Product Platform and Product Family Design: Methods and Applications
Abstract
Today's highly competitive and volatile marketplace is reshaping the way many companies do business. Rapid innovation and mass customization offer a new form of competitive advantage. In response, companies like Sony, Black & Decker, and Kodak have successfully implemented strategies to design and develop an entire family of products based on a common product platform to satisfy a wide variety of customer requirements and leverage economies of scale and scope. Designing products and product families so that they may be customized for the global marketplace and achieving these goals in an abbreviated time period, while maintaining mass production efficiencies, is the key to successful manufacturing operations. Research in this area has matured rapidly over the last decade, and "Product Platform and Product Family Design: Methods and Applications" discusses how product platform and product family design can be used successfully to:-Increase variety within a product line,-Shorten manufacturing lead times, an-Reduce overall costs within a product line. The material available here serves as both a reference and a hands-on guide for researchers and practitioners devoted to the design, planning and production of families of products. Included are real-life case studies that explain the benefits of platform-based product development. © 2006 Springer Science+Business Media, LLC. All rights reserved.
Chapters (22)
Nearly a century ago, Ford Motor Company was producing Model T’s in, as Henry Ford has been quoted, “any color you want—so long as it’s black”. Today, customers can select from more than 3.8 million different varieties of Ford cars based on model type, exterior and interior paint color, and packages and options listed on http://www.fordvehicles.com/. And that does not even include the staggering array of choices available with Ford’s minivans, trucks, and sport utility vehicles, or any of the models offered under Ford Motor Company’s “global family of brands”, namely, Lincoln, Mercury, Mazda, Volvo, Jaguar, Land Rover, or Aston Martin. Ford is not alone as nearly every automotive manufacturer produces a wide variety of vehicles so that nearly every customer can find one that meets his/her specific needs. And it is not only in the automotive industry—consumers can purchase a nearly endless variety of goods and services: bicycles, motorcycles, appliances, computers, audio and video equipment, clothes, food and beverage, pharmaceuticals, software, banking and financial services, telecommunications services, and travel services.
Platform Planning is increasingly being adopted by companies seeking to provide customization while maximizing economies of operation. Platform Planning is defined as the proactive definition of an integrated set of capabilities and associated architectural rules that form the basis for a group of products. When implemented effectively, Platform Planning can provide distinct benefits in cost and market leverage to provide a competitive edge in the marketplace.
Firms in many industries increasingly are considering platform-based approaches to reduce complexity and better leverage investments in new product development, manufacturing and marketing. However, a clear gap in literature still exists when it comes to discussing the problems and risks related to implementing and managing product families and their underlying platforms. Using a multiple-case approach, we compare three technology-driven companies in their definition of platform-based product families, investigate their reasons for changing to platform-driven development, and analyze how they implemented platform thinking in their development process and which risks they encountered in the process of creating and managing platform-based product families. The field study shows, that the companies involved in the study use a homogeneous concept of platform-based product families, and that they have similar reasons to turn to platform thinking and encounter comparable risks. However, the companies analyzed use mainly product architecture as a basis for their platforms (and ignore many of the platform types advocated in literature), while on the other hand they show divergent applications of the platform concept regarding the combinations of product families and market applications. Through this exploratory study, some important “gaps” in the literature became evident, and in the discussion, these “gaps” are discussed and directions for future platform research are proposed.
A platform must support several product variants at any point in time and it must survive several life cycles into the future. The technology composing the platform itself is usually the embodiment of the core value-added capability of the developing company, yet what makes a good platform? This question often arises, for instance, when comparing two alternative platform concepts or deciding whether to update or replace a platform. The decision is more complex than a standard concept comparison exercise, involving forecasts of several applications and alternative technologies. Multiplicity and uncertainty characterize platform concept evaluation.
As firms begin to adopt product family and product platform principles in the beginning stages of the product development process, an essential component is to have a cohesive market segmentation strategy for the product family. Managing innovation throughout the product family can be achieved by leveraging three elements within the organization: (1) the market applications for the technology, (2) the company’s product platforms, (3) and the common technical and organization building blocks that form the basis of the product platform (Meyer and Lehnerd, 1997). Implementing this strategy can allow the organization to attack different market segments and gain market share while benefiting from the cost advantage of using product families and sharing key common technological modules. This chapter builds upon the product platform planning methods described in Chapter 2 and explores the history of the market segmentation of product platforms. We describe the principles and tools behind market segmentation and include several examples showing how companies have used this process.
Due to the development of modern technologies and global manufacturing, it becomes harder and harder for companies to distinguish themselves from their competitors. To keep the competitive advantage, the companies intend to provide a variety of products by differentiating their product lines with the belief that product variety may stimulate sales and thus conduce to revenue (Ho and Tang, 1998). A large product variety does improve sales by providing the customers more choices. However, companies with expanding products face with the challenges of controlling costs. The costs exponentially increase with the variety growth. Further, high variety will result in the proliferation of products and processes and in turn inefficiencies in manufacturing (Child, et al., 1991). Mass customization aims at satisfying individual customer needs with the efficiency of mass production (Pine, 1993a). Customization emphasizes the uniqueness of, and the differences among, products (Jiao and Tseng, 2000). To optimize the product variety, a company must assess the level of variety at which customers will still find the company’s offerings attractive and the level of complexity that will keep the costs low (Jiao, et al., 1998). Developing product families has been recognized as a natural technique to facilitate increasing complexity and cost-effective product development (Meyer, et al., 1997).
Manufacturing companies need to satisfy a wide range of customer needs while maintaining manufacturing costs as low as possible, and many are faced with the challenge of providing as much variety as possible for the marketplace with as little variety as possible between products as discussed in Chapter 1. The challenge, then, when designing a family of products is in resolving the tradeoff between product commonality and distinctiveness: if commonality is too high, products lack distinctiveness, and their individual performance is not optimized; on the other hand, if commonality is too low, manufacturing costs can increase substantially (Simpson, et al., 2001). Commonality has many advantages beyond improving economies of scale: decreased lead-time and risk during product development (Collier, 1980); decreased inventory, handling costs and processing time; reduced product line complexity, set-up and retooling time, and increased productivity (Collier, 1979; Collier, 1981). However, too much commonality within a product family can hinder innovation and creativity and even compromise product performance (Krishnan and Gupta, 2001). Commonality is best obtained by minimizing the non-value added variations across the products within a family without limiting the choices of the customers in each market segment, i.e., make each product within a family distinct in ways customers notice and identical in ways that customers cannot see.
Optimization has been used for many years during product design to help determine the values of design variables, x, that minimize (or maximize) one or more objectives, f(x), while satisfying a set of constraints, {g(x), h(x)}, and the design variable lower and upper bounds, x1 and xu, respectively. The typical notation for formulating the optimization problem is as follows: $$
\begin{gathered}
Find: x \hfill \\
Min: f(x) \hfill \\
Subject to:g(x) \leqslant 0 \hfill \\
h(x) = 0 \hfill \\
x^1 \leqslant x \leqslant x^u \hfill \\
\end{gathered}
$$ (1)
Product variants with similar architecture but different functional requirements may have common parts or elements. We define a product family to be a set of such products, and refer to the set of common elements as the product platform. Product platforms enable efficient derivation of product variants by keeping development costs and time-cycles low. In many cases, however, the individual product requirements are conflicting when designing a product family. The designer must balance the tradeoff between maximizing commonality and minimizing individual product performance deviations. The design challenge is to select the product platform that will generate family designs with minimum deviation from individual optima.
The design optimization paradigm provides us with a rational synthesis means for the engineering design of products, machines, etc. The essential outcome from computational design optimization is that it can generate the best solution under mathematical representation and procedures, if an original design problem is appropriately translated into a formal style. The outcome is more effective if the original design problem is complicated, since human expertise cannot precisely manipulate such content. This situation is obvious when design concerns shift from component-level to system-level optimality (e.g., Papalambros and Wilde, 2000).
Most products are neither designed nor manufactured as one piece. They are decomposed into parts that are developed individually before they are assembled to form the final product. Typically, this partitioning-based development process matches the hierarchical structure of the product-offering organization. Design tasks are assigned to divisions, departments, and teams according to expertise. An example from the automotive industry is depicted in Figure 11-1. Obviously, this decomposition is not complete and serves only as an illustration of the decomposition paradigm.
Typically, it is assumed that a product family will be derived from a single platform once a firm has decided on platforming as an appropriate strategy. The totality of all variants represented in a single-platform family defines the lower and upper performance and value bounds which have to be supported by the platform. This difference between upper and lower bounds of a platform is commonly referred to as platform extent (Seepersad, et al., 2000). However, the average number of variants built from a single platform has been steadily increasing in a number of industries (automotive, electronics, aircraft) since the early 1990’s. Figure 12-1 shows that the number of models per platform in the automotive industry has been increasing since 2002. This trend had started in the mid 1990’s and is likely to continue in the future. The consequence is that each platform has to accommodate a larger number of variants, whereby the extent of the platform is constantly being challenged with each new variant that is assigned to it. There is general agreement that mass customization has led to an increasing fragmentation of the automotive market with the number of individual models for sale in the U.S. rising each year from 33 in 1947, to 198 in 1990 to an estimated 277 in 2009 (Simmons, 2005).
In recent years many markets have exhibited increasing demand heterogeneity; they are fragmenting into more and smaller market niches. This development threatens the large-scale assumption of many mass production processes. As a result, firms face the dilemma of how to provide a wide variety of goods for prices that can compete with mass produced products. To respond to these challenges, many firms have begun searching for ways to combine the efficiency of mass production with the variety of customer-oriented product offerings. A major focus of these efforts has been the fundamental structure of the product: the product architecture. Examples for this development are Sony's personal music players (Walkman) that use common drives across different models (Sanderson and Uzumeri, 1995), different power tools that use similar motors (Meyer and Lehnerd, 1997), PDAs (personal digital assistant) that can be turned into an MP3 player, a camera, or a telephone with different attachments (Biersdorfer, 2001), and automobiles with common components across models (Carney, 2004).
As companies are being challenged to produce a wider variety of products to satisfy customers that have different needs while maintaining competitive prices, platform-based product family development has become a cost-effective method for reducing production costs (Roberson and Ulrich, 1998). In general, production costs are generated by production activities ranging from purchasing raw materials to distributing finished products, and those activities consume direct and indirect resources (Horngren, et al., 2000). These costs are identified and collected through management accounting systems that companies have developed for accounting purposes and used to estimate the production costs of existing products. However, many management accounting systems are incapable of providing the necessary information to support platform-based product development because many companies have developed their own accounting systems to help them remain profitable and eliminate unnecessary costs in production. In many cases, the primary objective of management accounting systems is to support management to control overall equipment efficiency (OEE) and keep it as high as possible.
Product strategy at the platform level simplifies the product development process and encourages a long-term view, because there are fewer platforms than products and major platform decisions are only made every few years. A move towards implementation of a platform strategy, which is significantly different from design and development of each product separately, can be a challenging undertaking. While the move is difficult, potential benefits from product family approach include decrease in development cost and time over a range of products. Consequently, key questions and issues that need to be addressed to justify a company’s decision to allocate resources for refocusing their product strategy at the platform level are:
1.
What will be the potential decrease in development cost for implementing a product platform strategy?
One of the pressing needs faced by manufacturers nowadays is quick response to the requirements of individual customers while achieving high quality and near mass production efficiency, namely mass customization (Pine, 1993a). Due to product proliferation, manufacturing organizations are confronted with difficulties in dealing with frequent design changes and recurrent process variations, which augments the complexity of product and process structures (Westkämper, et al., 2000). Developing multiple products as product families based on common platforms has been well recognized as a successful approach in many industries (Sanderson and Uzumeri, 1997). Current practice in developing product families only encompasses the design domain — dealing with the transformation of diverse customer needs to functional requirements and subsequently the fulfillment of these requirements through a variety of design parameters (Simpson, 2004). It seldom, if not at all, explicitly considers the input from the backend of product realization, viz., production processes. While seeking technical solutions is the major concern in design, it is at the production stage that product costs are actually committed and product quality and lead times are determined per se. For a given design, the actual cost depends on how the production is planned and to what extent the economy of scale can be realized within the existing manufacturing capabilities.
The current market has become customer driven and heterogeneous, and these shifts in the market have caused companies the additional problem of providing greater variety with existing challenges of providing greater quality, competitive pricing, and greater speed to market. Many companies are moving towards a platform approach to address the challenges posed by the market, which requires aggregation of the existing varieties to design and develop common platforms. Product platform aggregation is a bottom-up approach that focuses on development of a common platform for an existing family (see Chapter 1). In a given product family, each product will have a basic/core function in combination with a unique set of functions to appeal to the targeted market segments (Kota, et al., 2000; Kota and Sethuraman, 1998). Consequently, one of the important questions that need to be addressed is “What is common among the different products of the family?” A key factor to answer this questionis measuring commonality of components across the product family to identify common components that have the potential to be included in the platform for the family.
Offering product variety affordably is the crux of mass customization. Unfortunately, this is the foremost difficulty that enterprises face in making the transition to this paradigm. Anderson (1997) addresses the problem of offering affordable variety through the identification of the cost of variety. The cost of variety is the sum of all the costs of attempting to offer customers variety with inflexible products that are produced in inflexible factories and sold through inflexible channels. This cost includes the cost of customizing or configuring products, the cost of excessive variety, the cost of excessive procedures, and the cost of excessive processes and operations, among others. The key to mass customization, therefore, is the development of products and production processes that minimize the cost components.
Many companies have developed product families based on common platforms with varying degrees of success. Many studies of these platforms are based on product dissection. It can be challenging to gain complete information from the companies about the product development for a number of reasons including intellectual property protection. Also, many new products are developed by teams, making it difficult to get the complete picture from any individual. The case study in this chapter comes from a small company of only two primary people, so it was possible to gain insight about the complete process. Of particular interest is that the company started their design with full intent of using platform strategies for developing their product family. The following is a description of their top-down approach to platform-based product development.
Every company has the business objectives of maximizing customer choice as well as its profitability. Typically, companies address maximum customer choice through a large spectrum of variants in their products and complete flexibility in creating engineered solutions to satisfy varying customer needs. For example, a camera manufacturer may wish to offer various choices such as fixed focus, auto-focus, variable zoom, different zoom ranges, SLR, APS, and digital cameras, and in different combinations, to satisfy customers with different demands (including the price that they wish to pay). The business goal, therefore, is to design a family of products or systems that satisfy many customers but at a minimum cost. These goals, customer choices and profit margin, are not as contradictory as they seem.
A Product Design Generator is a web-based tool, developed for a specific product platform, for automatically creating all of the design artifacts and supporting information necessary for the design of a particular product. The PDG is modeled as a transformation function where a set of customer requirements is transformed into finished designs that will meet those requirements. Several methods have been presented for configuring and defining a product platform and are not reviewed here. Once the concept and embodiment have been selected, scaling, reconfiguration, artifact creation, and testing must occur to complete the design. Variants of the product platform are achieved by modifying the customer requirements. The development of the transformation function must account for the envelope of variation desired to encompass the range of product family members. The development of the PDG demonstrates how this is accomplished
Cetetherm is a company developing and manufacturing different types of heat exchanging systems (HES). It has two different product lines, one for small HES and one for large HES. The case example being described in this chapter is about the implementation of a product platform for the large HES, systems that are used by professional users in buildings connected to district heating system. The other product line consists of smaller HES that are mainly used in family houses. The market for large HES is exceptionally heterogeneous, meaning that there are many difficulties involved for individual firms trying to increase their market shares. In the large HES business, there are different rules and regulations in each country, and there are even often several different regions with specific technical demands on products within each country. That is why it is nearly impossible for an individual manufacturer to cover all these policies with a narrow set of standard products and thereby becoming a superior player.
... Banks are facing a growing number of FinTech businesses that are claiming every single business process of banks for their own [45][46][47]. Against the backdrop of digital transformation, this development will continue to accelerate considerably, and companies need to be able to anticipate possible disruptive changes in their industries [48][49][50]. ...
... Digitizing the products and processes is not enough. Transforming their own business model is risky, as is having little understanding of how digital transformation can disrupt the entire ecosystem [48][49][50]. Tools needed to anticipate digital platforms in an ecosystem are difficult to find and often remain in the theoretical realm. The results from this study take a step towards filling this gap, and present a practical approach. ...
For many, digital transformation is the new normal. However, in particular, pipeline businesses in traditional industries, such as standard-setting organizations (SSOs), are reluctant to radically rethink their business models, as they have often successfully prevailed for decades. The literature shows that there is a great deal of theory to be found on digital transformation, but a practical and, at the same time, scientific approach is yet missing. Following the design science framework, this paper introduces a two-step approach to transform a pipeline business model into a digital standardization platform. This is achieved by mapping the incumbent pipeline business mode146l and its ecosystem with the Platform Business Model Canvas introduced by Eisape. The representation of the current ecosystem is then digitally transformed according to the three key transformation points introduced by Alstyne et al., shifting the current ecosystem into the digital realm. The illustrative case study on DIN e.V. (the German SSOs) demonstrates the new methodology and its suitability for real applications. The result is a platform business model for a digital standardization platform, which, compared via an index to the traditional business model, has the potential to disrupt the entire standardization industry.
... Few studies have systemically addressed the classes of design problems. As proof, Simpson et al. [3,9] organized several methods for designing product families and platforms into collections of articles. However, while these works allow seeing the methods approaching particular classes of problems in the context of the product family design, they do not explore the interactions or interrelations among the methods within the design process, thus not addressing how the classes of design problems relate to each other. ...
... Many methods for designing product families have been developed in the past 20 years [5]. Besides the ones described in Sect. 1 [3,5,9,13], the method by Jiang and Allada [19] also encompasses the four classes of design problems together. However, it presupposes that the modules' set already exists, which makes the method sensitive to extant modules' ability to meet the customer desired attributes. ...
Designing product families is an enabling strategy for mass customization. In general, there are four prevalent classes of problems when designing product families: (i) product family positioning; (ii) customer preferences modeling; (iii) product family modeling; and (iv) product family configuration. Although these classes are interwoven through design problems stemming from marketing, engineering, and economic areas, they are rarely handled together in product family design methods. The lack of a systemic, integrated design perspective may lead to locally optimal solutions and ultimately result in product families not making the economic benefits of customization worthwhile. Over the years, some methods have attempted to overcome this absence of holistic design view. However, because they are restricted to theoretical levels or lack detailed applications, their practical implementation is often not possible. To bridge the pathway between theory and practical implementation, this paper uses the market-driven modularity (MDM) method to design a family of autonomous mobile palletizers economically oriented to market requirements. The empirical application of the method points out the palletizers family as being economically feasible. Furthermore, it also indicates which modules should be developed in successive design phases, as well as reveal the definition of the product family structure as the MDM’s outcome that is more sensitive to the variation of parameters/variables composing the configuration model. The main contribution of this work lies in the presentation of practical implementation details of the MDM method, which, to the best of our knowledge, has not been reported since its proposition.
... Through product development companies face many challenges mainly due to constant market changes caused by globalization, which are very quickly reflected in many factors. The international competition, shorten product life cycles and improvement of the production process are some of the most important factors during product development (Simpson et al., 2006). Over time, customer demands were increase that involve the development of new product variants that companies should satisfy with their offerings. ...
During product development it was very important to do research in comparing the system functionality throughout its working operation and when unexpected parameters occur. In modelling of the system behaviour, the uncertain effects of the system due to the influence of the changing working environment was also considered here. The observed system was presented with its two models: structure model and behavioural model. The system structure model was represented in the matrix notation with the Design Structure Matrix (DSM). The second domain of the system, i.e. its behaviour, was modelled by the Model Predictive Control (MPC) method and the stability of the system was studied by using the second Lyapunov method. The mutual transfer of information between these two domains allows the creation of system variants suitable for working in uncertain situations. By combining the developed environment (Behaviour Prediction Framework) and its algorithm, and using existing computer tools (©LOOMEO and ©MATLAB), the research results were verified. This was done with a case study of an air handling unit used for air treatment in the food industry and clean rooms. While carrying out the simulation of the behaviour regarding the air conditioning system, the following facts about influential parameters were established. The most influential parameter for the systems stability was the environment temperature from -40 to -25 °C. The reason was that the heat transfer coefficient was then very large. In addition, the high air flow rate, which ranged from 3.6 to 7.8 m3/s, also greatly influenced the stability. System instability occurred with the smaller geometries (cross-sectional area – height x weight) of the air handling unit.
... Their use has attracted increasing interest over the last 10 years (e.g., [58,59]), and from the fields of Service Operation, Marketing, and Management. These are usually adopted for managing the conception of more variants from the same system architecture, and for managing the trade-off between flexibility and differentiation costs (e.g., [60]). Platform design has been applied to a variety of service sectors, such as healthcare (e.g., [61]), logistics (e.g., [62]), finance (e.g., [63]), and IT (e.g., [64]); while it has rarely been applied to public services, though excluding healthcare. ...
Different design traditions address the design of services. When adopted alone, they can limit design, especially if services systems are complex. Some combinations among service design traditions are theorized in the literature and a cultural synthesis is considered a priority. This paper discusses a practical application of that synthesis: systematic/data-driven methods from engineering and service innovation are applied within a participatory and transformative environment. Intangible versus material and functional versus experience service elements are considered. The case study on urban markets shows that economics and the transactional/functional mechanics of a service system must be understood for proper design actions, and that overcoming separations between strands of literature is necessary to achieve this aim. Moreover, since urban markets are two-sided platforms in a physical setting, the study allows easier investigation than in modern digital platforms regarding how platform economics affect the design of a service system, providing insights for digital services as well. Originality is due to generally scant contributions on urban market design, since markets are often regulated, rather than designed, beyond the rare practical attempts of cultural synthesis.
... Finally, producing or buying components in larger quantities triggers further saving, enabling economy-of-scale strategies. Traditionally, in a module-based product family, new products are instantiated by adding, substituting, and removing one or more functional modules (Simpson et al., 2006), such as the battery pack or the electric motor. This strategy is called horizontal leveraging and concerns more products sharing the same modules for different applications. ...
Electric vehicles still account for a small share of the total amount of cars on the road. One of the major issues preventing a larger uptake is their higher upfront cost compared to petrol cars. We aim to address this issue by investigating a module-based product-family approach to take full advantage of economy-of-scale strategies, reducing research, development, and production costs of electric vehicles. This paper instantiates a concurrent design optimization framework, whereby diffierent vehicle types share multiple modular powertrain components, whose size is jointly optimized to minimize the overall operational costs instead of being individually tailored. In particular, we focus on sizing battery and electric motors for a family of vehicles equipped with in-wheel motors. First, we identify a convex model of the powertrain, capturing the impact of modules’ sizing and multiplicity on the mechanical power demand and the energy consumption of the vehicles. Second, we frame the concurrent powertrain design and operation problem as a second-order conic program that can be efficiently solved with global optimality guarantees. Finally, we showcase our framework for a family of three different vehicles: a city car, a compact car, and an SUV. Our results show that concurrently optimizing shared components increases the operational costs by 3.2% compared to individually tailoring them to each vehicle, a value that could be largely overshadowed by the benefits stemming from using the same components for the entire product family.
... In this sense, current research has tried to bridge this gap of integrative connection among the existing design methods. For instance, the works of Simpson, Siddique, and Jiao (2006) and Simpson et al. (2014) present several methods for designing product families and platforms, organized in a collection of articles. Although this format allows seeing the methods (parts) in the context of the product family design (whole), it does not favor the exploration of how these methods interact or interrelate within the design process, which is particularly limiting since the whole emerges from the interactions among its parts (Senge, 1990). ...
This paper carried out a systematic literature review and meta-synthesis of 72 peer-reviewed articles and book chapters
(1998–2021) that addressed front-end issues in product family design. As a result, 72 methods were identifed using 120
techniques to solve 25 design problems. Converted into sub-functions, the design problems gave rise to a functional model
and structured classes of design problems that describe the joint behavior of the methods analyzed. From these elements,
it was found how the lack of a systemic view of design might lead to sub-optimal solutions, mainly when moderated by
specifc design criteria. Concerning its contributions, this work ofers a holistic view of design, which is not perceived from
an independent evaluation of the articles and book chapters. Besides organizing existing knowledge on the front-end product family design, this holistic view contributes to the difusion of design knowledge to industrial practices, by allowing
practitioners’ access to a multitude of methods and techniques from a single source. Finally, by considering other design
approaches than just modular, connecting two independent design strategies, as well as identifying fve sub-functions and 51
techniques not yet reported, this work complements the foregoing research on the integrative connection among the product
family design methods.
... This was shown by Hicks et al. (2000) by means of multiple case studies conducted with diverse capital equipment producers. Second, modularity drives the reuse of already existing designs, thus making the engineering process more standard and more accurate, as the study by Simpson et al. (2014) in a bas valve production company showed. ...
Engineer-to-order (ETO) firms design and manufacture products according to customers' orders. As such, they induce a high level of variety in production, which makes the management of their operations particularly complex. This complexity, coupled with increasing competitive pressure, led many ETO firms to leverage product modularity to improve their operational efficiency. Extant literature on the impact of modularity on operational performance in ETO firms is mainly focused on construction and neglects other relevant sectors. Existing studies, however, point to a potentially positive impact of modularity on products and processes in ETO. First, although ETO companies using modularity create lower variety, they are more efficient than ETO companies that provide fully tailor-made solutions. Second, modular design may decrease the risk of misunderstanding customers' needs, thus preventing reworks. Third, the reuse of designs among many customer orders can reduce engineering lead times and their variability. Fourth, modularity makes production activities more decoupled and allows task parallelization. Finally, modularity can improve scale economies in purchasing and production. In this paper, we choose an exploratory, inductive approach, and focus on ETO machine tools production, a highly relevant sector for the Italian economy. In particular, we deal with the question: how can modularity impact the operational performance of ETO machine tool producers? To answer this question, we conduct expert interviews in seven ETO machinery companies in Northern Italy. We integrate the key insights from the interviews into a causal map that illustrates how ETO machine tool producers can leverage modularity to improve their operational performance, in particular their delivery times. We also highlight the importance of our research for practitioners and for academics.
... The sensor system modulation strategy can facilitate the reconfiguration of the ROV system to suit different activities or tasks, as well as facilitate maintenance and repair tasks by adding, replacing or removing specific modules or components. This proposal is based on Simpson's approach [59] for the development of system platforms and products based on modules (module-based). To achieve this modular proposal, similarity metrics are introduced that help establish the benefits of a family of products based on modules. ...
This paper provides a methodological proposal for the design and development process
of a remotely operated vehicle (ROV). The design core and product design specifications (PDS) of Pugh’s Total Design model are considered, with a focus on the early stages of the product design and development process. A modularization of the functional groups of an ROV is proposed, focusing attention on the sensor system. The main concepts regarding ROVs are presented, Pugh’s Total Design model is explained, justifying the application interest in technological projects, a methodological proposal adapted to ROV projects is provided, based on Pugh’s Total Design model, with special interest in the early stages of the new product development process (NPD), the suitability of applying our own model of industrial design engineering in an ROV system is analyzed, and the contribution
of this study is evaluated, proposing future work and lines of research.
... Nowadays, for reducing development time and cost, manufacturers prefer to use common components, as a platform, in multiple different vehicles, as a product family (Simpson et al. 2006;Hosseini et al. 2019). Modern automotive body platforms are comprehensive mechanical systems with numerous attributes and requirements. ...
In this paper, the V-shape development model approach for designing an automotive floor panel made by laminated structure is investigated to attain the best trade-off between the system and subsystem level requirements while improving the local and global performance of the vehicle. For this purpose, the bending and torsional stiffness of the body structure, as well as mass, strength, and vibration attenuation of the floor panel, are considered as design objectives at the system and subsystem levels. A multi-objective discrete optimization of a laminated configuration is performed using the Taguchi-based grey relational analysis. Material grades and thicknesses of the sandwich panel face sheets and thickness of the viscoelastic core with five levels are taken as discrete design variables. Moreover, the contribution ratios of each design factor on the performance characteristics are determined using the analysis of variance. By observing the results of the proposed approach, it is revealed that with an appropriate combination of layers, an optimum sandwich structure can be designed to fulfill all objectives simultaneously. Compared to the initial model, static strength and vibration attenuation are improved by 47.18% and 17.14%, respectively, while the global level characteristics, i.e., overall body structure stiffness, are properly preserved. Furthermore, the floor panel mass is decreased by 59.69%, which reduces the total mass of the structure by 1.75%.
... Of primary importance in product family design is the interaction with customers and the market (Simpson et al., 2006). This interaction happens at early design stages, wherein business strategies are established; market segmentation is performed; customer needs and choice-related issues are modeled; and engineering aspects of design are conceptually defined (Colombo et al., 2020). ...
This paper uses Design Science Research to build a method, entitled Market-Driven Modularity (MDM), intended to conceptually design modular product family structures that justify the economic benefits of customization. Through a series of detailed design and evaluation procedures, the proposed method is designed and further improved, as well as the theoretical framework underlying its development is adjusted. The main result of this research is the proposal of a holistic approach to product family design, particularly when the front-end issues are involved. In terms of contributions, two emerged from the abductive process of design: (i) the systematic integration of four classes of design problems prevalent in the literature; and (ii) the use of throughput for selecting the design parameters instances to compose the product family structure. Regarding the shortcomings, three arose from the artifact's evaluation: (i) the potential inability of modularity in dealing with aesthetic variety and low heterogeneity of customers' needs; (ii) the incompleteness of constructs, ‘functional requirements’ and ‘product family architecture’; and (iii) the assumption of not considering the uncertainty in the product family design. These findings counterpoint the tendency of Design Science Research in usually making practical rather than theoretical contributions.
... Cluster analysis has been used frequently in product positioning and market segmentation studies [4]. Based on Simpson et al. [5], Moon et al. [6] defined a product family as a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments costeffectively. We can obtain product groups for existing products by clustering as a data mining technique. ...
Introduction: Firms’ production strategy consists of interrelated strategic decisions, including pricing, demand forecasting and demand response planning, capacity planning, capital and cost structure. Production strategy is also the most important factor affecting the overall return of companies. Companies must estimate future situations in order to maintain their current position. Nowadays, with the developing technology, companies contain a wide variety of data. With various data analytics and optimization methods and tools, the data that can help the decision making process of companies can be made meaningful and usable. Objective: For companies like sanitary wares with large number of product variants, product groups based on estimated requirements will emerge. The ceramic sanitary ware sector, where the product variety is very high, shows seasonal effects in itself and seasonal and trend effects in its products. This situation makes it difficult to make estimations in the ceramic sector. The aim of this study is to increase the accuracy rate of demand estimation ratio by more than 70%. Methods: First of all, k-means clustering algorithm is used to obtain product groups. Then, an artificial Neural Networks model is used to estimate demands of product groups. Results: The obtained estimation error ratios are compared with those which are obtained by exponential smoothing and moving average methods in time series methods. It is observed that the most suitable method is Artificial Neural Networks (ANNs) for obtaining best results. Conclusion: The results prove the efficiency of the applying ANNs to clustered products as a nature-inspired method for demand estimation problem.
This paper presents a sizing procedure for a rocket family capable of fulfilling multiple missions, considering the commonalities between the vehicles. The procedure aims to take full advantage of sharing a common part across multiple rockets whose payload capability differs entirely, ultimately leading to cost savings in designing a rocket family. As the foundation of the proposed rocket family design method, an integrated sizing method with trajectory optimization for a single rocket is first formulated as a single optimal control problem. This formulation can find the optimal sizing along with trajectory results in a tractable manner. Building upon this formulation, the proposed rocket family design method is developed to 1) determine the feasible design space of the rocket family design problem (i.e., commonality check), and 2) if a feasible design space is determined to exist, minimize the cost function within that feasible space by solving an optimization problem in which the optimal control problem is embedded as a subproblem. A case study is carried out on a rocket family composed of expendable and reusable launchers to demonstrate the novelty of the proposed procedure.
As discussed in Chap. 1.2.3, modeling of adaptable products serves as a foundation for adaptable design. Key issues for adaptable design modeling include.
Construction firms are taking up both digital delivery and product platforms for industrialized construction to benefit from economies of scale and scope. The digitally-enabled product platforms becomes important, which such platform is defined as a collection of common and stable modules and interfaces that can derive products effectively using digital delivery. Existing construction management literatures have focused on the usage of product platforms; however, there is relatively less on platforming, which encompasses both the development and implementation of digitally-enabled product platform. This paper takes a comparative case study approach from nine international case firms to examine how construction firms strategize for platforming. Findings show that three typologies of platforms that firms developed a kit of parts only, and also developed structured interface, and also developed design rules. This paper articulates the influencing role of customer requirement certainties across multiple market segments in shaping these strategies. The contribution is to extend work on construction product platform strategies, by providing a novel classification of platforming strategies with a focus on digitally-enabled product platforms, under varied certainties of customer requirements across market segments. This has implications for practitioners and opens new areas for research, taking the characteristics of customer requirements within or across markets into account in strategic decision-making on digitally-enabled product platforms.Keywordsindustrialized constructionproduct platformdigital deliveryfirm strategy
The current production and market contexts are coping with the advent of the mass customization paradigm, in which customers ask for a high variety of differentiated products. To be competitive and survive in such a scenario, industrial companies need to identify the most suitable production strategy for their product families, balancing the minimization of the time-to-market from one side, and the optimization of their inbound operations from the other side. Moreover, traditional strategies such as Make-to-Stock (MTO) and Make-to-Order (MTO) are no longer enough. Hence, hybrid production strategies rose in the last years to best manage challenges and barriers of the mass customization. In such a scenario, the aim of this paper is to propose a novel decision-making structure based on the application of the Analytic Network Process (ANP) methodology to select the most suitable production strategy for product families in a reference manufacturing system. A categorization of the main criteria available in literature affecting the production strategy choice is provided, introducing some novel criteria. The proposed method is then applied to a reference case study to show its applicability in practice.KeywordsMass customizationProduction strategyAnalytic network processMulti-criteria decision-making
Digitally-enabled product platforms are becoming prominent approaches for industrialized building. Such a platform is a collection of common and stable modules and interfaces that can derive products effectively using digital delivery. The usage of construction product platforms has been studied in the existing buildings literature; however, there is relatively less on firms' strategies of platform elements for platforming, which encompasses both the development and deployment of a digitally-enabled product platform. This paper examines how construction firms strategize for platforming, through a comparative case study approach with nine international case firms. Findings indicate that three typologies platforms that firms implemented: those rely on a kit of parts only; those have also developed structured interfaces; and those have also established design rules. Inferring from findings, this paper articulates the influential role of customer requirement certainties across multiple market segments in shaping these strategies. By offering a novel classification of platforming strategies under varied certainties of customer requirements across market segments, this paper contributes to the research on construction product platforming strategies. This has implications for practitioners and opens new areas for research, taking the characteristics of customer requirements within or across market segments into account in strategic decision-making on digitally-enabled product platforms.
Due to the recent trend of sustainability and socio-economic changes and to expand the research on resilient supply chains in Korea, this study targets Korean venture companies to ensure their success and growth. This study aims to analyze the reliance factors affecting the business performance of Korean manufacturing venture enterprises by considering two types of business performance: technology and financial performance. Regarding the factors influencing business performance, this study analyzes five resilience factors: product structure intensity, brand intensity, research and development intensity, cooperation, and corporate social responsibility. Business years were used as control variables and the causal relationship between the factors was analyzed using SPSS 22. The results show that all resilience factors positively influenced technology performance. The results for the financial performance show research and development intensity and corporate social responsibility. However, cooperation only shows different results between technology performance and financial performance. Based on these results, this study provides implications and contributions for manufacturing venture enterprises.
Modularization has played a significant role in product design and system configuration for both manufacturers and customers. Modularization enables mass customization, collaborative product design, concurrent engineering, and short product development cycle from the view point of manufacturers, while it enables high reusability, easy system configuration, and quick installation of parts from the viewpoint of customers. One of the key enablers of modularization is standardized interfaces that connect parts. The standardization has facilitated computational product design by enabling the automation of product design processes. As technology evolves, challenges from the variants of standardized interfaces, such as different versions of an interface, have emerged. A version mismatch causes incompatibility. In order to increase compatibility, interfaces are designed to support backward compatibility. This paper proposes an artificial intelligence planning–based mathematical framework for computational system configuration to support backward compatibility. The case study shows the significance of the design with the consideration of backward compatibility by demonstrating the capability of the proposed framework that automatically discovers a better design solution that cannot be identified when backward compatibility is not considered. Finally, experiments are conducted to prove the optimality of the solutions from the mathematical framework and to showcase the advantages of the framework. The proposed mathematical framework is expected to serve as a benchmarking tool, in terms of solution quality and time, for heuristic methods to be developed in the future.
In the recent years, the diversity in customer requirements asks industrial companies to move from mass production to mass customization, overcoming the traditional strategy of realizing a large volume of a single product in favor of the manufacturing of multiple product variants matching different customer needs. In such a scenario, traditional production strategies such as Make to Stock (MTS) and Make to Order (MTO) show some limitations, leading to the advent of new hybrid production strategies. The Delayed Product Differentiation (DPD) is one of the most relevant, which attempts to join the dual needs of high variety and quick customer response time by using the so-called product platforms. This working paper proposes a preliminary indicator to assess the similarity among a set of parts, i.e., product variants, according to their production cycle, acting as a first criterion to assess the feasibility of implementing the DPD strategy. The application of the proposed indicator to an operative industrial instance showcases its effectiveness to suggesting to the company whether to implement the DPD or to using traditional production strategies.
By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method; (2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms; and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large.
Purpose
This study aims to propose a quantitative approach to reduce the number of suppliers in an organization. This method is based on grouping, and different parts are grouped based on the capabilities they need and are allocated to suppliers who have these capabilities. In this regard, an integrated model for supplier reduction and grouping of parts using a group technology-based algorithm is proposed.
Design/methodology/approach
Design science research methodology was used in this study. The main problem under investigation is a large number of suppliers in an organization’s supply base. The proposed model was used to solve this problem in the electric motor industry.
Findings
The results of implementing the proposed model in the electric motor industry showed that reducing suppliers had a significant effect on reducing cost, increasing information sharing, increasing supplier innovation and technology, enhancing the relationship between buyers and sellers and reducing risks in the production process.
Practical implications
From a managerial point of view, reducing the number of suppliers plays an important role in the company’s overall strategy, and seems to be a prerequisite for building a strong supplier partnership and an effective supply chain, and will have many benefits for the focal company and suppliers.
Originality/value
To the best of the authors’ knowledge, grouping and formation of product families have never been performed based on the similarity of the operational capabilities required for producing parts, and it has not been addressed as a solution for reducing suppliers.
Dieser Abschnitt untersucht das Markt- und Wettbewerbsprofil der Automobilindustrie und bietet Einblicke in die Gestaltungsfaktoren und Herausforderungen für das Geschäftsmodell von OEMs und Zulieferern. Die Automobilindustrie ist gekennzeichnet durch massive Anfangs- und Versunkenkosten sowie weitreichende Komplexitäten beim globalen Einkauf, in der Lieferkette, der Produktion, dem Lagerung und der Verteilung. Die „Unbeweglichkeit“ der Industrie hat jedoch ernste Auswirkungen auf die Flexibilität der Hersteller, auf kurz- und mittelfristige Trends zu reagieren. Der staatliche Druck auf die ökologische Nachhaltigkeit und der Fortschritt der ethischen Konsumismus haben existentielle Auswirkungen auf das gesamte Geschäftsmodell – von Konsolidierungsbewegungen bis hin zum Aufkommen und der Stärkung von Marktteilnehmern. Der Wechsel von produktzentrierter zu serviceorientierter Mobilität beeinflusst auch die Beziehungen zwischen OEM und Zulieferer. Diese Arbeit untersucht verschiedene Hebel – einschließlich der Produktvielfalt, Innovationszyklen, der Produktdifferenzierung, der Integrationsintensität und der Beschaffungsstrategien -, um die Schnittstelle zwischen OEM und Zulieferer zu optimieren und sieht sich andere gesellschaftliche Treiber wie die Urbanisierung, die sich ändernden Wahrnehmungen der persönlichen Mobilität, die Sharing-Economy und das ökologische Bewusstsein an.
Die Umgestaltung des Automobillandschafts wird von der Konvertierung von Städten in multi-modale smarte Städte mit urbanen Infrastrukturen begleitet, die die Grundlage für die neuen fortschrittlichen Formen der Mobilität bilden. Gesetzgeber, Gemeinden und Hersteller müssen zusammenarbeiten, um eine funktionierende digitale und datenbasierte Infrastruktur als Rückgrat der smarten Stadt bereitzustellen. Darüber hinaus müssen sich die OEMs in einem zunehmend komplexen Wettbewerbsumfeld neu ausrichten, das neue, früher nicht-industrielle Akteure, Start-ups und globale IT-Unternehmen umfasst. Die Bildung von Partnerschaften und strategischen Allianzen wird eine Schlüsselrolle bei der Aufrechterhaltung ihrer wettbewerbsfähigen Fähigkeiten spielen. Der Umfang der Wertschöpfungsintegration, d. h. die Neuausrichtung der horizontalen und vertikalen Tiefe der eigenen Wertschöpfung, ist entscheidend für die Positionierung am Markt und für die Zugänglichkeit zu neuen Umsatzquellen. Nachverkaufserträge und wiederkehrende Erträge für Dienstleistungen, die während des Lebenszyklus bereitgestellt werden, bestimmen die Zuordnung und Monetarisierung von Umsatzpools – sowohl im Automobilsektor als auch in früheren Nicht-Branchen-Industrien. In einem Exkurs werden die rechtlichen Implikationen neuer Versicherungs- und Preismodelle diskutiert.
Technologiegetriebene disruptive Entwicklungen haben die Art und Weise, wie Kunden miteinander interagieren, und die Gestaltung der Kundenerfahrung mit neuen digitalen Touchpoints grundlegend verändert. Eine gezielte Nutzung von Daten ist entscheidend, um den Wandel von einem produktorientierten hin zu einem serviceorientierten Geschäftsmodell zu gestalten. Die Vernetzung, die V2X-Kommunikation und das Internet der Dinge spielen bei der Transformation der Automobilindustrie eine entscheidende Rolle – insbesondere auf dem Weg zum vollständig autonomen Fahren. Aber auch die Elektrifizierung und die Bereitstellung von Mobilität als Dienstleistung (MaaS) prägen die Disruption im Automobilsektor. Die Veröffentlichung untersucht nicht nur die Prämissen und Hürden für die weitere Entwicklung der neuen Formen der Mobilität, sondern zeigt auch, welche individuellen und gesellschaftlichen Vorteile – einschließlich Verkehrssicherheit, Zugang zur Mobilität, Verkehreffizienz, verbesserter Raumnutzung, Verringerung der Luftverschmutzung, reduzierte Transportkosten, Verbesserung der Dienstleistungen – daraus resultieren. Darüber hinaus wird der Einfluss der Digitalisierung und Vernetzung auf die klassischen Bereiche der Wertschöpfungskette analysiert und mögliche Anwendungsfälle und Entwicklungsszenarien der „intelligenten“ Wertschöpfung diskutiert. Die Art und Weise, wie sich die Hersteller untereinander und mit Kunden verhalten, wird sich grundlegend ändern – digitale Ökosysteme und die darunter liegenden Netzwerkeffekte spielen hier eine entscheidende Rolle. Diese Veröffentlichung untersucht die möglichen Auswirkungen dieses Ökosystemmodells.
Product platform development has become a norm in practice for manufacturing companies, enabling firms to develop and produce products more efficiently, shortening the time needed to develop new derivatives, and reducing unit procurement costs. However, the required organizational activities for product platform development with different architectural knowledge need further exploration. We conduct a case study of an automotive manufacturer in China to explore how firms engage in organizational learning processes when implementing product platforms with different architectural knowledge. This study illustrates four organizational learning approaches rooted in exploration and exploitation, yields four configurations between architectural knowledge and the required organizational learning approaches and reveals that each of these architecture knowledge‐organizational learning configurations produces different organizational performance.
When mathematical optimization is applied to a multi-objective design problem, it is the common practice of decision support to generate Pareto optimal solutions as alternatives and clarify their trade-offs. The decision-maker selects any design solution according to his or her preferences. Although any comparison of alternatives is beneficial for its selection, in the case of combinatorial design problems, a simple comparison study cannot find the significant relationships between alternatives because of their discrete nature. This paper proposes a decision-making support method based on the dendrogram of hierarchical clustering. It is assumed that an optimal solution can be represented as a piling-up of smaller pieces, which correspond to building blocks, in the combinatorial solution space. The distance of alternatives is surveyed using schema representation defined at various levels. The structure of the solution space is reconstructed into a dendrogram through hierarchical clustering. When essential parts of schemas are identified as building blocks, they are organized into a tree-shaped graph. It is expected to help the designer's exploration of the solution space. The effectiveness of the proposed method is verified through two case studies: the global product family design problem and the permutation flowshop scheduling problem. Schemas are defined that consider the characteristics of the problems. The set of alternatives is systematized using a decision bifurcation diagram and the relationships between the building blocks identified are discussed.
The simultaneous and integrated design of a series of products has become extremely important under the diversification of customer demands due to the maturity of society. The complicatedness and associated difficulties of their design activities are caused not only by the diversity of products but also by the entire shape of the supply chain. When viewing such a situation through a chain of parts, intermediate products, and final products, the lineup design problem of intermediate products must be another focus toward enhancing the integrative product performance. The lineup design problem is characterized by the simultaneous design of the ranges where respective products cover and the contents of those products. This paper proposes a two-phase method for the lineup design problems. The method consists of designing a framework and optimizing contents under the framework. The latter phase is formulated as a nested mini-max optimization problem. An effective and efficient optimization scheme is constructed by employing monotonicity analysis. Finally, an application to universal motors is demonstrated for ascertaining the validity and promises of the proposed design method.
The higher the share of components that are not related to an order (i.e. order-neutral) and therefore can be used across all product variants of a product family, the more internal costs and delivery times can be reduced. Due to low sales volumes and individual customer requirements, the identification and development of order-neutral components in the custom machine manufacturing sector is highly challenging. Accordingly, this paper presents a methodology that makes it possible to identify order-neutral components and increase their share by fixing individual operating parameters. Subsequently, the paper shows how to standardise variant components with long procurement lead times to optimise delivery time. The methodology is based on a product architecture determined for an existing product family, comprising an assessment of the order dependency of product functions and components using a novel classification matrix. A case study conducted on broaching machines demonstrates the applicability and validity of the methodology.
In order to improve the efficiency of the general platform optimization, a new mean value-based collaborative method (MVCM) for structural optimization of general platform is proposed. The key idea of this method is to decompose the general platform optimization problem into a system-level optimization problem and several independent subsystem-level optimization problems by introducing coordination variables. While meeting the design requirements and performance requirements, the subsystem optimizes each aircraft so that the design variables of each aircraft are as close as possible to coordination variables. The system-level coordination adjusts the coordination variables according to the results of each subsystem. With the continuous iteration between the system and the subsystems, the coordination variables that can maximize the generality of the product family are finally found. The numerical test results show that the MVCM can greatly improve the efficiency of the general platform optimization.
The purpose of this paper is to address a gap of missing modularization methods for engineer-to-order (ETO) companies. The research project was initiated by clarifying the challenges facing ETO companies, based on these challenges synthesis of existing methods was done to conceptualize a method. This article presents the modular candidate identification (MCI) method aimed at identifying modular candidates in ETO companies. The method analyzes five dimensions, namely, market segments, customer requirements, product architectures, cost and lead time to find modular candidates. The method was applied in a Danish ETO company and shown to be successful in identifying two modular candidates. Both were recognized by management and redesigned in modular product development projects.
This book provides an integrated perspective of the automotive market for the next decade. It shows how customers and producers are shaping the market simultaneously and contends that the first steps of the mobility revolution have already been taken. It compels automotive companies to strike new paths to participate in this journey.
The authors provide a comprehensive analysis of the automotive industry, including prevailing business models of OEMs and 'tier-n' automotive suppliers, the competitive environment they are embedded in as well as socio-economic changes affecting future market conditions. Subsequently, elements of the automotive disruption are presented; these enable the provision of novel urban mobility concepts and offer a new source for additional services accompanying the user. A comprehensive insight into consumer behavior, potential automotive business models which can be sustained by 2030, smart city models, transformation strategies, and diverse market penetration scenarios are also provided in the book. It also outlines the challenges and key actions that shape the automotive sector even beyond 2030 as well as knock-on effects across different industries arising from the technological and economic changes in the automotive market are projected.
The disruption of the automotive landscape will be accompanied by the conversion of cities into multi-modal smart cities with urban infrastructures that provide the basis for the new advanced forms of mobility. Legislators, municipalities, and manufacturers will need to work together to provide a functioning digital and data-driven infrastructure as the backbone of the smart city. In addition, OEMs must reorient themselves in an increasingly complex competitive landscape that includes new, formerly non-industry players, start-ups, and global IT companies. Forming partnerships and strategic alliances will play a key role in maintaining their competitive capabilities. The extent of value chain integration, i.e., the realignment of the horizontal and vertical depth of one’s own value creation, is crucial for positioning in the market and for determining the accessibility of new revenue sources. Aftersales revenues and recurring revenues for services provided throughout the lifecycle shape the allocation and monetization of revenue pools—both in the automotive sector and in formerly non-branch industries. In an excursus, the legal implications of new insurance models and pricing models are discussed.
The smart factories that are already beginning to appear employ a completely new approach to product creation. Smart products are uniquely identifiable and know both their current status and alternative routes to achieving their target state. Smart factories allow individual customer requirements to be met, meaning that even one-off items can be manufactured profitably. In smart industry, dynamic business and engineering processes enable last-minute changes to design and production, delivering the ability to respond flexibly to disruptions and failures on behalf of suppliers. This paper presents a case study of product development and design process renovation according to changeability paradigm in one-of-a-kind industrial environment. It demonstrates how integration of changeability with agile design strategies crucially contribute to improve the operations of a highly individualized product development business. Successful management of ‘never-ending’ engineering changes appears to be the most important aspect in this field. Contribution of the presented work is a generalized framework that demonstrates how companies in such specific environments can improve competitiveness through the utilization of changeability concepts. The included case study validated the proposed changeability model and offers valuable insights into how to implement this in practice.
Main objective of this book chapter is to explain design procedure of a mixed compression supersonic air intake. Conceptual design of air intake, modelling and simulation of supersonic flow, design of sub-components, design point of view, the objectives, constraints, phenomenon observed during the design period are mentioned. “Optimum” supersonic air-intake configuration for high speed transport aircraft are introduced briefly. Furthermore, strategy for the air-intake design of previous projects and design methodology are mentioned. Although it seems the performance requirements is enough for the successful design; operability, endurance and low-cost of the air intake are other important design goals. The aim of achieving that integration of the air-intake configuration to the propulsion system is investigated. Employing wind tunnel tests for all design alternatives makes the design period extremely long and inefficient. Instead, a simulation method of the air intake is proposed. Comparison of the results of the simulations and wind tunnel experiments are represented.
Information systems are key enablers for the integration and reliable management of the product development process. Fast, robust, and cost-efficient product adaptation is especially important in one-of-a-kind production. This paper presents an implementation of information supported tools of the product development (PD) design process for large power transformers. One-of-a-kind production is specific, as each product must be customized, wherefore a robust design process well supported by information technologies (IT) plays a key role in creating a digital twin and the product's final value. Goal of this research was to develop the product information model and smart supporting tools for customization and integrate them into the design process. Based on a systematic analysis of the sample company, this paper proposes a model for the complete renewal of information systems and of working methodology, where reorganization is demonstrated in an increase of overall effectiveness. The results clearly show a considerable drop in engineering changes, increased productivity and improved business competitiveness. The proposed framework is generalized, which makes it directly applicable in similar business environments and thus helpful for establishing the best-practice guidelines for promoting competitiveness in one-of-a-kind PD processes.
Increasing product complexity and individual customer requirements make the design of optimal product families difficult. Numerical optimization supports optimal design but must deal with the following challenges: many design variables, non-linear or discrete dependencies, and many possibilities of assigning shared components to products. Existing approaches use simplifications to alleviate those challenges. However, for use in industrial practice, they often use irrelevant commonality metrics, do not rely on the actual design variables of the product, or are unable to treat discrete variables. We present a two-level approach: (1) a genetic algorithm (GA) to find the best commonality scheme (i.e., assignment scheme of shared components to products) and (2) a particle swarm optimization (PSO) to optimize the design variables for one specific commonality scheme. It measures total cost, comprising manufacturing costs, economies of scales and complexity costs. The approach was applied to a product family consisting of five water hose boxes, each of them being subject to individual technical requirements. The results are discussed in the context of the product family design process.
The continuous management of a manufacturing company's product portfolio is a key aspect of successful product development. Managers determine when and which products should be updated or terminated. This process influences inhouse Industrial design teams, thus prompting a range of development strategies they might deploy. In product portfolio management there is a tension between standardisation and customisation. From a marketing perspective this is may be addressed through brand DNA, from engineering through modularization. The design perspective (merging those two) has been ill-explored, particularly from professional designers. Previously we proposed a theoretical model describing different industrial design projects and how they influence industrial designers strategic thinking. It was developed through literature reviews and examples found in manufacturing industry. Through a multi-case interview study with 16 participants from five manufacturing companies with strong brands, this article aims to empirically evaluate the proposed model. The results show that the respondents could describe all but one industrial design projects, the cause maybe that they had not been exposed to saving a company by doing a total makeover.
Ballard’s concept of work structuring for production planning in construction can be applied to improve production flow in construction projects formed by repetitive units with extensive customization. Customization can increase the value of repetitive units (apartments in a building, houses in an allotment, or stores in a shopping mall) forming a project, by meeting clients’ specific requirements. However, in traditional construction production systems, it commonly causes delays, stoppages, rework, increased amounts of work in progress, and excessive movement of crews and materials. The problem has been thoroughly documented and various production systems have been designed to address the trade-off between flow and value. We identify five such systems, which were originally developed following exploratory design science principles. In this work, we analyse and compare them using nine metrics to assess their performance in terms of flow and value, and to explore the role of work structuring in customized projects. The systems with the most effective flow are the Fit-Out Company and the Ecosystem Platform systems. The analysis led to the theoretical proposition of a hybrid production system called Product/Process Modularization, which may be applied and tested in the future.
Purpose
Facility design standardization strategy has considerable advantages, highlighted by its widespread and consistent use in the shipbuilding and manufacturing industries. However, capital projects have failed to realize these benefits. The primary rationale behind this problem is the lack of proper understanding of design standardization, more specifically the benefits and equally importantly, the trade-offs of design standardization in capital projects. Therefore, this study highlights 13 benefits and six trade-offs of standardization in connection to design standardization, along with specific examples.
Design/methodology/approach
To achieve the study objectives, the researchers identified the most impactful benefits and trade-offs in terms of economic impact by surveying prominent players in the industry. Furthermore, the researchers examined 43 actual case projects (a case study) executed with the standardization strategy to evaluate the industry's status in terms of the levels of advantage achievement and disadvantage incurrence.
Findings
The results of this survey show that design once, reuse multiple times and design and procurement in advance are the most impactful benefits. Similarly, susceptible to changes in the market conditions is one of the top trade-offs that can be incurred in capital projects when implementing standardization. The results also highlight that design once, reuse multiple times is one of the most achieved benefits in standardized capital projects today, while cost of establishing the design standard is the most incurred trade-off.
Originality/value
This study provides important insight into how standardization strategy can be advantageous while also enriching the literature about pitfalls expected from standardization. Moreover, this study's results will help the industrial sector achieve higher levels of design standardization by providing a better understanding of the benefits and trade-offs of design standardization.
Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives—process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for small-scale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the meta-heuristic algorithm were demonstrated through case studies and numerical experiments.
A key enabler for personalised product design is an open product architecture that allows the integration of personalised modules to create unique products. Decisions regarding product variety, module combinations, and configurations for personalised modules need to be coordinated with the decisions of manufacturing process and supplier selection when developing personalised product architectures. Conventionally, product architecture, processes, and suppliers are independently determined at different product development stages. However, this sequential design process lacks connection between product architecture, process, and supplier, and may lead to suboptimal or even infeasible design solutions with compromised performance. In this study, a concurrent optimisation approach is proposed to integrate manufacturing process and supplier selection into personalised product architecture design. A cost model is developed as a nexus of product architecture, process, and supplier. Then, a mixed-integer optimisation model is established to maximise the potential profit of a product family based on a profit formulation that incorporates customer preference, process resource, supplier, and manufacturing cost. A genetic algorithm is utilised to solve this optimisation problem. The method is demonstrated on the architecture design for a family of personalised bicycles. The result shows that concurrent optimisation can achieve design solutions with higher profitability compared to sequential design strategies.
A method for modular design of structural products such as automotive bodies is presented where two structural products are simultaneously decomposed to components considering the structural performances of each structure and the component sharing between two structures. The problem is posed as an optimization to minimize the reduction of structural strength due to the introduction of spot-weld joints and the number of redundant joints, while maximizing the manufacturability of the component and component sharing between two structures. As an extension to our previous work, this paper focuses on the simultaneous decomposition of two 3D beam-based structures. The major extensions include 1) a new, realistic definition of feasible joining angles based on the local geometry of joining components, 2) a component manufacturability evaluation that eliminates the need of specifying the number of components prior to decomposition and 3) a multi-objective optimization formulation that allows an effective exploration of trade-offs among different criteria. A case study on the simplified, 3D beam models of automotive bodies is presented to demonstrate the developed method.
Solid models are the critical data elements in modern
computer-aided design environments, because they describe the
shape and form of manufactured artifacts. Their growing ubiquity
has created new problems in how to effectively manage the many
models that are now stored in the digital libraries for large
design and manufacturing enterprises. Existing techniques from
the engineering literature and industrial practice, such as
group technology, rely on human-supervised encodings and
classification; techniques from the multimedia database and
computer graphics/vision communities often ignore the
manufacturing attributes that are most significant in the
classification of models. This paper presents our approach to
comparing the manufacturing similarity assessments of solid
models of mechanical parts based on machining features. Our
technical approach is threefold: perform machining feature
extraction, construct a model dependency graph (MDG) from the
set of machining features, and partition the models in a database
using a measure of similarity based on the MDGs. We introduce
two heuristic search techniques for comparing MDGs and present
empirical experiments to validate our approach using our testbed,
the National Design Repository.
This paper presents two complimentary approaches to comparing the shape and topology of solid models. First, we develop a map-ping of solid models to Model Signature Graphs (MSGs)—labeled, undirected graphs that abstract the boundary representation of the model and capture relevant shape and engineering attributes. Model Signature Graphs are then used to define metric spaces over arbi-trary sets of solid models. This paper introduces two MSG metric spaces: first, a euclidean distance measure via a mapping of MSGs to a high-dimension vector space; second, an edit distance measure between model signature graphs based on spectral graph theory. In practice, exact computation of the edit distance between model signature graphs is believed to be an NP-hard problem. We show that properties of the design signature graph's spectra, derived from the eigenvalues of its adjacency matrix, can be used as a ef-ficient and tractable approximation of the edit distance. Lastly, we provide empirical results using real test data from the National De-sign Repository (http://www.designrepository.org) to validate the approach. We argue comparisons among solid mod-els in these metric space are immune to problems caused by inex-actness and ambiguity arising from basic modeling transformations (scale, translation, rotation, sheer, etc.). It is our belief that this work contributes to a growing body of techniques for comparing models and indexing CAD media types in database systems.
Despite a steady increase in computing power, the complexity of engineering analyses seems to advance at the same rate. Traditional parametric design analysis is inadequate for the analysis of large-scale engineering systems because of its computational inefficiency; therefore, a departure from the traditional parametric design approach is required. In addition, the existence of legacy data for complex, large-scale systems is commonplace. Approximation techniques may be applied to build computationally inexpensive surrogate models for large-scale systems to replace expensive-to-run computer analysis codes or to develop a model for a set of nonuniform legacy data. Response-surface models are frequently utilized to construct surrogate approximations; however, they may be inefficient for systems having with a large number of design variables. Kriging, an alternative method for creating surrogate models, is applied in this work to construct approximations of legacy data for a large-scale system. Comparisons between response surfaces and kriging are made using the legacy data from the High Speed Civil Transport (HSCT) approximation challenge. Since the analysis points already exist, a modified design-of-experiments technique is needed to select the appropriate sample points. In this paper, a method to handle this problem is presented, and the results are compared against previous work.
We propose object-oriented Petri nets with changeable structure (OPNs-CS) in this paper for effective modelling of production
systems with changing structures. Two mechanisms for carrying out the structural changes in OPNs-CS are defined. One is for
the modification of message passing relationships between distinct OPNs and the other is for adding of OPNs to, and the removing
of OPNs from, a model. A case study is provided to show that OPNs-CS can cope with both the uncertainties which may exist
during the model-building phase of a one-of-a-kind production (OKP) system and the structural changes of the system which
often occur while the production system is in operation.
This work shows the relationship between product life-cycle modularity and product life-cycle costs. Previous statements tying increased modularity to improved costs, specifically product retirement costs, motivated this work.
The benefits of modularity with respect to product functionality, product development, production, the supply chain, and other life-cycle elements have been expounded by many works in several fields. Increased modularity has been widely considered to lead to decreased costs. Many have stated that, including modular design tradeoffs early in the design process will decrease life-cycle costs. Some even propose the hypothesis that modular architecture will lead to decreased life-cycle costs even if the modules are not made with other life-cycle characteristics specifically in mind. However, this desirable relationship - decreased costs driven by the increased modularity - has never been shown. No research has been done to prove if there exists a relationship between modularity and cost. Many products and research projects have been based on this unproven assumption.
This work begins the exploration into whether a relationship exists between life-cycle product modularity and life-cycle cost for a wide range of consumer products and a wide range of life-cycle issues. The purpose of this paper is to expand initial results in this vain to the whole life-cycle for a more comprehensive look. The results of our work differ significantly from conventional thought and are therefore interesting to both the research and application communities. It is our hope that this paper will motivate others to take a second look at the science behind product modularity and its application.
Presents a novel way of using object-oriented design metrics to support the incremental development of object-oriented programs. Based on a quality model (the factor-criteria-metrics model), so-called multi-metrics relate a number of simple structural measurements to design principles and rules. Single components of an object-oriented program like classes or subsystems are analyzed to determine whether they conform to specific design goals. Concise measurement reports, together with detailed explanations of the obtained values, identify problem spots in system design and give hints for improvement. This allows the designer to measure and evaluate programs at an appropriate level of abstraction. This paper details the use of the multi-metrics approach for the design and improvement of a framework for industry and its use for graphical applications. Multi-metrics tools were used with several versions of the framework. The measurement results were used in design reviews to quantify the effects of efforts to reorganize the framework. The results showed that this approach was very effective at giving good feedback, even to very experienced software developers. It helped them to improve their software and to create stable system designs
A technique for constructing shape representation from images using free-form deformable surfaces is presented. The authors model an object as a closed surface that is deformed subject to attractive fields generated by input data points and features. Features affect the global shape of the surface, while data points control its local shape. This approach is used to segment objects even in cluttered or unstructured environments. The algorithm is general in that it makes few assumptions on the type of features, the nature of the data, and the type of objects. Results for a wide range of applications are presented: reconstruction of smooth isolated objects such as human faces, reconstruction of structured objects such as polyhedra, and segmentation of complex scenes with mutually occluding objects. The algorithm has been successfully tested using data from different sensors including grey-coding range finders and video cameras, using one or several images
Modular design can be achieved by taking the variations of the surrounding components as conceptual noise and designing the part to be conceptually robust. Design-in-modularity, which has demonstrated its success in electrical/electronic industry, receives less attention in the other engineering disciplines. Previous work focuses on descriptive discussions as well as coding and classifying designs based on product features; none has outlined a design method for modular design. This paper articulates a procedure for design-in-modularity based on the idea of conceptual robustness. We claim that, by properly defining the conceptual noise, the conceptually robust design is the modular design. Issues on how to generate the minimum number of modular designs for a given set of systems are included. An algorithmic procedure is outlined for the practitioners to follow. This paper also discusses future research issues in this new research area of interest. The entire concept is illustrated by an example.
The article presents the results of a study conducted at Sargent and Lundy that can be used to assist the estimator in using scaling factors with more accuracy than may have been possible with previously published data concerning the subject. The process that a cost estimator could use to develop scaling factors unique to his/her own type of work is also addressed. This article is not, however, an endorsement for using scaling factors when other more sound methods are available for developing costs.
This paper discusses the ramifications of current Department of Defense (DoD) Acquisition Reform policies on Navy equipment standardization initiatives and provides an overview of the objectives and benefits of making "best value" end item selections during the design and construction process. The DoD initiative to implement acquisition reform by changing the processes by which defense system and equipment requirements are defined and communicated to contractors is having significant impacts on equipment standardization programs. The emphasis on the use of non-developmental and commercial-off-the-shelf items (NDIs/COTS) combined with naval ship system and equipment requirements being expressed primarily in performance terms creates the potential for the introduction of large numbers of commercial equipment to the supply support system. Approaches to maximizing equipment standardization efforts in the era of commercial-based acquisition strategies are described and examples of standardization approaches using recent ship acquisitions (Strategic Sealift, LHD 1,DDG 51, and LPD 17) are presented.. Possible approaches for the use of performance-based equipment databases and real-time linkages through the Internet with COTS manufacturers are discussed. Impacts that could change the structure of existing logistics support systems and result in substantial improvements in both cost and performance of shipboard equipment and components are addressed.
Dana Corporation has developed a rolling chassis module for DaimlerChrysler South American Dakota which sees history repeating itself, but with an element of role reversal. Instead of building a chassis and engine with the body being supplied by another company, DaimlerChrysler has established an operation in Brazil where it puts the body on to a complete chassis made by an outside company and then installs the engine. Differing from the standard chassis made for North American versions, the version for the Mercosur market has been modified and strengthened to cope with the rougher conditions. Dana was responsible for overseeing these developments and the engineering changes.
Two key data areas of the production data base in computerized production and inventory control systems are bill of material (BOM) and routing. A bill of material is a listing or description of raw materials and components that make up a product, along with the quantity of each required. The BOM may take various forms and be used in a number of ways. A routing specifies a sequence of operations to be performed at corresponding work centers and the processing times at each for making a product. The great majority of existing systems regard the BOM and routing as two separate subsystems, possibly with some degree of cross-referencing. For many purposes, however, our own experience and that of other authors have highlighted advantages in the integration of the BOM and routing.
An existing modular design method was focused on product retirement. A single flashlight was taken and redesigned making it more modular. Following completion of the modular design method, the product modularity and retirement cost of the product at each intermediate state of redesign were measured. Costs associated with retirement including, recycling, reuse, remanufacturing, and disposal were measured. Statistical analysis of the flashlight data was carried out to look at the relationships between relative modularity, number of design changes made, and retirement cost.
Outsourcing is a process for restructuring a firm's operations to create a more flexible, focused and competitive production structure. It involves differentiating between commodity components and those that are strategic to the firm's products. The objective of outsourcing is to enhance the company's competitive advantage and thereby, to improve it's financial performance. However, the risk involved in outsourcing must be identified and used to structure an outsource agreement that minimizes the risk. While the analysis of short and long run production costs takes time and effort to developed, their costs may be relatively insignificant, compared to the potential benefit of the information it provides for making more informed outsourcing decisions.
Manufacturers have carefully examined their current new product development cycles due to changes in customer demands. The use of common parts and designs brought about by the greater prevalence of computer-integrated manufacturing systems is now considered a powerful and increasingly accessible way for firms to compete in the increasingly competitive global market. Commonality can provide inventory-related benefits, however an example provided in this study shows that commonality can provide far-reaching competitive advantages. This indicate that commonality has become a strategic competitive weapon.
This paper discusses the optimal design of common components used for a class of products. While simultaneously designing multiple products has become an important concept in manufacturing in these days, alliances involved in such activities are extended from the traditional form. This means the existence of a chance that an integrator designs a set of components apart from particular products or a supplier commonalizes components independently from integrators. That is, any methodology for simultaneously designing a set of components becomes necessary behind ones for simultaneously designing a set of products. This paper formulates the design problem of common components as an optimization problem, investigates the condition of optimal design through the tradeoff among the level of system-level performance, the number of different components, etc. Then a computational procedure is configured for optimizing the commonalization of components apart from designing a particular set of products by using multivariate analysis, an optimization code based on mini-max operation and a genetic algorithm for constrained nonlinear mathematical programming. Finally the proposed optimization procedure is preliminarily applied to a design problem of liftgate dumpers for passenger cars for demonstrating the meaning of the levels of optimal design and tradeoff structure.
In an effort to improve customization for today’s highly competitive global marketplace, many companies are utilizing product families to increase variety, shorten lead-times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design research has matured rapidly in the past decade, and this paper provides an extensive review of the research activity that has occurred during that time to facilitate product platform design and optimization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with optimization-based approaches to help automate the design of a product platform and its corresponding family of products. Examples from both industry and academia are presented throughout the paper to highlight the benefits of platform-based product development, and the paper concludes with a discussion of promising research directions to help bridge the gap between planning and managing families of products and designing and manufacturing them.
This paper describes a procedure for calculating costs concurrent with the design process. The different modules of the computer program 'IKF' are described. The modules include those for cost calculation, comparison and forecast. Prior to developing the computer program, a manual procedure was designed and developed. Upon testing, the group with IKF took 40-50% more time and designed products that were about 80% more costly than the group using the IKF system. This procedure was then used as the model for the cost modules in a feature-based computer-aided design program. Test cases dealing with sheet-metal design are described which demonstrate and quantify the advantages of this system.
In this paper an integrated approach for the formation of parts and machine families in group technology is developed. The integrated approach is used to solve cell formation, process planning, and production planning simultaneously. The given information is part processing sequence, part production volume, part alternative processing plans, and part processing times. The approach is used to determine the machine-part cells and part processing plans, while the total intercell part flow is minimized. Also, the convergence of the algorithm is investigated. The approach goes across and beyond the group technology methods by considering sequencing, production planning, process planning, and part-machine cellular information simultaneously. Two methods are investigated: exact (optimal) and heuristic. The approach first solves an integer programming problem to find processing plans and then uses a procedure to form the machine-part cells. The proposed approach solves the problem iteratively until a set of plans for machine-part cell formations is obtained with minimal intercell part flow or interflow cost. An example is presented to explain the developed approach. Experimental results are also provided. An extension of the approach for solving the operations planning of an emergency room is also covered. In this extension of the approach, the application of cell formation provides a solution to efficiently managing patients and utilizing resources. By grouping patients by their needed medical procedures, time and resource efficiency is accomplished. An application to ER of University Hospitals of Case Western Reserve University is given.
This paper is based on the experiences of the Volvo Car Corporation and examines pilot production and manufacturing start-up- processes that are here combined under the rubric final verification of product and production system. The purpose of the paper is to discuss results from the pilot production and the manufacturing start-up of the Volvo S80 model in order to contribute to an understanding of how the process of ramping up production capacity and improving quality is affected by various types of disturbances - disturbances that affect final verification throughput time and efficiency. In addition, drawing on the results of the case study, the paper contributes a framework of concepts for studying and analysing the final verification processes. A case study methodology was employed, using a combination of quantitative and qualitative methods for data collection and analysis. The results show that disturbances that affect performance during the final verification are felt in four areas (materials supply, machinery/equipment, personnel and product concept). It is proposed that, in order to succeed with a rapid and efficient start-up, that production organizations should focus on identifying the sources of disturbances, as early as possible during pilot production, in order to gain control of these during start-up. The means for gaining improved disturbance control is to apply certain principles for designing, planning and controlling the pilot production and the manufacturing start-up phase.
Many market forces are driving companies to improve their targeting of increasingly small market niches. To accomplish this efficiently, products are organized into product families that typically share common platforms. To reorganize the current product offerings or new products into a product family, using a platform approach, requires estimating the savings for such a modification. One of the problems encountered in estimating development and design cost is the lack of availability of hard information during the initial design phases. The purpose of this paper is to estimate the design and development cost, when moving towards a platform approach, using simple models. The activity based product family cost models are developed from existing single product design activities, which are modified and extended to reflect activities related to development of product platform and subsequent product family members supported by the platform. Uncertainty related to cost associated with activities are included in the model, which is solved using Monte Carlo simulation. The approach is demonstrated using a hard disk drive spindle motor platform development for a family of hard disks.
Bally Engineered Structures, Inc. has figured out how to mass customize many different types of end products—such as walk-in coolers, freezers, insulated outdoor structures, refrigerated warehouses, and environmentally controlled rooms—based on a set of standard panel modules and accessories that can be put together in a virtually limitless number of ways to meet the needs of individual customers.
The recycling of manufactured products depends greatly on the efficiency with which material can be separated from each other. For the long term, recycling can be made more effective by the design of products for greater use of disassembly and recycling. This requires the development of suitable product analysis tools to enable design teams to evaluate the ease of disassembly and recycling of alternative product concepts during the early stages of design. This paper describes the development of product analysis procedures for combined disassembly and bulk recycling such that consequences of material selection upon the end-of-life recovery of materials can be investigated.
Product costs are calculated by many different departments in a company: cost engineering, industrial engineering, design, manufacturing, etc. The aims of these groups, the methods used by them and the uses to which their results are put are different. Cost engineering provides cost estimates based on finished designs. Industrial/manufacturing engineering groups calculate costs with a high emphasis on processing costs. Management uses these figures for making decisions on quantities and the product mix to be produced. Designers use (or should use) models to predict costs at the various design stages. These results help in making decisions during designing—where they are most effective. Most of the cost tools today address the problem only in the final design stages, whereas the major portion of the product cost is set much earlier. Generally, however, the designer often does not have sufficient knowledge of the manufacturing processes and the costs incurred therein. Design decisions influence all of the subsequent costs of the product. In the traditional organization, design and production planning are carried out sequentially and independently. The costs are calculated after the fact, when it is too late to have significant impact on them. The paper describes types of cost structures and costing methods. A comparison of conventional and activity-based costing is made with the help of an example. The latter has proven itself to be a fairer method of allocating overhead costs when a mix of products is involved.
Companies' attempts to adapt to today's technological capabilities and globally competitive environment have been greatly constrained by antiquated accounting systems. Improved management accounting systems can be designed:1) for operational control, to motivate the learning and improvement activities for managers and employees, and to provide feedback on the efficiency of operating processes;2) for activity-based costing, to calculate accurately the profitability of individual products and customers; and3) for capital investment decisions, to guide decisions on acquiring advanced technological capabilities. The paper summarizes recent advances in all three applications.
Based on the product information structure related to the Manufacturing Engineering Reference Model, a method for variant-based cost estimation is proposed. The structure defines a product in terms of elements and their relations. The elements and their properties constitute product characteristics that can be used to compare products. For proper use in different engineering processes, the product characteristics are related to the four cost drivers - geometry, material, processes and production planning. The main advantage of the proposed method is a more flexible way of defining and valuing product characteristics without the use of rigid product classes and codes.
The process of product design is driven toward achieving design specifications while meeting cost targets. Designers typically have models and tools to aid in functional and performance analysis of the design but have few tools and little quantitative information to aid in cost analyses. Estimates of the cost of manufacture are often made through a cost multiplier based on material cost. Manufacturing supplies guidelines to aid in design, but these guidelines often lack the detail needed to make sound design decisions. The need for a quantitative way for modeling manufacturing costs at Motorola was identified. After benchmarking cost modeling efforts around the company, an activity-based costing method was developed to model manufacturing cycle time and cost. Models for 12 key manufacturing steps were developed. The factory operating costs are broken down by time, and cost is allocated to each product according to the processing that it requires. The process models were combined into a system-level model, capturing subtle yet realistic operational detail. The framework was implemented in a software program to aid designers in calculating manufacturing costs from limited design information. Because the tool provides an estimate of manufacturing costs at the design prototype stage, the development engineer can identify and eliminate expensive components and reduce the need for costly processing. Using this methodology to make quantitative trade-offs between material and manufacturing costs, significant savings in overall product costs are achieved.
The goal of this paper is to raise awareness and generate discussion about research methodology in engineering design. Design researchers are viewed as a single communicating community searching for scientific theories of engineering design; that is, theories that can be tested by formal methods of hypothesis testing. In the paper, the scientific method for validating theories is reviewed, and the need for operational definitions and for experiments to identify variables and meaningful abstractions is stressed. The development of a design problem taxonomy is advocated. Generating theories is viewed as guided search. Three types of design theories are described: prescriptive, cognitive descriptive, and computational. It is argued that to seek prescriptions is premature and that, unless the human and institutional variables are reduced to knowledge and control, cognitive descriptive theories will be impossibly complex. A case is made for a computational approach, though it also shown that computational and cognitive research approaches can be mutually supportive.
Accountants have developed tools to evaluate firms' quality performance; however, a focus on evaluating one aspect of quality ?? conformance to pre-established specifications ?? has limited unnecessarily accountants' contribution to quality improvement. Product and process design are the most effective levers in quality improvement and cost reduction; yet, product designers often base decisions on cost estimates that do not reflect the experience of the firm. This paper presents a quality management framework that spans product design, production and consumption or use by end users. We describe and identify shortcomings of cost estimation methods used by product design engineers and propose a framework for new accounting information that focuses on achieving "design quality." The framework incorporates data from the relatively new practices of target costing and activity-based costing and identifies opportunities to develop accounting data that promotes quality being designed into, rather than inspected into, products.
Reply---On "Comment on Aggregate Safety Stock Levels and Component Part Commonality" (McClain, J. O., W. L. Maxwell, J. A. Muckstadt, L. J. Thomas, E. N. Weiss. 1984. Note---Comment on "Aggregate safety stock levels and component part commonality". Management Sci. 30 (6) 772--773.).
Four algorithms for locating an “optimal” new product in a multiattribute product space—Albers and Brockhoff's PROPOPP; Gavish, Horsky, and Srikanth's Method IV; May and Sudharshan's PRODSRCH; and GRID SEARCH—are compared in terms of the relative share of preferences the new product will capture under different simulated market environments. These environments were both ones for which the algorithms were designed as well as other “more realistic” environments. Results indicate that algorithm performance is sensitive to the number of customers or segments, and the presence of probabilistic choice, and less sensitive to the numbers of existing products. Gavish, Horsky, and Srikanth IV (GHS IV) and PROPOPP performed best under the market conditions for which they were designed and GHS IV proved quite robust under variation from these conditions. PROPOPP's performance deteriorated, however, in large sample size problems ( ≥ 200). PRODSRCH (a general purpose optimizer) was inferior under these special market conditions, but superior under other more general ones.
This paper examines the economics of substituting tube-hydroformed parts for stamped assemblies. Tube hydroforming has been
heralded for its ability to decrease weight and increase stiffness compared with stamped solutions. The economics of this
substitution question is examined in three case studies where tube-hydroformed components have replaced stampings using technical
cost modeling, a technique developed at the Massachusetts Institute of Technology. The cases illustrate different factors
that influence the relative cost of tube hydroforming compared to stamping.
The process of product design is driven toward achieving design specifications while meeting cost targets. Designers typically have models and tools to aid in functional and performance analysis of the design but few tools and little quantitative information to aid in cost analysis. Estimates of the cost of manufacture often are made through a cost multiplier based on material cost. Manufacturing supplies guidelines to aid in design, but these guidelines often lack the detail needed to make sound design decisions.
A need was identified for a quantitative way for modeling manufacturing costs at Motorola. After benchmarking cost modeling efforts around the company, an activity-based costing method was developed to model manufacturing cycle time and cost. Models for 12 key manufacturing steps were developed. The factory operating costs are broken down by time, and cost is allocated to each product according to the processing it requires. The process models were combined into a system-level model, capturing subtle yet realistic operational detail.
The framework was implemented in a software program to aid designers in calculating manufacturing costs from limited design information. Since the information tool provides an estimate of manufacturing costs at the design prototype stage, the development engineer can identify and eliminate expensive components and reduce the need for costly manufacturing processing. Using this methodology to make quantitative trade-offs between material and manufacturing costs, significant savings in overall product costs are achieved.
This paper describes the OptdesX design optimization software. The software provides a design environment for optimization
of engineering problems. The software supports interactive variable and function selection, optimization with continuous and
discrete algorithms, design space graphics, tolerance analysis, and control of noise in numerical derivatives, as well as
numerous other features. The software is described and illustrated in terms of a small example problem. The software is available
on Unix platforms only.
This paper demonstrates a methodology to quantify past product development efforts in an attempt to better utilize past experiences. The methodology is centered around conducting an observational study, using regression analysis, to expose relationships between various aspects of past product development efforts, including cost and time. The applicability of the methodology is demonstrated by presenting 'generic' results obtained by making use of information and historical data from a well-established electronics company. The results from applying the methodology support certain design for manufacturability guidelines and the statistical models lend themselves to possibly serving as historical records or prediction tools.
Learning has long been established as a phenomenon of great interest in operations management. Cost reduction through task learning has a great potential. Its quantitative determination is performed in this work using activity-based costing (ABC) and compared with traditional costing, employing a practical example in a mechanical workshop. Results obtained by these two techniques may be significantly different.
Conjoint analysis has become a major tool in the process of designing and concept testing consumer packaged goods and industrial products. In most applications, however, product concepts are tested against existing sets of competing brands without considering potential competitive reactions. Although many researchers have recognized the need for models to incorporate competitive reactions, few methodological developments have been published thus far. Instead of what-if analysis, which depends heavily on the managers' intuition about the competitors' reactions, S. Chan Choi and Wayne DeSarbo propose a game theoretic approach that models competing firms' reactions in price. This price reaction model is incorporated in the conjoint simulator for evaluating product concepts against competing brands. They illustrate the methodology using a commercial data set previously collected.
The constraints addressed in decision making during product design, process planning and production planning determine the admissible solution space for the manufacture of products. The solution space determines largely the costs that are incurred in the production process. In order to be able to make economically sound decisions, costing data support must be integrated into the decision making processes. Regarding product design, the designer must be supplied with transparent costing data, that is ready for direct application. In this paper a functional architecture for costing data support during product design, as well as a corresponding data structure are presented.
Product modularization aims to improve the overall design, manufacturing, operational, and post-retirement characteristics of products by designing or redesigning the product architectures. A successful modular product can assist the reconfiguration of products, while reducing the lead-time of design and manufacturing and improving the ability for upgrading, maintenance, customization and recycling. This paper presents a new modular design method called the House Of Modular Enhancement (HOME) for product redesign. Information from various aspects of the product design, including functional requirements, product architecture and life cycle requirements, is incorporated in the method to help ensure that a modularized product would achieve the objectives. The HOME method has been implemented in a software system. A case study will be presented to illustrate the HOME method and the software.
The linchpin of process cost analysis (PCA) is the evaluation of the product's life cycle and an internalization of market factors. ■ Value engineering, function analysis, and cost-driver analysis are integrated under PCA to link functions and processes. ■ The cost of product characteristics must be evaluated in comparison to customer value, and it also guides the tracking of cost increments at the product design and production stages. ■ The new PCA approach brings to the fore quality-costing factors, and monitoring of error-and-default costs over the product's life cycle, so that pre-emptive action can be directed at the right place and time.
Designing and pricing new products is one of the most critical activities for a firm, and it is well-known that taking into account consumer preferences for design decisions is essential for products later to be successful in a competitive environment (e.g., Urban and Hauser 1993). Consequently, measuring consumer preferences among multiattribute alternatives has been a primary concern in marketing research as well, and among many methodologies developed, conjoint analysis (Green and Rao 1971) has turned out to be one of the most widely used preference-based techniques for identifying and evaluating new product concepts. Moreover, a number of conjoint-based models with special focus on mathematical programming techniques for optimal product (line) design have been proposed (e.g., Zufryden 1977, 1982, Green and Krieger 1985, 1987b, 1992, Kohli and Krishnamurti 1987, Kohli and Sukumar 1990, Dobson and Kalish 1988, 1993, Balakrishnan and Jacob 1996, Chen and Hausman 2000). These models are directed at determining optimal product concepts using consumers' idiosyncratic or segment level part-worth preference functions estimated previously within a conjoint framework. Recently, Balakrishnan and Jacob (1996) have proposed the use of Genetic Algorithms (GA) to solve the problem of identifying a share maximizing single product design using conjoint data. In this paper, we follow Balakrishnan and Jacob's idea and employ and evaluate the GA approach with regard to the problem of optimal product line design. Similar to the approaches of Kohli and Sukumar (1990) and Nair et al. (1995), product lines are constructed directly from part-worths data obtained by conjoint analysis, which can be characterized as a one-step approach to product line design. In contrast, a two-step approach would start by first reducing the total set of feasible product profiles to a smaller set of promising items (reference set of candidate items) from which the products that constitute a product line are selected in a second step. Two-step approaches or partial models for either the first or second stage in this context have been proposed by Green and Krieger (1985, 1987a, 1987b, 1989), McBride and Zufryden (1988), Dobson and Kalish (1988, 1993) and, more recently, by Chen and Hausman (2000). Heretofore, with the only exception of Chen and Hausman's (2000) probabilistic model, all contributors to the literature on conjoint-based product line design have employed a deterministic, first-choice model of idiosyncratic preferences. Accordingly, a consumer is assumed to choose from her/his choice set the product with maximum perceived utility with certainty. However, the first choice rule seems to be an assumption too rigid for many product categories and individual choice situations, as the analyst often won't be in a position to control for all relevant variables influencing consumer behavior (e.g., situational factors). Therefore, in agreement with Chen and Hausman (2000), we incorporate a probabilistic choice rule to provide a more flexible representation of the consumer decision making process and start from segment-specific conjoint models of the conditional multinomial logit type. Favoring the multinomial logit model doesn't imply rejection of the widespread max-utility rule, as the MNL includes the option of mimicking this first choice rule. We further consider profit as a firm's economic criterion to evaluate decisions and introduce fixed and variable costs for each product profile. However, the proposed methodology is flexible enough to accomodate for other goals like market share (as well as for any other probabilistic choice rule). This model flexibility is provided by the implemented Genetic Algorithm as the underlying solver for the resulting nonlinear integer programming problem. Genetic Algorithms merely use objective function information (in the present context on expected profits of feasible product line solutions) and are easily adjustable to different objectives without the need for major algorithmic modifications. To assess the performance of the GA methodology for the product line design problem, we employ sensitivity analysis and Monte Carlo simulation. Sensitivity analysis is carried out to study the performance of the Genetic Algorithm w.r.t. varying GA parameter values (population size, crossover probability, mutation rate) and to finetune these values in order to provide near optimal solutions. Based on more than 1500 sensitivity runs applied to different problem sizes ranging from 12.650 to 10.586.800 feasible product line candidate solutions, we can recommend: (a) as expected, that a larger problem size be accompanied by a larger population size, with a minimum popsize of 130 for small problems and a minimum popsize of 250 for large problems, (b) a crossover probability of at least 0.9 and (c) an unexpectedly high mutation rate of 0.05 for small/medium-sized problems and a mutation rate in the order of 0.01 for large problem sizes. Following the results of the sensitivity analysis, we evaluated the GA performance for a large set of systematically varying market scenarios and associated problem sizes. We generated problems using a 4-factorial experimental design which varied by the number of attributes, number of levels in each attribute, number of items to be introduced by a new seller and number of competing firms except the new seller. The results of the Monte Carlo study with a total of 276 data sets that were analyzed show that the GA works efficiently in both providing near optimal product line solutions and CPU time. Particularly, (a) the worst-case performance ratio of the GA observed in a single run was 96.66%, indicating that the profit of the best product line solution found by the GA was never less than 96.66% of the profit of the optimal product line, (b) the hit ratio of identifying the optimal solution was 84.78% (234 out of 276 cases) and (c) it tooks at most 30 seconds for the GA to converge. Considering the option of Genetic Algorithms for repeated runs with (slightly) changed parameter settings and/or different initial populations (as opposed to many other heuristics) further improves the chances of finding the optimal solution.
The surface-matching problem is investigated in this paper using a mathematical tool called harmonic maps. The theory of harmonic maps studies the mapping between different metric manifolds from the energy-minimization point of view. With the application of harmonic maps, a surface representation called harmonic shape images is generated to represent and match 3D freeform surfaces. The basic idea of harmonic shape images is to map a 3D surface patch with disc topology to a 2D domain and encode the shape information of the surface patch into the 2D image. This simplifies the surface-matching problem to a 2D image-matching problem. Due to the application of harmonic maps in generating harmonic shape images, harmonic shape images have the following advantages: they have sound mathematical background; they preserve both the shape and continuity of the underlying surfaces; and they are robust to occlusion and independent of any specific surface sampling scheme. The performance of surface matching using harmonic maps is evaluated using real data. Preliminary results are presented in the paper
Nippondenso Co. Ltd (NDCL) is Japan's foremost manufacturer of automotive components. Over the past twenty-five years it has developed a variety of approaches to automating the assembly of products in order to meet the high-variety, just-in-time production requirements of its customers, notably Toyota. The approach evolved by NDCL is to design the product intelligently and to make massive use of the simplest automation technology possible consistent with the technical challenges of the product and its production strategy. The result is the capability to manufacture products with considerable model mix at high volume, with little or no changeover time between models. This is essentially a technological response to a business environment challenge.
In pursuit of this strategy, NDCL has categorized the problems of assembly automation into distinct classes, identified applicable solutions for each class, and successively attacked and solved increasingly difficult problems. This paper describes this strategy, gives examples of its evolution, and indicates how NDCL has managed production technology, notably robots, as part of the overall attack. NDCL's approaches to concurrent engineering (CE) and new product risk management are also described. The paper is based both on seven personal visits to NDCL during the period 1974 to 1991, which included extensive interviews with NDCL engineers and managers and plant tours, and on papers published by NDCL and interviews with their authors.