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Recently, online user-generated data have emerged as a valuable source for industrial applications. In the consumer product area, many studies analyze online data and draw implications on product design. However, most of them treat online customers as one group with the same preferences, while customer segmentation is a key strategy in conventional market analysis. This paper proposes a new methodology based on text mining and network analysis for online customer segmentation. First, the method extracts customer attributes from online review data. Then, a customer network is constructed based on these attributes and predefined networking rules. For networking, a new concept of "topic similarity" is proposed to reflect social meaning in the customer network. Finally, the network is partitioned by modularity clustering, and the resultant clusters are analyzed to understand segment properties. We validate our methodology using real-world data sets of smartphone reviews. The result shows that the proposed methodology properly reflects the heterogeneity of the online customers in the segmentation result. The practical application of customer segmentation is presented, illustrating how it can help companies design target-customer-oriented products. [ABSTRACT FROM AUTHOR]

The optimal design of hybrid power generation systems (HPGSs) can significantly improve the technical and economic performance of power supply. However, the discrete-time simulation with logical disjunctions involved in HPGS design usually leads to nonsmooth optimization model, to which well-established techniques for smooth nonlinear optimization cannot be directly applied. This paper casts the HPGS design optimization problem as a multidisciplinary design optimization problem with complementarity constraints, a formulation that introduces a complementarity formulation of the nonsmooth logical disjunction, as well as a time horizon decomposition framework, to ensure a fast local solution. A numerical study of a stand-alone hybrid photovoltaic/wind power generation system is presented to demonstrate the effectiveness of the proposed approach. [DOI: 10.1115/1.4002292]

Product take-back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. 'Selling a service' (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back but current methods assume a fixed leasing period. What is needed is a method for fine tuning the time span of customers' life cycles in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market and also across different lifecycles within each segment. Post-optimal studies help determine feasibility of component redesign in addition to parts consolidation. The proposed method is illustrated through an example involving personal computers. [DOI: 10.1115/1.4002142]

Product design is one of the most important sectors influencing global sustainability, as almost all the products consumed by people are outputs of the product development process. In particular, early design decisions can have a very significant impact on sustainability. These decisions not only relate to material and manufacturing choices but have a far-reaching effect on the product's entire life cycle, including transportation, distribution, and end-of-life logistics. However, key challenges have to be overcome to enable eco-design methods to be applicable in early design stages. Lack of information models, semantic interoperability, methods to influence eco-design thinking in early stages, measurement science and uncertainty models in eco-decisions, and ability to balance business decisions and eco-design methodology are serious impediments to realizing sustainable products and services. Therefore, integrating downstream life cycle data into eco-design tools is essential to achieving true sustainable product development. Our review gives an overview of related research and positions early eco-design tools and decision support as a key strategy for the future. By merging sustainable thinking into traditional design methods, this review provides a framework for ongoing research, as well as encourages research collaborations among the various communities interested in sustainable product realization. [DOI: 10.1115/1.4002308] Keywords: sustainable design, eco-design, product design, manufacturing, supply chain

Product recovery has become a field of rapidly growing interest for product manufacturers as a promising solution for product stewardship as well as for economic viability. Because product recovery is highly dependent on the way a product is designed, it should be considered in the design stage so that the product is designed to have high recovery. potential. To make a product easy to recover, manufacturers first need to understand the links between product design and recovery profit and be able to evaluate which design is better than others and why. This study proposes a framework for analyzing how design differences affect product recovery and what architectural characteristics are desirable from the end-of-life perspective. For better design evaluation, an optimization-based model is developed, which considers product design and recovery, network design simultaneously. For illustration, a comparative study with cell phone examples is presented. Three cell phone handset designs that share the same design concept but have different architectural characteristics are created, and the recovery potential of each design variant is evaluated under three different recovery scenarios. The results show that the framework can highlight preferred design alternatives and their design implications for the economic viability of end-of-life recovery. [DOI: 10.1115/1.4001411]

Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC)--a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving this class of problem. In this paper, we propose a new decomposition algorithm for the MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one solution. [DOI: 10.1115/1.4001206]

Environmental protection legislation, consumer interest in 'green' products, a trend toward corporate responsibility and recognition of the potential profitability of salvaging operations, has resulted in increased interest in product take back. However, the cost effectiveness of product take-back operations is hampered by many factors, including the high cost of disassembly and a widely varying feedstock of dissimilar products. Two types of decisions, must be made, how to carry out the disassembly process in the most efficient. manner to 'mine' the value-added that is still embedded in the product, and then how to best utilize that value-added once it is recovered. This paper presents a method for making those decisions. The concept of a transition matrix is integrated with mixed integer linear programming to determine the extent to which products should be disassembled and simultaneously determine the optimal end-of-life (EOL) strategy for each resultant component or subassembly: The main contribution of this paper is the simultaneous consideration of selective disassembly multiple products, and the value added that remains in each component or subassembly. Shared disassembly operations and capacity limits are considered. An example using two cell phone products illustrates application of the model. The obtained results demonstrate the most economical level of disassembly for each cell phone and the best EOL options for each resultant module. In addition, the cell phone example shows that sharing disassembly operations between different products makes disassembly more cost effective compared with the case in which each product is disassembled separately. [DOI: 10.1115/1.4001207]

Recently, online user-generated data have been used as an efficient resource for customer analysis. In the product design area, various methods for analyzing customer preference for product features have been suggested. However, most of them focused on feature categories rather than product components which are crucial in practical applications. To address that limitation, this paper proposes a new methodology for extracting sub-features from online data. First, the method detects phrases in the data and filtered them using product manual documents. The filtered phrases are embedded into vectors, and then they are divided into several groups by two clustering methods. The resulting clusters are labeled by analyzing items in each cluster. Finally, cue phrases for sub-features are obtained by selecting clusters with labels representing product features. The proposed methodology was tested on smartphone review data. The result provides feature clusters containing sub-feature phrases with high accuracy. The obtained cue phrases will be used in analyzing customer preferences for sub-features and this can help product designers determine the optimal component configuration in embodiment design. [ABSTRACT FROM AUTHOR]

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit. [DOI: 10.1115/1.2838336]

Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation. [DOI: 10.1115/1.2205870] Keywords: hierarchical multilevel optimization, analytical target cascading, uncertainty, probabilistic approach, design targets and consistency, coordination strategy, moments

In this article the process of rigorously setting supersystem targets in an enterprise context is explored as a model-based approach termed 'analytical target setting.' Engineering design decisions have more value and lasting impact if they are made in the context of the enterprise that produces the designed product. Setting targets that the designer must meet is often done at a high level within the enterprise, however, with inadequate consideration of the engineering design embodiment and associated cost. For complex artifacts produced by compartmentalized hierarchical enterprises, the challenge of linking the target setting rationale with the product instantiation is particularly demanding. The previously developed analytical target cascading process addresses the problem of translating top level design targets into design targets for all systems in a multilevel hierarchically structured product, so that local targets are consistent with each other and top targets can be met as closely as possible. The effectiveness of linking analytical target setting and target cascading is demonstrated in a hybrid electric automotive truck vehicle example. The manufacturer introduces a new product (hybrid electric truck) in the market under uncertainty in fuel prices during the life cycle of the vehicle. The example demonstrates a clear interaction between the enterprise decision making and the engineering product development. [DOI: 10.1115/1.2125972]

Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This article proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter. [ABSTRACT FROM AUTHOR]

The importance-performance analysis (IPA) is a widely used technique to guide strategic planning for the improvement of customer satisfaction. Compared with surveys, numerous online reviews can be easily collected at a lower cost. Online reviews provide a promising source for the IPA. This paper proposes an approach for conducting the IPA from online reviews for product design. Product attributes from online reviews are first identified by latent Dirichlet allocation. The performance of the identified attributes is subsequently estimated by the aspect-based sentiment analysis of IBM Watson. Finally, the importance of the identified attributes is estimated by evaluating the effect of sentiments of each product attribute on the overall rating using an explainable deep neural network. A Shapley additive explanation-based method is proposed to estimate the importance values of product attributes with a low variance by combining the effect of the input features from multiple optimal neural networks with a high performance. A case study of smartphones is presented to demonstrate the proposed approach. The performance and importance estimates of the proposed approach are compared with those of previous sentiment analysis and neural network-based method, and the results exhibit that the former can perform IPA more reliably. The proposed approach uses minimal manual operation and can support companies to take decisions rapidly and effectively, compared with survey-based methods. [ABSTRACT FROM AUTHOR]

Short life cycle products are frequently replaced and discarded, even though they are resource-intensive products. Technological advances and rapid changes in demand have led manufacturers to develop their innovative next-generation products quickly, which not only enables multiple generations to coexist in the market but also speeds up the technological obsolescence of products. Diversity of collected end-of-life (EoL) and rapid technological obsolescence make the effective recovery of EoL products difficult. The low utilization rate of EoL products causes serious environmental problems such as e-waste and waste of natural resources. To deal with the conflict between the technical evolution of products and the promotion of social benefits in solving environmental problems, this paper focuses on the impact of generational commonality effects on the overall production process including manufacturing and remanufacturing. Generational commonality leads to an increase in the efficiency of manufacturing due to reducing related costs. Additionally, from the remanufacturing perspective, the interchangeability between generations can help collect the EoL products needed for remanufacturing. On the other hand, it causes a weakening of the level of performance and technology evolution between generations that significantly affect the demand for short life cycle products. Therefore, this study identifies these trade-offs of generational commonality levels in both manufacturing and remanufacturing based on a quantitative approach. This study finds how different pricing strategies, production plans, and recovery costs are based on the designs of a new generation with a different degree of generational commonality. [ABSTRACT FROM AUTHOR]

This paper presents a novel mixed integer linear programing (MILP) formulation for finding the optimal layout of a fixed number of identical turbines that maximizes wind farm power generation. Previous MILP formulations with power maximizing objectives discretize the feasible space by using a grid of possible turbine locations. The proposed MILP formulation takes a different approach by allowing unrestricted placement of turbines, but treats wake cone overlap as a binary outcome. The rationale behind the proposed formulation is that the expansion of the feasible space for turbine placement in the proposed formulation would offset the disadvantage of using a lower fidelity binary wake cone overlap model. For small wind farms, the proposed formulation was able to produce superior layouts compared to a grid-based MILP formulation. [ABSTRACT FROM AUTHOR]

As awareness of environmental issues increases, the pressures from the public and policy makers have forced original equipment manufacturers (OEMs) to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical due to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying a better predictive model. Unlike other major approaches in literature such as distributed lag models or DLMs, the predictive model selection algorithm focuses on the predictive power over new or future returns and extends the set of candidate models. The case study of reusable bottles shows that the proposed algorithm can find a better predictive model than the DLM. [ABSTRACT FROM AUTHOR]

In order for remanufacturers to succeed in the market, it is important for them to be capable of ensuring in advance that a product is suitable for remanufacturing and that a remanufactured product will provide greater economic and environmental value than a brand-new product. This paper provides an approach to estimate the economic and environmental advantages of a remanufactured product. Focusing on the fact that advantages are greatly influenced by the nature of a product (i.e., its design and lifetime characteristics) as well as the timing of the remanufacturing, this paper proposes a model for assessing the time-varying advantages of remanufacturing a given product. The model provides an objective, quantitative method to compare a remanufactured product with an equivalent brand-new version of the product. Focus is placed on three perspectives: unit production cost, environmental impact, and net profit. By providing a multidimensional assessment tool for measuring product remanufacturability, the model is expected to assist remanufacturers make informed and effective decisions concerning product planning. It also helps marketing activities by supporting an effective sustainability communication with customers. Two versions of the model are presented, each of which targets a specific product type: (1) a product with only physical deterioration and (2) a product with both physical deterioration and technological obsolescence. Each version of the model is illustrated by utilizing an automotive alternator and a desktop computer, respectively. [ABSTRACT FROM AUTHOR]

In a market with rapid changes in technology and customer preferences, technological obsolescence of end-of-life products poses a significant challenge to product recovery. Remanufacturing with optimal part upgrades can be a promising solution for overcoming the obsolescence. This paper proposes a model for positioning a remanufactured product. By considering original product design, target market (i.e., customer preferences and competing products), and recovery economics, the model helps to find optimal specifications and the selling price of a remanufactured product at which maximum remanufacturing profit is expected. Two versions of the model are presented under different assumptions on product takeback. The first model assumes that the remanufacturer passively accepts all returns without paying any financial incentives. The second model assumes that the remanufacturer buys back end-of-life products so as to control the quality and quantity of returns. The two models are illustrated with the example of a desktop computer. [ABSTRACT FROM AUTHOR]