## 1 Introduction  
The mass customization (MC) concept sounds quite paradoxical to traditional manufacturing practices, since it combines mass production with individual consumer de-sites. This manufacturing strategy presents an important challenge for companies by providing custom products for individual customers at acceptable cost levels. For manufacturers of customized goods, it means they can provide customers with more options, which requires frequent product changeovers in the shortest possible times. Thus, MC leads to the need for manufacturing systems endowed with a higher level of flexibility and resource reconfiguration capabilities. Such manufacturers' ability can be achieved only by systematic implementation of well-known instruments of Industry 4.0 [1]. In general, it is possible to state that manufacturing lead times (MLTs) in mass customization environments are longer than in a case of standard production. Therefore, manufacturers of mass customized products usually expend a lot of effort to minimize MLTs in order to shorten delivery times and thereby increase their competitiveness. Principally, this objective can be achieved in two ways. The first of them is based on using common traditional methods which are applied in the production of standard products. The second possible way is to employ a so-called tailormade innovative approach for the given problem. It is known that product modularity helps to produce many product variants by keeping the costs to a minimum. In this context, our intent here is to present a novel approach which is aimed at increasing throughput rate (THR) performance of assembly processes and simultaneously to reduce MLTs. This approach is based on the assumption that process modularity can improve manufacturing process performance in terms of mass customization, since modular processes can be easily reconfigured to meet the changing demands of production volumes and product variability [2; 3; 4]. An important motivation for this research stems from the fact that modularity, as a design principle, is considered as an important criterion in designing manufacturing and assembly process structures [5; 6]. Moreover, we took into consideration the known fact that modular design is a useful tool for reducing complexity in manufacturing [7; 8; 9] by breaking a manufacturing system into smaller segments to easier manage them. Naturally, a problem regarding investigation of process structure modularity is about finding its optimal modularity level. As optimal modularity does not equate to maximal relative modularity [10; 11; 12], our focus in this paper is to especially employ two competitive indicators (optimal modularity indicators Qd and M(G)) for identification of the optimal modularity level of assembly process structures. In addition, another estimator, called cross-module independence indicator (CMI), that quantifies relative modularity of process structures will be used in order to provide comparison among them. The abovementioned relationships will be explored here using two case studies--the theoretical and practical ones. For this purpose, the main goal of this article can be compiled into two research questions (RQs):  
RQ 1: Can we adequately measure modularity of any real assembly process?  
RQ 2: If yes, how does modularity impact manufacturing lead time and throughput rate in a specific realistic case?  
This research article is organized in the following manner. Firstly, the literature sources that are the most related to this work are described in Section 2. Subsequently, methodology and research object specification are provided. Next, problem exploration is studied using theoretical and practical case study. Then, obtained results from the case studies are summarized in Section 5. Finally, positive research findings from this study and future works are summarized in Section 6.