Potentials and challenges of analyzing use phase data in product planning of manufacturing companies

Abstract

The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

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.…”

Creating data-driven products and services in Industry 4.0 – a case study on companies in the German machine and tool industry.

“…It was also determined that current DDPD studies could outperform classical design methods in well-de ned tasks, but still cannot master creative/innovative design tasks that require cognitive ability. Meyer et al [12] present 17 use cases that provide an overview of the possibilities offered by data analysis in product planning, reiterate the importance of data analysis, and point out that this topic is relatively new to many manufacturing companies and that many of these have little experience and/or expertise in this eld [13]. Meyer et al [14] offered practitioners a number and variety of investigations that can be performed with data in the use phases and at the end emphasizes that empirical research on this topic should gain even more importance.…”