One of the aims of systems engineering is to develop systems with a number of pre-defined configurations, in order to operate effectively and efficiently in different contexts and environments. Early in the design phase, system reconfiguration allows to propose and optimize these configurations. With regard to the literature review and industrial observation, pre-defining the standard configurations without relying on hints from end users has been raised as a major difficulty within the industry. In this paper, we propose a reconfiguration framework which considers data collected from the use phase in order to generate valid and optimized configurations with regard to stakeholders needs.

By applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.

The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.

This paper is contextualized in a research project that aims to create a new paradigm to support the design process, substituting the sequential nature of design process models by a flexible structure. To implement this paradigm, we must identify the final and intermediate results of the design process, such as documents, models, artefacts, among others. However, design research is wide and multidisciplinary, resulting in non-uniformity of the terminology across research communities, what hinders the results identification by means of a literature review. This paper aims to identify the terms employed by different research communities to refer to the intermediate and final results of the design process, structuring synonym terms across research communities and establishing how those terms interrelate in the design ontology. Using literature review, the following terms were analysed: design objects, elements, deliverables, entities, information, components, data, and artefacts. The results provide a holistic view of how the terms are employed throughout research communities, supporting the creation of search strings and pointing out opportunities for improving the design ontology.