# Decision Support Simulation Method for Process Improvement of Intermittent Production Systems
## 4 Possible Applications of a Decision-Making Simulation Method  
Continuous changes of the product types and amounts to be produced based on customer needs results in frequent re-planning of the logistics processes of intermittent production systems. The effects of the alteration to be realized can be difficult to forecast because of the examined system's complexity; consequently, it is necessary to reduce the decision risks by high precision determination of different logistics indicators. The value of the needed indicators can be determined by the elaborated simulation investigational method; thus, the improvement decision can be made with minimum risk. We examine a planned system's working by application of the investigational method, where the decision's result can be realization of the examined system or execution of a new examination with modified parameters based on the values of the determined indicators. We have to execute the simulation examinations while obtaining the necessary data for the final decision. This can be a repetitive process. The elaborated investigational method is suitable for the following cases:  
(1). Decision support for determination of the storage capacity to be created: Precise determination of the storage capacity needed for a system to be realized can cause significant difficulty because of the complexity of the processes. In such cases the necessary controls and alterations can be executed by the elaborated simulation method. Most important indicator:  
Maximum stock level: This is how to determine the maximum storage capacity need(s) regarding one or more storage systems. The maximum stock level of the storage system s can be calculated as follows:  
\[\mathrm{Q}_{\mathrm{s}}^{\mathrm{Max}}=\underset{\mathrm{t}\in\Theta^{ \mathrm{S}}}{\mathrm{max}}\{\mathrm{q}_{\mathrm{So}}+\mathrm{q}_{\mathrm{sbz}} -\mathrm{q}_{\mathrm{skt}}\}, \tag{1}\]  
where \(\Theta^{\mathrm{S}}\) is the date when stock movements were executed, \(\mathrm{q}_{\mathrm{So}}\) is the starting stock level, \(\mathrm{q}_{\mathrm{sbt}}\) contains the stocked in amounts, \(\mathrm{q}_{\mathrm{skt}}\) the stocked out product quantities regarding date t.  
(2). Decision support for determination of the material handling equipment's efficient utilization: The working strategy and efficiency of the material handling equipment can be difficult to evaluate before the realization of the examined system regarding the intermittent production systems, but the necessary controls and alterations can be executed by using elaborated simulation investigational method. Most important indicators:  
Rate of the effective route length: This expresses what percentage of the material handling equipment's total traveled route length was executed without an unladen vehicle. The rate of the effective route length in case of the material handling equipment m can be calculated as follows:  
\[\mathrm{R}_{\mathrm{m}}^{\mathrm{U}}=\underset{\mathrm{d}\in\Theta_{\mathrm{m }}^{\mathrm{U}}  
Capacity utilization of the material handling equipment: This introduces what percentage of the material handling equipment's transportation capacity is utilized during the examinational period. The transportation capacity of the material handling equipment m can be defined as follows: \[\mathrm{C_{m}^{U}}=\sum_{d\in\Theta_{m}^{U}}\left(\mathrm{I_{d}\cdot c_{m,d}^{U} }\right)/\sum_{d\in\Theta_{m}}\left(\mathrm{I_{d}\cdot c_{m,d}^{max}}\right),\] (3) where \(\mathrm{c_{m,d}^{U}}\) indicates the delivered products' quantity regarding route section d of material handling equipment m and \(c_{m,d}^{max}\) contains the transportable maximum quantity regarding route section d.
* Stock in, stock out and commission efficiency of the material handling equipment: This introduces the material handling equipment's average stock in, stock out and commission quantities per unit time.