Industrial revolutions (IRs) are mostly associated with how transformations regarding the operations of an enterprise affect said enterprise’s manufacturing systems. However, the impact of these transformations exceeds the production systems themselves; rather, they affect the entire value chain, from the product design and development process (PDDP) through manufacturing and supply-chain management to marketing and disposal. As the new PDDP to a large extent defines the value chain for a company, the challenge lies in ensuring that the designed product will help the company fully benefit from the IRs. By analysing the 4th IR, the authors reveal that few publications shed light on this aspect. Consequently, the purpose of this study is to establish features and properties that will shape the PDDP throughout the 4th IR and into a smart design engineering. To accomplish this, the authors conduct a systematic review of the literature, which provides ten findings. These findings are then analysed by 11 specialists both from academia and the industry, and the findings’ relations to the 4th IR and their impact on the product development process is discussed. By establishing these findings, this paper provides a platform for the understanding of what could potentially shape smart design engineering and its design-related activities.

New information and communication technologies are changing the way products are developed, manufactured, serviced and managed over the product’s lifecycle. Today’s smart products not only consist of their physical components, but are also endowed with intelligence. Data and the capabilities to process data into knowledge and eventually decisions have become critical components of the product itself and of the process to develop/operate the product. This paper investigates how engineers and a new functional role, data scientists, can effectively collaborate in a mixed team for new product development with data-driven features (NPD3). We focus on the concept development stage, typically the fuzziest phase of product development. In this paper, an integrated process model is explored by revisiting the traditional new product development (NPD) process model as well as the knowledge discovery and data mining (KDDM) process model. Then a case study of the development of an application-specific unmanned aircraft system (UAS) is used to examine the proposed model.

Ethnography, a tool traditionally used by social scientists, has been adopted by product design engineers as a tool to build empathy, understand customers and their contexts, and learn about needs for a product. This tool is particularly valuable for designers from the developed world working on products for customers in developing communities as differences in culture, language, and life experience make the designer’s intuition less reliable in these communities. This paper reports the use of design ethnography under a variety of conditions in the developing world. The data analyzed here come from field studies completed in four different developing communities on four different continents. Researchers had varying degrees of cultural familiarity, language fluency, and community partner participation in each location. Other factors were also included in the study such as the effects of gender and age of the respondents, the ethnographic activity used, and others. Some of the results are intuitive and some are surprising, but all are quantified through rigorous statistical analysis. The results of this study can help design teams of all types including NGOs, student teams, industrial teams, and any other team with an interest in product design in developing communities. These results can help teams plan their own ethnographic activities to increase the likelihood of collecting information that is useful for making product design decisions based on the conditions of their particular project.

Decomposing complex design problems is an important component of design processes. When a design problem is too complex to solve all at once, the problem is decomposed into manageable subproblems. Previous work on design processes has identified some general decomposition patterns and has studied how individual designers decompose design problems; this study examines the way variables are grouped into subproblems, the process of decomposition, and whether small teams use similar decomposition patterns. Data were collected from five teams as they solved a facility design problem, and the subproblems that they considered were analyzed and compared. Using a mix of qualitative and quantitative analysis techniques, we examined (1) whether their subproblems group tightly coupled design variables (and separate weakly coupled variables); (2) whether their decompositions (subproblems and the sequence in which they were solved) follow a top–down design process; and (3) whether different teams used the same decompositions. Our results suggest that teams followed a partial top–down design process that moved from breadth- to depth-first search, and that subproblems were often driven by two types of coupling among design variables. However, the inconsistency of observed approaches suggests that there is room for improvement in how human designers decompose problems. By identifying these issues, the results lay a foundation for future research to provide better support for human designers in decomposing problems.

Automated manufacturing machines in the discrete manufacturing domain frequently face changes in requirements, such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical, and software changes. In industrial practice, experts individually perform these adaptation processes without methodological support, which is time-consuming and highly error-prone. This article proposes a systematic approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

Using ideation methods in an optimal way has a great potential to increase the number of ideas which a team can contribute during the conceptual phase of product development in industry. Previous studies on ideation methods have been mainly statistical studies in a laboratory setting. In the present study, however, the aim was to develop tailor-made ideation methods in a specific context, among actors on a deregulated railway market, through close interaction between researchers and engineers in a real-world context. Considering previous relevant research on ideation methods, associative memory models and the findings from tests of established ideation methods performed in the same group, a number of preliminary design principles were formulated and implemented in an ideation method that combines individual phases of rotational and gallery viewing with phases of verbal group interaction. The method was tested and refined in a cross-functional inter-organizational group comprising participants from different actors in the railway sector. Besides its provision of qualitative and quantitative test results, the present study has considered the opinions of the participants in detail, which can give important insights into the factors determining whether such a method will be implemented in industry. The participants found the method to be more useful and to generate more ideas that could be used in practice compared to the established ideation methods which they had tried. The learning derived from the specific case was formalized into a number of design principles for ideation methods to be used in cross-functional inter-organizational groups.

The Kano model of customer satisfaction provides product developers valuable information about if, and then how much a given functional requirement (FR) will impact customer satisfaction if implemented within a product, system or a service. A limitation of the Kano model is that it does not allow developers to visualize which combined sets of FRs would provide the highest satisfaction between different customer segments. In this paper, a stepwise method to address this shortcoming is presented. First, a traditional Kano analysis is conducted for the different segments of interest. Second, for each FR, relationship functions are integrated between x = 0 and x = 1. Third, integrals are inserted into a matrix crossing segments and FRs, where FRs with the highest sum across the chosen segments are identified. Finally, the functions of the chosen segments with the smallest interval, define the FRs appealing to the biggest target group. The proposed extension should assist product developers within to more effectively evaluate which FRs should be implemented when considering more than one combined customer segment. It shows which segments provide the highest possibility for high satisfaction of combined FRs. We demonstrate the approach in a case study involving customers’ preference for outdoor sports equipment.

This study employs fuzzy logic to evaluate uncertain component end-of-life (EOL) options in the design stage. Determining EOL strategies during the product design stage can be complex. For example, EOL strategies for retired bicycle components are various and may change with geographic location. Thus, adopting fixed EOL strategies in the product design stage may not always be appropriate; the element of uncertainty should be considered. Limited research has examined uncertainty of EOL strategies during the design stage. Moreover, the evaluation of EOL strategies in a comprehensive manner has not been shown in a realistic case study. These facts motivate this investigation. Fourteen evaluation criteria are used to generate a comprehensive framework for assessing seven EOL strategies. The evaluation process generates the likelihood for each of these strategies by aggregating fuzzy set operations and a left–right fuzzy ranking method. Using SUMPRODUCT calculation for these weights/probabilities and input sustainability value (i.e., cost, environmental impact and labor time), expected values are derived to represent the sustainability values for each EOL strategy. A Technique-for-Order-of-Preference-by-Similarity-to-Ideal-Solution (TOPSIS) based method is employed to identify the appropriate EOL strategy for each component/product. A refrigerator is used as a case study to illustrate the methodology. This study addresses the uncertainty involved in identifying an EOL strategy for a specific product component during the design stage through the use of fuzzy logic. The method closes a gap in the current EOL strategy assessment criteria and introduces a comprehensive evaluation framework to capture multiple strategic perspectives by incorporating 14 key evaluation criteria.