This chapter discusses the implementation of data analytics and artificial intelligence (AI) in the circular economy. The case studies cover various domains, such as resource tracking and tracing, resource recovery, decision-making support systems, and machining optimization. The outcomes include improved supply chain management, extended product lifecycles, reduced waste generation, enhanced product quality, and cost savings. Data analytics and AI have the potential to shape a sustainable circular economy by optimizing resource utilization, improving processes, and enabling informed decision-making.
TopIntroduction
The circular economy is a systemic approach to economic development that aims to decouple economic growth from the consumption of finite resources. It is an alternative to the traditional linear economy model, which follows a “take-make-dispose” pattern. In contrast, the circular economy is designed to maximize resource efficiency, minimize waste generation, and promote sustainable practices.
Data analytics and artificial intelligence (AI) are powerful technologies that enable organizations to extract valuable insights from large datasets and make intelligent, data-driven decisions. Data analytics involves the process of collecting, organizing, and analyzing data to uncover patterns, trends, and correlations. It encompasses various techniques, such as statistical analysis, data mining, and predictive modeling. Data analytics allows organizations to gain a deeper understanding of their operations, customers, and markets, leading to improved efficiency, better decision-making, and identification of new opportunities(Ghoreishi & Happonen, 2020a).
AI, on the other hand, refers to the development of intelligent machines that can perform tasks that typically require human intelligence. AI technologies mimic cognitive functions like learning, reasoning, problem-solving, and decision-making. Machine learning, a subset of AI, focuses on the development of algorithms and models that enable computers to learn and make predictions or take actions based on data. AI technologies, including natural language processing, computer vision, and robotics, have a wide range of applications across industries. Both data analytics and AI have significant implications for various sectors, including the circular economy. In the context of the circular economy, data analytics and AI can be used to optimize resource allocation, identify opportunities for resource recovery, track and trace materials, and support decision-making in adopting circular economy practices. These technologies enable organizations to analyze complex systems, identify patterns and inefficiencies, and develop data-driven strategies for achieving sustainability goals.
By leveraging data analytics and AI, organizations can gain valuable insights into material flows, waste streams, consumer behavior, and supply chain dynamics. These insights can inform the development of circular business models, design of sustainable products, implementation of waste reduction strategies, and improvement of overall resource efficiency. However, it is important to consider ethical considerations, such as data privacy, security, and bias, when applying data analytics and AI technologies. Ensuring responsible and ethical use of these technologies is crucial to build trust, protect sensitive data, and ensure fairness in decision-making processes(Bag et al., 2021).
In summary, data analytics and AI technologies have the potential to revolutionize industries and drive sustainable practices, including in the circular economy. By harnessing the power of data and intelligent algorithms, organizations can unlock new opportunities, optimize resource utilization, and contribute to a more sustainable and efficient future.