Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

大规模定制下多CODP的定位模型及算法

在分析大规模定制系统中单客户订单解耦点（Customer Order Decoupling Point,CODP）生产模式不足的基础上,提出为不同属性的产品设置不同的多CODP的大规模定制生产模式,通过分析企业订单及其产品定制属性,设计多CODP的定位机制及模型,并给出算法。最后以某电动车生产企业为例,验证模型及算法的可行性。     

Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing

Mass customization (MC) relates to the ability to provide individually designed products and services to every customer through high process flexibility and integration. For responding to the mass customization trend it is necessary to develop an agility-based manufacturing system to catch on the traits involved in MC. An MC manufacturing agility evaluation approach based on concepts of TOPSIS is proposed through analyzing the agility of organization management, product design, processing manufacture, partnership formation capability and integration of information system. The 2-tuple fuzzy linguistic computing manner to transform the heterogeneous information assessed by multiple experts into an identical decision domain is inherent in the proposed method. It is expected to aggregate experts’ heterogeneous information, and offer sufficient and conclusive information for evaluating the agile manufacturing alternatives. And then a suitable agile system for implementing MC can be established.     

Product family modeling for mass customization

With growing reliance on modeling in product development, it is imperative to describe product families in a cohesive way. In particular, mass customization calls for a close integration of product life cycle from customer recognition to delivery and services. This paper proposes a triple-view scheme for modeling product families. Technical challenges are discussed by comparing product family modeling with modeling single products. Individual modeling formalisms for different views are discussed. An example of product family modeling in power supply design is presented to illustrate the feasibility and potential of the proposed approach.     

Mapping Product Design Specification for Mass Customization

Mass customization is an emerging field in manufacturing research community where customer satisfaction is achieved by complying with exact customer requirements at mass production efficiencies. Product varieties have mostly failed in the aspect of satisfying customer requirements, since knowing beforehand exactly what customer needs is beyond the scope of any manufacturing or marketing paradigm. So design to order and build to order is widely talked in the industry to capture heterogeneous market segments. In order to achieve the challenging task of customization at mass production efficiencies, a tight integration of the various entities involved is necessary. Gathering customer requirements, finalizing product design and eventual manufacturing need to be seamlessly integrated to achieve mass production efficiencies. Product designs are classified into customizable product platform families, and then searched based on customer requirements to identify the most conformal design family. Finally the design parameter transport is carried out from customer domain to product domain. Eventually a valid and realizable product specification is generated. The paper will address these issues of classification, selection and ultimately the mapping of parameters. A methodology to classify the product design information, which can easily accommodate design variations based on product platform architecture is proposed. Further, adaptive design customization, which relates most design parameters with the scalable platform design parameters using matrix formulation, is discussed. The proposed design parameter classification and adaptive synthesis of design parameters is applied to a spring design example.     

A review of soft computing applications in supply chain management

It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers’ attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research.     

Mass production of tooling product families via modular feature-based design to manufacturing collaboration in PLM

Product Lifecycle Management (PLM) is recognized as one of the most effective approaches for better, fast and cheaper product development and management. Mass customization is one of the key technologies in PLM to provide tailored product to end customers with the cost of mass production. Characterized by short production lead-time and dynamic customer requirements with low batch size and high variety, tooling product families are facing the challenge of high production cost. New technology with modular feature-based design to manufacturing collaboration to satisfy the needs of mass production with low cost in tooling industry is developed in this study. A corresponding prototype system is demonstrated to show the efficiency of the technology developed.     

A comprehensive survey and future trend of simulation study on FMS scheduling

Since the late 1970s when the first collection of papers on scheduling of flexible manufacturing systems (FMSs) has been published, it has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods, whereas the former is the most widely used tool for modeling FMSs. The objective of this paper is to review scheduling study on FMSs and analyse future trend that employed simulation techniques as the analyzing tool. Scheduling methodologies are categorized into, namely traditional simulation techniques with single criterion scheduling approaches, traditional simulation techniques with multi-criteria scheduling approaches, and artificial intelligence (AI) approaches in FMSs. It is concluded that AI approaches will be dominating in future study.     

Agents and the Internet: infrastructure for mass customization

Major market trends are driving the manufacturing complex from mass production, where the manufacturer tells the customer what to buy, to mass customization, where the customer tells the manufacturing complex what to make. The Internet supports this transformation with global communication between customers and manufacturers. However, the physical realities of manufacturing impose requirements for more than just communication. In some sense, manufacturing enterprises must actually exist over the Internet as an efficiently managed distributed enterprise. Software agents offer a means to achieve this link and thus a reliable global infrastructure for mass customization. The AARIA project provides a demonstration of how the manufacturing complex can move toward mass customization by using the Internet as a natural platform for managing distributed operations and by using autonomous agents as the tools for efficiently reconfiguring available productive resources. We begin by looking at the unique requirements manufacturing imposes on the infrastructure for virtual enterprises and describing the AARIA project components for meeting them. We then describe our scheduling technologies for efficient distributed resource management     

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.     

Design by customer: concept and applications

Customer satisfaction can be increased by reducing the gap between what customer really needs (customer requirements) and what manufacturer can provide (product specifications). The approach of Design for Customer where products are generated by translating customer needs into product specifications (in mass production system) or into product variety (in mass customization system) is not able to give optimum satisfaction to all customers. Some customers are still forced to relax their requirements and to accept predefined product in the assortment. This study proposes a new concept of Design by Customer to increase customer satisfaction by opening maximum possible channel for customers to involve in value creation so that they are no longer only searching for goods but they can also, when necessary, involve in production cycle to specify their own design. In order to ensure the viability of the proposed concept, the integration of multi customer involvement decoupling point, product attribute analysis, crowdscreening and new manufacturing strategy are introduced in this paper. Real products of resin-based table clocks are used as practical example to verify the concept applicability and to demonstrate its merit.     

Implementing mass customization

Total Customer Satisfaction today can mean embarking on “Mass Customization”: giving every customer a product tailored specifically to his or her needs. In the past, manufacturing was usually “high volume, low mix”, characterized by keeping costs down with economies of scale, or “low volume, high mix”, incurring costs and time for changeovers and special handling. Today's mass customization, however, can result in a challenging manufacturing environment with both high volume and high mix, where customers expect individualized products at the same price they paid for mass-produced items. Meeting this challenge requires changes in the manufacturing processes. Equipment must be more flexible. Most important are the computer systems which support the manufacturing enterprise. Never has data been so essential to define, control, and monitor manufacturing as with mass customization. Motorola's product lines — from the pagers with millions of possible options, to the cellular phones and semiconductors — are all experiencing the move to mass customization. This presentation will describe some Motorola examples and the methods used to achieve world-class manufacturing under these conditions.     

大批量定制产品的动态组织管理方法研究

用户的个性化需求决定了最终定制产品的多样性,多样化的定制产品的有效管理与组织生产是大批量定制生产的重要内容。研究了大批量生产模式下个性化产品(Customized Product for Mass Customization,CPMC)的数据模型,提出了基于产品属性信息的CPMC多维视图管理方法,将定制产品不同层次的属性信息投影到树型的数据结构从而实现CPMC的动态组织管理。最终,以定时器定制产品的管理为例对上述管理方法进行了实践。     

面向客户集成的产品网络化定制模式研究

生产力及社会需求的发展驱动了人类生产模式的变革。客户是企业激烈争夺的生存资源,蕴含着巨大的创新力。提出了一种面向客户集成的产品网络化定制(NCFC)应用模式,以集成客户创意,提高企业新产品开发能力,同时增强企业获得和保持客户的能力。客户是该应用模式的核心要素之一,对客户对象进行了深入研究,不同的客户,应采用不同的客户集成策略。建立了NCFC的业务过程模型,强调了该应用模式与企业内部工程设计分系统、管理信息系统、制造分系统集成的重要性。建立了以客户、制造商为角色主体的运行模式,并适用于多层定制的情形。     

Cognitive shape similarity assessment for 3D part search

Mass customization aims to satisfy diverse customer requirements with high product variety while maintaining reasonable manufacturing cost and lead time. Allowing customers to perceive product differentiation is a critical factor for most design methods developed for mass customization. This study examines 3D part search from the human cognitive perspective. We designed and conducted a quasi-factorial experiment to understand how structured variations of four factors—the shape, type, dimension, and location of the feature volume of a part model—affect human judgment of part similarity. The corresponding factorial similarity values were computed with different shape signatures in the form of the feature adjacency graph. The human responses were obtained by paired comparisons of test parts, and quantified as the cognitive similarity. Statistical analysis of the experimental results showed that the type and shape factors played an important role in the subjects’ judgments. Back-propagation neural networks were trained to model the correlations between the cognitive and the factorial similarity values. The performance of the networks validates our idea of incorporating human cognition into assessment of 3D part similarity. This study presents a systematic approach for personalized part search that reflects individual perception of shape similarity.     

Resource planning for direct fabrication of customized orthopedic implants using EBM technology

Medical innovations and patient expectations are pushing healthcare toward personalized medicine. In orthopedics, the concept of patient-specific implants could be economically realized with the use of additive manufacturing. Knee and hip replacements are some of the most common musculoskeletal procedures performed in the United States. Joint replacement implants are typically offered in standard sizes and geometries. The mass customization of theses prostheses, however, can improve patient outcomes and reduce medical costs. Mass customization is not economically feasible with traditional manufacturing methods because of the high fixed tooling costs for each geometry. The freedom of design offered by additive manufacturing presents a viable production alternative for unique personal geometry. The objective of this paper is to develop two new analytic models that can be used to investigate a complex additive manufacturing supply chain. The focus of the model is to provide planning tools and a methodology for the direct production of customized orthopedic implants using electron beam melting, an additive manufacturing technology. First, a production model for an additive manufacturing-based system is created. Next, resource planning for a single customized implant system is performed using a simulation model. A queuing model is developed for rapid systems analysis. The staffing requirement predictions of the two models align closely for production of a singular, customized implant. A detailed systems analysis of an additive manufacturing supply chain is conducted to illustrate the use of these models. The queueing model is analytically tractable, so it is extended to describe the production of standard and customized versions of multiple implant families.     

基于蚁群算法的MC供应链调度优化研究

为解决大规模定制模式下客户订单分离点后的动态供应链调度问题，提出了包括供应商选择及企业合作时序安排的优化调度模型，设计了基于蚁群算法的求解过程。通过多组数据实验及结果比较分析，对模型算法的有效性、稳定性进行了验证。     

A Knowledge-based Customization System for Supply Chain Integration

Hostile environmental pressure on supply chain management increases emphasis on supply chain agility, integration, and visibility to respond rapidly, effectively and efficiently to changes in the marketplace. There is a need for new methods and tools to visualize the supply chain topologies which captures and recognizes the complexity of the supply chain network. This paper presents a Knowledge-based Customization System for Supply Chain Integration (KCSSI) which is developed based on three core technologies: visualization of topologies, network analysis, and knowledge-based system so as to obtain quantified actionable information and formulating strategies for supply chain configuration leading the long term success. The performance of the system is verified by a series of controlled simulation experiments conducted in a selected reference site. It is verified that the KCSSI improves supply chain visibility by recognizing the structure clustering and interconnection of the supply chain network, quantifying and exploiting holistic supply chain performance to provide measurable insights for the customization of the supply chain configuration leading to long term success.     

A queuing model on supply chain with the form postponement strategy

The form postponement (FP) strategy is an important strategy for manufacturing firms to utilize to achieve a quick response to customer needs while keeping low inventory levels of finished products. It is an important and difficult task to design a supply chain that uses FP strategy to mitigate the conflict between inventory level and service level. To this end, we develop a two-stage tandem queuing network to model the supply chain. The first stage is the manufacturing process of the undifferentiated semi-finished product, which is produced on a Make-To-Stock basis: the inventory is controlled by base-stock policy. The second stage is the customization process based on customers’ specified requirements. There are two types of order: ordinary order and special order. The former can be met by customizing from semi-finished product, while the latter must be entirely customized beginning from the first stage. The customer orders arrive according to a Poisson process. We first derive the inventory level and fill rate, and then present a total cost model. It turns out that the model is intractable due to the Poisson distribution in the objective function. To analytically solve the problem, we use normal distribution as an approximation of the Poisson distribution, which works well when the parameter of the Poisson distribution is quite large. Finally, some numerical experiments are conducted and managerial insights are offered based on the numerical results.