Exploring a Data-Augmented Approach for Improved Module Driver Analysis

Since the invention of the modular function deployment (MFD) method in the 1990s, the amount and volume of available data in manufacturing companies has increased substantially. This paper explores the possibility of augmenting the analysis of module drivers using data commonly available in manufacturing companies to provide decision support to managers and analysts during design or re-design of modular product platforms. This is achieved by analyzing the 12 module drivers of the MFD method against available data in a case company to propose novel metrics for evaluation. It is found that while some module drivers lend themselves favorably towards data-based analysis, others are inherently more complicated to quantify through available data. Utilizing a data-driven approach to augment the module driver analysis provides a less subjective estimate of the module drivers and presents analysts with an improved decision foundation for modular product platform design.