## 2 Literature Review  
There is a broad literature focusing on methods and tools which have been developed to facilitate the performance improvement of HMLV manufacturing plants. Recently, new options have become accessible with the digitization of the everyday processes, or with the introduction of \"Industry 4.0\" technologies, in short. Group Technology (GT) methods' main benefit lies in the simplification, standardization, and harmonization of processes and in transferring these well-proven practices of mass production to HMLV production [3]. Commonalities of features of products, parts, processes, technologies, and equipment can all be considered when defining product families. Family clusters' leveling is a relatively straightforward and pragmatical solution for managing the smooth flow of HVLM production [4]. This method is easily adaptable by human planners, too. Note that the approach of grouping tens of thousands of product variants into manageable numbers of families has also been taken in our factory's everyday practice, well before the start of this particular research project. Proper handling of components and semi-finished products forming the so-called Work in Process (WIP) is suggested in [5] for reducing the lead time and improving the delivery performance of HMLV production. This work, implicitly, also emphasizes the importance of the right sizing of internal buffers. As reported in [6], a pull system with rigorously managed customer takt time and with a properly calculated WIP level supportedby a Kanban system can increase the throughput of the system, even under the conditions of HMLV production. Again, this management methodology was already adopted in our factory. It is warranted that essentially no WIP level can increase without limits [7]. Overall equipment effectiveness (OEE) is a major key performance indicator of any production environment. To evaluate the OEE of a HMLV production site, a machining equipment effectiveness (MEE) method is recommended in [8]. However, there are always priorities and constraints in an existing lean manufacturing environment. Primarily, cost of levelling is an important factor when a decision must be made [9]. Capacity scaling effects or lot size selection can have different financial impacts on the cost of manufacturing. Sometimes, decisions need to be at the expense of neglecting the lean principles. Job shop control methods presented in [10] analyze situations when the capacity increase of some workstations considerably improves the delivery performance of the whole system. However, cost as a key factor cannot be neglected and a trade-off must be found, in any case. In a production environment where capacities are doubled, the possibility of machine underutilization due to improper scheduling can be expected, as shown in [11]. In case of batch production, lot-splitting can be an especially effective tool in a HMLV environment to reduce the makespan. However, there is always a trade-off between capacity availability and makespan reduction, as shown in [12]. With efficient lot-splitting, one can achieve the same makespan with lower capacity and at lower costs. Lot-splitting and load-limiting methods were worked out considering the special limitations in a high-variety production shop [13]. With these methods, the performance and service level of a shop can be improved. However, if synchronization is enforced at every routing step, the results deteriorate. The lot-streaming (or job-splitting) methods suggested for a batch production environment can help in reducing the main KPIs, such as makespan, WIP, mean flow time, and total cost [12; 14]. The workload control method presented in [14] was mainly elaborated for make-to-order (MTO) production, however, the solutions could be good starting points for improving the performance of HMLV production, too. Finally, the evolution of cyber-physical production systems opened new opportunities for autonomous control, process optimization, and decision-making [15; 16].