## 1 Introduction  
To be competitive on the global market, a company must deliver consistently with a high delivery precision, on a good-quality and economical cost level. Exploiting more and more on the faculties of the internet, and social media in particular, customers' behavior has changed significantly, requiring much more responsive services and being more impatient than ten-fifteen years ago. Plants with expensive manufacturing technologies and trained human resources need to keep their utilization at a high level. This is especially so in high-mix and low-volume (HMLV) manufacturing environments where order sizes are low, numbers of orders are high, and meanwhile products' variability is increasing. To have a competitive advantage in HMLV production, the most essential faculties that a factory needs are flexibility, responsiveness, and cost efficiency. The pragmatic motivation of the work presented here was to improve the actual managerial decision support methodology of a HMLV manufacturing plant across these dimensions. Specifically, the key issues were how to adapt the structure and behavior of the production system to a typical HMLV demand, and to find a balanced trade-off between the suggested changes and the overall costs. Additional motivation was to discover the potentials existing in highly digitalized cyber-physical production systems (CPPS) [1]. Such systems provide the technological background for so-called \"Industry 4.0-ready\" companies, operating with the support of Enterprise Information Systems (EIS), Manufacturing Execution Systems (MES), and advanced Production Planning and Control (PPC) systems [2]. On the one hand, these systems presume articulated models of the factory, on several levels of abstraction and along different functional decompositions. On the other hand, CPPSs employ massive sensing, reporting, data processing, and storage systems. The large data repositories are subject to data analytics and machine learning, but the pieces of knowledge generated in this way are typically independent from the managerial and engineering background knowledge which are encoded in the above models. The main objective of this research is to make past data accumulated during the work of a HMLV facility operational in adapting its structure and behavior to changing external and internal conditions. It is important to emphasize that the machine learning process will comply, as far as possible, with the existing technological and managerial know-how and background knowledge captured in our production information systems. Hence, we are not going to learn blindly from data gathered from sensors, logs, or large database repositories. We are also not relying simply on correlations found between different key performance indicators (KPI). Instead, we are going to exploit the power of big data analytics in the context of our accumulated business intelligence. In particular, smart companies often use techniques and models of discrete event simulation (DES) to support decision-making. These simulation models describe the real systems with a fine granularity, semantic clarity, and transparency. However, simulation models may have limited generality, may only run with some restricted future scenarios, and at times, only with extreme computational efforts. How can we make a better use of these models by relying on the novel techniques of data-driven analytics? How can we generalize results of specific simulation runs? How can we push analytics towards useful findings by relying on the engineering and managerial background knowledge captured in these DES models? Is there any way to combine model- and data-driven analytics with the purpose to improve the performance of production in an HMLV environment at an affordable cost? The paper provides answers to the above questions according to the following logic. Section 2 surveys those methods which were developed for and are also applied in the industrial practice to facilitate reliable customer service in HMLV production.