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.     

Integrated product and manufacturing system platforms supporting the design of personalized medicines

Pharmaceutical product customization, a prerequisite for personalized medicines, is currently a widely researched topic. Patient characteristics can be mapped and translated into parameters for designing patients’ individual treatment, i.e., the dosage form. However, current pharmaceutical manufacturing is dominated by mass production and lacks the capability and flexibility required to produce customized products. Mass customization is a proven successful approach in, for example, the manufacturing industry and thus has been discussed as an enabler for pharmaceutical product customization but has never been fully explored in a pharmaceutical context. Inspired by mass customization approaches in the manufacturing industry, this study proposes a novel methodology to develop integrated product and manufacturing system platforms for pharmaceutical products supporting a mass customization paradigm. The proposed methodology establishes sets of product and manufacturing system platform variants and suggests an approach to feasible platform design selection. The applicability of the proposed methodology is illustrated for diabetes treatment as a selected case example. Integrated platform designs are developed for the conventional treatment of a fully integral tablet design and for a design enabling product customization with a modularized tablet design. The manufacturing platforms are still embracing a mass production design in the methodology illustration and should elicit knowledge on the utility of the current production design in a mass customization context. The performance and utility of the respective platform are assessed in terms of production cost and patient benefit. The results suggest a substantial increase in patient benefit afforded by the modularized tablet design, however the production cost is increased. This trade-off between the production cost and patient benefit thus calls for novel manufacturing system concepts to achieve the feasible manufacturing of customized pharmaceutical products.     

A heuristic solution procedure for the dynamic lot sizing problem with remanufacturing and product recovery

Here we discuss the lot sizing problem of product returns and remanufacturing. Let us consider a forecast of demands and product returns over a finite planning horizon — the problem is to determine an optimal production plan. This consists of either manufacturing new products or remanufacturing returned units, and in this way meets both demands at minimum costs. The costs of course are the fixed set-up expenses associated with manufacturing and/or remanufacturing lots and also the inventory holding costs of stocks kept on hand.In addition to showing that a general instance of this problem is NP-Hard, we develop an alternative mixed-integer model formulation for this problem and contrast it to the formulation commonly used in the literature. We show that when integrality constraints are relaxed, our formulation obtains better bounds. Our formulation incorporates the fact that every optimal solution can be decomposed into a series of well-structured blocks with distinct patterns in the way in which set-ups for manufacturing and remanufacturing occur. We then construct a dynamic programming based heuristic that exploits the block structure of the optimal solution. We also propose some improvement schemes as well. Finally, our numerical testing shows that the heuristic performs very well as intended.     

A multi-representation re-ranking model for Personalized Product Search

In recent years, a multitude of e-commerce websites arose. Product Search is a fundamental part of these websites, which is often managed as a traditional retrieval task. However, Product Search has the ultimate goal of satisfying specific and personal user needs, leading users to find and purchase what they are looking for, based on their preferences. To maximize users’ satisfaction, Product Search should be treated as a personalized task. In this paper, we propose and evaluate a simple yet effective personalized results re-ranking approach based on the fusion of the relevance score computed by a well-known ranking model, namely BM25, with the scores deriving from multiple user/item representations. Our main contributions are: (1) we propose a score fusion-based approach for personalized re-ranking that leverages multiple user/item representations, (2) our approach accounts for both content-based features and collaborative information (i.e. features extracted from the user–item interactions graph), (3) the proposed approach is fast and scalable, can be easily added on top of any search engine and it can be extended to include additional features. The performed comparative evaluations show that our model can significantly increase the retrieval effectiveness of the underlying retrieval model and, in the great majority of cases, outperforms modern Neural Network-based personalized retrieval models for Product Search.     

Sustainable product development and manufacturing by considering environmental requirements

Sustainability in the development and manufacture of new products is a strategy that is widely accepted in principle, although not yet widely practiced. The integration of environmental requirements throughout the entire lifetime of a product needs a new way of thinking and new decision tools to be applied. This paper describes the concept of an approach to product development, based on a paradigm for sustainable manufacturing. Four examples of methodologies and decision tools are discussed, representing the most important sources of environmental impacts of a product. The emphasis lies on the integrating concept rather than on the details of the methodologies.     

面向企业集成的产品设计共享信息建模

企业间的信息集成是实施敏捷制造的基础,而产品设计信息的集成与共享是实现企业集成的重要手段之一。文章提出了一种基于中性文件机制实现企业间产品设计信息交换的模式,建立了支持该模式的共享信息模型,并给出了基于共享信息模型开发企业间信息交换接口的实现途径。所提出的模型和实现途径为实现企业间产品设计信息的集成提供了一种具有普遍意义的解决方案。     

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.     

A personalized recommendation procedure for Internet shopping support

The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.     

A taxonomy and decision support for the design and manufacture of types of product families

The realization that designing products in families can and does have significant technological and economic advantages over traditional single product design has motivated increasing interest in recent years in formal design tools and methodologies for product family design. However, currently there is no guidance for designers in the first key strategic decisions of product family design, in particular determining the type of product family to design. Hence, in this paper, first a taxonomy of different types of product families is presented which consists of seven types of product families, categorized based on number of products and time of product introduction. Next a methodology is introduced to support designers in deciding which type of product family is appropriate, based upon early knowledge about the nature of the intended product(s) and their intended market(s). From this information it follows both which manufacturing paradigm and which fundamental design strategies are appropriate for each type of product family. Finally, the proposed methodology is illustrated through a case study examining a family of whitewater kayaks.     

An approach to competitive product line design using conjoint data

In today’s highly competitive environment, where market oriented firms aim to maximize profits through customer satisfaction, there is an increasing need to design a product line, rather than a single product. The main goal of designing a profit maximizing product line is to target the ‘right product’ to the ‘right customer’. Although conjoint analysis has turned out to be one of the most widely used techniques for product line design, it falls to explicitly consider retaliatory reactions from competitors. In this paper, we propose a new conjoint-based approach to competitive new product line design, employing the Nash equilibrium concept. The optimal product line design problem for each firm is formulated as a nonlinear integer programming problem. In the absence of a closed-form solution, to compute the Nash equilibrium and to determine the optimal product line, we propose a two-phase procedure: a sequential iterative procedure in the first phase, and backward induction in the second. To solve the optimization problem in each of the iterations of the sequential procedure, we used the branch-and-bound method. The proposed approach is illustrated under several scenarios of competition using previously published conjoint data.     

Research on optimization of setup time for product in the multi-product production system

Consumers' consumption habits are more and more personalized and diversified, which makes the multi-product production system has been applied extensively in the factory worldwide. This brings a difficult problem to a large number of manufacturing enterprises: how to optimize the setup time of the product to achieve the purpose of improving the time efficiency. Based on this problem, this paper proposes the TCP technology for the optimization of setup time, that is, using the Times Series model, the Clustering Algorithm, and the Parallel Job technology in the Single Minute Exchange of Die (SMED), to form an application framework focusing on optimizing the product setup time. The validity of the technology is verified by a case study. This paper enriches the research field of setup time optimization, production planning, and the application of the Clustering Algorithm in the multi-product production system. It provides a new way for manufacturing enterprises to pursue an excellent efficiency of product setup time.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

Earliness/tardiness production planning approaches with due-window for manufacturing systems

Although the earliness/tardiness production planning approaches for manufacturing systems with due-date have appeared in the literature, in practice, customers prefer a time duration rather than an exact due-date. This kind of due-date is called due-window. This paper focuses on the production planning problems to minimize the total earliness and tardiness penalties with a due-window subject to the manufacturing resource constraints. Two models, one for mass manufacture and another for one-of-a-kind product (OKP) manufacture, are discussed separately. By means of mathematical deduction, the model for mass manufacture is translated into a linear programming problem and solved by a simplex method. In the case of OKP manufacture, the problem is reduced to a linear 0–1 programming model, using the elaborate definition of variables. The computational results show that both algorithms achieve the optimal production planning and are applicable to practical manufacturing systems.     

The effects of individually personalized computer-based instructional program on solving mathematics problems

This study investigated the effects of an individually personalized computer-based instructional program on the achievement and attitudes regarding mathematics computational problems and word problems of 104 middle school American students. Students were blocked by math entering knowledge based on pre-test scores, then randomly assigned to a personalized or non-personalized version of the computer-based instructional program. A significant two-way interaction (treatment by math entering knowledge) reflected that personalized higher-level math entering knowledge students and non-personalized higher-level math entering knowledge students had similar post-test scores but personalized lower-level math entering knowledge students scored significantly higher on the post-test than non-personalized lower-level math entering knowledge students. Another significant two-way interaction (math entering knowledge by problem type) reflected that students with higher-level math entering knowledge scored considerably higher on the computational problems than on the word problems while students with lower-level math entering knowledge scored significantly higher on the computational problems than on the word problems. Student attitudes were significantly more favorable toward the personalized computer-based instructional program.     

Design synthesis of machining systems using co-platforming

Modern manufacturing environment is characterized by frequent product design changes in order to satisfy evolving customer requirements. Various strategies are implemented in order to efficiently manage the consequences arising from the product design changes starting from product design to planning and manufacturing. This paper focuses on synthesizing manufacturing system using the co-platforming concept which maps product platform features and components to the manufacturing system candidate platform machines. A matrix-based mapping model is proposed in order to determine the candidate platform and non-platform machines. Product-related characteristics including manufacturing features, feature orientation, dimensional and geometrical tolerance, cutting power requirements, workpiece volume and surface finish are considered. Characteristics of machines in the manufacturing system include machining axes, accuracy, working envelop and available power. A case study adopted from an automotive engine cylinder block manufacturer is used for demonstrating synthesizing manufacturing systems, based on co-platforming, which are capable of adapting to new products variants without changes to the platform machines. This prolongs the life of the manufacturing system and reduces costs associated with retooling and replacing it.     

云制造环境下个性化产品配置研究

在分析了云制造环境下的产品配置典型过程的基础上,总结了云制造环境下参与 者及制造资源组织的特点,包括产品配置过程需要兼顾不同参与方的利益,以及在产品配置求 解过程中需要覆盖不同制造服务实例的差异化属性的基础,提出了面向云制造的个性化产品配 置技术框架,阐述了面向产品配置的云服务模型、基于关联规则挖掘的产品初步配置以及基于 蚁群算法的产品完整配置 3 个关键问题的求解方法,为云制造环境下开展个性化产品配置提供 了一种新的思路。在模型设计及方案求解过程中,以轮式装载机产品配置作为示例进行了阐述, 验证了该方法的有效性和可行性。     

Personalized product configuration rules with dual formulations: A method to proactively leverage mass confusion

Mass confusion is a metaphor of the burdens for customers as a result of attractive but probably overloaded options in mass customization interaction process. This perceived risk of mass customization has been revealed and discussed in last decade and most research concentrates on the personalization of the interaction behaviour between front-end roles, i.e. customers and salesmen. Although the research in the front-end helps to improve customers’ satisfaction, the risk of mass confusion cannot be well leveraged by isolated personalization in the front-end: the intensive involvement of customers and salesmen also brings challenges to their patience, qualification and the responsiveness of configuration activities. Differing from most existing research, this research tries to propagate the effort of personalization from front-end roles to back-end configuration knowledge by proposing a kind of personalized configuration rules (PCRs). To simultaneously alleviate the difficulties in PCR acquisition, representation and deduction, both symbolic and connectionist methodologies are adopted to establish dual formulations for PCR development. Although these two methodologies are theoretically disparate, approaches based on specific rule extraction technique are proposed to coordinate the dual formulations and make them complementary in application. A case study of the proposed methodology for bicycle configuration is presented.     

搜索引擎个性化关键技术刍议

目前,中文个性化搜索引擎的开发已经越来越引起了国人的注意,虽然现在已经有了一定的基础,但它距离成为一个成熟的产品。道路还很漫长。本文从网络机器人、索引引擎、Web服务器三个方面对搜索引擎的“个性化”进行探讨。     

Logic programs with stable model semantics as a constraint programming paradigm

Logic programming with the stable model semantics is put forward as a novel constraint programming paradigm. This paradigm is interesting because it bring advantages of logic programming based knowledge representation techniques to constraint programming and because implementation methods for the stable model semantics for ground (variable‐free) programs have advanced significantly in recent years. For a program with variables these methods need a grounding procedure for generating a variable‐free program. As a practical approach to handling the grounding problem a subclass of logic programs, domain restricted programs, is proposed. This subclass enables efficient grounding procedures and serves as a basis for integrating built‐in predicates and functions often needed in applications. It is shown that the novel paradigm embeds classical logical satisfiability and standard (finite domain) constraint satisfaction problems but seems to provide a more expressive framework from a knowledge representation point of view. The first steps towards a programming methodology for the new paradigm are taken by presenting solutions to standard constraint satisfaction problems, combinatorial graph problems and planning problems. An efficient implementation of the paradigm based on domain restricted programs has been developed. This is an extension of a previous implementation of the stable model semantics, the Smodels system, and is publicly available. It contains, e.g., built‐in integer arithmetic integrated to stable model computation. The implementation is described briefly and some test results illustrating the current level of performance are reported. This revised version was published online in June 2006 with corrections to the Cover Date.     

Applying case-based reasoning for product configuration in mass customization environments

Product variation and customization is a trend in current market-oriented manufacturing environment. Companies produce products in order to satisfy customer's needs. In the customization environment, the R&D sector in an enterprise should be able to offer differentiation in product selection after they take the order. Such product differentiation should meet the requirement of cost and manufacturing procedure. In the light of this, how to generate an accurate bill of material (BOM) that meets the customer's needs and gets ready for the production is an important issue in the intensely competitive market.

The purpose of this study is to reduce effectively the time and cost of design under the premise to manufacture an accurate new product. In this study, the Case-Based Reasoning (CBR) algorithm was used to construct the new BOM. Retrieving previous cases that resemble the current problem can save a lot of time in figuring out the problem and offer a correct direction for designers. When solving a new problem, CBR technique can quickly help generate a right BOM that fits the present situation.