Standardization and modularization driven by minimizing overall process effort

Abstract

Faster product development is a major goal for companies in competitive markets. Product platform architectures support planning for addressing diverse markets and fulfilling future market desires. Applying standardization or modularization on product platform components leverages current product design effort across future products. This work introduces a method—SMDP (standardization and modularization driven by process effort)—for focusing engineering effort when applying standardization or modularization on product platform components. SMDP calculates the total design effort from current to future generations of the platform following standardization or modularization of components. By comparing the total design cost of different simulations, we can direct the design team to standardization or modularization opportunities. The contribution of this work is in using an estimation of design effort as the basis for decision in contrast to commonly used static measures of components' interactions. Such a computational approach allows conducting sensitivity studies that address the subjective nature of various estimations needed for exercising SMDP. SMDP is illustrated in a product platform design of an external-drum plate-setter for the digital prepress printing market.

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.