Understanding Usage Data-Driven Product Planning: A Systematic Literature Review

Abstract

Cyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.

Four Patterns of Data-Driven Design Activities in New Product Development

“…Two studies on DDD for product portfolio planning are identified, which are conducted by the same scholars (Meyer et al, 2021(Meyer et al, : 2022. The scholars argue that even if product planning and data analytics are two established and independent research areas and together, they span the new research areas, research on this topic is absent.…”

“…Therefore, they explore the concepts, advantages, and challenges of utilising usage data in product planning through a systematic literature review and interview. According to Meyer et al (2021), usage data-driven product planning consists of six main concepts: capturing of user-generated data, product operating data and environmental data; data feedback into product planning; data analysis with data mining and machine learning techniques; fact-based decision making; improvement of existing and future products; and reinforcing loop of inverse and forward design.…”

Generative Product Design Processes: Humans and Machines Towards a Symbiotic Balance

“…With Manovich ( 2001), "we are facing the shift of all cultures to computer-mediated forms of production, distribution, and communication". Digitization, therefore, has a deep impact on design culture as it addresses production, organization, and transmission, returning a scenario more relevant than ever for design which is challenged by a profoundly transformed design process (Meyer et al, 2021), operating in an increasingly fluid environment with blurred boundaries between physical, digital, and biological spheres. Yet nowadays it seems that technology is dictating the rules of change: the extent of technological advancement will entail a complex underlying cultural maneuver, since industrial products, both in appearance and in performance, will be placed in contexts in which technology will offer new social, environmental and cultural values.…”

Planning the Analysis of Use Phase Data in Product Planning

“…These data can be investigated with statistical analysis, data mining, and machine learning methods (Hou and Jiao, 2020;Igba et al, 2015). The results of the data analysis lead to new insights about the product and its users (Meyer and Wiederkehr et al, 2021). For the planning of a future, improved product generation, they enable decisions based on facts instead of assumptions (Holler et al, 2017;Wuest, Hribernik and Thoben, 2014;Xu, Frankwick and Ramirez, 2016).…”