Market-driven Design Research Articles

Teaching Market-Driven Engineering Design with an Agent-Based Simulation Tool

In product design, there is often a disconnect between the engineers creating the product and the marketing team determining the best characteristics for the product. The research areas of “design for market systems” and “decision-based design” seek to bridge that disconnect through quantitative approaches that facilitate simultaneous technical design and marketing decisions. However, these market driven design frameworks have been primarily evaluated in the context of industry case studies, with limited integration into engineering education. This article presents the development and implementation of a simulation tool for teaching market-driven design in undergraduate engineering design courses. This classroom tool demonstrates the basic relationships between product design decisions, pricing and marketing choices, and predicted market success, by simulating the interactions among producers and consumers in a market system. The product attributes influence the cost and consumer utility of the product, which, along with price, affect market dynamics. The simulator was implemented in a third-year undergraduate design course to introduce the concept of market-driven product design, allowing student design teams to assess the impacts of different design variables on the market success of their design projects. Surveys and written reflections by the students were used to evaluate the simulator’s value in contributing to self-reported learning. The results showed that a majority of students expressed that the simulator provides a meaningful and engaging way to learn about market-driven design concepts.

An Agent-Based Model for Adoption of Clean Technology Using the Theory of Planned Behavior

Abstract Technology adoption in low-income regions is among the key challenges facing international development projects. Nearly 40% of the world's population relies on open fires and rudimentary cooking devices exacerbating health outcomes, deforestation, and climatic impacts of inefficient biomass burning. Clean technology alternatives such as clean cookstoves are among the most challenging technologies to approach their target goals through sustainable adoption due to a lack of systematic market-driven design for adoption. Thus, a method is needed to provide insight regarding how target customers evaluate and perceive causes for adopting a clean technology. The holistic approach of this study captures technology adoption through lenses of social networks, individual and society scale beliefs, and rational decision-making behavior. Based on the data collected in the Apac region in Northern Uganda, an agent-based model is developed to simulate emerging adoption behavior in a community. Then, four different scenarios investigate how adoption patterns change due to the potential changes in technology or intervention strategy. These scenarios include influence of stove malfunctions, price elasticity, information campaigns, and strength of a social network. Results suggest that higher adoption rates are achievable if designed technologies are more durable, information campaigns provide realistic expectations for users, policymakers, and education programs work toward women's empowerment, and communal social ties are recognized for influence maximization. The application of this study provides insight for technology designers, project implementers, and policymakers to update their practices for achieving sustainable and to the scale clean technology adoption rates.

A systematic estimation approach for the importance of engineering characteristics based on online reviews

Online reviews are a new source for the valuable voice of customers. By identifying the customer’s opinion, designers can comprehend the important features of a product to satisfy customer demand, thus enhancing the market competitiveness of the product. Customers have opinions on multiple aspects of products hidden in reviews, and sentiment divergence may exist. Moreover, there is a gap between customer requirements and the product’s system requirements. How to effectively analyze a large number of reviews to extract the aspect-level customer opinion and thus determine the most important product engineering characteristics in design are the critical challenges for market-driven design. A systematic requirement analysis framework is proposed in this work. First, a convolutional neural network and sentiment analysis are used for opinion mining of online reviews. Then, based on fuzzy logic, the customer sentiment divergence (which is quantified by controversy indexes) and the average sentiment of a requirement are used to determine the degree of satisfaction. Finally, based on the product’s quality function development matrix, the satisfaction and frequency of the customer requirements are used to estimate the importance of the product’s engineering characteristics, which identifies the focus of product design. A case study of a hair dryer is given to demonstrate the effectiveness of the proposed methods.

Exploiting user experience from online customer reviews for product design

Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data.

Influence of voluntary standards and design modifications on trampoline injury in Victoria, Australia

PurposeTo examine the influence of the voluntary Australian trampoline standard (AS 4989-2006) and market-driven design modifications on relevant trampoline injuries.MethodsTrend and intervention analysis on frequencies and proportions of hospital-treated trampoline-related...

Consequential Life Cycle Assessment With Market-Driven Design

Summary This article describes the development of a consequential life cycle assessment (cLCA) with endogenous market-driven design (MDD). Incorporation of MDD within cLCA (cLCAMDD) is beneficial because design decisions, influenced by market forces, are a major source of environmental emissions and resource consumption in many life cycle systems. cLCAMDD captures the environmental impact of these design responses resulting from industrial and policy decisions. We begin by developing the concept of cLCA-MDD, then present a case study that demonstrates how design responses can be endogenously captured in a cLCA analysis. The case study is in two parts: First, we incorporate endogenous design responses into a cLCA of a mid-size vehicle and, second, we conduct a policy analysis using a cLCA-MDD approach. The case study illustrates that cLCA-MDD can capture multiple “ripple effects” resulting from an industrial decision (e.g., downsizing a vehicle’s engine) or a policy decision (e.g., raising gasoline taxes) and that these effects significantly influence results. A key challenge of the approach is appropriately managing and communicating uncertainties associated with the choice of economic parameters or models. We discuss sources of uncertainty in cLCA-MDD and demonstrate a presentation scheme to facilitate communication of result sensitivity to uncertainties from input parameters, models, and model structure.

Market-driven design of innovative services: The case of multimedia home banking

This paper is aimed at empirically analyzing how user needs may drive the development of a special class of innovative services, namely multimedia home banking services. Homogeneous groups of potential users are identified in a sample of 400 customers of Italian retail banks, while a discrete-choice model is estimated relying upon a sub-sample of 148 individuals; the attitude of typical individuals towards a number of hypothetical designs of the new service is simulated. It is shown that differences in both socio-economic conditions of living and individual skills cause potential consumers to select different designs of multimedia home banking.