Customizability analysis in design for mass customization

Product customization has been recognized as an effective means to implement mass customization. This paper focuses on the customizability issue of design, that is, to evaluate the cost effectiveness of a design to be customized in order to meet individual customer needs. Three aspects of customizability are identified, namely, (1) design customizability: the intrinsic nature of product by design, which renders customization to be easy or deficient for either customers or the manufacturer, (2) process customizability: the economic latitude of (production) process variations due to product customization, and (3) the value of customization as perceived by the customers. While design customizability is measured based on the information content metric, the evaluation of process customizability follows the general gist of process capability indices. Conjoint analysis is employed to explore customer preference for multiple product features in terms of utility. Customizability analysis thus exhibits a maximization of customer-perceived value while exploiting the potential of design to be customized by achieving optimal design and process customizability indices.     

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.     

Simulation-supported change process for product customization - A case study in a garment company

Due to the increasing competition in global markets, many European garment manufacturers have had to undergo significant restructuring and were forced to create leaner value-adding processes. As a result, the majority of the manufacturing operations have been outsourced to low labor cost countries. At the same time, production logistics as well as information and communication technologies have gained importance, in order to keep job functions requiring higher qualifications within Europe. Another challenge concerns the introduction of customized products into the production processes which previously had been designed for manufacturing of large volumes. Prior to implementing such changes, the consequences with respect to production logistics as well as the financial impacts should be examined. The case study presented in this paper uses a simulation tool for these purposes. Different scenarios developed for a garment company were analyzed. Based on the results, recommendations for the further development of the regarded company were deduced.     

Applying principles of mass customization to improve the empirical product development process

Applying methods of mass customization to the empirical process can improve product development process efficiency and reduce time and cost. Empirical methods are used to develop predictions of product behavior in conjunction with analytical methods or instead of analytical methods. These empirical methods represent a complete sub-product development process within the overall product development process. Application of process decomposition and planning used in mass customization can improve the efficiency, lower the time and cost of these empirical processes. This paper presents a method for applying principles of mass customization to the empirical sub-processes within a product development process. Two case studies are also presented to demonstrate the method.     

A methodology of developing product family architecture for mass customization

Mass customization, aiming at delivering an increasing product variety that best serves customer needs while keeping mass production efficiency, has recently received numerous attention and popularity in industry and academia alike. This paper presents a methodology of developing product family architecture (PFA) to rationalize product development for mass customization. Systematic steps are developed to formulate a PFA in terms of functional, technical and physical views. The diverse needs of customers are matched with the capabilities of a firm through systematic planning of modularity in three consecutive views. The development of a PFA provides a unifying integration platform to synchronize market positioning, commonality employment and manufacturing scale of economy across the entire product realization process. A case study in an electronics company is reported to illustrate the potential and the feasibility of PFA methodology.     

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.     

Bio-surfaces and geometric references for mass customization in bio-interface design

Mass customization of products that interface with the human body poses unique problems due to the complexities of bio-interface design, the lack of biomechanical techniques in traditional mechanical design, and the absence of specific parametric strategies. Current biomechanical design often follows craftsman-like design processes using less than state-of-the-art tools and techniques. Thus, products that interface with the human body are not readily parameterized or automated. This paper presents a strategy for implementing mass customization in the design of mechanical devices that interface with the human body. This strategy is based on three methods that include: a method for capturing and representing the human body so that the model can be used with state-of-the-art tools and solid modeling techniques, a design methodology based on feature structure planning allowing the design process to be reused and automated, and a strategy for identifying parametric variables tied to the human body. A case study is presented to illustrate the proposed process.     

Data-driven engineering design: A systematic review using scientometric approach

In the last two decades, data regarding engineering design and product development has increased rapidly. Big data exploration and mining offer numerous opportunities for engineering design; however, owing to the multitude of data sources and formats coupled with the high complexity of the design process, these techniques are yet to be utilised to the best of their full potential. In this study, a comprehensive assessment of the state-of-the-art data-driven engineering design (DDED) in the last 20 years was conducted. A scientometric approach was employed wherein first, a systematic article acquisition procedure was performed, where a dataset of 3339 articles related to engineering design and big data analytics applications were extracted from Web of Science (WoS) and Scopus. Thereafter, this dataset was reduced to a dataset of 366 articles based on concise data screening. The resulting articles were used to analyse the dynamics of research in DDED throughout the last 20 years and to detect the primary research topics related to DDED, the most influential authors, and the papers with the highest impact in the DDED domain. Furthermore, the co-occurrence network of keywords/keyphrases and co-authorship networks were constructed and analysed to reveal the interconnection of the research topics and the collaboration between the most prolific authors. Finally, an insight how big data analytics is being applied through product development activities to support decision-making in engineering design was presented.     

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.     

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.     

Development of a module based service family design for mass customization of airline sector using the coalition game

The Airline industry faces various issues and difficulties related to its management, which have a peculiar and a complex solution. The objective of this paper is to introduce new and simplified methodologies for improving the airline sector by introducing cost efficient and relatively high payout services. In this paper, we have applied strategic sharing of modules in service family design using coalition game and Cournot game theories to model the related situations. Service family design is a cost-effective way for achieving mass customization by developing highly differentiated products from a common platform while laying emphasis on individual products. A coalitional game is applied which is used to model the potential module sharing and thus determines which modules that are being used in the platform proves to be the most beneficial ones. The empirical evidence suggests that convenience, safety and service quality also have a major influence on the choice of airline passengers. In a Cournot model, new services are predicted to be introduced by an airline and the effect of that service on its profits and thus response from other competitor airlines are carefully examined and result of this, decides the layout of the service in the functionality of the airline. This research can be efficiently used to assist the working of airline companies and help them to select services and have a better understanding of the role of quality of different services in the airline sector as a whole, thus giving them an edge over its competitors.     

A practical generative design method

A generative CAD based design exploration method is proposed. It is suitable for complex multi-criteria design problems where important performance criteria are uncomputable. The method is based on building a genotype of the design within a history based parametric CAD system and then, varying its parameters randomly within pre-defined limits to generate a set of distinctive designs. The generated designs are then filtered through various constraint envelopes representing geometric viability, manufacturability, cost and other performance related constraints, thus reducing the vast design space into a smaller viable design space represented by a set of distinctive designs. These designs may then be further developed by the designer. The proposed generative design method makes minimal imposition on the designer’s work process and maintains both flexibility and fluidity that is required for creative design exploration. Its ability to work seamlessly with current CAD based design practices from early conceptual to detailed design is demonstrated. The design philosophy behind this generative method and the key steps involved in its implementation are presented with examples.     

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 generative design technique for exploring shape variations

Because innovative and creative design is essential to a successful product, this work brings the benefits of generative design in the conceptual phase of the product development process so that designers/engineers can effectively explore and create ingenious designs and make better design decisions. We proposed a state-of-the-art generative design technique (GDT), called Space-filling-GDT (Sf-GDT), for the creation of innovative designs. The proposed Sf-GDT has the ability to create variant optimal design alternatives for a given computer-aided design (CAD) model. An effective GDT should generate design alternatives that cover the entire design space. Toward that end, the criterion of space-filling is utilized, which uniformly distribute designs in the design space thereby giving a designer a better understanding of possible design options. To avoid creating similar designs, a weighted-grid-search approach is developed and integrated into the Sf-GDT. One of the core contributions of this work lies in the ability of Sf-GDT to explore hybrid design spaces consisting of both continuous and discrete parameters either with or without geometric constraints. A parameter-free optimization technique, called Jaya algorithm, is integrated into the Sf-GDT to generate optimal designs. Three different design parameterization and space formulation strategies; explicit, interactive, and autonomous, are proposed to set up a promising search region(s) for optimization. Two user interfaces; a web-based and a Windows-based, are also developed to utilize Sf-GDT with the existing CAD software having parametric design abilities. Based on the experiments in this study, Sf-GDT can generate creative design alternatives for a given model and outperforms existing state-of-the-art techniques.     

Data-driven cleaner production strategy for energy-intensive manufacturing industries: Case studies from Southern and Northern China

Cleaner production is an effective strategy for improving material utilization, reducing energy consumption, maximizing product output, and minimizing emissions during the entire manufacturing processes. An increasing number of enterprises promote the implementation of cleaner production. In the Industry 4.0 context, product-embedded information devices are widely used to capture product lifecycle data. Product lifecycle management provides an opportunity to achieve cleaner production strategy. This paper presents a data-driven cleaner production strategy by product lifecycle management for energy-intensive manufacturing industries. The data-driven strategy is proposed and explained by the lifecycle of cleaner production data. Implementation models are developed to illustrate the proposed data-driven cleaner production strategy. Furthermore, two case studies from Southern and Northern China are presented to demonstrate the strategy. Results show that the unit energy consumption and the energy cost of production in Company A are reduced by at least 3% after applying the proposed strategy. In addition, the “cradle-to-gate” lifecycle data analysis indicates that the costs of environmental pollution in Company B are diminished significantly. Finally, energy-intensive manufacturing companies in China are compared and evaluated, and the effectiveness of the proposed strategy is discussed.     

Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system

As the field of design automation and generative design systems (GDS) evolve, more emphasis is placed on issues of design evaluation. This paper focus on the presentation of different applications of GENE_ARCH, an evolution-based GDS aimed at helping architects to achieve energy-efficient and sustainable architectural solutions. The system applies goal-oriented design, combining a genetic algorithm (GA) as the search engine, with the DOE2.1E building energy simulation software as the evaluation module. Design evaluation is based on energy spent for heating, cooling, ventilation and artificial lighting in the building, and on sustainability issues like greenhouse gas emissions associated with the embodied energy of construction materials. The GA can work either as a standard GA or as a Pareto GA, for multicriteria search and optimization. In order to provide a broad view of the capabilities of the software, different applications are discussed: (1) standard GA: testing and validating the software; (2) standard GA: incorporation of architecture design intentions, using a building by architect Alvaro Siza; (3) Pareto GA: choice of construction materials, considering cost, building energy use, and embodied energy; (4) Pareto GA: application to Siza’s building, considering thermal and lighting behavior separately; (5) standard GA: shape generation with single objective function; (6) Pareto GA: shape generation with multicriteria evaluation; (7) Pareto GA: application to an urban and housing context. Overall conclusions from the different applications are discussed, as well as current challenges and limitations, and directions for further work.     

Robotic material handler scheduling in flexible manufacturing systems for mass customization

The scheduling problem of robotic material handlers in flexible manufacturing systems (FMSs) is NP-hard. This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in make-to-order (MTO) environments for mass customization (MC). A mathematical model of the problem is formulated. A computational study of the proposed algorithm is performed. The algorithm is compared to an effective FMS robot scheduling rule, the shortest remaining processing time first (SRPF) rule. The results reveal the effectiveness of the algorithm in increasing the productivity-based measures of the FMS. Practical application insights are discussed. Further research is also provided.     

A system framework for OKP product planning in a cloud-based design environment

Nowadays, one-of-a-kind (OKP) companies, which generally operate in an 'engineer-to-order' business mode, strive to deliver individualized products with quality to achieve customer satisfaction. Thus, an accurate and prompt analysis of customer requirements (CRs) in the early design stage is critical to its success. However, most OKP companies are small or medium-sized enterprises (SMEs). Due to the limited resources and low product planning budget, they often cannot obtain abundant CR information nor can they afford the expense of complicated planning process. To address these issues, a system framework is proposed in support of OKP product planning process in a cloud-based design (CBD) environment. The challenges and future market niches of OKP companies are presented. The comparison of typical distributed systems shows that CBD, which utilizes advanced information technologies and business model, has advantages in providing sufficient resources, decreasing product development time span for OKP companies in a cost-efficient way. This article describes the proposed system architecture, the business interaction process and the information communication among customers, designers and marketing analysts at the product planning stage. To validate the proposed framework, a prototype system module MyProduct is under development in the CBD environment with an illustrative example.     

Multiple-platform based product family design for mass customization using a modified genetic algorithm

A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products with the prerequisite of satisfying customers’ performance requirements. Optimization based methods are experiencing new found use in product family design to resolve the inherent tradeoff between commonality and distinctiveness that exists within a product family. This paper presents and develops a 2-level chromosome structured genetic algorithm (2LCGA) to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of the proposed approach is demonstrated through the design of a family of universal electric motors and comparison against previous work.