Summary table for all experiments

Regarding the base model, the results can be summarized in the following table. The scenario describes the experiment level. The throughput time reduction is calculated statistically, that is why

“significant” is added. The WIP and WIP value reduction are not calculated statistically, but are just the difference between the mean values. The costs row describes the costs of the intervention. If a result is colored red, it means that no reduction but only an increase of that concerned KPI is observed.

Table 53: Summarized results for interventions to the base model.

Scenario Sign. throughput

time reduction

WIP reduction WIP value

reduction

Extra costs Planning

approach

MRP model 23 days and 10 hours to 23 days and 14 hours.

59,4 €297000 n.a.

ConWIP model 23 days and 1 hour to 23 days and 7 hours. 60,4 €302000 n.a. Bottleneck control 23 days and 9 hours to 23 days and 15 hours. 60 €300000 n.a. Delivery variability Different intervals 4 days and 8 hours to 5 days and 23 hours. 4,14 €20716 n.a. Different delivery numbers 2 days and 5 hours to 4 days and 14 hours. 9,03 €45174 n.a.

Combination 5 days and 16 hours to 8 days and 16 hours.

98 Little variability 2 days and 4

hours to 2 days and 18 hours.

3,35 €167746 n.a.

Transport to MPP

Different day 3 days and 2 hours to 3 days and 6 hours

8,34 €41676 €21241

Two days 7 days and 16 hours to 7 days and 22 hours.

20,4 €100209 €42482

Three days 7 days and 16 hours to 7 days and 22 hours. 20,4 €100209 €63723 Adding a Grob machine Adding an extra Grob 0 0 0 €369481 Adding an extra Grob, while limiting the shift

2 days and 13 hours to 2 days and 18 hours. 1,18 €5900 €147141 Adding a Palmary machine Adding an extra Palmary 1 day and 23 hours to 2 days and 6 hours. 5,39 €26962,- €35000 Adding an extra Palmary, while limiting the shift

0 0,31 €1575,- - €17749

Based on the table, within the base model, it can be observed that the biggest time gain can be acquired by changing the planning. Another big time gain can be acquired by driving more often to the outsourcing company. Though, this comes with a cost. Total costs can be reduced by adding an extra Palmary and limiting shift-hours. This will not influence the throughput time significantly.

Another “costless” way of reducing the throughput time significantly is negotiating a fixed delivery

interval and number.

Though, some experiments have also been done within the MRP-model. This is mainly because the base model gives some disturbing values because of the week-to-week planning. The results of this model are summarized in the following table:

Table 54: Summarized results for interventions to the MRP-model.

Scenario Sign. throughput

time reduction

WIP reduction WIP value

reduction

Extra costs Transport to

MPP

Different day 4 days and 21 hours to 5 days and 6 hours.

99 Two days (Tu-Fr) 8 days and 11

hours to 8 days and 15 hours.

22,04 €110200 €42482

Two days (We-Fr) 8 days and 9 hours to 8 days and 18 hours.

22,00 €110016 €42482

Three days 10 days and 3 hours to 10 days and 6 hours. 26,25 €131259 €63723 Adding a Grob machine Adding an extra Grob 0 0 0 €369481 Adding an extra Grob, while limiting the shift

9 hours to 13 hours. 1,18 €5900 €147141 Adding a Palmary machine Adding an extra Palmary 2 days and 10 hours to 2 days and 18 hours. 6,59 €32941,- €35000 Adding an extra Palmary, while limiting the shift

18 hours and 20 minutes to 1 day, 1 hour and 22 minutes. 2,34 €11709,- - €17749 Delivery to customer X

Different day 3 days and 21 hours to 4 days and 2 hours.

10,26 €51308,- Unknown

Two days 6 days and 19 hours to 7 days and 1 hour.

17,80 €88983,- Unknown

Based on the table, it can be observed that driving two times to MPP is the most effective way to reduce the throughput time. Though, it (again) comes with a cost. Another interesting option is adding an extra Palmary machine since it saves costs while also reducing throughput time. The experiments in both the base model and MRP-model show that most gains can be acquired regarding transport. Improving things internally does help, but the time gains are relatively small regarding transport. This is mainly because products will have to wait for the truck later on in the process. Usually, costs are also much higher for improving things internally since it usually involves extra personnel or machines.

Eventually, the best results have been combined into one model. In order to acquire an average throughput time of four weeks, the planning should be changed to an MRP-based planning. In addition, an extra Palmary machine should be added together with driving to the outsourcing company twice per week. The internal throughput time incl. outsourcing is, in that case, already under four weeks. Though, if the total throughput time also needs to be under four weeks, also transporting to the customer should be done twice.

100

Chapter 7: Conclusions, recommendations, and discussion

Conclusions are drawn at the end of every chapter. These conclusions can be linked to the main research question. Though, in this chapter, only key-answers to the main research question will be summarized:

How should a production planning approach be applied at PMA such that the relevant characteristics and restrictions of the production are satisfied to reduce throughput time?

Based on these key-conclusions, recommendations will be given. The chapter will conclude by giving options for further research, analyzing shortcomings in this research, and outlining the contribution to practice.

In this thesis, multiple planning and control approaches were investigated and assessed on their applicability to PMA. By gathering data of the production of the bearing support, a simulation model was created. Within this simulation model, the planning and control approaches could be compared. In addition, other planning and control related subjects were tested in this simulation model.

7.1Conclusions

It was difficult to determine a clear planning for the bearing support at PMA. Though, the intended planning includes a lot of waiting time, resulting in a throughput time of 9 weeks. Potential

bottlenecks that substantially contribute to a higher throughput time are the turning and milling machine, grinding machine, and outsourcing to MPP that takes a week. According to literature, and assessing this literature, ConWIP, MRP and bottleneck control seem to be suitable planning and control approaches to apply to PMA. This is mainly because their implementation is relatively easy compared to other approaches.

The following conclusions can be drawn based on the simulation model:

- Bottleneck control on the grinding machine is the best-performing bottleneck control approach.

- MRP, ConWIP and bottleneck control outperform the current planning approach, mainly because the long waiting times are deleted. Deleting these waiting times can improve the throughput time with at least 23 days while the same capacity is needed.

- MRP and bottleneck control outperform the base model and ConWIP control. MRP and bottleneck control almost perform similarly.

- Decreasing or eliminating delivery variability significantly reduces total throughput time by approximately 2 to 3 days (little variability) and perhaps 3 to 6 days (a lot of variability). Furthermore, negotiating a fixed number is more effective than negotiating a fixed interval. - Choosing a different transport-day to the outsourcing company MPP can significantly reduce

throughput time. Regarding the assumed transport day in the base model, the throughput time can be reduced by approximately 3 days (in the base model) or 5 days (in an

MRP-model) by just choosing a different transport-day.

- Driving twice to the outsourcing company MPP can significantly reduce throughput time by approximately 8 days in the base model and 8 to 9 days in an MRP-model. One factor that massively influences the right transport day to MPP, is the day on which new bearing supports are delivered and finished bearing supports are acquired by customer X.

- Adding an extra turning and milling machine would not reduce total throughput time, while increasing costs. The shift hours cannot be limited enough in order to save more costs such that a second turning and milling machine is lucrative.

- Adding an extra grinding machine would reduce total throughput time, while it can also save costs. The shift hours can be limited enough in order to save more costs such that a second

101 grinding machine is lucrative. This is mainly because one operator can operate two grinding machines if these machines are the same.

- A different day for the delivery of new bearing supports and acquiring of finished bearing supports by customer X can improve total throughput time by approximately 4 days (based on the MRP-model). Furthermore, driving twice to customer X can significantly reduce the throughput time by approximately 7 days (based on the MRP-model).

- The experiments show that improving things internally is less effective than improving things regarding transport. This is because most efficiency gains also end up in longer waiting times for the truck later on in the process.

- A throughput time excluding the delivery to the customer of less than four weeks can be acquired by using an MRP planning and control approach, buying an extra grinding machine, and driving twice to the outsourcing company MPP. A throughput time including the delivery to the customer of less than four weeks can be acquired by doing the same but also driving two times to the customer.

- In order to produce the required number of bearing supports within four weeks, at least 9

FTE’s are needed to eventually do at least 4,82 FTE work, excluding the turning and milling machine. This machine needs a 24-hour shift, 6 days per week (of which 8 hours per day are automated). This could be done with 2 to 3 operators.