## 4 Applying the Model for Production Control to the Metallurgical Industry through Simulation
### Model Programming  
The simplified production flow simulated can be seen in Figure 4 below:  
The models were created using delay times as lead times for production processes, transport, waiting times, etc. The AnyLogic software allows providing a processing time or delaying time depending on the agent entering in a machine group. When an agent or production unit enters a machine, the processing time that depends on the agent is activated. After this time the unit goes out of the machine and waits to be transported to the next processing step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Difference** & **Push-Control** & **Pull-Control Applying TOC/DBR** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Order release} & Order release for improving capacity utilization in the first production steps & Regulated order release control based on the system load \\ \hline \multirow{2}{*}{2} & Determination of delivery dates & Same order release for units of the same product family & Adjusted based on the system load \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Sequence planning} & \multirow{2}{*}{First in First out (FIFO)} & Global Priority Rule: Priority determined based on time consumption on the delivery date \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between push and pull production control models for a metallurgical manufacturer.  
Figure 4: Simplified production flow of the simulation model: 6 main steps.  
Moreover, the agents within the simulation are the production units with their parameters associated to them as listed below:  
* Variant within the product family (number);
* Weight (tons);
* Processing lead time per production step (days);
* Days until security buffer (days);
* Transport lead times between all combinations of production steps (days).  
Based on the flow of a production unit along its production process, the following parameters are calculated dynamically within the agents:  
* Waiting time until order release is given (days);
* Production lead time (days);
* Transport lead time (days);
* Waiting times along the production process (days);
* Days before or after the promised production lead time (days).  
Demand is created within the model with gamma distribution and is equal for both models. Its value is quantified according to the rate of incoming units or positions of a certain product family and variant and it is recalculated every 90 days, assuming that a certain pattern remains for 90 days.  
The key performance indicators (KPIs) for the simulation model are:  
* Demand: production units ordered (units). The value is written in an Excel file in periods of 90 days.
* Production units started: production units released (units). The value is written in an Excel file in periods of 90 days.
* OTD (on-time-delivery): percentage of units produced before the promised delivery date for logistics (%). The value is written in an Excel file in periods of 90 days.
* Production throughput: cumulated production (tons).
* WIP: quantity of units in production process (units).
* Stock before and after production process: quantity of units before and after the production process (units).  
The KPIs per production family are: demand (units), OTD (%), production lead time since order release (days), lead times for technical processing, transport and waiting times (days), percentage of units that did not reach the security buffer (%), and the weight break-down of the units (tons and %).  
The KPIs per machine group are: capacity utilization (%), planned load (weeks), priority of a unit before a machine group (%), and direct stock (weeks).