Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.     

Product platform configuration for product families: Module clustering based on product architecture and manufacturing process

Product family design utilizes platform-based modularity to enable product variety and efficient mass-production. While product platform issues have attracted much attention from both academia and industry, traditional product platform design for product families emphasized the platform-based modularity that focuses on product structure dimension (functional or non-functional) to realize cost reductions during the design stage. Both the design architecture and manufacturing process are objectives that define product family modularity (PFM). They should be closely coupled with each other for the planning and configuration of platforms. This paper focuses on the product platform configuration by recognizing and utilizing shared product modules for product families. Instead of clustering product modules only based on their design structure, this approach differentiates each product variant, and considers the inherent relationship between product architecture and processing activities. The advantage is that similar components can be grouped and produced on a shared platform, thus benefitting from lower cost and shorter production time. First, both the architecture and manufacturing information of the product variety are captured in matrix format. Then, hierarchical clustering is applied over the components to generate PFM. Finally, a set of platforms are constructed to efficiently process most components of variants.     

Progressive sharing of modules among product variants

Recent market transition from mass production to mass customization forces manufacturers to design products that meet individual requirements. In order to address the high cost of this practice, manufacturers develop product families with a common platform, whose variants are designed to meet different customer demands. Parallel to this transition, the dynamics of the market forces designers to develop products composed of modules that are standardized as much as possible across products, thus can be more resilient than complete designs in a changing world.Starting from an original set of different components, our method designs a modular common platform and additional modules, shared by subsets of the designs, from which variants are composed.We applied the method to the layout design of a set of products. Consequently, the geometric aspect of the product family optimization is emphasized, but functional aspects related to the product features and to customer needs are also addressed due to their manifestation in the layout. The design search space is explored using shape grammar rules that alter component geometry and therefore, functionality. The search for optimal design is performed using simulated annealing. Given different objective formulations or parameter settings, the method can be used to explore the solution space. A simple example problem demonstrates the feasibility of the method.     

A cost-based module mining method for the assemble-to-order strategy

The assemble-to-order (ATO) strategy is one of the most popular operations management approaches to achieve mass customized products while maintaining lower costs. In the ATO system, manufacturers keep inventory at the component and module level, and postpone product differentiation until the final stage of production. However, most research on modularity assumes that modules are already known in advance. In fact, in the ATO system, the determination of which components should be pre-assembled as modules mainly depends on the types and volumes of products ordered by customers. That is, module composition and volume should be derived dynamically from the product database based on updated customer orders. To bridge this gap, a two-stage cost-based module mining method for the assemble-to-order strategy is proposed. The first stage determines which sets of components can be formed (pre-assembled) as modules based on a list of customer orders. In the second stage, a cost-based selection approach is developed to evaluate the total cost of each module implementation project generated from the set of feasible modules. The module implementation project with the lowest cost is thus found and suggested to production managers.     

An in-process customer utility prediction system for product conceptualisation

Customer involvement in new product development, especially in the early stage of product conceptualisation, plays an important role for a successful product. In this study, a customer utility prediction system (CUPS) is proposed. The system comprises two modules, namely design knowledge acquisition module and customer utility evaluation module. In the design knowledge acquisition module, a knowledge acquisition technique called general sorting is utilised to establish a design knowledge hierarchy (DKH), in which design options can be generated. In the same module, customer voices towards diverse design options called customer-sensitive design criteria are solicited from customer requirements. Subsequently, in the customer utility evaluation module, a measurement for customer desirability, i.e. customer utility index (CUI), is formulated using conjoint analysis (CA) technique. Finally, the rated criteria are also used as inputs to a radial basis function (RBF) neural network for in-process customer utility prediction. A case study on cellular phone design is used to illustrate the proposed approach.     

Integrated product line design and supplier selection: A multi-objective optimization paradigm

Product line design is commonly used to provide higher product variety for satisfying diversified customer needs. To reduce the cost and development time and improve quality of products, companies quite often consider sourcing. Conventionally, product line design and supplier selection are dealt with separately. Some previous studies have been attempted to consider product line design and supplier selection simultaneously but two shortcomings were noted. First, the previous studies considered several objectives as a single objective function in the formulation of optimization models for the integrated problem. Second, positions of product variants to be offered in a product line in competitive markets are not clearly defined that would affect the formulation of marketing strategies for the product line. In this paper, a methodology for integrated product line design and supplier selection is proposed to address the shortcomings in which a multi-objective optimization model is formulated to determine their specifications and select suppliers for maximizing the profit, quality and performance as well as minimizing the cost of the product line. In addition, joint-spacing mapping is introduced to help estimate market share of products and indicate positions of product variants. The proposed methodology can provide decision makers with a better tradeoff among various objectives of product line design, and define market positions of product variants explicitly. The results generated based on the methodology could help companies develop product lines with higher profits, better product quality and larger market share to be obtained. A case study of a product line design of notebook computers was performed to illustrate the effectiveness of the proposed methodology. The results have shown that Pareto optimal product line designs and the specifications of product variants can be determined. Suppliers of components and modules can be selected with considerations of minimum sourcing cost, and maximum performance and quality of product variants. Prices and positions of the product variants can also be determined.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

Dynamical mining of ever-changing user requirements: A product design and improvement perspective

Previous studies carried out customer surveys by questionnaires to collect data for analyzing consumer requirements. In recent years, a large and growing body of literature has investigated the extraction of customer requirements and preferences from online reviews. However, since customer requirements change dynamically over time, traditional studies failed to obtain the change data of customer requirements and opinions based on sentiments expressed in reviews. In this paper, a new method for dynamically mining user requirements is proposed, which is used to analyze the changing behavior of product attributes and improve product design. Dynamic mining differs from the traditional need acquisition mainly in three aspects: (1) it involves dynamically mining user requirements over time (2) it adds changes in manufacturers’ opinions to the analysis (3) it allows for product improvement strategies based on the changing behavior of product attributes. First, text mining is adopted to collect customer and manufacturer review data for different time periods and extract product attributes. A Natural Language Processing tool is used to measure the importance weight and sentiment score of product attributes. Second, an approach for dynamically mining user requirements is introduced to classify product attributes and analyze the changes of attribute data in three categories over time. Finally, an improvement strategy for next-generation product design is developed based on the changing behavior of attributes. Moreover, a case study on vehicles based on online reviews was conducted to illustrate the proposed methodology. Our research suggests that the proposed approach can accurately mine customer requirements and lead to successful product improvement strategies for next-generation products.     

Multiple platforms design and product family process planning for combined additive and subtractive manufacturing

Industry 4.0 promotes the utilization of new exponential technologies such as additive manufacturing in responding to different manufacturing challenges. Among these, the integration of additive and subtractive manufacturing technologies can play an important role and be a game changer in manufacturing products. In addition, using product platforms improves the efficiency and responsiveness of manufacturing systems and is considered an enabler of mass customization. In this paper, a model to design multiple platforms that can be customized using additive and subtractive manufacturing to manufacture a product family cost-effectively is proposed. The developed model is used to determine the optimal number of product platforms, each platform design (i.e. its features set), the assignment of each platform to various product variants, and the macro process plans for customizing the platforms while minimizing the overall product family manufacturing cost.The multiple additive/subtractive platforms and their process plans are determined by considering not only the commonality between the product variants but also their various manufacturing cost elements and the customer demand of each variant. The design of multiple product family platforms and their process plans is NP-hard problem. A genetic algorithm-based model is developed to reduce the computational complexity and find optimal or near optimal solution. Two case studies are used to illustrate the developed multiple platform model. The model results were compared with a single platform model in literature and the results demonstrate the multiple platform model superiority in manufacturing product families in lower cost. The use of the developed model enables manufacturing product families cost efficiently and allows manufacturers to manage diversity in products and market demands.     

Responsive consistency restoration in interactive product configuration by content-addressable memory

With the context of individualized product design in mass customization, the practice of product configuration is transformed from traditional unidirectional process into bidirectional design interactions. In this interactive configuration manner, maintaining the consistency of configuration assignments, often referred to as consistency restoration, is one of the primary issues which should be addressed. The task of consistency restoration faces two great challenges: the uncertainty and individualized flexibility of customer requirements. The uncertainty turns problem dynamic and deteriorates the computational complexity of existing consistency restoration methods. The individualized flexibility turns the task of consistency restoration ill-structured. Consequently, these two characteristics increase the response time in interactive configuration and make companies face the challenge of ‘customization-responsiveness squeeze’. To leverage this problem, this paper proposes a responsive approach to facilitate the task of consistency restoration. A methodology based on content-addressable memory (CAM) is established, which compiles a CSP-based consistency restoration into a memory recalling process and automatically corrects the incompleteness and inconsistency within the memory. Specific orientation mechanism based on customer preference is proposed to introduce customer flexibility into CAM model. The resulting oriented-CAM method is demonstrated by a case study and the performance and responsiveness are analyzed.     

Product portfolio identification based on association rule mining

It has been well recognized that product portfolio planning has far-reaching impact on the company's business success in competition. In general, product portfolio planning involves two main stages, namely portfolio identification and portfolio evaluation and selection. The former aims to capture and understand customer needs effectively and accordingly to transform them into specifications of product offerings. The latter concerns how to determine an optimal configuration of these identified offerings with the objective of achieving best profit performance. Current research and industrial practice have mainly focused on the economic justification of a given product portfolio, whereas the portfolio identification issue has been received only limited attention. This article intends to develop explicit decision support to improve product portfolio identification by efficient knowledge discovery from past sales and product records. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with requirement analysis enacted among customers, marketing folks, and designers. An association rule mining system (ARMS) is proposed for effective product portfolio identification. Based on a scrutiny into the product definition process, the article studies the fundamental issues underlying product portfolio identification. The ARMS differentiates the customer needs from functional requirements involved in the respective customer and functional domains. Product portfolio identification entails the identification of functional requirement clusters in conjunction with the mappings from customer needs to these clusters. While clusters of functional requirements are identified based on fuzzy clustering analysis, the mapping mechanism between the customer and functional domains is incarnated in association rules. The ARMS architecture and implementation issues are discussed in detail. An application of the proposed methodology and system in a consumer electronics company to generate a vibration motor portfolio for mobile phones is also presented.     

Adaptive generic product structure modelling for design reuse in engineer-to-order products

In product lifecycle management, the efficiency of information reuse relies on the definition and management of equivalence information between various product data and structure representations. Equivalence information ensures the consistency and traceability of product information throughout the product lifecycle. The sales-delivery process of engineer-to-order (ETO) products presents a great potential for design reuse, i.e. the reuse of previously validated design solutions in the design of new product variants according to customer-specific requirements. A product family data model that focuses on the interdependencies of viewpoints on information will therefore improve the setup of design reuse mechanisms such as modularity. This paper describes the Adaptive Generic Product Structure (AGPS), a dynamic structure-based product family modelling approach that enables the systematic aggregation of product variants and their distinctive components. The purpose of the approach is to capitalize on the expanding component variety developed within previous product variants as early as the sales lead phase of the sales-delivery process, in order to reduce customer-driven design costs and shorten lead-times. An illustrative example based on the aerospace industry is presented.     

An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

A knowledge system for integrated product design

This paper discusses the architecture and implementation issues for a knowledge system to assist in product design. The goals of the concurrent Design Advisor (CODA) are to enhance the quality of designs by 25 percent and the efficiency by a factor of 10. The improvement springs from the integration of diverse knowledge bases, ranging from customer needs to product evaluation, and from process configuration to production control. One source of efficiency is the automation of many routine tasks, thereby increasing user productivity. Another source is the increase in the quality of initial designs, which obviates the need for numerous iterations in the design process due to poor manufacturability. CODA is based on the general architecture of the Creativity Support System, an expert system for assisting users in specific domains requiring creative solutions. The bilevel structure of the system consists of a domain-independent module containing general tools and techniques for creative problem-solving, and a domain-dependent module incorporating knowledge specific to particular fields of application. The utility of this approach is illustrated in the realm of concurrent product design by demonstrating a CODA within the general architecture of the system.     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

Collaboration planning framework (CPF) to support distributed product development

Current marketplace is undergoing major changes that will affect the way organizations conduct their business. Organizations need to respond to a geographically dispersed marketplace. This can be achieved by leveraging globally distributed resources to fully understand and interpret individual customer needs. That is, organizations need to integrate their operations (product development) in a way that will allow dynamic response to market changes. Computer-supported collaborative engineering could provide the integrating mechanisms needed to integrate distributed operations. The change to collaborative engineering should be based on sound and comprehensive methodologies that can analyze current practices, assess their ability to be performed collaboratively, restructure organizational practices to enhance their performance in a collaborative environment, select appropriate tools to support practices, and provide an implementation plan. This paper presents a framework to build computer-supported collaborative product design and development operating in a distributed environment. The framework is composed of six modules that provide a systematic procedure to plan for computer supported collaborative engineering.     

Configuration optimization of service solution for smart product service system under hybrid uncertain environments

Configuration of service solution is recognized as a crucial task for smart product service system (PSS) development, which provides an effective way to meet dynamic customer requirements. New smart attributes, broader stakeholder value and hybrid uncertainty occur inherently in the service solution configuration for smart PSS. These emerging characteristics lead to urgent updating need of the existing approaches to service solution configuration optimization. Therefore, the current study proposes a systematic approach to optimization of smart product service (SPS) solution configuration, aiming to achieve the best smart capability and value symbiosis among stakeholders of the configured SPS schemes. In the proposed approach, a new attributes system is proposed to describe the smart service module instances, and then employed as criteria to determine the configuration parameters. A novel rough-fuzzy data envelopment analysis (DEA) method is proposed to obtain the configuration parameters of each service module, with objective of effectively handling the hybrid uncertainties involved in the configuration environments. In addition, a bi-objective optimization model for SPS solution configuration considering smart capability efficiency and value symbiosis efficiency is proposed, and it is solved by an adaptive non-dominated sorting genetic algorithm II (ANSGA-II) to acquire a Pareto set of optimized configuration solutions. Finally, an application of this systematic approach to smart vehicle service demonstrates the feasibility and effectiveness of the proposed approach.     

A new approach for product evaluation based on integration of EEG and eye-tracking

Exploiting biometric measures, especially neurophysiological data of evaluator for product evaluation is advantageous at avoiding bias and subjectivity in expert scoring process. This paper proposes an approach that integrates electroencephalograph (EEG) and eye-tracking (ET) data in a new way to derive multi-faceted supportive information for product evaluation. Firstly, emotion recognition from EEG signals of evaluator is carried out with a spatial–temporal neural network. Then, based on correlations between emotions and preferential judgement, general customer preference toward product design scheme is inferred from emotions by fuzzy system. Finally, general preference is integrated with ET data at application-level to quantify fine-grained customer preferences toward design modules and visual attractiveness. This approach is verified with a case study which evaluates six designs of frontal area of automotive interior, and valuable supportive information for design decision-making is yielded. Also, comprehensive analysis is conducted and the results verify the effectiveness of proposed approach.     

Generation and evaluation of product concepts by integrating extended axiomatic design,quality function deployment and design structure matrix

Existing product concept generation and evaluation methods are mainly based on designers' experience to determine design schemes in the process of product development, which is time-consuming and ineffective. This paper proposes an approach to generate and evaluate design concepts by integrating the extended axiomatic design, quality function deployment and design structure matrix. Different design domains are mapped for matrix operations to generate feasible concepts based on design criteria. A domain mapping matrix is built to determine technical measures, functional requirements and design parameters based on customer requirements. The proposed approach provides a structured method to quantify, validate and qualify design concepts. A case study of the design of a hand rehabilitation device demonstrates the effectiveness of the proposed method.