This study investigates the key factors influencing labor hour demand during the early-life Infant Mortality Phase of Complex Capital Goods (CCGs), using ASML lithography systems as a case. The objective of this research is not to construct a high-performing predictive model, but rather to uncover and understand the key factors underlying labor hours in CCGs when these are not yet known and only historical labor hour data from predecessor machines are available. By elucidating these factors, and understanding the causal mechanisms behind them, a stronger analytical foundation can be established, enabling a more accurate interpretation of the available data and serving as a bridge for future predictive modeling of the labor required to navigate the infant mortality phase.
A mixed-methods approach was adopted, combining expert interviews, focus groups, Multi-Criteria Decision Analysis (MCDA), and Fixed and Random Effects panel regression, combining qualitative expert insights with quantitative statistical modeling. The qualitative phase identified both pre-deployment factors and live operational factors influencing labor demand. These were subsequently quantified and tested using a balanced panel dataset of historical labor hour and performance records from predecessor machines.
The methodological findings demonstrate that even in data-scarce environments, structured inference models can uncover consistent and interesting patterns in early-life labor demand. However, several methodological limitations were identified. The reliability of time-writing and performance data posed challenges for model validation and may have led to the exclusion of potentially relevant factors from the analysis. Additionally, the limited sample size constrained the generalizability of certain results, while inconsistencies in labor hour recording practices introduced potential measurement bias that may further affect the robustness of the inference outcomes.
The contributions of this study are twofold: (i) the development of a methodological framework for inferring labor hour influencing factors of CCGs in the absence of direct input parameters, and (ii) the adaptation of classical statistical inference techniques for novel applications in CCG maintenance planning. While demonstrated using ASML lithography systems, the approach is transferable to other CCG contexts, such as wind turbines, jet engines, and railway vehicles.