ALBERT

Description: Technical drawings are constituted by three components that are strongly correlated: the geometry showing the part shape, the dimensions defining the part volume and the tolerances establishing the variability of the two previously described components. A general methodology to assign the tolerances, especially geometric tolerances, to all the components of an assembly has not been clearly defined up to now. This is probably due to the complexity of the problem and to the existing gap between the existing standards and the industrial designers’ common practices. In this work, a new general methodology to assign dimensional and geometric tolerances to all the components of an assembly with a concurrent design approach is proposed. It takes into consideration the relationships between tolerances and a set of design principles that have been naturally extracted from the standards and literature and from a deep discussion with the Italian Association of Industrial Designers. In order to demonstrate and validate the proposed approach, the methodology has been applied to two real case studies: a volumetric gear pump and a pneumatic actuator.

Description: We explore an approach to synthesize concepts of a class of sensors, where a quantity is sensed indirectly after nullifying its effect by using negative feedback. These sensors use negative feedback to increase the dynamic range of operation without compromising the sensitivity and resolution. The synthesis technique uses knowledge about existing phenomena to come up with an approach to synthesize concepts of sensors and also study their interactions with their surroundings, so as to generate robust designs. The approach uses a database of building blocks which are based on physical laws and effects that capture the transduction rules underlying the working principles of sensors. A simplified variant of the SAPPhIRE model of causality, which also uses physical laws and effects, has been adapted to represent the building blocks. SAPPhIRE model had been used earlier to understand analysis and synthesis of conceptual designs. We have adapted it here for automated generation of concepts. The novelty of the approach lies in the way and the ease with which it constructs a graph which is a super-set of the concept-space. The individual concepts are extracted out of the graph at a later point in time. The extraction of the concepts is done by using a modified breadth-first search algorithm which detects loops in the graph. The usage of breadth-first search algorithm for loop detection is novel, as we have demonstrated that it performs better than depth-first search algorithm for the specific problem. The technique has been implemented as a web-based application. For the sensor problems attempted, a number of existing patents were found that were based on the concepts that were generated by the synthesis algorithm, thus emphasizing the usefulness of the designs produced. The tool generated 35 concepts for accelerometers, out of which 2 concepts were found in patents. The synthesis approach also proposed new, feasible sensor concepts, thereby indicating its potential as a stimulator for enhancing creativity of designers. Automated generation of feedback-based sensor designs is a novel outcome of this approach.

Description: Agriculture is facing increasing innovation challenges to meet current societal expectations, yet very few design science studies are devoted to it. This paper highlights some of the particularities of the objects, reasoning and organization of design in agriculture that may open fruitful scientific dialogue between design scientists and agricultural scientists. We first provide an overview of the broad range of objects that are designed in agriculture and point out their specific characteristics with regard to design. We then identify some particular challenges of design activities in agriculture and review how they have been addressed up to now. Finally, we discuss how design challenges and characteristics in agriculture can contribute to current debate in the field of design science. We propose two main lines of enquiry and debate: enhancing the links between design reasoning and organization and further conceptualizing the status of use situations in design to deal with uncertainties and complexity in design processes.

Description: Product development (PD) workstream identifies and implements updates into the design of a product in order to keep up with the market demands. PD requires redesigning the product because an optimized strategy for a single generation may not be the best option in the multi-generation scenario. For this purpose, a mathematical model to estimate the product life-cycle cost for a multi-generation manufacturing-based product is proposed. The cost estimation model has three cost constituents: development cost, service cost, and associated risks cost. The article focuses on market risk and technical risk under risks, an element that most previous researchers have missed in their models. Operation risk related to uncertainty during project management has also been considered in the formulation. The model serves as a strategic decision-making tool and provides the guidelines for making project management efficient. This tool is very handy to those design engineers who have a little idea of cost–risk analysis when they design a product. It assists design teams by accurately forecasting and strategically planning a project and by making decisions which are compatible with future generations at a relatively cheaper cost. An empirical case study of a manufacturing-based product (gas turbine) has been discussed to illustrate and justify the use of proposed cost estimation model. The case study has three candidate concepts in the contention, and the best option needs to be selected. After a stringent analysis, concept-3 is found to be the most cost-effective option for most of the instances.

Description: According to several literature sources, Product Planning is acknowledged as a primary driver of future commercial success for new designed products, and it is schematically constituted by the identification of business opportunities and the selection of most promising alternatives. Despite the recalled relevance of Product Planning, it emerges that a marginal quantity of companies have adopted formal methods to carry out this task. The paper attempts to provide a major understanding about such a limited implementation of Product Planning techniques and other open issues emerging from the analysis of the literature concerning the initial phases of engineering design cycles. The presented study investigates the claimed benefits of methods described in the literature, the level to which such tools are diffused through educational programs in Technical Institutes, the expectations and the demands of a sample of enterprises with respect to new tools supporting Product Planning. It emerges that, whereas existing methods strive to fulfil relevant properties according to the perception of the companies, limitations come out in terms of the transfer of the proposed techniques and their perceived reliability.

Description: In this study, we developed a new multiple criteria optimization method in the context of engineering design. The design of experiment (DoE) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are combined to identify an objective function of the selected problem by fitting a polynomial to the experimental data in a multiple linear regression analysis. Then, the regression function is incorporated into a mathematical model with the criteria constraints to determine an optimal criteria set. The proposed method can be used to compare the functionality and results provided by different scenario analyses of a building design optimization problem, which represent design solutions. We applied the DoE–TOPSIS model to solve different multi-criteria design optimization problems using two examples from the literature and obtained satisfactory results. In the examples, the results obtained using the combined DoE–TOPSIS model are almost in agreement with those derived from conventional multi-criteria design optimization methods, which demonstrates the simplicity, usability, and flexibility of the proposed method in solving engineering design problems.

Description: 〈h3〉Abstract〈/h3〉 〈p〉In a previous paper, we introduced the process of developing a new prescriptive method for conceptual engineering design called ICE (idea-configuration-evaluation), and demonstrated its application in detail. The new design model is based on the well-established theoretical foundation of C–K theory and the empirically derived parameter analysis method. In this paper, we describe the testing and evaluation of the model’s formulation through a design experiment that examines the performance of external participants when applying it. Two categories of evaluation metrics are used: applicability and effectiveness of the method. Nine quantitative and qualitative metrics describe aspects such as the ease of teaching and using the design procedure, and its support of creativity and innovation. The experiment took place in an academic environment and the results were obtained by analyzing detailed written design reports, assessing characteristics of the final solutions, and examining replies to reflective questionnaires. The conclusions from the study are that the ICE model presents a clear and concise step-by-step procedure, is conducive to teaching and practicing design, captures the dynamics and rationale of the conceptual design process, and can therefore lead to viable and innovative conceptual designs. Another, general contribution of the paper is in delineating the application of a plurality of measures in a non-comparative assessment of a design method.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Selection of suitable computer-supported collaborative design (CSCD) technologies is crucial to facilitate successful projects. This paper presents the first systematic method for engineering design teams to evaluate and select the most suitable CSCD technologies comparing technology functionality and project requirements established in peer-reviewed literature. The paper first presents 220 factors that influence successful CSCD. These factors were then systematically mapped and categorised to create CSCD requirement statements. The novel evaluation and selection method incorporates these requirement statements within a matrix and develops a discourse analysis text processing algorithm with data from collaborative projects to automate the population of how technologies impact the success of CSCD in engineering design teams. This method was validated using data collected across 3 years of a student global design project. The impact of this method is the potential to change the way engineering design teams consider the technology they use and how the selection of appropriate tools impacts the success of their CSCD projects. The development of the CSCD evaluation matrix is the first of its kind enabling a systematic and justifiable comparison and technology selection, with the aim of best supporting the engineering designers collaborative design activity.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Design process models have a complex and changing relationship to the processes they model, and mean different things to different people in different situations. Participants in design processes need to understand each other’s perspectives and agree on what the models mean. The paper draws on philosophy of science to argue that understanding a design process model can be seen as an imagination game governed by agreed rules, to envisage what would be true about the world if the model were correct. The rules depend on the syntax and content of the model, on the task the model is used for, and on what the users see the model as 〈em〉being〈/em〉. The paper outlines twelve alternative conceptualizations of design process models—〈em〉frames〈/em〉, 〈em〉pathways〈/em〉, 〈em〉positions〈/em〉, 〈em〉proclamations〈/em〉, 〈em〉projections〈/em〉, 〈em〉predictions〈/em〉, 〈em〉propositions〈/em〉, 〈em〉prophecies〈/em〉, 〈em〉requests〈/em〉, 〈em〉demands〈/em〉, 〈em〉proposals〈/em〉, 〈em〉promises〈/em〉—and discusses when they fit situations that stakeholders in design processes can be in. Articulating how process models are conceptualised can both help to understand how process management works and help to resolve communication problems in industrial practice.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419, 〈span〉https://doi.org/10.1007/s00163-017-0270-7〈/span〉, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The research reported in this paper explored the feasibility of embedding multiple design structures into design definitions with a view of sharing design definitions across product life cycles. Two separate case studies using (a) lattice theory and (b) a qualitative data analysis (QDA) software tool were used to illustrate the benefits of embedding. In the first case study, of a robotic arm assembly, lattices in the form of partially ordered sets are used to embed multiple design structures into a given design definition. A software prototype has been built that allows a design bill of materials (BoM) to be extracted from a STEP AP214 file and translated into a lattice that is visualized as a Hasse diagram. This lattice is a sub-lattice of a complete lattice that includes all possible BoM structures for the given collection of component parts in the assembly. New BoM design structures can be defined by selecting the required nodes in the complete lattice and alternative product definitions are then exported as new STEP files. The second case study introduces a collision avoidance robot with associated design structures. It is used to illustrate management of design information using a current technique, design structure matrix (DSM), and compared with how embedding using QDA has the potential to support the establishment of relationships between design structures. Results from these case studies demonstrate that it is feasible to use lattice theory as an underlying formalism and QDA as a means for sharing design definitions.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The purpose of the study is to model the micro-scale process patterns which can be identified during team conceptual design activities. A state-transition model has been developed and used to empirically investigate the patterns of design operations during two types of team conceptual design activities: ideation and concept review. The presented work builds on the perception of design problems as ill-defined and implies that conceptual design activities involve the simultaneous development of problems and solutions using three distinctive design operations—analysis, synthesis, and evaluation. The three design operations have been defined as fine-grain design steps performed by design teams when exploring the content of both the problem and the solution dimensions of the design space. Moreover, design operations have been conceptualised as transitions between states of the explored design space, thus providing a basis for the state-transition model. The model’s ability to map and visualise proportions of design operation sequences emerging during ideation and concept review has facilitated the identification of both the activity-specific patterns and patterns that were likely to appear during both types of empirically investigated activities. The two activities exhibited similar patterns, such as alternation of solution synthesis and analysis, sequences of synthesis, analysis and evaluation within solution space, and the potential co-evolution episodes. Nevertheless, divergent traits have been identified for ideation, and convergent traits for concept review, based on the significant differences in proportions of design operations and their sequences.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Aided by the capabilities of additive manufacturing in building a part with multiple materials, dynamic sub-components, and complex geometries, the number of parts that are feasible for consolidation has increased drastically. However, to decide which components to consolidate is difficult. Therefore, to identify these potential candidates out of a complex product is highly demanded. We define this issue as a part consolidation candidate detection (PCCD) problem. To solve this problem, we proposed three principles that rationalize the PCCD process with regard to the maximum number and the priority of parts to be consolidated. Based on which, we developed a modularity-based PCCD (MPCCD) framework which is featured by the need for module division and community detection as well as two PCCD algorithms [i.e., strength-based numerical PCCD (NPCCD) and community-based PCCD (CPCCD)]. Two case studies of a throttle pedal and an octocopter are given to demonstrate the effectiveness of the proposed CPCCD algorithm and the MPCCD framework, respectively. In the end, this paper is wrapped up with important conclusions and future research.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Understanding the needs of consumers is essential to the success of product design. Affective responses are a reflection of affective needs, often encompassing many aspects. Therefore, the process of designing products capable of satisfying multiple affective responses (MARs) falls into the category of multi-objective optimization (MOO). To solve the MOO problem, most existing approaches require the information for decision-making before or during the solving process, which limits their usefulness to designers or consumers. This paper proposes a posterior preference articulation approach to Kansei engineering system aimed at optimizing product form design to deal with MARs simultaneously. Design analysis is first used to identify design variables and MARs. Based on these results, a MOO model that involves maximizing MRAs is constructed. An improved version of the strength Pareto evolutionary algorithm (SPEA2) is applied to solve this MOO model so as to obtain Pareto solutions. After that, the Choquet fuzzy integral, which has the ability to take into account the interaction among the MARs, is employed to determine the optimal design from the Pareto solutions in accordance with the consumer preference. A case study involving the design of a vase form was conducted to illustrate the proposed approach. The results demonstrate that this approach can effectively obtain the optimal design solution, and be used as a universal approach for optimizing product form design concerning MARs.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Recently, tradespace analysis and exploration has emerged as an important focus area within the Department of Defense Engineered Resilient System initiative, which draws upon engineering concepts, science, and design tools to produce trusted and effective solutions for a wide range of operational contexts. Most of the previous research on tradespace analysis, including those developed for rotorcraft, emphasize performance. However, non-functional requirements such as reliability, availability, and maintainability have received minimal consideration, despite their direct influence on program level concerns such as operation and support as well as affordability. This paper proposes a strategy to incorporate reliability engineering into tradespace analysis. We also develop a subsystem-level reliability investment model that is illustrated through a simplified example. Our results suggest that reliability investment could achieve significant savings over a systems lifecycle, thereby enabling improved fleet availability and a larger fleet size.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Understanding the role of iterations is an important topic within design research and design practice. Iterations often involve rework and thus often increased costs and completion time. Many theories and studies ascribe iterations either to the social or technical complexity of the design process. Here, we join the two perspectives by analysing metadata of more than 3000 documents produced during the design of a biomass power plant. We gain insights by using network analysis and by visualising the temporal unfolding of the design process. Subsequently, we develop a statistical model to rigorously test multiple hypotheses showing that iterations are a combination of technical and social factors. The paper shows that iterations increase when the number of stakeholders/participants increases and when external suppliers are involved. Iterations are lower in presence of integrative activities. Furthermore, the paper shows the existence of synergistic interactions between nodes’ in-going and out-going flow in both activity and team networks associated with an increase in iterations.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉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.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study introduces a method to build a multi-domain matrix (MDM), visualizing the intended architecture of a system within the component, function, and parameter domains. The MDM is generated from textual function specifications that are subject to a specific grammatical structure and vocabulary based upon the functional basis and interaction basis as presented in the literature. Two types of functions are distinguished: functions specifying what functionality a particular component provides to another component, and functions specifying the internal working (transformation of flow) of a particular component. The fixed grammar for the specification of the two types of functions allows for the automated derivation of dependencies between components, between functions of components, and between system parameters. A case study on a navigation lock demonstrates that the system architecture generated from function specifications matches the architecture of the real lock system fairly well. As such the method can be used in the early design phase to reveal the product architecture that is embodied in the function specifications of system components. The method may also support modeling of high-definition DSMs of existing engineering systems.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉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.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Design is essential to fulfil unmet or under-served needs of resource-poor societies, supporting their social and human development. A great deal of design research has been undertaken in such low resource settings, and is discussed under different names, such as ‘community development engineering’, ‘humanitarian engineering’, ‘appropriate technology’, ‘design for development’, ‘design at the Base of the Pyramid’, etc. This has created an important need to know what has been examined and learnt so far and to plan for further investigation. To address this, we review a broad range of literature, with close examination of 30 design studies in this field. This reveals a multifaceted picture, showing a great diversity in investigation and reporting of attributes of context (income, rural and urban, design sectors, countries, and gender), the roles of poor people (consumers, producers, and co-designers), characteristics of research methods employed (e.g. descriptive and prescriptive, data collection methods, qualitative and quantitative aspects, and unit of analysis), and design topics. Based on the review results, we offer recommendations for further research, identifying concerns that researchers ought to have about this field and suggesting ways in which research in this field can be undertaken and reported.〈/p〉

Description: 〈p〉Author would like to make corrections in the original publication. Author would like to add a reference to the part 4.3.1 (page 11, in the middle of line 12).〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents the outcomes of an exploratory research to clarify the performance of R&D designers when involved in design task for the ideation of the next generation of a technical system. The research aims also at clarifying if creative stimuli play a role in supporting ideation after idea generativity decreases because of natural exhaustion or the emergence of fixation. The effect of precedents (singular as patents, and structural as technology evolution trends), as well as design strategies (in the form of a design procedure for inventive problem solving) on idea generation, is compared by means of an experiment involving 24 R&D Iranian engineers. Precedents demonstrated to be more effective than design strategies in supporting productivity in idea generation, while generally they are not effective enough to support the generation of candidate ideas for the next generation of a technical system with a robust repeatability. The main recorded lacks depend on the capabilities of creative stimuli to support the generation of novel ideas, as they are generally effective in providing good results with reference to technical plausibility and relevance for a target audience. The results of the experiment are also discussed with reference to the efficiency of the design process (number of generated ideas per time unit). The outcomes of such studies, as part of a broader research objective, serve as input to support the development of a serious game to support R&D engineers to face design tasks for the next generation of technical systems with higher motivation and engagement, providing them with an improved design experience.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Teams are a major feature of engineering and are commonly thought to be necessary when solving dynamic and complex problems. Even though teams collectively provide a diversity of knowledge, skills, and perspectives to problem-solving, previous work has demonstrated that in certain scenarios, such as in language-based and more spatially oriented configuration design problems, the production by a team is inferior to that of a similar number of individuals working independently (i.e., nominal team). This research explores this comparison of individual versus group problem-solving within the domain of conceptual engineering design. Thus, a behavioral study was run with freshman engineering students, who solved a conceptual engineering design problem individually or collaboratively in a team. Results corroborate previous findings, exhibiting that individuals outperform teams in the overall quality of their design solutions, even within this more free-flowing and explorative setting of conceptual design. Exploiting this result, this work further considers whether adaptive feedback from a process manager can lessen the underperformance of collaborative design teams compared to individuals, by helping teams overcome potential deterrents that may be contributing to their inferior performance. Teams that are under the guidance of a process manager end up performing better than teams that are not in terms of solution quality, and almost as well as individuals, though not significantly different. This result suggests that process managers are able to mitigate some of the deficiencies in design teams. In an attempt to uncover some of the cognitive rationale and strategies that may be beneficial throughout problem-solving, the managerial interactions with the design teams are then investigated. Furthermore, to determine the reason of the collaborative teams’ underperformance, the effect of verbalization is studied as a possible cognitive hindrance. In the end, this work expands growing research on team problem-solving in engineering, and suggests that collaborative teams may not be optimal in every circumstance, but under the proper process management, can become substantially more effective.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉When resource constraints are taken into consideration in the deployment of engineering changes, assignments of resources and changes to design entities and selection of change propagation paths make the scheduling problem difficult to solve. Due to complicated dependency relations between engineering entities in the complex engineering design process making it difficult to build an explicit objective function to evaluate different change propagation paths, an integrated simulation and optimization approach is adopted in this research to identify the optimal change scheduling plans for renewable resource-constrained change propagations. First, a digraph model is built to represent the product structure or design process connected with input and output logical relations; second, mathematic formulations of change scheduling problems in two kinds of resource usage schemes are presented; third, a forward-propagate-later-rollback strategy is adopted to develop the resource-constrained change propagation simulation algorithms; fourth, simulation results are used to evaluate individuals in genetic algorithm (GA) during the optimization of propagation likelihood for each involved design dependency. Two case studies are conducted to demonstrate the effectiveness of the models and methods developed in this research. Results for both resource usage schemes are compared, and it can be found that: (1) shorter-duration change propagation plans can be found in the dedicated resource usage scheme than in the flexible scheme; (2) the flexible resource usage scheme can produce a fairer workload allocation between design resources than the dedicated scheme; and (3) the tradeoff between change duration and cost should be determined by design delivery urgency and cost budget in addition to choosing an optimal change propagation plan.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉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.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉A tangible understanding of the latent design knowledge base of a technology domain, i.e., the set of technologies and related design knowledge used to solve the specific problems of a domain, and how it evolves, can guide engineering design efforts in that domain. However, methods for extracting, analyzing and understanding the structure and evolutionary trajectories of a domain’s accumulated design knowledge base are still underdeveloped. This study introduces a network-based methodology for visualizing and analyzing the structure and expansion trajectories of the design knowledge base of a given technology domain. The methodology is centered on overlaying the total technology space, represented as a network of all known technologies based on patent data, with the specific knowledge positions and estimated expansion paths of a specific domain as a subgraph of the total network. We demonstrate the methodology via a case study of hybrid electric vehicles. The methodology may help designers understand the technology evolution trajectories of their domain and suggest next design opportunities or directions.〈/p〉

Description: This paper focuses on a generic paradigm for quantitative behavioral knowledge modeling. The goal is to support automated functional case adaptation with behavioral inputs rather than structure parameter assignments. Functional case is a kind of functional module, integrating functional knowledge, qualitative behavioral knowledge, quantitative behavioral knowledge, and flexible structure models to support multiple design stages in a “5R” case recycle. Behavior effect analysis has been proposed to analyze the roles of behavior variables and structure parameters in the function implementing process and to establish primary relationships between them. Behavioral constraint has been introduced for knowledge representing in detailed design of a functional case. Behavior constraint matrix is presented to guide construction of fully constrained behavioral constraint equations. Examples of functional case modeling and recycling have been demonstrated. The proposed method will benefit designers in reuse of a functional case for a rapid design response in detailed design phase. Automated functional case adaption is achievable, which is especially significant for unskilled designers.

Description: The act of introducing an innovation into an existing product by substituting or inserting new technologies is thought to be challenging due to the problem of integrating new components and sub-system architectures into existing ones. This article aims to challenge the foundation of this problem and develop new insights into the choice of functional architecture. The article will propose that the choice of functional architecture to achieve an intended purpose locks-in a design by influencing the cost of transformation. This paper studies functional lock-in based on the transformation cost of the functional architectures of products. The transformation cost for a set of biological and biologically inspired products is compared to that of engineered products. The results show that the biological and biologically inspired products have a statistically significant lower transformation cost than the engineered products. The results indicate that the structure of functions and flows in a product will constrain its transformation. More broadly, the paper proposes minimum transformation cost as an essential property of an optimal design.

Description: Optimisation of product family design has been emphasised and studied for many years. However, previous studies only took overall cost or profit as an optimisation objective, but ignored the supply risk of a product family. Moreover, the discount associated with the bidding price, which is common in practice, was not considered in the modelling. In this paper, we propose a new multi-objective optimisation approach integrating supplier selection into product family design. In our optimisation model, not only the profit but also the supply risk of a product family is formulated as optimisation objectives. Consequently, we can evaluate and optimise a product family from many perspectives. In addition, as a bidding price discount may affect product family’s configuration and supplier selection, we include linear piecewise discount of bidding prices from suppliers in our optimisation model. The NSGA-II algorithm is developed to achieve Pareto non-dominated solutions of the multi-objective optimisation model. Sensitivity analysis on the model parameters is performed, and several managerial insights for enterprises are achieved in the case study of a printing calculator product.

Description: The paper presents an analytical method for finding the expected duration and variance of a product development (PD) project network. A PD project network is a stochastic activity network (such as a PERT network) which allows for probabilistic repetition of activities (i.e., activity rework). When rework is allowed, estimating the process duration and variance becomes difficult. Most existing literature refers to the use of simulation in such scenarios; however, few analytical methods exist to solve this problem. One such method is called the reward Markov chain (RMC) which only considers sequential activity networks and which we use as a starting point in our proposed method. In this paper, we extend the RMC method to solve mixed networks (i.e., a combination of parallel and sequential activities) and more complicated practical issues that may arise in PD environments, specifically coupled activities and parallel rework.

Description: Conceptual modeling is an important initial stage in the life cycle of engineered systems. It is also highly instrumental in studying existing unfamiliar systems—the focus of scientific inquiry. Conceptual modeling methodologies convey key qualitative system aspects, often at the expense of suppressing quantitative ones. We present and assess two approaches for solving this computational simplification problem by combining Object-Process Methodology (OPM), the new ISO/PAS 19450 standard, with MATLAB or Simulink without compromising the holism and simplicity of the OPM conceptual model. The first approach, AUTOMATLAB, expands the OPM model to a full-fledged MATLAB-based simulation. In the second approach, OPM computational subcontractor, computation-enhanced functions replace low-level processes of the OPM model with MATLAB or Simulink models. We demonstrate the OPM computational subcontractor on a radar system computation. Experimenting with students on a model of an online shopping system with and without AUTOMATLAB has indicated important benefits of employing this computation layer on top of the native conceptual OPM model.

Description: Concurrent tolerancing becomes an optimisation problem to find out the optimum allocation of the process tolerances in the given design function constraints. In traditional optimisation methods, finding out the optimum solution for this advanced tolerance design problem is complex. The proposed algorithms (elitist non-dominated sorting genetic algorithm) and differential evolution extensively do better than the previous algorithms for attaining the optimum result. The aim of this paper is to suggest a model for optimal tolerance allocation by considering both tolerance cost and the present worth of quality loss such that the total manufacturing cost/loss is minimised. The suggested model takes into account the time value of money for quality loss and product degradation over time and consists of two new parameters: the planning horizon and the product user’s discount rate. From the outcome of this study, a longer planning horizon results in an increase in both tolerance cost and quality loss; however, a larger value of discount rate gives up a decrease in both tolerance cost and quality loss. Finally, a practical example is brought into reveal the effectiveness of the suggested method.

Description: In Design by Shopping, designers explore the design space to gain an insight into trades, and feasible and impractical solutions, as well as to learn about alternatives before optimization and selection. The design space consists of multidimensional sets of data and, in order to select the best design from among numerous alternatives, designers may use several different graphs. In this study, we test to find the most appropriate graph to indicate the best solution corresponding to a set of objectives represented by a design scenario (1). A further constraint is that this must be done in the shortest possible time (2). Three graph types are tested in three different design scenarios where one car has to be chosen from a total of 40. A response quality index is proposed which computes the quality of a designer’s choice for any given scenario. In total, 90 tests with 30 participants were performed. The parallel coordinates plot proved to be the best graph for selection in Design by Shopping.

Description: This paper presents a quantitative method for analysing process models of designing independently of the specific design domain. The method uses the situated function–behaviour–structure framework as the basis for a simulation model of a designer acting according to these models. The results of these simulations are sequences of design issues that are analysed using cumulative occurrence graphs with associated quantitative measures. The paper illustrates the approach by analysing and comparing three models of designing from different domains: Pahl and Beitz’ model of engineering design, the rational unified process of software design and a model of design for six sigma in service design. The quantitative results indicate some commonalities across the different models. These commonalities are related to the start of cognitive effort spent on design issues, the continuity of the cognitive effort throughout the design process and the constancy of the speed with which design issues are generated.

Description: This paper demonstrates how system performance and robustness can be improved by incorporating models of end-user decision making in the early stages of complex engineered system design. End-user decision making is incorporated into the system design models through the novel use of decision and game theory methods. Methods for representing, dispersing, and absorbing information by end users in engineered system models are explored. The benefits of using mechanism design of end-user information to manipulate end-user decisions, with the goal of improving system performance, are examined. Further, incorporation of end-user models is shown to improve system robustness with respect to variations in user population. Complex system examples of aircraft and buildings are explored as test beds that showcase the benefits of incorporating an end-user decision model in the early stages of design.

Description: On modern mechatronic products, incorporating multiple modes is a common and effective way of dealing with changes in task, requirements, and environment. Modes are established to enable the system to switch from one configuration state to another. However, using the traditional methodology in engineering design, products are considered and designed with fixed configurations. A systematic method to involve and enable the design of changeable configurations is lacking. This paper focuses on product functional models and investigates the conceptual design of multi-modal products, which are identified by their reconfigurability during the operation stage. The author connects the phenomenon of multiple modes to product reconfigurability, asserts function and technology multiplications as the basis of multiple modes, and then specifies that usability and robustness are the key drivers of incorporating multiple modes. At the end of the paper, the author reconciles the conceptual design procedures to derive the principle solutions specifically for multi-modal products. This research on the dynamic characteristics of the product functional model introduced by multiple modes complements the current systematic design methodology.

Description: This article describes tolerance analysis using a reliability-based approach to ensuring that a functional condition is satisfied. A particular feature of this procedure is that it defines the combined effects of geometric and dimensional ISO specifications for product parts, and the architectural parameters that define the relative positions of parts in contact. In the first part, we describe configuring the product parameters by geometric deviations, and from this, a global model is produced to characterise the variation in rotor/stator clearance in a turboshaft engine turbine. This model relies on tolerance zone dimensions, dimensional tolerances and architectural parameters. When clearance is examined using the reliability-based approach, links emerge between the turbine’s architectural parameters and its geometric and dimensional specifications. Indicators can then be deduced which guide the designer in selecting the optimum turbine architectures.

Description: The design of engineering systems like airports, communication infrastructures, and real estate projects today is growing in complexity. Designers need to consider socio-technical uncertainties, intricacies, and processes in the long-term strategic deployment and operations of these systems. Flexibility in engineering design provides ways to deal with this complexity. It enables engineering systems to change in the face of uncertainty to reduce impacts from downside scenarios (e.g., unfavorable market conditions) while capitalizing on upside opportunities (e.g., new technology). Many case studies have shown that flexibility can improve anticipated lifecycle performance (e.g., expected economic value) compared to current design and evaluation approaches. It is a difficult process requiring guidance and must be done at an early conceptual stage. The literature offers little guidance on procedures helping designers do this systematically in a collaborative context. This study investigated the effects of two educational training procedures on flexibility (current vs. explicit) and two ideation procedures (free undirected brainstorming vs. prompting) to guide this process and improve anticipated lifecycle performance. Controlled experiments were conducted with ninety participants working on a simplified engineering systems design problem. Results suggest that a prompting mechanism for flexibility can help generate more flexible design concepts than free undirected brainstorming. These concepts can improve performance significantly (by up to 36 %) compared to a benchmark design—even though users did not expect improved quality of results. Explicit training on flexibility can improve user satisfaction with the process, results, and results quality in comparison with current engineering and design training on flexibility. These findings give insights into the crafting and application of simple, intuitive, and efficient procedures to improve lifecycle performance by means of flexibility and performance that may be left aside with existing design approaches. The experimental results are promising toward further evaluation in a real-world setting.

Description: Complex systems are increasingly being developed as part of portfolios or sets of related complex systems. This enables synergies such as commonality between portfolio systems that can significantly reduce portfolio life-cycle cost and risk. While offering these benefits, commonality usually also incurs up-front as well as life-cycle penalties in cost and risk due to increased design complexity. The resulting trade-off needs to be carried out during the architecting stage of the portfolio life cycle when there is maximum leverage to improve life-cycle properties due to degrees of freedom available in architectural and design decisions. This paper outlines a 4-step methodology for the identification and assessment of commonality opportunities in complex systems portfolios during the architecting stage of the portfolio life cycle. The methodology transforms a solution-neutral description of a portfolio of aerospace systems based on system functionality, requirements, and metrics into a set of preferred portfolio design solutions with commonality. The methodology is based on a 2-stage approach that identifies preferred architectures for each system in the portfolio individually prior to heuristic commonality analysis between systems based on a pairwise assessment of system overlap in functionality, technologies, operational environments, and scale. Application of the methodology is demonstrated with a retrospective analysis of NASA’s Saturn launch vehicle portfolio.

Description: With the current rise in environmental concerns, customers want products that reduce environmental impacts. As a result, new multi-objective modular design methods are needed that can consider multiple design objectives, related to both the customers’ and the companies’ functional, environmental, and economical constraints and objectives. Most previous multi-objective modular design methods create module configurations, in which each module meets all of the design objectives. However, highly distinct independent modules are important for current product replacement processes. This paper presents an innovative multi-objective green modular design method that uses atomic theory and fuzzy clustering to create module configurations, in which each module meets one distinct design objective. This paper also presents case studies that demonstrate the capabilities of the new multi-objective green modular design method.

Description: While creativity is often seen as an indispensable quality of engineering design, individuals often select conventional or previously successful options during the concept selection process due to the inherent risk associated with creative concepts and their inadvertent bias against creativity. However, little is actually known about what factors attribute to the promotion or filtering of these creative concepts during concept selection. To address this knowledge gap, an exploratory study was conducted with 38 undergraduate engineering students. This study was aimed at investigating the impact of individual risk aversion, ambiguity aversion, and student educational level on the selection and filtering of creative ideas during the concept selection process. The results from this study indicate that individuals’ ability to generate creative ideas is not significantly related to their preference for creative ideas during concept selection, but individual risk aversion and ambiguity aversion are significantly related to both creative concept selection and creative idea generation. Our results also revealed that first- and third-year students’ creative ability is affected differently by varying levels of tolerance for ambiguity. These results highlight the need for a more directed focus on creativity in engineering education in both concept creation and concept selection. These results also add to our understanding of creativity during concept selection and provide guidelines for enhancing the design process.

Description: As a mechanism through which better solutions are developed, creativity is well-recognised as an important part of the engineering design process, but has to date largely only been studied in general or in early design process stages. This paper aims to study the occurrence of creative behaviour in engineering design with a particular focus on the later design process stages. Through the application of a detailed coding scheme to two studies of engineers’ work, this paper identifies patterns in creative behaviour through the design process stages, creative approaches employed by engineers, typical types of creative task, and fundamental differences within creative behaviour between early- and late-stage design. This understanding is then used to form ten characterisations of engineer behaviour within late-stage design, early-stage design, and throughout the design process. These characterisations can be used to direct future research and to improve the design process and output through development of specific, effective design support methods, selected to be appropriate to the design stage and type of creative behaviour that occurs within.

Description: This paper seeks to improve the understanding of how service-based companies can benefit from developing and delivering service offerings from a standardised core of service modules, which are organised through a service architecture. Research within the field is relatively sparse, and there is scope for an explicit definition of elements related to the development of modular service platforms and architectures. A study of the existing literature, combined with a comprehensive case study in a global engineering consultancy, has created the basis for development and evaluation of the conceptual model for modular service design synthesis presented in this paper. The case study is based on internal documentation and a high level of interview data. Inductive research methods have been used for the analysis. The presented conceptual model defines three suggested dimensions (Market Segmentation, Service Roadmap and Service Architecture Layout) to be included in development of modular service platforms and architectures. Testing indicates a significant standardisation potential for service configuration across service families. Our understanding is that the approach can increase strategic flexibility and adaptability to changes in a quick evolving service market. The empirical part of this paper is exploratory in nature and is limited to one provider of high-end engineering consultancy services. Thus, further research will be needed to verify the aspects of the presented methodology to allow a further generalisation of our findings. Nevertheless, this paper contributes to the emerging literature on service modularity by presenting a specific operational approach for description and utilisation of modular service platforms and architectures.

Description: With the emergence of environmental legislations in many countries, the importance placed upon environmental protection has been raised to a new level, especially for industrial activities. Considering environmental issues as early as possible, starting with the design stage, is expected in order to better manage and diminish adverse environmental impact. Commensurate progress has been made in method/tool development for use in environmental impact estimation; however, very few of these methods allow integrating this estimation early in the design process—a critical point of deciding for potential product concepts and suppliers. In this paper, we propose a tool that integrates environmental impact estimation into architecture and supplier identification, in order to conjointly consider requirements satisfaction as well as uncertainty due to new module and new supplier integration. This tool is developed to support original equipment manufacturer decision making in the context of an extended enterprise. A case study is presented to illustrate a plausible implementation.

Description: Concept selection is recognized as a crucial component of the design process that largely involves informal group discussions within design teams. However, little is known about what factors affect the selection or filtering of creative ideas during this process. This is problematic because in order for innovation to occur, individuals must first identify and select the creative concepts developed in the early stages of design. However, prior research has shown that individuals tend to select conventional alternatives during this process due to the inherent risk associated with creative concepts. Therefore, the current study was developed to understand how personality traits, risk attitudes, and idea generation abilities impact the promotion or filtering of creative ideas in a team setting. The results from our empirical study with engineering students reveal that teams who have higher levels of conscientiousness, agreeableness, and tolerance for ambiguity are more prone to select novel concepts. In addition, the results revealed that the teams who generate creative ideas did not necessarily select creative ideas during concept selection. These results add to our understanding of team-based decision making during concept selection and allow us to provide guidelines for increasing the flow of creative ideas through this process.

Description: Recent advances in design theory help clarify the logic, forms and conditions of generativity. In particular, the formal model of forcing predicts that high-level generativity (so-called generic generativity) can only be reached if the knowledge structure meets the ‘splitting condition’. We test this hypothesis for the case of Bauhaus (1919–1933), where we can expect strong generativity and where we have access to the structures of knowledge provided by teaching. We analyse teaching at Bauhaus by focusing on the courses of Itten and Klee. We show that these courses aimed to increase students’ creative design capabilities by providing the students with methods of building a knowledge base with two critical features: (1) a knowledge structure that is characterized by non - determinism and non - modularity and (2) a design process that helps students progressively ‘superimpose’ languages on the object. From the results of the study, we confirm the hypothesis deduced from design theory; we reveal unexpected conditions on the knowledge structure required for generativity and show that the structure is different from the knowledge structure and design process of engineering systematic design and show that the conditions required for generativity, which can appear as a limit on generativity, can also be positively interpreted. The example of Bauhaus shows that enabling a splitting condition is a powerful way to increase designers’ generativity.

Description: 〈h3〉Abstract〈/h3〉 〈p〉Engineering designers frequently use prototypes to gather input from stakeholders. Design guidelines recommend the use of quick and simple prototypes early and often in a design process. However, the type and quality of a prototype can influence how stakeholders perceive a new design concept and can, therefore, impact their responses. Additionally, different levels of experience, expertise, and preparedness for providing input to designers may lead stakeholders from different geographical or cultural settings to provide different responses, making the format of a prototype even more influential. Although design practitioners are known to intentionally align their prototyping approaches with the specific design question to be answered, it is unclear the extent to which prototyping approaches should vary based on the stakeholder, context, and setting of a design project. To investigate how the format and quality of prototypes influence stakeholders’ responses, we conducted a field study with various medical professionals in Ghana. We presented prototypes for a medical device in different formats to stakeholders and collected responses to the design through semi-structured interviews. Our findings indicate that professional expertise, prototype format, and question type influenced the types of responses that stakeholders provided. These findings suggest that designers seeking input from stakeholders on new concepts should consider context-specific prototyping strategies, especially when designing at distance and across cultures.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Engineering design and development projects involve a multitude of plans in various formats. Such documents are created, used and updated throughout a project. We argue that the interactions among plan documents, their content, their stakeholders and the planning processes can be usefully perceived as a planning system. The planning system is instrumental in coordinating a design and development project and thus strongly influences how it unfolds. In this article, a survey tool is developed to assess a planning system from its stakeholders’ perspective. Results from the survey are analysed using Multiple-Domain Matrix (MDM) methodology to derive insights and suggestions for improvement. The approach is developed and demonstrated through a case study in a company that develops advanced electronic scientific instruments.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Testing is an important aspect of design and development which consumes significant time and resource in many companies. However, it has received less research attention than many other activities in product development, and especially, very few publications report empirical studies of engineering testing. Such studies are needed to establish the importance of testing and inform the development of pragmatic support methods. This paper combines insights from literature study with findings from three empirical studies of testing. The case studies concern incrementally developed complex products in the automotive domain. A description of testing practice as observed in these studies is provided, confirming that testing activities are used for multiple purposes depending on the context, and are intertwined with design from start to finish of the development process, not done after it as many models depict. Descriptive process models are developed to indicate some of the key insights, and opportunities for further research are suggested.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper explores the use of designed experiments in an online environment. Motivated by real-world examples, we model a scenario where the practitioner is given a finite set of units and needs to select a subset of these which are expended toward a one-shot, multi-factor designed experiment. Following this phase, the designer is left with the remaining set of unused units to implement any learnings from the experiments. With this setting, we answer the key design question of how much to experiment, which translates to choosing the number of replicates for a given design. We construct a Bayesian framework that captures the expected cumulative gain across the entire set of units. We derive theoretical results for the optimal number of replicates for all two-level, full and fractional factorial designs with seven factors or fewer. We conduct simulations that serve as validation of the theoretical results, as well as enabling us to explore scenarios and techniques of analysis that are not captured in the theoretical studies. Our overall results indicate that the optimal allocation of units for experimentation varies from 1 to 〈span〉 〈span〉\(20\%\)〈/span〉 〈/span〉 of the total units available, which is mainly governed by the experimental environment and the total number of units. We conclude that experimenting with the optimal number of replicates recommended by our study can lead to a cumulative improvement which is 80–95% greater than the expected cumulative improvement gained when a practitioner chooses the number of replicates randomly.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Products continuously change over time through product innovations satisfying new customer needs and technologies. The successive emergence of new products within a product family can be considered as product family evolution that necessarily involves changes in a product family design structure. Although product family design has been widely discussed in the extant literature, inherent evolving properties in a product family design structure have not been sufficiently explored in an analytical manner. To tackle this issue, this research aims to characterize underlying properties in an evolving product family structure based on a network science approach. First, a product family structure is represented as a network to describe relationships among the components of a product family structure. Then, topological properties and patterns in a product family structure network at each time period are investigated through a case study using the smartphone models of a major company. The results show that each product family structure network follows the topological properties observed in other real networks; the product family structure network evolves with both scale-free and small-world properties and with common and specific motifs during each specific time duration. Also, findings from this study suggest that a design structure with a scale-free network topology with commonality can have topological robustness due to a decrease in structural complexity.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Engineering design is characterized, in many cases, by the involvement of multiple stakeholders. The variety of stakeholders’ expectations with regards to the output and outcome of a vessel design situation, and the differences in background, culture and information asymmetry among stakeholders, make it difficult to arrive at a common set of requirements and a mutually accepted vessel design solution. In this paper, we show how poor handling of expectations in multi-stakeholder arrangements may lead to overspecified design solutions and thereby, negatively affect business outcomes. We propose and test a set of metrics to measure the level of misalignment among stakeholders’ expectations to identify and measure overspecification in vessel design alternatives. The measure can be used in tradeoff analysis against cost, in the decision process for selection among design alternatives. Hence, at equal cost, a higher degree expectation fulfillment may be preferred and selected. A case study is presented for the design of an offshore ship design based on a joint-venture ownership.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Methods and tools to develop sustainable products are ever more required by companies to balance the efficiency of its operations with its responsibilities for environmental and social actions. This work aims at achieving improved products along its production stage by the application of a methodology, which consists of three different phases: (i) sustainability assessment; (ii) product redesign; (iii) comparing designs. Different impacts are assessed, sustainability strategies are proposed and more sustainable design alternatives are projected. The procedure is supported by the use of engineering metrics and sustainability indicators, conveniently selected to quantitatively measure the three dimensions of the sustainability: environmental, economic and social. The methodology is implemented in two case studies. First, the manufacturing process of an airbrush is analyzed. Next, the cow milk production in a dairy farm is studied. Strategies to reduce impacts and to achieve an improvement in the sustainability performance of the product are addressed in each case. Both, environmental and socio-economic improvements, are obtained. Thus, a practical method to carry out sustainability-oriented decision making in production processes is developed.〈/p〉

Description: Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input.

Description: The reuse of knowledge and information arising from the different phases of a product’s lifecycle is crucial for a company in order to achieve competitive advantage. This paper describes a case study from the oil industry investigating the transfer of knowledge within the service phase and also between the service and design phases. Interviews with engineering designers and service engineers were conducted. Knowledge arising from servicing the drilling equipment that was identified as relevant for service engineers was compared to that relevant for engineering designers. Furthermore, the mechanisms involved in the transfer of knowledge between service and design were investigated. Knowledge about changes, issues and improvements generated during service was found to be relevant to both groups; however, engineering designers were interested in knowledge of equipment at a component level whilst service engineers were more interested in obtaining an overview of the systems. The study showed that communication between the departments consisted prevalently of the service engineers pushing knowledge and information to the engineering designers. The reusing service knowledge (RSK) model is proposed based upon the findings and the understanding from a general framework for developing a knowledge management strategy. Additionally, the initial model was revised to explicitly address the factors that emerged from the case study. The RSK model was developed based on a case study from a customised industry; however, previous studies indicated that similar issues are also of relevance to a variant design industry.

Description: Capturing and managing requirements are crucial to the success of product design and development. This paper presents and analyzes the causes of requirements change during the design process of a complex aerospace system developed by Rolls-Royce during the course of 6 years. The study was part of a series of efforts ongoing at this original equipment manufacturer to improve requirements engineering practices and involved a group of engineers in the assessment of the root causes of change. A large data set was examined, containing 700 system requirements and more than 1,000 changes released during the project studied. The results showed that more than 80 % of the changes had internal root causes and change driven by the customer accounted for about 15 % of the total. The structured approach that was implemented to understand the causes of change allowed the group of engineers to identify and dissect various management guidelines aiming to further improve requirements engineering in practice. This paper thus also reports the guidelines captured at this aerospace manufacturer and intends to support practitioners across the industrial community.

Description: The reuse of knowledge and information arising from the different phases of a product’s lifecycle is crucial for a company in order to achieve competitive advantage. This paper describes a case study from the oil industry investigating the transfer of knowledge within the service phase and also between the service and design phases. Interviews with engineering designers and service engineers were conducted. Knowledge arising from servicing the drilling equipment that was identified as relevant for service engineers was compared to that relevant for engineering designers. Furthermore, the mechanisms involved in the transfer of knowledge between service and design were investigated. Knowledge about changes, issues and improvements generated during service was found to be relevant to both groups; however, engineering designers were interested in knowledge of equipment at a component level whilst service engineers were more interested in obtaining an overview of the systems. The study showed that communication between the departments consisted prevalently of the service engineers pushing knowledge and information to the engineering designers. The reusing service knowledge (RSK) model is proposed based upon the findings and the understanding from a general framework for developing a knowledge management strategy. Additionally, the initial model was revised to explicitly address the factors that emerged from the case study. The RSK model was developed based on a case study from a customised industry; however, previous studies indicated that similar issues are also of relevance to a variant design industry.

Description: This paper reports a case study of design and engineering change management in the design and manufacture of automated train systems in a company. The paper uses a combination of surveys and interviews to map the information flow processes in the design and engineering processes. Based on the interviews and data collected on information flow and communication processes, the paper identifies the sources of engineering changes in the design process. Further, the paper takes cognizance of the change in the organizational structure of the company’s engineering change processes to evaluate the engineering change notice (ECN) policies and its application in the company to identify the cause of errors in the engineering change process itself. Based on the analysis, the paper describes a support system that incorporates a novel, context-based ECN generation and workflow routing support system that is intended to reduce the cognitive load of the design engineer. The goal of the system is also to measure the causes of ECN to aid and improve managerial action.

Description: This research addresses a need in systems engineering to verify that a system can meet performance requirements; this is done by integrating failure behavior into the system’s nominal model during the initial stages of design. In general, failure behavior is not used in early assessments, lending toward increased uncertainty in the model’s validity. Current libraries do not model failures and thus cannot confidently address how a design will function in the intended operational environments. Since failures occur from effects on the environment, they should be included during verification and validation efforts. Current approaches capture off-nominal behavior using parameter variation where flow variables and parameters are varied to measure the system-level effect. This approach is ad hoc and does not accurately capture failure mode behavior. To address this limitation, an approach is developed to understand and implement failure mode behavior into nominal models. The Modelica Standard Library (MSL) is used as an example for the component library of nominal models. MSL has a significant amount of basic nominal component behavior and therefore is desirable for this research. Two approaches are developed to implement failure mode behavior; the first uses transfer function and use case graphs, and the second uses existing literature. In addition, complex systems often have a large number of components and an even larger number of failure modes. Since the goal is to limit the development time, we generate an approach to identify high-risk failure modes. This captures an early system-level effect of each failure mode and uses an occurrence to calculate risk. To show the usefulness of each method, two examples are provided including a vehicle drivetrain subsystem with a variety of failures and a diesel engine with fuel injector and valve failures.

Description: This paper introduces metrics derived from application of a Markov decision process to evaluate a design’s changeability. Changeability is known to improve product performance if conscientious early-stage design decisions are matched with structured management of the system in response to exogenous disturbances—shifts which may be environmental, market, technological, and political in nature. Included in the paper is a brief discussion of changeability’s role in ensuring strategic product performance as well as a review of past metrics developed, highlighting the open design challenge to more fully capture the managerial and process dimensions inherent to changeability. The proposed metrics are featured in a case study related to the ballast water system of an ocean-going vessel. The result of such application is greater context for the value changeability offers and an improved understanding of the resources required to manage uncertainty over the product’s life cycle.

Description: This research addresses a need in systems engineering to verify that a system can meet performance requirements; this is done by integrating failure behavior into the system’s nominal model during the initial stages of design. In general, failure behavior is not used in early assessments, lending toward increased uncertainty in the model’s validity. Current libraries do not model failures and thus cannot confidently address how a design will function in the intended operational environments. Since failures occur from effects on the environment, they should be included during verification and validation efforts. Current approaches capture off-nominal behavior using parameter variation where flow variables and parameters are varied to measure the system-level effect. This approach is ad hoc and does not accurately capture failure mode behavior. To address this limitation, an approach is developed to understand and implement failure mode behavior into nominal models. The Modelica Standard Library (MSL) is used as an example for the component library of nominal models. MSL has a significant amount of basic nominal component behavior and therefore is desirable for this research. Two approaches are developed to implement failure mode behavior; the first uses transfer function and use case graphs, and the second uses existing literature. In addition, complex systems often have a large number of components and an even larger number of failure modes. Since the goal is to limit the development time, we generate an approach to identify high-risk failure modes. This captures an early system-level effect of each failure mode and uses an occurrence to calculate risk. To show the usefulness of each method, two examples are provided including a vehicle drivetrain subsystem with a variety of failures and a diesel engine with fuel injector and valve failures.

Description: This paper introduces metrics derived from application of a Markov decision process to evaluate a design’s changeability. Changeability is known to improve product performance if conscientious early-stage design decisions are matched with structured management of the system in response to exogenous disturbances—shifts which may be environmental, market, technological, and political in nature. Included in the paper is a brief discussion of changeability’s role in ensuring strategic product performance as well as a review of past metrics developed, highlighting the open design challenge to more fully capture the managerial and process dimensions inherent to changeability. The proposed metrics are featured in a case study related to the ballast water system of an ocean-going vessel. The result of such application is greater context for the value changeability offers and an improved understanding of the resources required to manage uncertainty over the product’s life cycle.

Description: Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input.

Description: Managing uncertainty levels is important for organizations carrying out complex product development processes since it fosters design process improvements and optimization. Among the different uncertainties, design imprecision is known to represent uncertainty in decision-making that typically triggers changes to the value assigned to design variables during the early stages of the development process. This paper presents a method aiming to support large organizations understanding, quantifying and communicating this type of uncertainty. The imprecision management method that is proposed relies on five main steps: collection of historical records of change, time evolution reconstruction, statistical characterization of the typical levels of imprecision that should be expected, communication to new projects and continuous knowledge update. In addition, we present results from a case-study performed at Rolls–Royce that tested the method’s applicability in practice. The study shed light to interesting empirical findings about the typical level of imprecision in design variables and its evolution during real product development projects. The results from this initial evaluation suggest that the method provides useful support for design process management and thus has industrial value.

Description: This paper presents a model of the way that designers move between situations when interpreting during design activity. Three hypotheses are presented that arise from this model: that designers change their situation during interpretation, that small changes in a source can lead to large changes in the representation and that changes to the situation have their origins in the experience of the designer. The paper demonstrates how this internal movement between situations can be computationally implemented using three examples. The systems implemented demonstrate the way that interpretation can lead to changes in the situation and present an example of how the changes to a designer’s situation can be guided by past experiences.

Description: The variability of products affects customers’ satisfaction by increasing flexibility in decision-making for choosing a product based on their preferences in competitive market environments. In product family design, decision-making for determining a platform design strategy or the degree of commonality in a platform can be considered as a multidisciplinary optimization problem with respect to design variables, production cost, company’s revenue, and customers’ satisfaction. In this paper, we investigate evolutionary algorithms and module-based design approaches to identify an optimal platform strategy in a product family. The objective of this paper is to apply a multi-objective particle swarm optimization (MOPSO) approach to determine design variables for the best platform design strategy based on commonality and design variation within the product family. We describe modifications to apply the proposed MOPSO to the multi-objective problem of product family design and allow designers to evaluate varying levels of platform strategies. To demonstrate the effectiveness of the proposed approach, we use a case study involving a family of General Aviation Aircraft. We show that the proposed optimization algorithm can provide a proper solution in product family design process through experiments. The limitations of the approach and future work are also discussed.

Description: Search of design spaces to generate solutions affects the design outcomes during conceptual design. This research aims to understand the different types of search that occurs during conceptual design and their effect on the design outcomes. Additionally, we study the effect of other factors, such as creativity, problem-solving style, and experience of designers, on the design outcomes. Two sets of design experiments, with experienced and novice designers, are used in this study. We find that designers employ twelve different types of searches during conceptual design for problem understanding, solution generation, and solution evaluation activities. Results also suggest that creativity is influenced positively by the type and amount of searches, duration of designing, and experience of designers.

Description: This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions.

Description: Benefits of modularity are often achieved from module independence that allows for independent development to reduce overall lead time and economies of scale due to sharing similar modules across products in a product family. Current modularity methods tend to describe only one of these views, either the module–module independence or the product–product shared module similarity. This paper proposes a new hybrid module generation algorithm that balances both module independence and product similarity, allowing product similarity strategy to influence the coupling-driven architecture considerations. The proposed method builds on two popular matrix-based methods: the design structure matrix approach and modular function deployment that each has been developed to support these two different aspects of the module generation. This paper presents a novel algorithm that integrates both views and allows a balanced clustering that takes both interactions and company portfolio strategy into account. Usefulness of the algorithm is presented using a cordless handheld vacuum cleaner as a case study and by comparing it to alternative approaches.

Description: Complex systems are increasingly being developed as part of portfolios or sets of related complex systems. This enables synergies such as commonality between portfolio systems that can significantly reduce portfolio life-cycle cost and risk. While offering these benefits, commonality usually also incurs up-front as well as life-cycle penalties in cost and risk due to increased design complexity. The resulting trade-off needs to be carried out during the architecting stage of the portfolio life cycle when there is maximum leverage to improve life-cycle properties due to degrees of freedom available in architectural and design decisions. This paper outlines a 4-step methodology for the identification and assessment of commonality opportunities in complex systems portfolios during the architecting stage of the portfolio life cycle. The methodology transforms a solution-neutral description of a portfolio of aerospace systems based on system functionality, requirements, and metrics into a set of preferred portfolio design solutions with commonality. The methodology is based on a 2-stage approach that identifies preferred architectures for each system in the portfolio individually prior to heuristic commonality analysis between systems based on a pairwise assessment of system overlap in functionality, technologies, operational environments, and scale. Application of the methodology is demonstrated with a retrospective analysis of NASA’s Saturn launch vehicle portfolio.

Description: Introduced nearly 25 years ago, the paradigm of mass customization (MC) has largely not lived up to its promise. Despite great strides in information technology, engineering design practice and manufacturing production, the necessary process innovations that can produce products and systems with sufficient customization and economic efficiency have yet to be found in wide application. In this paper, the state-of-the-art in MC is explored in the context of an envisioned MC development process for both the firm and the customer. Specifically, 130 references are reviewed within the process frameworks (Sect. 3 ) and/or to highlight opportunities for future development in MC (Sect. 4 ) based on the review. This review yields opportunities in four primary areas that challenge MC development: (1) customer needs and preference assessment tools, (2) approaches for requirement specification and conceptual design, (3) insights from methodologies focused on the development of durable MC goods and (4) enhancements in information mapping and handling.

Description: While cyber–physical system sciences are developing methods for studying reliability that span domains such as mechanics, electronics and control, there remains a lack of methods for investigating the impact of the environment on the system. External conditions such as flooding, fire or toxic gas may damage equipment and failing to foresee such possibilities will result in invalid worst-case estimates of the safety and reliability of the system. Even if single component failures are anticipated, abnormal environmental conditions may result in common cause failures that cripple the system. This paper proposes a framework for modeling interactions between a cyber–physical system and its environment. The framework is limited to environments consisting of spaces with clear physical boundaries, such as power plants, buildings, mines and urban underground infrastructures. The purpose of the framework is to support simulation-based risk analysis of an initiating event such as an equipment failure or flooding. The functional failure identification and propagation (FFIP) framework is extended for this purpose, so that the simulation is able to detect component failures arising from abnormal environmental conditions and vice versa: Flooding could be caused by a failure in a pipe or valve component. As abnormal flow states propagate through the system and its environment, the goal of the simulation is to identify the system-wide cumulative effect of the initiating event and any related common cause failure scenario. FFIP determines this effect in terms of degradation or loss of the functionality of the system. The method is demonstrated with a nuclear reactor’s redundant coolant supply system.

Description: ‘Someone else has patented this before’. This is the last thing you would want to hear after months of intensive engineering design work. Yet, this is not uncommon. There is a link between engineering design and intellectual property (IP). However, the boundary between them is not always well defined, especially on when and how IP infringement checks should be conducted during the engineering design process. Hence, this letter seeks to reach out to researchers in the engineering design community to come together and tackle this common design issue.

Description: We propose a framework for measuring the complexity of aerospace systems and demonstrate its application. A measure that incorporates size, coupling, and modularity aspects of complexity is developed that emphasizes the importance of indirect coupling and feedback loops in the system. We demonstrate how hierarchical modular structure in the system reduces complexity and present an algorithm to decompose the system into modules. The measure is tested and found to be scalable for large-scale systems involving thousands of components and interactions (typical in modern aerospace systems). We investigate the sensitivity of the measure and demonstrate the ability of the framework to identify incorrectness in system representation. The merits of the framework are exemplified through a case study comparing three spacecraft. The framework provides the designer with three key capabilities that can positively influence the aerospace (or other) design process: the ability to identify complex subsystems, the ability to classify misrepresentations, and the ability to trade-off commercially of the shelf (COTS) and non-COTS components.

Description: We present a comprehensive robot development process and its evaluation. We designed this process in the context of a robotics course in high schools. The motivation for designing this new process was improving the robustness and reliability of robots developed by students and preparing students for becoming better designers. The newly designed process proved to be highly successful in designing top quality robots. In the process design, we explored and adapted existing design tools and methods to the specific designers, the nature of the product, the environment, the product needs, and the design context goals. At the end of this thorough design, we selected a synergetic integration of six tools and methods to compose the new comprehensive development process for this product context: conceptual design, fault-tolerant design, atomic requirements, fuzzy logic for control, creative thinking, and microprogramming-based design. The design skills of the students that learned the design process and the performance of robots they designed and participated in an international robotics contest were examined. The high school teams that studied the proposed process won the first places in an international contest. The robots developed by the students had better performance than robots built by engineers and faculty teams. Professional experts rated the robots’ designs as excellent. The students that studied the process demonstrated high level of diverse design skills including creativity and design management capabilities. Additionally, they improved their science subject grades and their attitude toward engineering. Both the results obtained by the study and the authors’ experience in teaching robotics demonstrate that the proposed robot development process could be taught successfully in high school and that it leads to superior robotic products. Our experience also indicates that this process could serve industry design by improving the robustness of robots operating in uncertain environments and supporting fast change management practices.

Description: To demystify the debate about the validity of selection methods that utilize aggregation procedures, it is necessary that contributors to the debate are explicit about (a) their personal goals and (b) their methodological aims. We introduce three additional points of clarification: (1) the need to differentiate between the aggregation of preferences and of performances, (2) the application of Arrow’s theorem to performance measures rather than to preferences, and (3) the assumptions made about the information that is available in applying selection methods. The debate about decision methods in engineering design would be improved if all contributors were more explicit about these issues.

Description: When designing mechatronic products, ‘complex dependencies’ are often reported to be a major challenge. This paper focuses on managing dependencies between attributes of the product during the design process. The literature study shows that there is a gap in the literature with regard to the classification of product-related dependencies. Traditionally, these dependencies have been described as appearing between the following product attributes: function, properties and structure. By analysing three mechatronic projects from industry, we identified and classified 13 types of product-related dependencies. Each product-related dependency is described and illustrated using the practical examples from the industrial projects. The value of the classification is evaluated by applying it to an industrial development setting not used for the analysis. The evaluation shows that delays in the project schedule, loss of functionality and quality issues can be avoided if attention is directed toward the product-related dependencies in the development process.

Description: We present a comprehensive robot development process and its evaluation. We designed this process in the context of a robotics course in high schools. The motivation for designing this new process was improving the robustness and reliability of robots developed by students and preparing students for becoming better designers. The newly designed process proved to be highly successful in designing top quality robots. In the process design, we explored and adapted existing design tools and methods to the specific designers, the nature of the product, the environment, the product needs, and the design context goals. At the end of this thorough design, we selected a synergetic integration of six tools and methods to compose the new comprehensive development process for this product context: conceptual design, fault-tolerant design, atomic requirements, fuzzy logic for control, creative thinking, and microprogramming-based design. The design skills of the students that learned the design process and the performance of robots they designed and participated in an international robotics contest were examined. The high school teams that studied the proposed process won the first places in an international contest. The robots developed by the students had better performance than robots built by engineers and faculty teams. Professional experts rated the robots’ designs as excellent. The students that studied the process demonstrated high level of diverse design skills including creativity and design management capabilities. Additionally, they improved their science subject grades and their attitude toward engineering. Both the results obtained by the study and the authors’ experience in teaching robotics demonstrate that the proposed robot development process could be taught successfully in high school and that it leads to superior robotic products. Our experience also indicates that this process could serve industry design by improving the robustness of robots operating in uncertain environments and supporting fast change management practices.

Description: Search of design spaces to generate solutions affects the design outcomes during conceptual design. This research aims to understand the different types of search that occurs during conceptual design and their effect on the design outcomes. Additionally, we study the effect of other factors, such as creativity, problem-solving style, and experience of designers, on the design outcomes. Two sets of design experiments, with experienced and novice designers, are used in this study. We find that designers employ twelve different types of searches during conceptual design for problem understanding, solution generation, and solution evaluation activities. Results also suggest that creativity is influenced positively by the type and amount of searches, duration of designing, and experience of designers.

Description: Design-by-analogy is a growing field of study and practice, due to its power to augment and extend traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. This paper presents the results of experimentally testing a new method for extracting functional analogies from general data sources, such as patent databases, to assist designers in systematically seeking and identifying analogies. In summary, the approach produces significantly improved results on the novelty of solutions generated and no significant change in the total quantity of solutions generated. Computationally, this design-by-analogy facilitation methodology uses a novel functional vector space representation to quantify the functional similarity between represented design problems and, in this case, patent descriptions of products. The mapping of the patents into the functional analogous words enables the generation of functionally relevant novel ideas that can be customized in various ways. Overall, this approach provides functionally relevant novel sources of design-by-analogy inspiration to designers and design teams.

Description: The parameter analysis method of conceptual design is studied in this paper with the help of C–K theory. Each of the fundamental design activities—idea generation, implementation of the idea as hardware and evaluation—is explained and defined as a specific sequence of C–K operators. A case study of designing airborne decelerators is used to demonstrate the modeling of the parameter analysis process in C–K terms. The theory is used to explain how recovery from an initial fixation took place, leading to a breakthrough in the design process. It is shown that the innovative power of parameter analysis is based on C-space “de-partitioning” and that the efficient strategy exhibited by parameter analysis can be interpreted as steepest-first, controlled by an evaluation function of the design path. This logic is explained as generalization of branch-and-bound algorithms by a learning-based, dynamically evolving evaluation function and exploration of a state space that keeps changing during the actual process of designing.

Description: Capturing and managing requirements are crucial to the success of product design and development. This paper presents and analyzes the causes of requirements change during the design process of a complex aerospace system developed by Rolls-Royce during the course of 6 years. The study was part of a series of efforts ongoing at this original equipment manufacturer to improve requirements engineering practices and involved a group of engineers in the assessment of the root causes of change. A large data set was examined, containing 700 system requirements and more than 1,000 changes released during the project studied. The results showed that more than 80 % of the changes had internal root causes and change driven by the customer accounted for about 15 % of the total. The structured approach that was implemented to understand the causes of change allowed the group of engineers to identify and dissect various management guidelines aiming to further improve requirements engineering in practice. This paper thus also reports the guidelines captured at this aerospace manufacturer and intends to support practitioners across the industrial community.

Description: When developing an artifact, designers must first capture and represent user needs. These needs can then be transformed into system requirements or objectives. The contribution of this work is rooted in the formalization of the affordance-based approach for capturing user needs in the early stages of design. This formalization comes in three forms: the first affordance basis for engineering design (a defined set of affordances), a formal structure for affordance statements, and a new relational model structure. This formalization is intended to improve model quality and consistency, while managing model creation resources. Further, this affordance-based approach to capturing user needs imposes a level of abstraction that forces solution independence yet is capable of capturing the large range of user needs. As such, the approach provides a structured approach to problem abstraction—the process of specifying user needs without reference to specific solutions. This affordance-based problem representation relies on other design process tools to help develop the actual artifact, which is also discussed.

Description: 〈h3〉Abstract〈/h3〉 〈p〉The need for more knowledge intensifies the complexity of product development. To understand how knowledge contributes to designing products and how learning improves the product development process, this paper introduces an agent-based model that represents product development as the learning and application of knowledge. Product development consists of a complex network of interdependent agents, such as product functions, design activities and designers. Knowledge is the link connecting these elements, since product functions are the embodiment of knowledge, since design activities require knowledge, and since designers provide knowledge. The simulation of model activities and agent interactions at the micro-level generated project performance, which was measured in terms of project effort and duration at the macro level. The model used product and development process data from GE Hydro. Results demonstrated that design effort and project duration increased exponentially with product knowledge (complexity), that product development was more sensitive to the design of interfaces as compared to the design of components, and that designer knowledge played an important role in mitigating complexity. The implications for managers were that attention should be paid to the management of interfaces, to coordination thorough communication and consultation, and to increasing the rate of designer learning.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Prototypes have been identified as critical artifacts for generating and developing innovative products and thus stimulating economic growth. However, prototyping is also associated with a large sunk cost including the extensive time and resources required to make physical prototypes. While a wide variety of prototyping methods have been proposed to reduce the cost and time of prototype development and increase the likelihood of final product success, the majority of research to date has explored the impact of these methods using simplistic measures of the technical performance of a design. Just as it is not enough to measure the effectiveness of ideation methods only by the quantity of ideas produced, we argue that it is not enough to measure the effectiveness of prototyping frameworks through technical performance alone. Without this fundamental knowledge, we cannot understand the impact of prototyping methods on final design success or failure. Therefore, the purpose in this work is to explore the effects of a structured prototyping framework on a variety of design attributes, including user satisfaction, perceived value, technical quality, and ease of manufacturability. Specifically, the overarching research question this study seeks to answer is: what attributes of a final design are affected by the implementation of a prototyping framework? A partial factorial experimental design was used to collect data from designs produced by 77 student design teams; designs were analyzed using five robust product metrics derived from the literature. Results indicate that a structured prototyping framework can lead to improved overall design quality and that differences in the implementation of such a prototyping framework can affect the achievement of these design attributes. The findings of this work deepen our understanding of the relationship between prototyping methods and design refinement during the product development process.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Project managers are continuously under pressure to shorten product development durations. One practical approach for reducing the project duration is lessening dependencies between different development components and teams. However, most of the resource allocation strategies for lessening dependencies place the implicit and simplistic assumption that the dependency structure between components is static (i.e., does not change over time). This assumption, however, does not necessarily hold true in all product development projects. In this paper, we present an analytical framework for optimally allocating resources to shorten the lead time of product development projects having a time-varying dependency structure. We build our theoretical framework on a linear system model of product development processes, in which system integration and local development teams exchange information asynchronously and aperiodically. Utilizing a convexity result from the matrix theory, we show that the optimal resource allocation can be efficiently found by solving a convex optimization problem. We provide illustrative examples to demonstrate the proposed framework. We also present boundary analyses based on major graph models to provide managerial guidelines for improving empirical PD processes.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉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 (NPD〈sup〉3〈/sup〉). 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.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The problem of collective decision by design teams has received considerable attention in the scientific literature of engineering design. A much debated problem is that in which multiple designers formulate their individual preference rankings of different design alternatives and these rankings should be aggregated into a collective one. This paper focuses the attention on three basic research questions: (1) “How can the degree of concordance of designer rankings be measured?”, (2) “For a given set of designer rankings, which aggregation model provides the most coherent solution?”, and (3) “To what extent is the collective ranking influenced by the aggregation model in use?”. The aim of this paper is to present a novel approach that addresses the above questions in a relatively simple and agile way. A detailed description of the methodology is supported by a practical application to a real-life case study.〈/p〉