## 4 Discussion  
By using the regression function presented above, it is possible to calculate productivity levels for all four mechanisms, whether bottleneck exists in the process or not, and for a given current condition of a production process in terms of variability, processing time and the desired level of WIP. By comparing of calculated values, one can decide which control mechanism to choose. Figure 2 presents just one of the possible combinations of independent parameters and level of productivity for that specific condition. The influence of the bottleneck in the process is obvious and for the same level of parameters, variability, processing time and WIP, the same pull control mechanism will not achieve the optimal level of productivity in the process with and without bottleneck (Figure 2a,b). Therefore, for this specific case, for the process with bottleneck DBR would be better choice, while for the process without bottleneck the better choice would be the Conwip mechanism.  
Figure 2: Comparison of effectiveness of pull control mechanisms (**a**) Productivity for set of parameters: za \(\mathrm{Cv}=0.25\), T = 5 min, WIP = 15, for the process without bottleneck (**b**) Productivity for set of parameters: za \(\mathrm{Cv}=0.25\), T = 5 min, WIP = 15, for the process with bottleneck.  
For further check and discussion, let us look at a production process with higher variability, Cv = 1.25, and WIP = 5 and the process with bottleneck. Processing time is 5 min as above. Figure 3 presents the levels of productivity for this combination of parameters. In this case with bottleneck Hybrid Kanban/Conwip would be better choice.  
The advantage and novelty of this study is that it is useful as u guideline for many other combinations of levels of independent parameters which are different for different production facilities. This could help managers in this field in making decision on which production control mechanisms to implement.  
In practice, lean implementation is not that simple task. There are well known general steps in transforming production processes according to lean principles. Many lean implementation projects start with great success but do not succeed in greater extant, rather slow down or stop due to challenges that emerge. Pull principle is a big challenge for many companies and demands a lot of prerequisites in order to be achieved and that is well described in literature [3, 71].  
One of the decisions that have to be made when introducing pull in the production is which production control mechanism to use. Effectiveness of pull control mechanisms under various production condition is not the same and for industrial practitioners it is not always clear which control mechanism would be suitable for their process. The process of implementation of pull production mechanisms can be significantly long and to test in real production and then make the decision mechanism to implement is impractical and expensive. Thus, findings gained from this study could help production managers to make decision and choose appropriate pull mechanism.  
As was the initial assumption, the results of simulation experimentation showed that the existence of bottleneck in the process affects the efficiency of pull control mechanisms in terms of productivity. That is, one mechanism which is optimal for the process without bottleneck is not optimal for the process with bottleneck. It is also confirmed that for the different level of input parameters, namely variability, process time and number of control cards, different pull control mechanism contribute to better levels of productivity, thus if in the case of a bottleneck one mechanism for one setting of production parameters is optimal for another setting of parameters, also with bottleneck, that same mechanism will not be the best choice.