Results

5.4.3.1 A macro approach to the simulation results

 Presentation of results

Because each experiment consists of 50 replications, much data is generated from one experiment. The results that are reviewed are the two KPIs that are exported from the simulation software and their further analysis in Excel. For this purpose, as it has been already mentioned the two KPIs called “Final inventories” from the Inventory Turnover KPI group and the KPI called “% Delivered on time versus Requested” from the Service Level KPI group are exported in Excel for all 12 scenarios that were tested. In particular, due to the 50 replications for every scenario, the result of the KPI values exported for every ISBN are the average values of those 50 replications in both the case of the inventory KPI and the service level KPI.

Using Excel, the data from every experiment is accumulated in a matrix with all the relevant details that the author wants to focus on. Hence, one matrix is generated for each KPI in Excel hose columns are: ISBN, lead time, supplier, ABC categorization, XYZ categorization, and then 12 columns (for the 12 scenarios) containing the KPI exported data from the model. The cumulative results can be seen in appendix A. Afterwards, a thorough analysis is conducted by first considering the results that regard the KPIs for all 12 scenarios and second, by making meaningful plots regarding the KPIs, taking into account the factors that are examined in this research, such as the different lead times and the ABC- XYZ classifications.

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Figure 44: Presentation of the way that the results are outputted from the model in Excel in order to be analyzed.

It is evident that the various scenarios can be compared and analyzed in a more efficient way in Excel if they are organized in a way that product clusters can be easily formulated in order to observe the simulation behavior in every scenario. The reason that the author wanted to approach the results of the simulation this way is that in essence every scenario includes the same amount of products that have different characteristics such as the lead time, their supplier and the different category according to the ABC and the XYZ categorization. Hence, formulating the outputted results in Excel the way that has been described, facilitates the analysis of the results and the conclusions drawing process.

 Generic averages from the simulation runs for the KPI “% Delivered on time versus Requested” :

In order to visualize the model behavior, usable plots have been created to show the cumulative results for both KPIs. Hence, for the KPI “% Delivered on time versus Requested” the following plot in Figure 45 shows the average values of the service level KPI “% Delivered on time versus Requested” for every one of the 12 scenarios.

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Figure 45: The average values of the service level KPI “% Delivered on time versus Requested” for all scenarios

This plot is only for demonstrating all the average values but it is not practical to use it in order to distill valuable observations. The reason for that is that scenarios 1 to 6 regard the current replenishment policy (the (s,S) policy), whereas scenarios 7 to 12 regard the new policy (the Min/Max policy). Thus, the following plot has been created and is presented below:

Figure 46: Average values of the service level KPI “% Delivered on time versus Requested” for both policies.

The plot summarizes the simulation behavior of the KPI “% Delivered on time versus Requested” regarding both policies. Moreover, the numbers in the x-axis represents the number of scenarios as they have been specified in chapter 4. Hence number 1 represents scenario 1 for the current policy and scenario 7 for the Min/Max policy.

It can be observed that in the case of the current inventory policy, the KPI is not fluctuating much as it presents small increases and decreases for the current policy. On the other hand, regarding the Min/Max policy, the service level expressed in terms of the KPI “% Delivered on time versus Requested” seems to diminish steadily until the 11th scenario and then at the 12th scenario the decrease is more intense.

93  Generic averages from the simulation runs for the KPI “Inventory final product” :

The respective plot for the inventory KPI “Inventory final product” is presented in the following figure. In this figure one can see the average values of the finished inventories for the scenarios 1 to 6 that regards the current policy and the same KPI for the scenarios 7 to 12 that refer to the new Min/Max policy:

Figure 47: Average values of the service level KPI “Inventory final product” for both policies

It can be observed by looking at the presented figure that the simulation behavior regarding the current policy seems somehow less consistent than the behavior regarding the Min/Max policy. Moreover the decrease is more observable at the Min/Max policy than at the current policy. It should also been noted that the scales of the x –axis are very different, since the current policy starts with very high inventories compared to the Min/Max policy. It is observed that the two different policies affect the results regarding the inventories: at the current policy the inventories are very high and at the Min/Max policy the inventories are substantially lower in scale.

 Conclusions on generic averages from the simulation run for the two KPIs:

It seems necessary at this point to highlight that there are some first conclusions regarding the two KPIs that have been presented above. First, the replenishment policy seems to define the simulation behavior substantially. Thus, the first six scenarios (Scenarios 1 to 6) that refer to the current policy need to be separately reviewed and the same applies to the rest six scenarios (7 to 12) that refer to the Min/Max policy. Secondly, it is observed that the unit of measurement for comparing the scenarios in the case of the service level KPI facilitates this process as the KPI is expressed as a % percentage. Hence, in all cases the KPI results are comparable. However, in the case of the second KPI that regards finished inventory, the KPI is expressed in units of product quantities. Thus, the comparison is not that simple. Moreover, it is noticed while reviewing the simulation results that the inventory values differ in scale substantially between the scenarios that regard the current policy and the scenarios that refer to the Min/Max policy. This means that the inputs regarding the inventories for the two different policy cases are very different. It can be seen that there are products with a stock of more than 1880 units on average in the current policy. However, the Min/Max policy works

94 with fewer inventories in general as it is observed that for the same products the highest value of kept stock does not exceed the 550 units on average.

Thus, it can be stated that Scenarios 1 to 6 differ substantially to Scenarios 7 to 12 due to inventory scales that were specified according to the requirements of each replenishment policy that has been selected. Therefore, it seems interesting to seek different relations between these scenarios by obtaining a more micro-approach on the analysis of the results.

5.4.3.2 A micro approach to the simulation results

To make more valuable observations on the simulation outputs, the results have been visualized with acquiring different perspectives. The way that was selected to present the results presented in Appendix A facilitates the process of obtaining a micro approach on them. The reason is that this way, valuable conclusions can be drawn when isolating specific clusters of results depending on which influencing factors the author chooses to investigate each time. Thus, for both KPIs, first for the “% Delivered on time versus Requested” and then for the “Inventory final product” KPI, the results are shown primarily from the ABC and subsequently from the XYZ perspective. Then, a presentation of the results regarding the different Lead times follows.

 “% Delivered on time versus Requested” KPI results regarding the ABC classification

The KPI will be visualized by showing the first six scenarios that regard the current policy separately than the scenarios 7 to 12 that refer to the Min/Max policy. Following this pattern, the graphs in Figure 48 and 49, present the ABC categorization in the case of the two policies.

Figure 48: Average values of the service level KPI “% Delivered on time versus Requested” for the current policy according to the ABC categorization.

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1 2 3 4 5 6

“% Delivered on time versus Requested” KPI averages for ABC product groups for current policy

A products B products C products

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Figure 49: Average values of the service level KPI “% Delivered on time versus Requested” for the Min/Max policy according to the ABC categorization

The figures that are presented above show the average values of the service level KPI “% Delivered on time versus Requested” first for the 6 first scenarios that refer to the current policy and then for the rest 6 scenarios (scenarios 7 to 12) that regard the Min/Max policy, according to the ABC categorization. The main conclusion here is that in the current policy the IRIs do not seem to influence the KPI substantially as they show a steady evolution when observing the different scenarios of different IRIs. However, in the case of the Min/Max policy, it seems that the IRIs influence in a more evident way the service level KPI. This is evident for all product clusters. Moreover, there is not any highly observable difference between the rate that the service levels decrease as we switch to higher IRIs between the A, B, and C product clusters as the rate of decrease seems steady.

In Appendix B a more extensive analysis is implemented to compare the “% Delivered on time versus Requested” KPI results regarding the ABC classification according to the different product clusters. More specifically, to shed more light on the different product clusters, the author decided to visualize the separate product clusters in order to be able to compare the A products in the current policy to the A products in the Min/Max policy, the B products in the current policy to the B products in the Min/Max policy, and the C products in the current policy to the C products in the Min/Max policy.

 “% Delivered on time versus Requested” KPI results regarding the XYZ classification

Again as well the KPI will be visualized by showing the first six scenarios that regard the current policy separately than the scenarios 7 to 12 that refer to the Min/Max policy. Following this pattern, the graphs below present the XYZ categorization in the case of the two policies in figires 55 and 56.

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1 2 3 4 5 6

“% Delivered on time versus Requested” KPI averages for ABC product groups for Min/Max policy

A products B products C products

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Figure 50: Average values of the service level KPI “% Delivered on time versus Requested” for the current policy according to the XYZ categorization.

Figure 51: Average values of the service level KPI “% Delivered on time versus Requested” for the Min/Max policy according to the XYZ categorization.

The figures that are presented above show the average values of the service level KPI “% Delivered on time versus Requested” first for the 6 first scenarios that refer to the current policy and then for the rest 6 scenarios (scenarios 7 to 12) that regard the Min/Max policy, according to the XYZ categorization. The main conclusion here is that in the current policy the IRIs do not seem to influence the KPI substantially as they show a steady evolution when observing the different scenarios of different IRIs. However, in the case of the Min/Max policy, it seems that the IRIs influence in a more evident way the service level KPI. This is evident for all product clusters. Furthermore, there is not any highly observable difference between the rate that the service levels decrease as we switch to higher IRIs between the X, Y, and Z product clusters as the rate of decrease seems steady. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 3 4 5 6

“% Delivered on time versus Requested” KPI averages for XYZ product groups for current policy

X products Y products Z products 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 3 4 5 6

“% Delivered on time versus Requested” KPI averages for XYZ product groups for Min/Max policy

X products Y products Z products

97 In Appendix C, a more extensive analysis is implemented to compare the “% Delivered on time versus Requested” KPI results regarding the XYZ classification according to the different product clusters. Moreover, separate product clusters are visualized in plots separately in order to be comparable: the X products in the current policy are compared to the X products in the Min/Max policy, the Y products in the current policy to the Y products in the Min/Max policy, and the Z products in the current policy to the Z products in the Min/Max policy.

 “% Delivered on time versus Requested” KPI results regarding the different Lead times

At this point the aim it to observe and compare the “% Delivered on time versus Requested” KPI results regarding the two different lead times that have been examined for both the replenishment policies. As it has been stated, the author decided to vary the lead time by choosing three different suppliers: The UK litho-production supplier with a lead time of 22 working days that correspond to one month. Further, the China supplier and the Malaysia supplier were selected and both have a lead time of 66 working days that correspond to 3 months.

Figures 52 and 53 visualize the behavior of the service level KPI “% Delivered on time versus Requested” at the current policy first and then at the Min/Max policy for the two different lead time product groups.

Figure 52: Average values of the service level KPI “% Delivered on time versus Requested” for the two lead time groups for the current policy.

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Figure 53: Average values of the service level KPI “% Delivered on time versus Requested” for the two lead time groups for the Min/Max policy.

In the figures presented above, the x-axis represents the service level KPI. The y-axis refers again to the different review interval selections as they have been specified in the scenarios that have run. Since the first figure is for the current policy, the y-coordinates correspond to the first six scenarios. The y-coordinates of the second figure that is for the Min/Max policy correspond to the scenarios 7 to 12.

The main conclusion on this comparison is that varying the lead times does not seem to affect in an abnormal way the service level within one policy. However, if we compare how the same lead time groups behave for the two different policies, it can be observed that for the current policy, the decrease of the service level is not that significant. It can be seen that the IRI does not affect much the service level in the current policy for both lead time product groups. On the other hand, while viewing at the Min/Max case, the decrease of the service level in both lead time product groups is more distinct. Furthermore, the rate of decrease seems to increase while we move to higher inventory intervals.

Moreover, it is evident that for the case of LT= 22 days, the KPI values fall down more quickly than in the case of LT= 66 days. This can be explained by the fact that when the lead tine is 22 days, you are going to order more often. When the lead time is 66 days, however, you have bigger orders hence, one can order less often, so he will have more inventories on average. This will make the chance of running out of stock smaller.

In Appendix D a more elaborate analysis is implemented to compare the “Inventory final product” KPI results regarding the two different lead times that have been examined for both the replenishment policies.

The same procedure that was presented so far for the presenting and explaining the behavior of the service level KPI “% Delivered on time versus Requested” is followed to explore the behavior of the inventory KPI. The procedure starts with presenting first the “Inventory final product” results from the ABC categorization perspective and subsequently from the XYZ categorization perspective. Then, a presentation of the results regarding the different Lead times follows.

0,6 0,65 0,7 0,75 0,8 0,85 0,9 1 2 3 4 5 6

“% Delivered on time versus Requested” KPI averages for two lead time groups for Min/Max policy

LT 66 days LT 22 days

99  “Inventory final product” KPI results regarding the ABC classification:

The KPI will be visualized by showing the first six scenarios that regard the current policy separately than the scenarios 7 to 12 that refer to the Min/Max policy. Following this pattern, the graphs below in Figures 54 and 55 present the ABC categorization in the case of the two policies.

Figure 54: “Inventory final product” KPI averages for ABC product groups for the current policy

Figure 55: “Inventory final product” KPI averages for ABC product groups for the Min/Max policy

Moreover, it is noticed that the inventory values differ in scale substantially between the scenarios that regard the current policy and the scenarios that refer to the Min/Max policy. As it has been previously stated, the Min/Max policy works with fewer inventories compared to the current policy. This is obvious if the x-axis is checked. The maximum averages of inventory units in the current policy

100 even exceed in the 4th scenario for the A products the value of 4000 for example, while for the 10th scenario in the second figure (y-coordinate=4) for the same A products at the Min/Max policy, this average corresponds to less than 1300 units of products.

Further, a lower rate of decrease is observed at the current policy compared to the rate of the inventory decrease at the Min/Max policy. In particular, at the Min/Max policy it is distinct enough to notice that the inventory KPI decreases in a faster rate as we switch to higher IRIs.

In Appendix E a more extensive analysis is implemented to compare the “Inventory final product” KPI