By using simulation, we evaluate the effect of changes made to the production processes of Company X. With the simulation model we want to answer, at first, the research question: “What is the impact of the new filling line on the internal supply chain and how can Company X optimally use this new filling line, considering the available resources?”. Second, we want to answer the research question: “How can we identify and elevate the bottleneck that arise, after growth in tonnage production on the new filling line, considering the internal supply chain?”. In this section we address the second question of research question 3: “What is the experimental
design used in this simulation study?”.
Table 4-4 displays our experimental design. We make a distinction between fixed factors, growth scenarios, and interventions. Now, we describe these concepts in more detail. First, the fixed factors. In the simulation model, we use the same list of orders for a replication. The list of orders in replication x in experiment y are the same as the orders in replication x in experiment y + 1. The list of orders in replication x differ from the list of orders in replication x + 1. Also, the shift distributions for all filling lines are constantly the same, with exception for the SFVision filling line. Finally, the OEE Rate, or the availability percentage, is fixed for all filling lines over all replications and experiments. These fixed factors make sure that we continuously compare the same system.
Page | 48 Second, for the growth scenarios we run and evaluate multiple future states to determine the consequences of the new filling line and to make sure that Company X could eventually reach the growth scenario. First, we want to analyse how Company X can reach the 700 tons. Second, we analyse how Company X can reach multiple growth scenario. We describe the growth scenarios that Company X would like to reach later in more detail.
Finally, before running future states, we discuss the interventions that we use to reach the growth scenario. Table 4-4 displays the interventions that we use. We create these interventions with the Manager Planning and Logistics. To reach a growth scenario, we use the what-if analysis as approach by analysing multiple interesting future states in which we exploit the bottleneck. We use one or more interventions in a future state. We determine the specific future states after we know the impact of the new filling line and thus when we find the bottleneck. First, we determine the impact of the new filling line to find out if the new filling line can produce, with the current resources, the amount of tonnages for which it is bought. If this is not the case, then we would like to know what department dictates the throughput.
We determine the impact of the new filling line by analysing multiple shift distributions. By analysing multiple shift distributions, we give FS recommendations on the way in which they should schedule a week, such that they can make sure that the internal supply chain is aligned, and flow is realized. We analyse one shift distribution for 5 different weeks of production, such that we make sure that we give recommendations that are applicable for multiple weeks of production. For this research we decide to make use of predefined shift distributions for the new filling line. It is infeasible to analyse all combinations of shifts distributions. So, we determine these distributions by hand. Appendix H displays all shift distributions for different number of shifts for the new filling line. We would like to know the impact of the new filling line on the production process of Company X, so the optimal shift distribution is not interesting to answer the research question. We create the shift distributions in collaboration with the Manager Planning and Logistics and these are created in such a way that we analyse multiple interesting situations that are also feasible in real life. The situations that we analyse are for example an even spread over the week or an extreme situation in which 8 shifts are active in the beginning or at the end of the week to find out if there are differences between shift distributions. In addition to the shift distributions, we use the following interventions:
- Alwid20L Shift Distribution. We vary the shift distributions for the Alwid20L. FS and the Manager Planning and Logistics presume that it is not optimal to schedule this filling line and the new filling line simultaneously for every shift. The reason for this presumption is the high production speed that can be realized on both lines.
- Broken shifts. The Manager Planning and Logistics is interested to find out if it is desired to break shifts in half. For this situation, we only use the new filling line and the Alwid20L, due to the fast production speed. Using broken shifts means that an operator processes the first four hours on the SFVision and the last four hours on the Alwid20L. - Extra RK. In consultation with the Specialist Processing, we determined that there is
the possibility at Company X to add a maximum of 2 10 m3 RKs at Jupiter.
- Extra BK. In consultation with the Specialist Processing, we determined that there is the possibility at Company X to add a maximum of two BKs for the new filling line by sharing the BKs of the GVP filling with the new filling line.
- Starting a new batch. The logic for when a new batch starts at mixing can be altered. Chapter 2 displays the way in which the mixing department currently start batches.
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Experimental Design
Fixed Factors Short Description 1. Orders
2. Shift distribution 3. OEE Rate
1. The orders, with their characteristics, used in the simulation is for every experiment in all future states the same. The list of orders used differ per replication.
2. The shift distribution for the other filling lines are fixed and are in tune with the shift distributions of 2018.
3. The calculated OEE Rate over data from 2018 remains the same as explained before.
Growth Scenario Short Description 1. Current situation
2. Future situation
1. We evaluate the current situation, where the number of tonnages for the new filling line is 700 tons a week.
2. We evaluate the future situation, where the number of tonnages required for the new filling line increases.
Intervention Short Description 1. Shift distribution 2. Alwid20L shift distribution 3. Broken shifts 4. Extra BKs 5. Extra RKs 6. Starting a new batch
1. We evaluate multiple shift distributions for the new filling line where we also vary the number of shifts between 8 and 15. 2. We evaluate the impact of changing the shift distribution of the
SFVision to the Alwid20L shift distribution.
3. We evaluate the system by using broken shifts for the new filling line and the Alwid20L filling line.
4. We evaluate the possibility to add a maximum of 2 BKs for the new filling line.
5. We evaluate the possibility to add a maximum of 2 10 m3 RKs at
Jupiter.
6. We evaluate multiple ways in which batches are started at mixing.
Table 4-4 Experimental Design