Digital Product Innovation in Manufacturing Industries - Towards a Taxonomy for Feedback-driven Product Development Scenarios

In the light of pervasive digitalization, traditional physical products get augmented with digital components that create the potential of making the whole product lifecycle visible for product developers. As numerous opportunities sketch out how feedback such as sensor data might be leveraged for future products, a comprehensive model to describe, particularly a classification model to organize and structure these opportunities seems analytically useful. Hence, this paper pursues a scenario-based approach and proposes a taxonomy for feedback-driven product development scenarios in manufacturing industries. Grounded on (1) empirical data from case studies and focus groups and (2) a systematic literature review, we follow an established taxonomy development method employing the general systems theory as meta-characteristic. With the limitation of a (1) qualitative, interpretive empirical research design and a (2) representative literature review, we contribute to the body of knowledge by shedding light on feedback-driven product development from a classification perspective which may act as structuring and creativity fostering tool

A Reference Process Model for Usage Data-Driven Product Planning

Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage data-driven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice

How can Data Analytics Results be Exploited in the Early Phase of Product Development? 13 Design Principles for Data-Driven Product Planning

The megatrend digitalization turns mechatronic products into continuous collectors and generators of use phase data. By analyzing this data, manufacturers can uncover valuable insights about the products and the users. Especially in product planning, these insights could be used to plan promising future product generations. The systematic exploitation of data analytics results, however, represents a serious challenge, as research on the topic is still scarce. In this paper, we present 13 design principles for exploiting data analytics results in product planning. The results are based on a systematic literature review and a workshop with a research consortium. The evaluation of the design principles is demonstrated with a real case of a manufacturing company. The identified design principles represent a first contribution to a still scarcely explored research field