## 6 Simulation Results
In the middle part of the presented graph, the dependence of the maximum price for a service on \(f_{\text{BC}}\) can be approximated using a linear function, as the production function in this range is linear as well. The more \(f_{\text{BC}}\) increases, the higher the maximum price is. The increase of the blockchain network's frequency in the linear part of the production function means that there is proportionally more time before congestion; consequently, there is proportionally more time to generate supply in the market and to increase the price of services. In the third part, logarithmic relaxation occurs due to the limiting of the production function (in this case, it was set to 20 s). Figure 19 presents how the time to network congestion depends on the transaction confirmation frequency. The time to network congestion is the first point in time in the game at which the frequency of the provided services is equal to the frequency of transaction confirmation by the blockchain network. Again, the presented function does not indicate linear dependence, because the time to network congestion depends on the defined production function. In a case where there exists a group of players who play the optimal strategy and want to achieve the global optimum manufacturing system, this function has a lower set limit. This means that the blockchain network cannot be congested sooner than the production function allows. A similar form of the function can be observed in Figure 16, which shows the times at which the initial fluctuations in the frequency of service execution disappear. It can be seen that the value fluctuates, i.e., a local maximum appears at about every 0.02 Hz. This oscillation occurs because a point in time is observed at which the function reaches a certain limit; because this function is periodic, the obtained point in time depends on the phase of the observing function. Figure 18: Maximum price of provided services in the game for different frequencies of transaction confirmation. From the presented simulation results, it can be seen that the scalability properties of blockchain technology in BBSM define the kind of regime in which the BBSM system operates. If the throughput of the blockchain network is lower than the requirements of the users, the transaction fees are high, service prices are low, and manufacturing capacities are underused. This causes depreciation of the manufacturing role compared to the financial role, as shown in the preliminary experiment. On the other hand, if the throughput of the blockchain network is much higher than the requirements of the users, the financial role is depreciated, as the value of the transaction fees are minimal compared to service prices. Both of these two roles, manufacturing and financial, are necessary in order for the BBSM system to exist. Therefore, either extreme regime inhibits the operation of the BBSM system and is not desirable. There must be a balance between the requirements of the users and the throughput provided by the blockchain network in order for the BBSM system to operate. Such a balance is dependent on the user requirements in the system. The main limitation of the presented results is that the analyzed BBSM system is simplified due to the methodology of employing a virtual environment. In reality, the observed system would be more complex, as the manufacturing world is more diverse with a large number of different types of services, there are additional external factors affecting operation of the system, such as supply chains, and other aspects such as security issues are present. In order to improve this study, experiments with a higher number of repetitions than the sole experiment presented here are necessary. Furthermore, he trade-off between the complexity of the virtual environment and the intuitiveness of the user interface should be optimized in order to increase the complexity of the observed system.