Adaptive decision-making method of life cycle options by using process data collected over multiple life cycle stages

Abstract

The appropriate design of product life cycles is crucial for the realization of a sustainable society. Since Alting proposed the concept of life cycle engineering in 1993, many concepts, approaches, methods, and tools have been proposed and developed for the purpose. On the other hand, rapid development of Internet of Things (IoT) technology enables the development, operation, and maintenance of a new generation of products: so-called ‘smart’ products. One characteristic of smart products is the use of data collected throughout the product’s entire life, making it possible to improve energy and resource efficiency. In this context, many efforts have been devoted to developing a variety of algorithms, methodologies, and data models for supporting IoT-enabled engineering activities such as maintenance, production planning, and logistical planning. However, there exists a gap between life cycle engineering and emerging IoT-based technologies; that is to say, little research has addressed the life cycle design of products (including end of life treatment stages) using IoT-based technologies, whereas most IoT-based research focuses on improvement of individual life cycle stages such as production, operation, or maintenance. It is not currently clear how IoT-based technologies can practically contribute to the adequate design of product’s life cycle as a whole. As a first step toward answering this question, this paper proposes a process data model that captures the complex interdependency of product data over multiple life cycle stages. We additionally present a method based on this data model for determining a product’s appropriate life cycle options. Finally, through an illustration of the method with a simplified case study, we discuss the advantages and the future development needs of IoT-based life cycle design.