degree at least) or aiding in achieving inventory accuracy. One of the more interesting examples is the Belgian brewery De Halve Maan, which in an effort to reduce congestion on the city streets, built a beer pipeline under the streets. The pipeline is capable of carrying 1,500 gallons of beer an hour at 12 mph to a bottling facility two miles away. Autonomous technology is seen in warehouses and stores, on highways and in mines, and in last mile deliveries.

Digital technology is widely accepted as a necessary part of driving improved supply chain efficiency and effectiveness. But some business leaders think it could go a step further, suggesting the technology could propel not only greater productivity, but help craft an autonomous supply chain able to identify, forecast and fix problems on its own. A combination of technologies, from artificial intelligence (AI) and machine-learning to digital twins and cloud computing, have made it increasingly possible for end-to-end supply chains to make decisions without the need for human intervention. These technologies, which appear to have all the right ingredients for the formation of an autonomous supply chain, are slowly permeating the market. Ocado is using advanced autonomous supply chain processes to help make accurate predictions.

Supply Chain TV - Autonomous Supply Chain - Pt. 1 Listen as Fab Brasca, JDA GVP of Solutions Strategy, and Sarah Barnes-Humphrey, Let's Talk Supply Chain, walk you through the ins and outs of Autonomous Supply Chain. In part one, you'll hear the benefits of artificial intelligence (AI), machine learning (ML), and the autonomous supply chain. By the end of this series, you'll be on your way to an AI/ML driven supply chain.

The Internet of Things (IoT) is set to spread into every area of the enterprise over the next year, in order for that to happen it will need to start employing some of the newer, emerging technologies. This will be particularly true of Industrial IoT (IIoT) as more and more organizations start using the opportunities it offers to achieve business goals. Frank Vella, COO of Information Builder, points out that with the emergence of smart cities and new manufacturing processes, for example, there is a growing need for large pools of data. This data will be used to build more efficient, broader ecosystems that provide proactive insights in verticals like manufacturing, health and safety. "AI [artificial intelligence], predictive analytics, IoT and blockchain are all technologies that require strong data capture and use. Consequently, the way data is accessed will change to enable broader visibility and create cohesive ecosystems that support a convergence of data access and provides better operational and predictive capabilities," Vella said.

The internet of things (IoT), artificial intelligence (AI), and blockchain are having a media moment, especially in the context of the supply chain. For instance, IDC predictsone-third of all manufacturing supply chains will be using analytics-driven cognitive capabilities – a version of AI – by the end of 2020; increasing cost efficiency by 10% and service performance by 5%. Each of these technologies has the potential to shift global supply chains. Taken together, they have the power to completely revolutionize the process via the first truly'autonomous' supply chain. To understand the combined impact, it's important to examine each.

Picture a scenario in which flow-rate sensors in a water pump inside a car's engine detect a drop in water pressure. The sensor information would be automatically transmitted to a local service center via a telematics link. The service center would be tasked with deciding whether the water pump needs to be replaced, and with connecting into the car manufacturer's service portal. There, the service center would gain access to a machine-learning platform that examines a combination of factors, including historical information. That data would help the service technician determine when the water pump is likely to fail.