Modularization is a strategy used for handling the demand for external complexity with less internal complexity, which leads to higher profits and more efficient product development processes. However, modularity is often driven in silos, not crossing into the engineering fields of mechanics, electronics, and software. Therefore, we present the MESA (Mechanics, Electronics, and Software Architecture) tool—a tool that can be used to visualize modular product architectures across mechanics, electronics, and software. The tool demonstrates how a change in one domain affects the rest and how well aligned the modularity in the different domains is. The tool has been tested in two case companies that were used for case application and has helped provide information for making key design decisions in the development of new product families.

This study investigates how traditional costing methods hinder the development of modular product architectures. A structured literature review identifies the gap between Management Accounting (MA) and Innovation and Operations Management (IOM), revealing that current costing approaches often fail to provide an accurate cost assessment of the impact of modularity effects across the product life cycle. A hypothesis is proposed, suggesting that defining cost allocation by levels of abstraction—product, subsystem, and component—can address the challenges in current methods. The analysis found that modularity effects are predominantly assessed at the product level in four out of five life-cycle phases, despite product costs being incurred at lower levels, leading to inaccuracies in cost allocation. This study concludes that current costing practices do not offer a sufficient level of detail for informed design decisions based on cost when it comes to developing modular product architectures and proposes the development of more refined cost models. It offers a new perspective on how to assess product variety and its related effects in a product portfolio. This lays the foundation for future research combining the fields of MA and IOM. This paper highlights the absence of a universal method to assess the total life-cycle cost of product portfolios and outlines directions for future work, including the development and testing of refined allocation models through industry case studies.

Manufacturers increasingly face the challenge of delivering high product variety while managing the internal complexity and costs this creates across the value chain. Modular product architectures are often promoted as a solution, yet adoption remains limited due to the absence of robust, quantitative tools for evaluating their systemic effects. This study develops and applies a data-driven framework that explicitly links product variety and complexity to overhead activities across the value chain. The framework integrates principles from time-driven activity-based costing (TDABC), complexity management, and hierarchical product decomposition, and is operationalized through a structured methodology that combines semi-structured interviews, enterprise resource planning (ERP) data analysis, and model-based simulations. This enables the allocation of previously untraceable cost pools such as engineering, procurement, production preparation, and sales hours to the product structure. Application in an engineer-to-order (ETO) equipment manufacturer demonstrates how the framework can identify high-impact subsystems, quantify potential reductions in engineering and procurement hours, and support scenario testing of alternative product architectures. The results indicate that even approximate estimates provide valuable, directional insights into customization-driven cost distributions. The study concludes that the framework constitutes a scalable and flexible decision-support tool for bridging the gap between theoretical modularization benefits and their quantification in industrial practice.

This study examines the challenges regarding the quantification of modularity effects, focusing on data quality, availability, and management practices within four industrial production companies in Denmark. While the literature identifies many effects of modularity, a substantial gap persists regarding data availability and quality for accurate and practical quantification. This study reveals that less than half of the modularity-related effects have sufficient data for analysis. Furthermore, critical data such as bill-of-material have observed frequent inconsistencies with far-reaching consequences that further undermine data reliability. Silo effects also hinder knowledge sharing and limit holistic assessments of modularity effects. Traditional cost accounting methods often overlook indirect costs, leading to underestimations of modularity’s economic impact, while this study finds that even direct costs lack accuracy and consistency. The results in this paper imply that the Danish industry, and likely other sectors too, may benefit from more rigorous data practices, including tracking time expenditures in overhead departments, to facilitate modularity quantification. Future research may include how accounting systems impact data management, developing structured data management frameworks, including artificial intelligence, to enhance data quality, improve quantifiability, and encourage knowledge sharing within and across departments. Ultimately, these advancements would support informed, data-driven decisions regarding modular product development and support bridging the gap between research and successful industrial application.

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 study introduces the Product Variety Costing Method (PVCM), a data-driven framework that addresses the limitations of existing costing approaches, which fail to accurately present the cost of product and part variety, thereby constraining cost-informed decision-making in modular product development. Traditional cost allocation methods often lack one or more of the following: a full life-cycle perspective, a lower level of granularity according to the product structure, or a combined integration of qualitative and quantitative data. The PVCM bridges these gaps by combining Time-Driven Activity-Based Costing (TDABC) with hierarchical product structures and empirical enterprise data, enabling the quantification of variety-induced resource consumption across components, subsystems, and complete products. An industrial application demonstrates that the PVCM enhances cost accuracy and transparency by linking resource use directly to specific product abstraction levels, thereby highlighting the true cost impact of product variety. In this case, results revealed deviations of up to 60% in the adjusted contribution margin ratio relative to traditional overhead-based methods, clearly indicating the influence of product variety on cost assessments. The method supports design and managerial decision-making by allowing evaluation of modularization based on detailed cost insights. While the study’s scope is limited to selected life-cycle phases and a single company case, the findings highlight the method’s future potential as a generalizable tool for evaluating economic benefits of modularization. Ultimately, the PVCM contributes to a more transparent and analytically grounded understanding of the cost of variety in complex product portfolios.