Output

3It should be mentioned that the costs for both items are similar. The reason for this could be that the items are similar with for example a different angle, but therefore resulting in a similar price. Furthermore, both items are mechanical.

50

Table 10, Replenishment levels, item 1

Output Theoretical model Model 1 and 3 Model 2 and 4

𝐒̃_𝟏^∗ 2.36 3

The results of the simulation of model 1 and 3 are given in Table 11. The average performance of model 1 is with 99.71% well above the 98% constraint, However, a probability of 0.9 on a lead-time of 5 weeks decreases the performance with 2.14% to 97.57%. A decrease of the probability with 0.1, results in an average decrease of the performance of 3.12%, an average decrease of the on hand inventory of 0.12 items, and a decrease in the costs of on average €0.61.

Table 11, Results of model 1 and 3, item 1

Table 12 displays the results for item 1 in model 2 and 4. Model 2 meets the service level constraint, but a decrease of 0.1 on the probability of a lead-time of 5 weeks, results in an average decrease of the performance of 1.28%, an average decrease of the on hand inventory of 0.08 items, and a decrease in the costs of on average €0.42. Again, little uncertainty, P(L(t)=L)=0.9, results in a performance level under the service level constraint.

Table 12, Results of model 2 and 4, item 1

A comparison of the models with adjusted and with a fixed replenishment level is illustrated in Graph 7.

A decrease in the probability on a lead-time of 5 weeks has a bigger impact in the model with an adjusted replenishment level than it has on the model with a fixed replenishment level. Model 1 performs slightly better than model 2, but model 4 performs better than model 3. The decrease of the performance of the models with an adjusted replenishment level is on average 2.98%, and the decrease of the models with a fixed replenishment level is on average 1.28%. The costs of the models with an adjusted replenishment level are always lower than the costs of the models with a fixed replenishment level. On average, the costs for holding inventory in the adjusted replenishment models are €2.09 lower than the costs for the models with a fixed replenishment level.

Three conclusions can be derived:

 If there is no lead-time uncertainty, a model with an adjusted replenishment level results in a better performance than a model with a fixed replenishment level;

 Lead-time uncertainty has a bigger negative impact on a model with an adjusted replenishment level than it has on a model with a fixed replenishment level;

 The costs of the models with an adjusted replenishment level are always lower than the costs of the models with a fixed replenishment level.

51

Graph 7, Average performance and cost comparison, item 1

Item 2

The tables and graph that display the results for item 2 are given in Table 26, Table 27, and Graph 9 in Appendix N, and this section elaborates on the conclusions drawn from the results.

The cumulative probability 𝑃(𝐷𝑖 ≤ 86) > 0.995 , therefore the upper boundary of the truncated demand is 86. The theoretical model resulted in an average adjusted replenishment level of 35.20 items, and a fixed replenishment level of 38 items.

Model 1 has an average performance that meets the service level constraint of 98%, whereas model 2 has an average performance that is under the service level constraint, namely 97.72%. On top of that, the cost for model 1 are €44.43 and the costs for model 2 are €61.70. Both model 3 and 4 fail to meet the service level constraint for all different probabilities on lead-time uncertainty. Again, at the moment lead-time uncertainty is added, model 4 performs better than model 3.

Lead-time uncertainty has a big impact on the average performance. A decrease in the probability on a lead-time of 5 weeks of 0.1 results in a decrease of the average performance of on average 9.82% for the models with an adjusted replenishment level, and 9.60% for the models with a fixed replenishment level. The costs of decrease with on average €4.36 for the models with an adjusted replenishment level, and €6.17 for the models with a fixed replenishment level.

If the probability on a lead-time of 5 weeks is equal to a lead-time of 6 weeks, the average performance of model 3 is 49.43%, and the average performance of model 4 is 49.70%. The difference between the average performance of the models with an adjusted and a fixed replenishment level is on average 0.19% in favor of models with a fixed level, whereas the cost difference is on average €10.50 in favor of the models with an adjusted level. The costs for model 3 are again smaller than the costs for model 4.

Three conclusions are derived:

 If there is no lead-time uncertainty, only a model with an adjusted replenishment level results in an average performance that meets the service level constraint;

 Although the difference is only 0.19%, lead-time uncertainty has a bigger negative impact on a model with an adjusted replenishment level than on a model with a fixed replenishment level;

0,00%

Costs Model 1 and 3 Costs Model 2 and 4 Performance Model 1 and 3 Performance Model 2 and 4

52

 The costs of the models with an adjusted replenishment level are always lower than the costs of the models with a fixed replenishment level.

Sensitivity analysis

A small sensitivity analysis on model 1 is performed to analyse the impact of the probability of an item in a component. Instead of using historic ratios, one can decide to compute the probability based on the occurrence of the item in the variants of the component. A distinction is made between the probability in the theoretical model and in the simulation:

𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑚𝑜𝑑𝑒𝑙: 𝑝_𝑗^𝑖 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛: 𝑝̃_𝑗^𝑖

The probability in the simulation is based on the historic ratios, whereas the probability in the theoretical model is based on the occurrence (Appendix L):

𝑝₁^𝐵= 0.35, 𝑝̃₁^𝐵 =12 24= 0.5 𝑝₂^𝐶 = 0.42, 𝑝̃₂^𝐶 = 6

24= 0.25

Table 13 shows the results. If the probability based on occurrence is higher than the probability based on historic ratios, all output increases a bit. However, if the probability based on occurrence is lower than the probability based on historic ratios, this has a big negative impact on the average performance, costs and replenishment level (Table 14). Thus, using a higher probability to compute the replenishment level increases the costs for holding inventory, but diminishes the probability on a low average performance level.

Table 13, Sensitivity analysis, item 1

𝒑̃𝒋𝒊 ∝𝒋 𝑪𝒋 𝑺̃𝒋∗

0.35 99.71% €9.61 2.36

0.5 99.86% €12.02 2.62

Table 14, Sensitivity analysis, item 2

𝒑̃_𝒋^𝒊 ∝𝒋 𝑪𝒋 𝑺̃𝒋

∗

0.42 97.72% €61.70 35.15

0.25 20.83% €2.15 23.39

53 9. CONCLUSIONS AND RECOMMENDATIONS

This chapter describes the conclusion and recommendations, based on results of the redesign and simulation, and describes further research possibilities.

9.1 Conclusion

The research question defined in section 5.6 is the following:

“What is the effect of proactive inventory control for the Posisorter equipment on performance of the factories and suppliers and how can Vanderlande better control its supply?”

This question is answered in this section, by discussing each of the seven sub-questions.

9.1.1 Measurable performance indicators for the factories and suppliers

The first sub-question is related to the identification of the key KPIs for the factories and suppliers to increase material availability at the factories. Three KPI’s are defined in section 5.1:

1. On time and complete performance of the second tier suppliers;

2. Material availability, being the percentage of order lines fulfilled from stock;

3. Total costs for holding inventory.

The three KPIs consider the ETO trade-off defined by Radke and Tseng (2012) that includes a high service level, inventory budgets, and the delivery lead-time.

9.1.2 Current performance with uncontrolled stock points

The second sub-question is related to the performance of the current stock points. There is no data available about the inventory control policy of the current stock points, nor about the performance of the inventory model. Moreover, the yearly holding costs computed by Vanderlande are time independent. The performance in delivering order on time and complete to SCCE of VIM in week 2 to 37 of 2016 is 83% and of VIS in week 10 to 37 of 2016 is 65%, while the threshold for both factories is 95%.

The performance gap between the actual performance and the desired performance is therefore 12%

for VIM and 30% for VIS. These results are displayed in section 3.1.

In section 3.2, the causes are categorised. This brings to light that 35% of the delays are caused by overdue purchase parts. An analysis of the Delivery to request of the second tier suppliers, executed in section 3.4, showed that both suppliers that deliver to VIM, and suppliers that deliver to VIS are underperforming. The second tier suppliers of VIM have an average performance of 80%, and the suppliers of VIS have an average of 83.3%. The threshold for the Delivery to request performance of the second tier suppliers is 98%. The research addresses three factories and a subcontractor, but due to information unavailability only the performance of VIM and VIS and their second tier suppliers is evaluated.

To conclude, the second tier suppliers are underperforming in delivering material at or before the request date set by Vanderlande. Consequently, the material availability at the factories is low and this affects the performance of the factories.

54 9.1.3 Five local stock points and local decisions

The third sub-question investigates the impact of local decision making and local stock points. A conceptual design of a two-echelon distribution system is defined, and in section 5.3, demand data is analysed in order to estimate the demand parameter. The analysis in section 5.3 led to the conclusion that Vanderlande can determine PI demand for multiple items at the end of the conceptual design phase.

A PI demand analysis, executed in Chapter 6, shows to what extend Vanderlande can determine PI demand based on SPO Future demand. The PI analysis proved that nearly all PI demand is defined at the end of the conceptual design, and that Vanderlande should redesign its control structure, instead of stock raw and intermediate material. As a result, the third sub-question became irrelevant.

9.1.4 A single stock point at the second tier supplier and central decisions

The fourth sub-question also became irrelevant after the conclusion of the data analysis revealed that a redesign will provide a better solution for Vanderlande.

9.1.5 Five local stock points and central decisions

The fifth sub-question also became irrelevant after concluding that a redesign leads to a better solution.

9.1.6 Compare the performance of the proactively and non-proactively controlled stock points The goal of the sixth sub-question was to compare the inventory models, though due to the new insights, these models were not developed and the performance was not compared.

9.1.7 Effect of a redesign on the performance of the factories and suppliers

The objective of the redesign was to increase the performance of the factories by increasing the material availability. Therefore, the process structure, control structure, and decision structure are redesigned insection 7.4. Figure 24 illustrates the qualitative redesign. The main difference in the process structure, discussed in sub-section 7.4.1, involved transmitting information between sales engineering and SCCE.

This adjustment will save time and will result in better communication.

The redesigned control structure, elaborated on in sub-section 7.4.2, leads to more improvements. First, translating the SPO Future demand into PI Demand provides insight in the resources required for the production of sub-components. Moreover, the ordering and signalling of PIs by SCCE increases material availability as second tier suppliers have more time to produce material. In addition, central and weekly ordering of material results in larger batches and can lead to quantity discounts. Material coordination is increased by central stock points for the few items that still require inventory. Furthermore, the redesigned control structure can decrease product uncertainty, process uncertainty, complexity of structure of goods flow, and complexity of the multi-project character of the ETO situation.

The adjustments in the decision structure, discussed in sub-section 7.4.3, ask for closer cooperation between departments, and can decrease technical risks, increase workload control, and increase the flexibility for VIM.

55

Altogether, the redesigned process structure, control structure, and decision structure impact the performance of the factories, as they can decrease material unavailability and increase the flexibility of VIM, resulting in less capacity issues. The SPO dataset for purchase items consists of 76 items of which 37% are longer lead-time items that currently require expediting or arrive too late. This percentage is reduced to 3% by the redesign.

9.1.8 Other effects

There are some other effects that result from analysises performed during the research project.

Effect of lead-time uncertainty

In Chapter 8, the effect of lead-time uncertainty on the performance of inventory models is investigated.

A periodic review, single item, single location, inventory control policy with base-stock levels is used to simulate four types of models with either an adjusted or fixed replenishment level, and either deterministic or stochastic lead-time. The demand and ratios of two items is used to estimate a demand distribution used as input for the model. The first item is part of component (B) and has little demand with an average of 2.70 items per week, the second item is part of component (C) and has a larger demand, with an average of 64.43 items per week. The lead-time of the two stochastic models can be one week longer than the standard lead-time. Different probabilities are used as input to compute the effect of the variation. The computational results in sub-section 8.10.2 show that the models with an adjusted replenishment level perform slightly better than the models with a fixed replenishment level.

Moreover, the costs for the models with the adjusted level are a lot smaller.

Little lead-time uncertainty has a big impact on the service level of the models. For the first item, the impact was bigger on the model with an adjusted replenishment level and for the second item, the impact was similar between the items. The model with an adjusted replenishment level has lower costs, therefore resulting in better output for item 2. Furthermore, the effect of lead-time uncertainty is bigger on the models of the item with large weekly demand.

A small sensitivity analysis on the probability of an item in a component, conducted in sub-section 8.10.2, shows that little differentiation in the probability can have a big impact on the performance. Therefore, it is important to find the right probabilities.

9.2 Recommendations

This section elaborates on the recommendations to increase factory performance.

9.2.1 Redesign the control structure

In order to increase factory performance, it is recommended to redesign the process and control structure by increasing the interaction between departments, translating the SPO Future demand into PI demand, central ordering of PIs with a lead-time longer than four weeks, signalling of other items, and central inventory control, as it will increase material availability. Furthermore, it is recommended to redesign the order acceptance functions as they affect worload control and increase flexiblity. This solution will directly increase factory performance, as it tackles the two biggest causes (Appendix F).

56 9.2.2 Analyse other equipment

This master thesis project solely focused on SPO data as these projects are always produced by Vanderlande factories and due to the high future demand illustrated in the SPO Future demand. By analysing the list with all purchase items ordered in VIM and VIS, it is concluded that 15% of the items currently require extra effort to be delivered on time (Table 15). It is recommended to analyse other equipment similar to the analysis conducted in section 5.3 and Chapter 6, as this might lead to the conclusion the redesign is applicable to other equipment.

Table 15, Lead-time division of all purchase items

9.2.3 Reduce lead-time uncertainty

The results of the simulation of the inventory control policy showed that little uncertainty has a large impact on the performance of an inventory model. For the purchase items with a small demand, a fixed replenishment level is advised as it results in the best performance, whereas for purchase items with larger demand, an adjusted replenishment level results in similar performance as a fixed level, but has lower costs. It is suggested to decrease this uncertainty by close cooperation and information sharing with the second tier suppliers. Signaling demand is part of this closer cooperation and can reduce lead-time uncertainty. Moreover, lead-lead-time control can often be reduced by adding an additional crashing cost, or by long-term partnerships between suppliers and vendors (Ouyang et al., 2004).

9.3 Academic relevance

Little research is conducted on control policies in an ETO or capital goods industry. Bertrand and Muntslag (1993) analyzed production control in an ETO firm, but did not test their model in a real-life situation. This master thesis project contributes to literature by providing a case study on the model of Bertrand and Muntslag (1993). No evidence was found for literature that studies control policies for a supply chain with multiple factories. By this research, we have tried to fill this gap by using a business case study from a capital goods company.

Van Aken (2007) states that a typical research product of a design science is the technological rule: “A technological rule gives, for a specific solution concept, the objectives the application of the solution concept would serve, and for which setting it would be valid.” By this project, the conceptual control system of Bertrand and Muntslag (1993) is field-tested in its intended field of application.

9.4 Suggestions for further research

Multiple assumptions are used in order to scope the problem. For this reason, a first suggestion for further research is to relax these assumptions and analyse the effect of the relaxation on the results.

The validation of the redesign involved four aspects that ask for further research. First, the redesign is only feasible for SPO projects. The SPO is part of Parcel and Postal projects. There are multiple other

Items Ratio

Lead-time ≤ 4 weeks 5052 85%

Lead-time > 4 weeks 857 15%

57

projects that contain purchase items used for the SPO. Consequently, it is suggested to first analyse those projects and extend the redesign over those items, and lateron analyse other projects.

Secondly, in Chapter 7 and 8, it is assumed that the lead-time to America is equal to the lead-time in Europe. Relaxing this assumption will have a big impact, as the lead-time to America can be a couple of weeks longer due to extra transportation time. The new sub-order allocation rule defined in sub-section 7.4.3 will make it possible to estimate the amount of demand that is required in America, however Vanderlande first has to gain information about all different characteristics before it can implement the true situation in America. Shipping material asks for batch sizing and consolidation of items, two aspects that are not considered in this master thesis project. On top of that, multiple political issues arise when shipping material to America instead of buying it there. Therefore a suggestion would be to first analyze the control structure of the factory and the differences between Europe and America in a logistical but also political manner.

Another limitation of the research is the lack of quantitative support for the redesign. It is suggested to analyse the quantitative effect of a redesign of the processes, control structure, and decision structure.

The fourth aspect for further research involves the IT capabilities. It is validated whether it is possible to adjust the IT system, but the detailed adjustments are not clarified yet.

The fourth aspect for further research involves the IT capabilities. It is validated whether it is possible to adjust the IT system, but the detailed adjustments are not clarified yet.

In document Eindhoven University of Technology MASTER Redesign of the control structure and inventory control at Vanderlande Gubbels, P.M. (pagina 66-0)