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.     

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.     

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.     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

Modular and platform methods for product family design: literature analysis

After the industrial revolution, the literature has mentioned different principles to allow a better management of the production and product life cycle activities. For example the principle of standardization was first mentioned in the literature by an automobile engineer and placed in a real context by Henry Ford. Standardization has made possible the configuration of different products using a large set of common components. Another strategy called modularization was first mentioned in the literature in the 60s. The modularity proposed to group components of products in a module for practical production objectives. Today, modularity and standardization are promising tools in product family development because they allow to design a variety of products using the same modules of components called platforms. Using platforms allows important family design savings and easy manufacturing. In this paper we give a literature review of the platform concept with a special interest on the efficient product family development. This paper is organized as follows. Section 1 mentions the general context of modularity to develop product variety. Section 2 details the importance of product architectures in the literature for a modular design. Section 3 points on some important works that apply some modular and platform methodologies.This revised version was published in June 2005 with corrected page numbers.     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

Discovering relationships between modularity and cost

While much has changed in product modularity research in the 18 years since the independence axiom, some basic questions remain unanswered. Perhaps the most fundamental of those questions is whether increasing modularity actually saves money. The goal of the research behind this paper was to clearly define the fundamental relationship between product modularity and product cost. Our previous work in modular product design provided a complete package of a product modularity measure and a modular design method. The “best” measure was created and verified after correcting common performance problems among the seven measures, finally subtracting the averaged relationships external to modules from the averaged relationship within modules. After comparing and finding better design elements among four representative modular design methods, the “best” method was developed that includes product decomposition, multi-component reconfiguration and elimination, and an extended limiting factor identification. The “best” method/measure package quickly yields redesign products with higher modularity. To seek out relationships between product life-cycle modularity and product life-cycle cost, modular product design experiments were implemented for four off-the-shelf products using the new measure/method package applied to increase both functional and retirement modularity. The modularity data recorded for each redesign included retirement modularity, manufacturing modularity and assembly modularity. Each redesign’s life-cycle cost was also obtained based on several classical cost models. The cost data recorded for each redesign included retirement cost, manufacturing cost, and assembly cost. The best relationships came from the retirement viewpoint. However, there is not a significant relationship between any life-cycle modularity and any life-cycle cost unless there are significantly large modularity changes. Life-cycle modularity-cost relationships are more likely to exist in data pools generated from that life-cycle redesign viewpoint. The beginning of modular redesign, where greater modularity improvements are seen, is more effective at reducing costs. Cost savings depend the appropriateness of the modularity matrix’s product architecture representation from a cost savings viewpoint.     

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.     

Co-development of product and supplier platform

The platform strategy has been implemented to efficiently manage the increased variety in products and manufacturing systems domains by achieving their effective and rapid re-configuration. Despite the increased development of platforms research, their back-end issues such as the supply chain and supplier selection have received little attention. In this research, a methodology that integrates the product platform synthesis with the selection of suppliers to form a supplier platform is introduced. The formed supplier platform is a collection of suppliers capable of supplying the components/modules of the product platform. The supplier platform remains unchanged for product generations, and non-platform suppliers are added or removed as needed for producing different product variants in different production periods. The presented co-development methodology consists of three phases. First, co-platforming is used to map the product requirements to the supplier’s domain; then an intuitionistic fuzzy TOPSIS method is employed to assign weights to the suppliers according to selected criteria. The suppliers are chosen next and their platform is synthesized. A laptop product family is used to illustrate the developed methodology. The significance of this research is the synthesis of a supplier platform which can be used without change for many product variants and many product generations. Its implementation enables the planning and creation of strategic alliances with the product platform suppliers.     

Product platform design through sensitivity analysis and cluster analysis

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.     

A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm

With highly fragmented market and increased competition, platform-based product family design has been recognized as an effective method to construct a product line that satisfies diverse customer’s demands while aiming to keep design and production cost-effective. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss. In this paper, a systematic multi-platforming product family approach is proposed to design a scale-based product family. In the light of the basic premise that increased commonality implies enhanced manufacturing efficiency, we present an effective platform decision strategy to quantify family design configuration using a commonality index that couples design varieties with production variation. Meanwhile, unlike many existing methods that assume a single given platform configuration, the proposed method addresses the multi-platforming configuration across the family, and can generate alternative product family solutions with different levels of commonality. A modified genetic algorithm is developed to solve the aggregated multiobjective optimization problem and an industrial example of a planetary gear train for drills is given to demonstrate the proposed method.     

Product family design and platform-based product development: a state-of-the-art review

Product family design and platform-based product development has received much attention over the last decade. This paper provides a comprehensive review of the state-of-the-art research in this field. A decision framework is introduced to reveal a holistic view of product family design and platform-based product development, encompassing both front-end and back-end issues. The review is organized according to various topics in relation to product families, including fundamental issues and definitions, product portfolio and product family positioning, platform-based product family design, manufacturing and production, as well as supply chain management. Major challenges and future research directions are also discussed.     

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.     

Extension of the target cascading formulation to the design of product families

The target cascading methodology for optimal product development is extended to product families with predefined platforms. The single-product formulation is modified to accommodate the presence of shared systems, subsystems, and/or components and locally introduced targets. Hierarchical optimization problems associated with each product variant are combined to formulate the product family multicriteria design problem, and common subproblems are identified based on the shared elements (i.e. the platform). The solution of the overall design problem is coordinated so that the shared elements are consistent with the performance and behaviour of the product variants. A simple automotive design example is used to demonstrate the proposed methodology.     

Platform-based product design and development: A knowledge-intensive support approach

This paper presents a knowledge-intensive support paradigm for platform-based product family design and development. The fundamental issues underlying the product family design and development, including product platform and product family modeling, product family generation and evolution, and product family evaluation for customization, are discussed. A module-based integrated design scheme is proposed with knowledge support for product family architecture modeling, product platform establishment, product family generation, and product variant assessment. A systematic methodology and the relevant technologies are investigated and developed for knowledge supported product family design process. The developed information and knowledge-modeling framework and prototype system can be used for platform product design knowledge capture, representation and management and offer on-line support for designers in the design process. The issues and requirements related to developing a knowledge-intensive support system for modular platform-based product family design are also addressed.     

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.     

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.     

Product platform design to improve commonality in custom products

Many companies find it difficult to maintain commonality and economies of scale in products with strict customer design requirements that may vary greatly from contract-to-contract or piece-to-piece. These strict and varied requirements typically result in highly customized products that are costly to manufacture, involve small production runs, and require long delivery times. In this paper, we discuss how the strategic incorporation of product platforms into the design process can leverage the design effort of individually customized products. The example involves the design of cross-sections for yokes used to mount valve actuators in the nuclear power industry. Through this example we demonstrate the process of creating a market segmentation grid, choosing a targeted segment, creating a product platform for the yoke cross-section, and subsequently defining the yoke product family using the product platform concept exploration method. The end result is a platform-based product family that will improve response to customer requests, reduce design and manufacturing costs, and improve time to market for companies that make small production runs of highly customized products.