Delft University of Technology
Faculty Mechanical, Maritime and Materials Engineering Transport Technology
R.J. van der Kolk Logistieke parameters bij de Cargill Eiwitfabriek. Definiëring van deze parameters en het bepalen van de relaties hiertussen m.b.v. een simulatiemodel geschreven in Must.
Masters thesis, Report 93.3.LT.4120, Transport Engineering and Logistics.
In this paper a method is described to determine relationships between different global logistic parameters. With parameters the production process can be described on a high level of aggregation. For instance production lines instead of machines etc. The goal of this study is to determine what in a certain situation the state of the system is/will be and how it can be influenced.
In this paper the folowing two parameters are used:
1. Performance indicators (parameters that indicate the performance of the process, compared to the goal of the process) 2. Influencing parameters (parameters which influence the value of the performance indicators)
In total four different performance indicators are distinguished: Fixed costs per produced number of tons
Number of hours overtime per produced number of tons Percentage of orders delivered in time
Variance of the percentage of orders delivered in time A number of influencing parameters are used, for example:
The composition of the orders (number of different products, average number of tons etc.) The order in which the orders are manufactured
The capacities of the different production departments The capacities of the storage facilities
The disturbances of the different departments (number and average length etc.) etc.
To determine the relationships between these parameters, a simulation model of the production process is developed. With this model experiments can be done. The assumption in this model is that the performance indicators 'percentage of orders delivered in time' and 'variance of the percentage of orders delivered in time' are constant. The output of the model is among others the performance criteria 'number of hours overtime per production line'. The value of the performance indicator 'fixed costs per produced number of tons' has to be determined afterwards. With these experiments and so called regression analysis relationships between parameters and results can be determined.
The general conclusion is that with this simulation model and regression analysis the relationships can be determined. For three different ordercompositions a number of those relationships are determined. The conclusions are: Ordercomposition 1
In this ordercomposition, the situation of October/November 1992, none of the investigated changes in the influencing factors improves the performance of the system. The 'number of hours overtime per production line' can not, or can hardly, be reduced while the 'fixed costs per produced number of tons' will increase by the changes in the influencing factors.
Ordercomposition 2
This is the same composition as Ordercomposition 1, but with an increased number of orders for Texture products. In this situation two influencing factors can improve the performance of the process. These two are: the capacity of two of the production lines (an increase of 25% of the lines Rotopacker and Texafzakker) and the number of production hours (an extra shift: 8 extra hours per day) on those lines. Because the 'number of hours overtime per production line' will decrease and the 'fixed costs per produced number of tons' will increase in both cases, it depends on the weight of the two factors whether the performance of the process as a whole will inor decrease.
An increase in capacity on those two lines of 25% gives a reduction of 35% of the number of hours overtime, the effect of an extra shift is a reduction of 70% of the number of hours overtime. Important is that when the number of orders for Texture products decreases, the effect of both the influencing parameters decreases significantly. Because of this effect a (temporary) extra shift is the only solution, especially when the number of orders for Texture products is as variable as it is in this situation.
Ordercomposition 3
In this Ordercomposition the number of orders for Flour products (out of the DT) increases. Tabel 1 shows the effect of the influencing factors when the value of the performance indicators except for the, 'fixed costs per number of produced tons', are constant.
desired number of tons.
Table 1. The increase in production of Flour (DT) products caused by inluencing parameters.
Combination of influencing parameters
Extra production in tons of Flour (out of the DT)
Increase (in %) compared to the volume of orders of Flour (DT) products of ordercomposition 1
Extra grinder like the C-line 200 44,9
Extra tank like the F902B 30 4,6
Extra tank like the F903 0 0
Extra grinder and extra F902B tank 345 53,5
Extra grinder and extra F903 tank 310 48,0
Extra grinder and extra F903 and extra F902B tank 390 60,4
Reports on Transport Engineering and Logistics (in Dutch)
