Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We show this by assessing the 14 most-recognized log parsing approaches in the literature using (i) nine publicly available datasets, (ii) one dataset comprised of combined publicly available data, and (iii) one dataset generated within the infrastructure of a large bank. Subsequently, toward improving log parsing robustness in real-world production scenarios, we propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts through generating synthetic log data that resemble industry logs. Our contributions serve as a foundation to consolidate past research efforts, facilitate future research advancements, and establish a strong link between research and industry log parsing.