In this chapter, we present the results from the simulation study. For each scenario and intervention, we analyse the outcomes of the simulation and provide explanations on the performance. In Section 7.1, we give the results from scenario I. In Section 7.2, we give the results from scenario II. Finally, in Section 7.3, we give the conclusion of the results.
7.1 Outcomes of scenario I: historical material flow
The results of the scenario I are not available in the public version of this report.
7.1.1 Intervention I.1: Flexible workforce
The results of intervention 1 of scenario I are not available in the public version of this report.
7.1.2 Intervention I.2: All items arrive at the start of the day
The results of intervention 2 of scenario I are not available in the public version of this report.
When all items arrive at the start of the day, there is a need for a large buffer to store all the items before they are received. If the items were delivered in a perfect time window, meaning there is no waiting time in front of the receive station, the performance of the system increases.
7.2 Outcomes of scenario II: increased material flow
The results of scenario II are not available in the public version of this report.
In the situation of an increased material flow, VMI Holland should make investments to increase the maximum capacity of workstations to increase the throughput and decrease the waiting times of these workstations. This may prevent backlog in the system that causes a poor performance.
7.2.1 Intervention II.1: Increase capacity of bottleneck station
The results of intervention 1 of scenario II are not available in the public version of this report.
After 5 iterations, the simulation model meets the desired performance. Over 99% of the items are placed within 8 working into their storage locations. If we summarize the iterations, the company should invest in the following resources:
Increase the maximum capacity of the accept workstation for RB/EP items, from 11 to 13 employees;
Increase the maximum capacity of the put away workstation for RB items, from 4 to 6 employees;
Increase the maximum capacity of the put away workstation for EP items, from 3 to 4 employees.
The recommended expansion of the maximum capacities of the workstations ensures that, with the current variability in the material inbound flow, the company is able to process at least 99% of the items within 8 working hours. Appendix E visualizes the improvement on the maximum dock to stock time per day of the simulation. Here, we see that for most days the maximum dock to stock time lies far below the norm. The occupation rates of the workstations, shows that employees do not have work all the time. During the day there is no need to use the maximum capacity of a workstation. Using a flexible workforce will increase the efficiency per employee to let them work at multiple workstations.
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In this situation, we need fewer employees for the same amount of work. Intervention 1 of scenario I confirms this statement.
7.2.2 Intervention II.2: No anonymous items
The results of intervention 2 of scenario II are not available in the public version of this report.
7.2.3 Intervention II2.3: All items arrive at the start of the day
The results of intervention 3 of scenario II are not available in the public version of this report.
7.3 Conclusion
The conclusion of this chapter is confidential. We do give some general results from the simulation outcomes.
Conclusions from (current) scenario I
In order to deal with the variability in workload and to increase the performance of the system, we recommend the company to use a flexible workforce.
When all items arrive at the start of the day, there is a need for a large buffer to store all the items before they are received. If the items were delivered in a perfect time window, meaning there is no waiting time in front of the receive station, the performance of the system increases.
Conclusions from (future) scenario II
After 5 iterations, the simulation model meets the desired performance. Over 99% of the items are placed within 8 working into their storage locations. If we summarize the iterations, the company should invest in the following resources:
Increase the maximum capacity of the accept workstation for RB/EP items, from 11 to 13 employees;
Increase the maximum capacity of the put away workstation for RB items, from 4 to 6 employees;
Increase the maximum capacity of the put away workstation for EP items, from 3 to 4 employees.
When we eliminate the anonymous items from the inbound, the average dock to stock time decreases. It would increase the performance of the system, but there will be a need to invest in the maximum capacity of the accept station for RB/EP items.
When all items arrive at the start of the day, there is again a need for a large buffer to store all the items before they are received. If the items were delivered in a perfect time window, meaning there is no waiting time in front of the receive station, we could say that the performance of the system has slightly increased.
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