## 6 Conclusions  
This chapter was divided into theoretical, managerial, and empirical conclusions explaining also the limitations of the research performed as well as describing the potential research work derived:  
Theoretical conclusions: the research paper pursued the following objectives, the improvement of the OTD (delivery schedules), the reduction of the stock in WIP and the reduction of the production lead times. In order to achieve these goals, a pull production control with a so-called \"bottleneck control\" was developed for an ETO manufacturer providing steps as guidelines to follow in order to apply it successfully in real manufacturers. * A new concept using a TOC approach was developed. * Steps for the implementation of the new concept developed were described. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Performance Indicator**} & \multirow{2}{*}{**Push-Control**} & **Pull-Control Applying** \\  & & & **TOC/DBR** \\ \hline
1 & OTD (\%) & 74 & 95 \\
2 & Production throughput (tons) & 75 & 77 \\
3 & WIP (units) & 339 & 285 \\
4 & Waiting time (\%) from total production lead time & 51 & 34 \\
5 & Capacity utilization (\%) & 58 & 61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results for push and pull-demand production control—base demand scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Description**} & **Name** \\ \hline
1 & Scenario 1 &
\begin{tabular}{c} Demand in an average level around 30 units \\ per week \\ \end{tabular} & Base \\ \hline
2 & Scenario 2 &
\begin{tabular}{c} High demand for all product families \\ compared to base scenario \\ \end{tabular} & High \\ \hline
3 & Scenario 3 &
\begin{tabular}{c} Low demand for all product families \\ compared to base scenario \\ \end{tabular} & Low \\ \hline
4 & Scenarios 4–10 &
\begin{tabular}{c} High demand for one product family and \\ base for the others \\ \end{tabular} & High for product family X \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Demand scenarios. Managerial conclusions:
* The current challenges of ETO manufacturers were described and those influencing the production system were integrated in the push control model. * Internal efficiency was proved as key for metallurgical companies, in particular, for the steel industry. * The importance of a controlled and synchronized communication between sales and production to assign realistic delivery dates that lead to higher customer satisfaction. Empirical conclusions: to prove the utility of the new concept a simulation for a metallurgical example was created by including the necessary instruments and control systems developed as well as the validation by means of simulation. The model considered and simulated the variety of complex process such a batch production in the steelworks and unique production items in the other production steps as well as the interrelationships between them. That is why the planning and control system was robust, simple, and transparent to every employee and for the end-customer. * Both concepts, pull and push-control, were implemented in AnyLogic software with agent-based modeling and simulated to compare the performance in different demand scenarios. * The benefits of the change from a common push control approach to an approach using DBR are:  
Limitations of the research work:  
* Assumed that existing ETO producers used a production control based on a push approach. * Complexity of an ETO manufacturer was partially built in the simulation model. * Complexity of processes such as for steelmaking was not built in detail. * Organization structure and interfaces were not considered in the simulation model. * Quality problems were not considered. * Concept was not proved in any company.