Standardization and modularization driven by minimizing overall process effort
Introduction
The increasing heterogeneity in contemporary marketplaces, the wider income distribution, and the slower growth within the market, are driving the need for increased product variety. Developing robust product platform architectures with modular and standardized components could enhance the ability of companies to bring products to market faster and gain an important competitive advantage. The major benefits from this are reduced design effort and time-to-market for future generations of the product [1], [2].
Within the growing interest in planning for product platform, there are studies that encourage the use of platform architecture in early development stages and include the consideration of marketing, design, and manufacturing issues [3]. Fujita and Ishii [4] discussed design for product variety in terms of structuring essential tasks and issues associated with variety design. They tried to optimize the system structure and the configuration of product families simultaneously. Simpson et al. [5] applied goal programming and statistical analysis techniques to provide a method that facilitates the synthesis and exploration of a common product platform concept that can be scaled into an appropriate family of products. Messac et al. [6] described the identification of common and scaleable parameters in a family of scaleable products. Sosa et al. [7] identified modular and integrative systems and Dobrescu and Reich [8] developed flexible product architecture composed of progressively shared modules. Kreng and Lee [9] described a QFD-based approach combined with linear integer programming for translating customer requirements and a product into recommendations for modularizing components. The last three studies employed information on components’ static interactions. In contrast, we based our method on modeling the dynamics of design processes.
We focus our attention on the use of DFV (design for variety) [1], [10] as a tool for gathering design information for the product platform architecture. DFV uses specifications ‘flows’ within the project, employed by two indices, the generational variety index (GVI) and the coupling index (CI), to develop a decoupled architecture that requires less design effort for follow-up products. The utilization of the above indices is static without taking into account an actual analysis of the design process.
In today's products development and in particular, architecture design of products family, improved management of development processes contributes to competitive advantage. The key to process improvement lies in better process understanding. One can achieve this by using modeling techniques that simplify the complexity of the design process by viewing it as a set of simpler activities with interrelationships. This simple model could be simulated and analyzed to find process bottlenecks that can be addressed to shorten the development cycle time and reduce the design cost [11]. Gonzalez-Zugasti et al. [12] used a meta-model of the technical performance requirements and costs to optimize the design of a family of spacecraft based on a common platform.
Steward's [13] Design Structure Matrix (DSM) can be used as a model for analyzing decision-making processes. DSM provides a simple, compact, and visual representation of a complex system and supports methods for decomposition and integration. Smith and Eppinger [14] employed an extended version of the DSM, to model design as a sequential iteration approach, which involves the sequential execution of coupled tasks. Converting Martin's CI matrix to a task-based DSM allows for studying the relationship between design tasks, arriving at alternative strategies by ordering the design tasks, evaluating design cost, and improving the overall design process.
In the following sections, we introduce a method called SMDP (standardization and modularization driven by process effort), for focusing engineering effort when applying standardization or modularization on product platform components. For this purpose, SMDP is the first method to use dynamic simulations of design processes instead of the static information about the interface between product components (Jose and Tollenaere [15]). SMDP is a framework for integrating several former ideas into one working model. We selected particular methods for inclusions (e.g. DFV) but they are not necessary for the use of the framework. Alternative methods to collecting relevant information or modeling design processes could be exploited if found suitable. As in any integrative effort, our work proposes extensions and adjustments to achieve its own goals. SMDP focuses on standardization and modularization for minimizing design effort as a means for reducing cost and time to market. Nevertheless, other concerns such as manufacturing, marketing, service, or maintenance cost (Gershenson et al. [16]; Ishii [17]; Pine [2]) could be incorporated by modeling them mathematically and modifying the objective function minimized by SMDP. This modeling, however, might not be trivial and might require similar work as presented in this paper for modeling design effort.
Given that many studies deal with a focused problem and propose method to address it, there is significant importance to demonstrating the need to integrate several methods for addressing a larger problem. In addition, our work provides a methodological test of the utility of previous studies and the ability to replicate their results.
Referenced work such as DFV, sequential iteration, and DSM, are reviewed in order to establish their integration as part of the overall process. Simulating the product platform design and obtaining better cost considerations in planning future designs, improves decision-making. This contribution is illustrated through a genuine test case.
Section snippets
The SMDP method
In order to minimize the total design effort (DE) across the platform generations, one needs to find the set of components to undergo standardization (Is) and the set of components to be modularized (Im), out of the group of platform components (I). Eq. (1) presents the corresponding mathematical model.
In this work, we do not solve this problem explicitly or optimally. Rather, we provide an algorithmic heuristic solution that is computationally feasible and
Application of SMDP
We applied SMDP for designing product platform architecture of laser direct imaging (LDI) plate/image-setter for the prepress market industry. For the purpose of illustration, we used a simplified example where only eight main subsystems (components) of the LDI plotter system were considered. The input of this case study was given by leading engineers that took an active part in the design and development of prepress imaging systems for market-brand names such as CreoScitex Inc. These engineers
Sensitivity analysis
SMDP employs many subjective evaluations from its users as well as various parameters in its calculations. Consequently, we run sensitivity analysis of the results (i.e. total design effort and task ordering) with respect to the input values and SMDP parameters. We run studies in three categories:
- a.
Overall inputs change to mimic an overall variation in the team's initial estimations:
- 1.
10% increase or decrease in CI values (matrix multiplied by 1.1 or 0.9).
- 2.
10% increase or decrease in components
- 1.
Conclusions
We presented a method for selecting components for standardization and modularization when designing product architecture for multi-generation products. SMDP is based on the integration of several design tools previously developed by different research groups into coherent steps that lead to well informed design decisions. The essence of this work and its contribution is by improving decisions that are based on subjective measures (e.g. DFV) using detailed process simulation and by developing a
Acknowledgements
The authors thank Amir Ziv-Av for his assistance in the case study.
Yuval Sered is a Technical Lead at ClickSoftware Inc. a world-wide leading provider of service optimization software solutions; received his BSc and MSc in Mechanical Engineering, Tel Aviv University. His master's thesis introduced a method for designing a family of products by minimizing the overall process effort, utilizing computer simulated design processes, knowledge modeling and genetic algorithms. Yuval served as a Captain in the Technology & Logistics Headquarter of the Israel Defense
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Yuval Sered is a Technical Lead at ClickSoftware Inc. a world-wide leading provider of service optimization software solutions; received his BSc and MSc in Mechanical Engineering, Tel Aviv University. His master's thesis introduced a method for designing a family of products by minimizing the overall process effort, utilizing computer simulated design processes, knowledge modeling and genetic algorithms. Yuval served as a Captain in the Technology & Logistics Headquarter of the Israel Defense Forces. As a senior officer, took active part in implementing a Product Data Management system that enables design decision support, knowledge conservation and integration to legacy, CAD and e-commerce systems.
Yoram Reich is an associate professor at the Faculty of Engineering, Tel Aviv University, Israel; received his BSc (Summa Cum Laude) and MSc (Magna Cum Laude) in Mechanical Engineering, Tel Aviv University. Before obtaining the PhD degree in Civil Engineering from Carnegie Mellon University, he practiced design for over 7 years in the audio, structures, and marine industries. Yoram Reich has authored about 150 papers and is a member of the editorial board of the journals Advanced Engineering Informatics, International Journal of Mass Customization, and Research in Engineering Design. His industrial activities included consulting and serving on the management board and as chair of the Israeli chapter of SME. His research focuses on product design methods and theories, computer-aided design, data mining, and design research methodology.