A modular architecture is a strategic means to deliver external variety (to the customers) and internal commonality (to the manufacturing organization). A common view is that a module should be a physical and functional building block, with well-defined and standardized interfaces between modules, and that it should be chosen for company specific reasons. Existing methodologies, such as Modular Function Deployment with the Modular Indication Matrix (MIM) representation of identified company-specific module drivers, can be used to assist the task to identify modules. Other approaches, such as clustering of the Design Structure Matrix product representation, may be used to identify modules from a technical complexity point of view. A new methodology for product modularization that integrates technical complexity and company strategies is proposed in this paper. The core of the presented methodology is to adapt the component-DSM with MIM-strategies, before clustering this hybrid representation with the previously presented IGTA++ clustering algorithm. The proposed methodology is exemplified and logically verified with an industrial test rig modularization case. The modular test rig architecture chosen with the new methodology is shown to have 53% less complexity, as defined by Pugh, compared with the original architecture, and it could potentially reduce the risk of design mistakes, and reduce the development time by up to 70%. It is also estimated that it would be possible to reuse up to 57% of the modules, in future test rig redesign projects, which indicates potentially large savings in cost and development time.

A modular architecture is a strategic means to deliver external variety and internal commonality. A methodology for product modularization that integrates complexity and strategies is proposed and logically verified with an industrial case from the heavy truck business area. The case study indicates that the new methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company specific strategies.

A common view is that a module should be a functional building block, with well-defined and standardized interfaces between the modules, and that it should be chosen for company specific reasons. A modular product architecture is a strategic means to deliver external variety and internal commonality. Today, multiple modularisation methodologies exist to support the highly complex task to identify module candidates in the product architecting phase. One methodology is Modular Function Deployment with the Modular Indication Matrix (MIM) representation of company-specific module drivers. Other methodologies, such as Design Structure Matrix (DSM) clustering, may be used to identify modules from a technical complexity point of view. In this paper, the performance of the newly proposed Integrated Modularization Methodology (IMM), which is based on clustering of a strategically adapted DSM, is conceptually verified. The core of the IMM is to transfer company specific module drivers from the MIM into the component-DSM, before clustering this hybrid representation. A re-architecting industrial case, where a truck manufacturer with a unique business strategy had to redesign parts of its modular gearbox architecture to also become a First-Tier OEM-supplier to another large truck manufacturer, is used as test bench. Reverse engineering of the investigated gearbox architecture indicates that the current modules are most likely not only based on technical complexity concerns. They are rather derived from different types of business strategic aspects, e.g. outsourcing. The study also indicates that the IMM is capable of identifying clusters without strategic conflicts, and with the most similar result to the analysed architecture, which is assumed to be based on expert judgements.

A modular product architecture is a strategic means to deliver external variety and internal commonality. In this paper, we propose a new clustering-based method for product modularization that integrates product complexity and company business strategies. The proposed method is logically verified by a studied industrial case, where the architecture of a heavy truck driveline is analyzed in terms of how it has evolved over a couple of decades, due to changed business strategies and the evolution of new technology. The presented case indicates that the new methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company-specific strategies. Furthermore, the case study clearly shows that the business strategic reasons for a specific architecture can be found by analyzing how sensitive the clusters are to changes in the module drivers (MD).

A modular product architecture is a strategic means to deliver external variety and internal commonality. In this paper, a heavy duty modular gearbox architecture is represented and analyzed. In focus is re-engineering of hidden technical complexity and business strategy concerns behind an existing product architecture. The architecture of the investigated gearbox is represented and analyzed with a Product Architecture DSM and the Integrated Modularization Method (IMM). Furthermore, a Cluster Match Matrix (CMM) is proposed as a means to compare multiple clustering results. The case study indicates that the IMM methodology and CMM can be used for analyzing and finding the explicit and/or implicit reason for a targeted existing product architecture.

We propose a new extended version of the previously introduced Integrated Modularization Methodology (IMM) that integrates technical complexity and business strategic concerns when clustering the architecture. The extended IMM (eIMM) adds physical interference and implementation dependent behavior into product architecture clustering. A presently developed battery electric truck is used as a test bench for studying if and how the product architecture DSM and eIMM approach may enable us to identify module candidates that are reasonable trade-offs between technical complexity, business strategies and physical interference. The presented case study indicates that eIMM is able to propose a modular product architecture without conflicting business strategies or intra-modular physical interferences, as well as reasonable module candidates from a technical complexity point of view.

Product architecting involves conceptual system design, module identification (clustering) and product layout design. In this paper, we propose a new extended version of the previously introduced Integrated Modularization Methodology (IMM) that integrates technical complexity and business strategic concerns into product architecture clustering. The extended IMM (eIMM) adds physical interference and implementation dependent behavior into architecture clustering. The proposed method is logically verified by an industrial case, where the architecture of a presently developed battery electric truck is used as a test bench for studying if and how the product architecture DSM and eIMM approaches may enable us to identify module candidates that are reasonable trade-offs between technical complexity, business strategies and physical interferences. The case study indicates that the eIMM is able to propose a modular product architecture with reasonable module candidates from a technical complexity point of view, and without conflicting business strategies or intra-modular physical interferences.

Product architecting involves conceptual system design, module identification (clustering) and product layout design. In this paper, we propose a new clustering-based process and methodology for heavy-duty truck modularization that integrates technical complexity, company business strategies and physical interference. A quite complex industrial case of a conceptual Battery Electric Vehicle (BEV) variant is used to illustrate the proposed process and methodology, and to show that it is capable of identifying module candidates that are reasonable trade-offs between technical complexity, business strategies and physical interferences. The presented case confirms that the proposed process and methodology for Integrated Modularization is capable of supporting the Systems Engineering process by improving the quality of the architectural decomposition,i.e. the module identification stage by adding business strategies and physical interference to product architecture clustering. Furthermore, the case study clearly shows that the clustering results depend on the relative weights of the different types of component relations that are represented in the product architecture DSM. The importance of these weights are reduced when multiple business strategic aspects and physical interference constraints are introduced