This paper introduces a method for analysing motion patterns that can be utilised to optimise data-driven systems. The aim is to use surveillance cameras and artificial intelligence to track multiple objects in a reliable manner, thereby preserving the authenticity of movement patterns for numerous and similar objects. In a case study, this method is applied to optimize lighting conditions in animal husbandry. Furthermore, this approach can be utilized not only in animal husbandry but also in other domains.

The amount of information in our society and its opportunities have given rise to Big Data research. The systems supplier industry needs suitable tools and methods to ensure the harvest and utilization of Big Data in their product development. This paper used Systems Thinking to analyze the current state in the industry and suggested leverage points for further research direction. The findings suggest that the research project should emphasize the industry cases, the collaboration between the companies and academia, develop a Big Data systems architecture, and maintain a socio-technical view.

Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.

Product development in today's manufacturing companies is characterized by multiple development projects under intense time constraints. This means that the success of projects impacts the company's success significantly. However, industrial practices show that many projects fail to meet their time targets. This paper presents a methodology to systematically improve project schedule adherence of development projects by combining exploratory data analysis of historic project data with project scheduling optimizations to enhance the project schedules and enable more successful projects.

The ongoing digitalization of products offers product managers new potentials to plan future product generations based on data from the use phase instead of assumptions. However, product managers often face difficulties in identifying promising opportunities for analyzing use phase data. In this paper, we propose a method for planning the analysis of use phase data in product planning. It leads product managers from the identification of promising investigation needs to the derivation of specific use cases. The application of the method is shown using the example of a manufacturing company.

Industrial Data Analytics needs access to huge amounts of data, which is scattered across different IT systems. As part of an integrated reference kit for Industrial Data Analytics, there is a need for a data backend system that provides access to data. This system needs to have solutions for the extraction of data, the management of data and an analysis pipeline for those data. This paper presents an approach for this data backend system.

The paper presents the application of non-specialized lexical database and semantic metrics on transcripts of co-design protocols. Three different and previously analyzed design protocols of co-creative sessions in the field of packaging design, carried out with different supporting tools, are used as test-bench to highlight the potential of this approach. The results show that metrics about the Information Content and the Similarity maps with sufficient precision the differences between ICT- and non-ICT-supported sessions so that it is possible to envision future refinement of the approach.

Crucial in the design process, Technology Readiness Levels are a common form of technology maturity assessment. Studies suggest that the TRL scale can be subjective and biased. Automating the assessment can reduce human bias. This paper highlights important challenges of automation by presenting data collected on 15 technologies from the nanotechnology sector. Our findings show that, contrary to claims from the literature, patent data exists for low maturity technologies and may be useful for automation. We also found that there exists unexpected trends in data publications at TRL 2, 3 and 4.

In the digital era, products’ forms do not necessarily follow their function. Design fixation may happen when a designer attempts to generate diverse concepts. New design heuristics for digital design were extracted to support designers in the early conceptual design stage. Ten design heuristics were extracted from 998 RedDot award-winning concept designs (2013-2017) through a five-step process. It was preliminarily tested by four practitioners and proved to have positively influenced their conceptual design.

It is important to be able to compare and evaluate different solutions early in development. This paper proposes a method for structuring historical data into a data model that can support the evaluation of new design concepts. The data is contextualized by linking it to a hierarchical decomposition of existing products. Two case studies were conducted to evaluate the value of using historical data when evaluating new concepts. The cases confirm that the proposed method is useful for evaluation of new concepts.

An important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.

This study evaluated the annual prevalence of anogenital warts (AGW) caused by human papillomavirus (HPV) and analysed the trend in annual per cent changes (APC) by using national claims data from the Health Insurance Review and Assessment of Korea, 2007–2015. We also estimated the socio-economic burden and co-morbidities of AGW. All analyses were performed based on data for primary A63.0, the specific diagnosis code for AGW. The socio-economic cost of AGW was calculated based on the direct medical cost, direct non-medical cost and indirect cost. The overall AGW prevalence and socio-economic burden has increased during the last 9 years. However, the prevalence of AGW differed significantly by sex. The female prevalence increased until 2012, and decreased thereafter (APC + 3·6%). It would fall after the introduction of routine HPV vaccination, principally for females, in Korea. The male prevalence increased continuously over time (APC + 11·6%), especially in those aged 20–49 years. Referring to the increasing AGW prevalence and its disease burden, active HPV infection control surveillance and prevention in males are worth consideration.