The online shopping has been much easier and popular, and meanwhile brings new challenges and opportunities to the field of product design and marketing sale. On one hand, product manufacturers find it challenging to produce new popularly accepted products to meet the customers’ needs; on the other hand, end customers usually feel it difficult to buy ideal goods that they really want, even if navigating a huge amount of commodities. There are indeed a ‘communication gap’ between the customers and manufacturers. As an effort to partially resolve the issue, this paper proposes a novel product synthesis approach from ‘voice of the customer’ over product knowledge graphs. Here the voice of customers mainly refer to the buyers’ product reviews from online shopping platforms or blogs, while the product knowledge graph is constructed containing professional hierarchical product knowledge on its properties based on ontological models. Using the technologies of natural language processing, we first extract the customs’ polarities on each specific aspect of a product, which are then transited to design requirements on the product’s design components. Based on the requirement extractions, and the pre-built product knowledge, semantic web and reasoning techniques are utilized to synthesize a novel product that meets more customer needs. Typical case studies on mobile phones from raw online data demonstrate the proposed approach’s performance.

Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.

Improving design for market systems analysis relies on understanding the motivations and interactions among producers and consumers. Producers should theoretically develop their strategies for designing new products based on consumer demand and the expected profits from their sales. In this study, an agent-based modeling approach is proposed to simulate consumer and producer behavior for use in market systems analysis, and it is demonstrated through a simplified automobile market. In the model, consumers make heterogeneous purchasing decisions based on product attributes, which provides the producers with insights into their preferences and how to improve upon these design attributes over time. Emergent behavior of the model shows that analyzing the behavior of consumers provides the opportunity for producers to compete which one another with different strategies to improve their designs by investing in technology improvements. This lays the foundation for future work that can model how different business and regulatory strategies, social structures, and policies influence consumer and producer behavior, which in turn influences economic, environmental, and social impacts.

Existing research on the design and analysis of front-loading washing machines has primarily focused on maximizing performance of a single product based on specific disciplinary analyses (e.g., vibration, dynamics). This design approach does not necessarily guarantee low costs, high sales, and maximum profit. Moreover, in order to target a variety of different customer needs, washing machines should be thought of and designed as a product family. In this paper, we suggest a value-driven design approach for a family of front-loading washing machines to identify promising solutions based on stakeholder’s preference. To create a value function, the sales volume for front-loading washers is estimated based on sales rank data, and then the net present value (NPV) is formulated by using the estimated sales volume and a demand sensitivity curve derived from the literature and publicly available data. The result shows that we can determine product family design candidates that maximize NPV, performance, and commonality of scalable and platform (i.e., shared) variables in the washer family. We also investigate the effectiveness of reducing the complexity of the value function based on rank ordering and parametric studies of the attributes used to compute NPV.

Online product reviews have become an efficient source to gather consumer needs, instead of going through the labor-intensive surveys. The contribution of the paper is to relate the content of online reviews to a product’s sales rank, that implicitly reflects the needs and motivation behind what drives customers to purchase the product. In particular, the review content includes product features stated in the review, together with the sentiment expressed towards the feature. Part-of-speech tagging is used to extract the features and sentiment from the reviews. The extracted data from reviews and price then subsequently become independent variables in the regression model, while sales rank is the dependent variable. An experiment is run for the wearable technology products to illustrate the methodology and interpret the results. In general, the features in reviews that are related to sales rank significantly are button, calorie tracker, design, time functions, and waterproof abilities. Moreover, the products are further stratified based on price average. In the cluster of the most expensive items, the sales rank is found to be not significantly related to price.

This paper presents a case study in engineering for global development. It introduces the Village Drill, which is an engineered product that has now — five years after its introduction to the market — enabled hundreds of thousands of people across 15 countries and three continents to have access to clean water. The Village Drill creates a 15 cm (6 inch) borehole as deep as 76 m (250 feet) to reach groundwater suitable for drinking. It was designed and developed by the authors together with a team of talented individuals from various disciplines. The case study presents facts and figures for the actual development and sustaining scenario and are unaltered for the purpose of publication. This approach provides the reader with a realistic view of the development time, testing conditions, fundraising, and the work needed to sustain the drill through five years of sales and distribution. The purpose of the case study is to provide sufficient and frank data about a real project so as to promote discussion, critique, and other evaluations that will lead to new developments that inspire and inform successful engineering for global development. As part of the case, the paper describes six fundamental items related to the endeavor; the product, the customer, the impact, the manufacturing, the delivery, and the revenue model of the drill.

Different from typical mechanical products, tickets for movies and performing arts can be considered as a special type of consumer products. Compared to widely known box-office receipts prediction with single-output in movie industry, estimating the market share and price for performing arts is still a challenging problem due to high dimensional datasets yet limited number of samples. This paper describes a data-driven decision support system to help arts managers make strategic decisions, especially on session-determination and price-setting, considering price discrimination and prediction on the corresponding sales volume. Eight different attributes from the database, with multiple labels in each attribute, are used to accurately and comprehensively represent and classify the characteristics of performing arts in each genre. A web-based influence factor is also defined to quantify the popularity and publicity of performing arts. For this multi-input and multi-output problem, support vector regression (SVR) is employed and its optimal parameters are determined using genetic algorithm (GA) and particle swarm optimization (PSO) respectively. Price utility axiom with the law of demand is applied to maximize the receipts. Compared to artificial neural networks (ANN), those two optimization based SVR methods perform much better, in terms of effectiveness and reliability.

As the availability and affordability of consumer products continues to increase around the world, consumers — especially those in developing countries and living on less than $10/day — will express more discerning in their tastes and preferences. Design teams have already been operating in design for the developing world contexts for many years and more are moving into the arena on a regular basis. Many designers do not have cultural knowledge of the customers cultures they are designing for. Cultural ignorance can lead to misinterpretation of customer needs that can lead to products that do not satisfy customer needs and results in disappointed customers, low sales figures, and a frustrated design team. The Customer Needs Cultural Risk Indicator (CNCRI) method introduced in this paper provides a method for design teams to rapidly analyze customer needs for “Risk Indicators” in customer needs based upon cultural differences between the customers and the design team. By understanding early on in the design process where a lack of cultural knowledge may be a risk to the design, the design team can make informed decisions on how to satisfy customer needs effectively.

As awareness of environmental issues increases, the pressures from the public and policy makers have forced OEMs to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical with regards to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying better predictive models. Unlike other major approaches in literature (distributed lag model or DLM), the predictive model selection algorithm focuses on the predictive power over new or future returns. The proposed algorithm extends the set of candidate models that should be considered: autoregressive integrated moving average or ARIMA (previous returns for future returns), DLM (previous sales for future returns), and mixed model (both previous sales and returns for future returns). The prediction performance measure from holdout samples is used to find a better model among them. The case study of reusable bottles shows that one of the candidate models, ARIMA, can predict better than the DLM depending on the relationships between returns and sales. The univariate model is widely unexplored due to the criticism that the model cannot utilize the previous sales. Another candidate model, mixed model, can provide a chance to find a better predictive model by combining the ARIMA and DLM. The case study also shows that the DLM in the predictive model selection algorithm can provide a good predictive performance when there are relatively strong and static relationships between returns and sales.

In this paper, we consider a company including both a forward supply chain producing and supplying products to markets and an after-sales supply chain providing spare parts to fulfill after-sales commitments. Uncertainties in estimating product demand in the pre-markets, spare parts demands in the after-sales markets and qualified outputs of production facilities throughout these two chains are considered in this problem. Stochastic qualified output of production facilities are due to their imperfect productions systems. We show that uncertainties propagate and qualified flow depreciates by moving material, components and products from the upstream to the downstream of these supply chains and these phenomena should be quantified to determine the service levels of this company in the pre and after-sales markets. Reliable flows preserve chains’ service levels against uncertainties and their propagated effects. The consideration of the interactions of pre- and after-sales operations as concurrent planning of these two chains and considering both demand and supply side uncertainties and their propagated effects are the important contributions of this paper. Here, we propose a mathematical model for determining the best marketing strategies for this company (price, warranty length and service levels) and preserving reliable flow dynamics throughout the chains’ networks. The proposed model and its solution approach are tested using data from an engine production company. We derive some managerial insight by analyzing the correlations among the marketing strategies and how they affect each other.

One requirement in our sophomore design class is for the students to work in teams to design and build devices that satisfy several requirements subject to physical constraints. The evaluation of these devices is based on how individuals and the team perform on written documents as well as how well the device performs during testing and satisfies given design criteria. The performances in various parts of this assignment are used as a basis for student outcomes assessment. The paper will provide an example of a team project description including the figure of merit that assesses the functionality of the device and the rubric used to assess the quality of the design in four areas: • creativity demonstrated in the design concept, • creativity demonstrated in the execution of the concept, • overall craftsmanship and esthetics, and, • robustness, plus a sales brochure. Examples of student work and assessment documentation will also be provided.

Recent trends towards service orientation make manufacturing, especially in developed economies, to move into a more service oriented strategy. One solution is to fulfill customer requirements by a product and its after-sales services. However, the actual impact of this transformation on manufacturers has never been explored. This study aims to understand how evolution of service in terms of service-width and length takes place over time, and how it impacts on the manufacturer and is impacted from it. We carry out a longitudinal case-based study on a particular service family (iTune) in consumer electronics using Pearson correlation and linear regression using SPSS software package. Considering consistent intention to improve existing ones and introduce new services, following results are gained: (1) Manufacturing with higher research and development capabilities are expected to have great potential to have strong after-sales portfolio both in terms of service-width and length. A strong portfolio in this type of manufacturing is expected to bring economic advantage. (2) Manufacturing with higher liabilities is expected to have more economic advantage in increasing service-width. (3) Manufacturing with robust infrastructure are expected to have more economic advantage in increasing service-length.

In competitive market environments, strategies that adding services to products for sales promotion are now moved to integrate products and services for satisfying diverse customer needs, and the number of these cases is gradually increasing. Trends of integrating products and services lead to the emergence of a product-service system (PSS). To implement and embody a PSS solution in new product development, a comprehensive design framework is allowed designers to facilitate the design factors of the PSS in complex business environments. The objective of this paper is to propose a PSS model to identify design factors for developing products and services by integrating object-oriented concepts and blueprinting in context of a business ecosystem. The proposed model is developed based on relationship between products and services matching with their design factors. The products and the services are then brought together to form a PSS. Functions and processes can be categorized to identify the design factors in different levels using the object-oriented concepts. Interaction between products and services lies on a PSS platform to form a product service system in blueprinting. To demonstrate of the effectiveness of the proposed model, we use a case study involving a smart phone.

To achieve “green profit” in their business, manufacturers who produce both new and remanufactured products must optimize their pricing and production decisions simultaneously. They must determine the buy-back price and take-back quantity of end-of-life products as well as the selling prices and production quantities of new and remanufactured products. With an aim to assist in optimal pricing and production planning, this paper presents a mixed-integer programming model that optimizes the three prices (of buyback, new and remanufactured products) and the corresponding production plan simultaneously. The model considers the two conflicting objectives of maximizing economic profitability and maximizing environmental impact saving. The model helps address potential barriers to remanufacturing, which include limited economic, and/or environmental sustainability of remanufacturing, imbalance between the supply of end-of-life products and the market demand for remanufactured products, and cannibalization of the sales of new products. The developed model is illustrated with an example of engine water pump.

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter ® and Facebook ® , have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

This article describes an advance in design optimization that includes consumer purchasing decisions. Decision-Based Design optimization commonly relies on Discrete Choice Analysis (DCA) to forecast sales and revenues for different product variants. Conventional DCA, which represents consumer choice as a compensatory process through maximization of a smooth utility function, has proven to be reasonably accurate at predicting choice and interfaces easily with engineering models. However the marketing literature has documented significant improvement in modeling choice with the use of models that incorporate non-compensatory (descriptive) and compensatory (predictive) components. The non-compensatory component can, for example, model a “consider-then-choose” process in which potential customers first narrow their decisions to a small set of products using heuristic screening rules and then employ a compensatory evaluation to select from this set. This article demonstrates that ignoring consider-then-choose behavior can lead to sub-optimal designs, and that optimality cannot be “recovered” by changing marketing variables alone. A new computational approach is proposed for solving optimal design problems with consider-then-choose models whose screening rules are based on conjunctive (logical “and”) rules. Computational results are provided using three state-of-the-art commercial solvers ( matlab , KNITRO , and SNOPT ).

Developing multiple-generation of products has become a mainstream tactic in today’s markets. The most notable case is Apple Inc.’s huge success with its iPod, iPhone and iPad product lines. Multiple-generation product lines require carefully planned strategies. Under a multiple-generation product development strategy, companies introduce a line of products to the market instead of introducing a single product to better utilize technology assets and resources in an elongated time span. For such product development and launch scenarios, cannibalization can occur, however. That is, multiple product generations compete in the same market and partition the company’s market shares. In the paper, we propose a new framework to predict the sales and introduction timing for every product generation in a multiple-generation product line while considering cannibalization. We demonstrate a case study implementing the proposed framework on Apple Inc.’s iPhone product line. The results show that the forecast performance of the model matches the realized real data.

The determination of modular product structures requires a continuous evaluation of the measures taken. Modularization is an appropriate method for product structuring. It provides the opportunity of taking into account the requirements from different product life phases, such as the development, production or after-sales phase. The assembly, as an essential part of the production for the company, demands easy mountable products. Short lead times and flexible processes are the main requirements among others. The methodical procedure presented in this paper proposes the integrative representation of the product structure and its resulting assembly sequence in a single diagram. The developed tool provides the opportunity to perform structuring measures to the product and coincidently show the impact on the assembly process. For the evaluation the use of key figures is proposed. The relevant input for the calculation of these key figures is similarly extracted from the diagram. Conclusively, the procedure is applied to an example of aircraft interior development.

An extensive range of companies have moved from providing a tangible product to offering long-term integrated product service solutions. The revenue from such offerings, in particular at the in-service stage of a product service system, has become a key area for profit generation within some companies. However, estimating the cost of providing such a service is one of the major challenges that companies encounter. The research in this paper presents a new framework for estimating the cost of in-service provision for a product service system. Within this framework the service cost factors are identified through a review of the literature and an analysis of industrial practice. Analysing seven years data of the service demanded by customers for a Chinese machine manufacturer and service provider, the key relationships are identified and described. The service provider in this study offers repair and maintenance services to ensure the machines are available for use at the customer’s sites. The results from the study found that there is not a strong correlation between the distance from the service provider to customer companies and the number of call outs for after-sales service. It was also found that there was no strong evidence to show that when there is convenient and economical transportation links between the service provider and the customer, the demand for after-sales service increases. However, there was a strong correlation between machine failure rate and the number of years in service. It was also found that preventative maintenance, which occurred after the second year of machine service, did not improve the reliability of the machine during the in-service phase. Based on the initial findings and outcomes of the study the next stage of the research is then discussed, describing how the framework will be used to estimate the cost of the in-service provision for a product service system.

In order to survive in the modern market, manufacturing companies need to invest in Advanced Product Development Technologies (APDTs). The problem many companies face, however, is the huge variety of possible technologies available. Examples of these are Stereo Lithography Apparatus (SLA), Selective Laser Sintering (SLS), Fused Deposition Modelling (FDM), Virtual Prototyping and many others. A literature review revealed that there were many attempts at implementing decision support tools, for the selection of the most appropriate APDT for a particular application. These tools were limited in scope to a small range of technologies. Examples of this are the several attempts in the field of Rapid Prototyping. Furthermore the approaches which exist focus on the technical requirements only of the machine and very rarely take into consideration the needs of the manufacturing organization as a whole. Therefore as a result, there is a lack of decision support tools for selecting APDTs while considering the different Product Development (PD) stages. One area in which these APDTs excel is in the reduction of lead times to develop a product. Therefore the implementation of such technologies can potentially reduce the time-to-market of companies investing in them. The question which remains unanswered is: how does one choose the technology which will deliver the greatest improvement with regards to time-to-market? To address this question, this research developed a framework which allows stakeholders in the organization to select the most appropriate technology to reduce time-to-market for companies following the Integrated Product Development (IPD) model. This framework is based on their requirements and a structured approach. This paper presents the framework developed to this end, which was implemented in an Information and Communication Technology (ICT)-based prototype tool. The novelty of the proposed framework lies in the fact that it combines Multiple Attribute Decision Methods (MADM), more specifically Analytical Hierarchical Process (AHP) and Simple Additive Weighting (SAW) along with the IPD model and continuous improvement practices. The validity of the proposed ICT tool was evaluated qualitatively by stakeholders from different PD perspectives such as sales and marketing, product design and manufacturing. The evaluation results provide a degree of evidence that the proposed framework and ICT tool were effective at meeting the time-to-market shortcomings of current product development practices of companies.