## 3 Problem Statement and Solution Approach  
In the research presented here, the ultimate goal was to develop such a practically adaptable and affordable decision support method which can help production planners improve the performance of an HMLV production facility by combining the strengths of model- and data-driven analytics. From among the many key performance indicators(KPIs) relevant in our business, the so-called confirmed delivery date (CDD) emerges as the most important performance criteria. CDD is a main differentiator factor for a company, which defines its reliability in fulfilling the customers' order requirements. We were seeking a solution which could be turned operational by the adaptation of our earlier investments into digitalized models and decision support mechanisms. Finally, the most important measure of affordability is, as in any business, cost. All in all, to obtain a method to be used by our production planners in their everyday activity, several practical requirements had to be fulfilled:  
* The method had to be built, as far as possible, on existing CPPSs solutions routinely applied in our factory.
* Compliance with the overall managerial decision-making workflow of the factory was a must.
* We did not intend to automatize the process completely and to replace our production planners in any way. Instead, we wanted to give them a new tool that can be used in combination with their accumulated experience, to obtain faster, more reliable, and better solutions.
* The method had to be flexible and easy-to-adapt to changing conditions, as in the case of the unexpected coronavirus situation as well. Under whatever circumstances, the method should assure the efficient running of the plant.
* Finally, we had to find an acceptable balance (i.e., compromise) between the expected gains and costs of the new method.  
The solution approach was based on the used simulation models for analyzing the impacts of potential changes--both structural and operational--in our production system. The main features of this new method are the following:  
1. Simulations were run in a number of systematically collected scenarios which captured both external and internal changing conditions. Specifically, causes of these unexpected changes can be of many types, such as changes of market demand or particular customer order levels, unreliable material supply, or volatile workforce availability due to epidemic-prone diseases or quarantine, to name a few.
2. When focusing on the most promising changes in the structure and operation of our manufacturing facility, we were experimenting with generalized, compressed past records. These old sources were available in various digitalized records, such as order books, production schedules, production parameters gathered through different sensors, escalations of unexpected events, KPI statistics, etc. From this pool of data, machine learning (ML) helped to extract and condense useful information when defining characteristics simulation scenarios.
3. Finally, the suggested, most promising structural and operational changes were validated, evaluated, and compared in simulation runs.  
Some elements of this workflow have been presented earlier in [15; 23]. In what follows, we provide a concise summary of the industrial motivations and present the complete solution process, along with the impact of the results on the actual operations.