## 8 Conclusions and Outlook  
This paper discussed both the practical and theoretical aspects of the challenges in HMLV production and pointed to decision support as the main critical factor in providing the proper responses. In particular, it was explained that correct, and at the same time cost-efficient actions have to be found in a complex web of interrelated factors. Mathematical modeling, albeit desirable, can capture only a fragment of issues and their relations.  
However, in a competitive setting which is most characteristic to HMLV production, misses and mistakes are barely tolerated.  
What remains at disposal to improve the quality and precision of decision-making under such circumstances are detailed simulation models and big datasets collected during the normal operation of a factory. Both are essential elements of modern cyber-physical production systems and readily available in factories which are equipped with quasi-standard Industry 4.0 tools and techniques.  
This paper suggested a novel approach for the combined use of model- and data-driven analysis and decision-making, all in the ultimate service of improving the delivery performance of a HMLV facility. All of these new results were integrated into a workflow whose phases were presented in detail, while elaborating on a case study. Key findings were validated and verified by special series of simulation experiments. The everyday practice corroborated the results, even in the hard times of the COVID-19 pandemic [28].  
Beyond the above improvements, the main benefits of the new approach are the following:  
* Theoretical and practical analysis resulted in a comprehensive and structured set of factors, which essentially influence delivery performance in a factory. These findings have relevance well beyond the scope of HMLV production.
* Companies at the advanced level of Industry 4.0 maturity (which are not few, anymore) typically have some DES models of their production facilities and some consolidated datasets of their past demand. This paper presented a workflow for making new use of these digital assets. The rough-cut sensitivity analysis suggested here can help to set the focus on the most promising and cost-efficient factors to change.
* As it was shown, the need for massive simulation studies can be reduced by compressing demand data of past periods into a much smaller set of characteristic demand patterns. For this, an unsupervised learning method was suggested which retained the intrinsic structure of a complex HMLV demand mix.
* Simulation experiments made explicit the observation that the different demand patterns produced by learning can be best served by a fitting combination of sequencing and lot-splitting rules. Our day-to-day operation could be improved, accordingly, and we are convinced that the lesson is generic, applicable even beyond HMLV production.  
Further improvement possibilities in managing our HMLV production system can be achieved through extending the simulation model into a continuously functioning digital twin. This would entail a nonstop back and forth communication between the simulation software and the real factory, opening new opportunities for a continuous correction capability, self-optimization, and self-learning.  
This research received no external funding.  
Not applicable.  
I would like to thank my colleagues, [PERSON], for her contribution in building and calibrating the simulation model of the real line, [PERSON], [PERSON], and [PERSON], for their support in our production planning modeling, [PERSON] for transferring his practical knowledge into the theoretical simulation model, as well as [PERSON] (Haris Digital) and [PERSON] (SZTAKI) for their challenging questions and continuous support.  
The author declares no conflict of interest. The views, thoughts, and opinions expressed in the text belong solely to the author, and not necessarily to the author's employer.