To ensure a successful implementation of the benchmarking model we have to consider three aspects. Firstly, the fit between the final clustering derived in Chapter 6 and the managerial structure of PostNL. Secondly, the lay-out of the benchmarking-model and finally the implementation plan to cover the last steps of the benchmarking process.
Fit between Clustering and Managerial Structure of PostNL
In Chapter 6 we have developed clusters which do not encounter the managerial structure of PostNL. As defined in Section 5.4, the information required to calculate our performance measure “time per mail item” has only been provided on a team and postmen level until now. Thus, before we can implement the new clustering and performance measure in the benchmarking model, we have to assess the fit with the managerial structure and to clarify possible implications on the quality of the benchmarking.
The main idea is to compare the same clusters between the different delivery areas. Therefore, ideally we sum up the required delivery time and the weighed mail volume of all PC5 areas that are in the same delivery area and same cluster. Whereas the mail volume is given per PC5 area, the delivery time is currently only given per team and per postman. PostNL has assigned each PC5 area to a depot and each depot to a team. However, as we can see in Figure 7.1, the majority (84%) of teams manages PC5 areas which are in more than four different clusters given the new clustering. As the delivery times of car and scooter areas are booked separately from (e-) bike and foot deliveries, we can extract Cluster 1 (interdrop >100m) and Cluster 2 (interdrop >50m) from the remaining clusters. However, still teams contain different clusters and thus implementing the clustering given the current information availability on team level is only possible by combing different clusters within a team at the expense of the overall cluster homogeneity.
To assess the impact on cluster homogeneity, we evaluate the proportion of the cluster membership of PC5 areas per team. As we can extract the information about the delivery time of cluster 1 and 2, we only consider Cluster 3 to 7 for the cluster proportion. Looking at Figure 7.3, we can see that 90% of all teams incorporate one dominant cluster, meaning that at least 40% of the PC5 areas of a team are in the same cluster. Hence, we can conclude that PC5 areas within a team show a tendency towards one cluster. However, for 72% of the teams Cluster 5 has the highest proportion of all clusters (see Figure 7.2), meaning that Cluster 5 is most often the dominating cluster within a team. This is as expected as 47% of all PC5 areas of delivery area Utrecht are within Cluster 5. Thus, clustering the teams based on their dominating cluster is not only on the expense of the overall cluster homogeneity, but also has the consequence that Cluster 1, 2 and 7 will be empty.
In order to realise all clusters, we advise to couple PC5 areas to the postman that delivers those areas during his delivery tour. The data to do the coupling is available as the tours (including all delivery addresses) of each postman for each day are known, one only has to extract from it the PC5 areas. The advantage is that the number of PC5 areas covered by a postman in on average 2.2, which is significantly less than the PC5 areas of a team with an average of 30.6 (see Appendix XIII). Therefore, the chances of homogeneity between PC5 areas are higher and thus a better cluster quality can be achieved. The disadvantage is that if the tours of a postman vary, it is a higher workload to link a PC5 area to a postman as we have to determine the link for each day separately. However, the majority of postmen always covers the same tour, thus we expect that taking the delivery time per postmen as an information source for our performance measure is realisable and therefore we advise to combine PC5 areas covered by the same postman into one cluster object.
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FIGURE 7.1:NUMBER OF CLUSTERS WITHIN A TEAM
Layout of the Benchmarking Model
In Section 3.2 we conclude that the benchmarking model has to be clear and easy to understand. The clarity especially concerns the number of performance measure. By using the efficiency measurements defined in Section 5.2 (two measures for internal efficiency, one for the delivery and one for the depots, and one measure for external efficiency, the numbers of complaints per customer), we are able to combine different performance measures. By that we reduce the complexity of the benchmarking model, which has been one major drawback of the current benchmarking model (see Figure 3.2 – Requirement 1).
We design a new lay-out of the benchmarking model, which fulfils the requirements named in Section 3.2. It consists out of three surfaces, the further we go, the more detailed it get.
The first surface of the benchmarking model allows process managers to select the performance measure of interest. Furthermore important information can be added (“you are the top scorer”) to gain the interest of process mangers (see Figure 7.4).
On the second surface (see Figure 7.5), which can be entered by clicking on the performance measure of interest, seven column graphs are presented by default showing the top and button three delivery areas for each cluster; showing all 28 delivery areas at the beginning would only lead to confusion. If process managers want to know more about the performance measure he can click on the question mark positioned next to the heading of the surface and the performance measure record sheet of Neely et al. (1997) (see Table 4.2) for that specific performance measure will appear. This record sheet (see Table_Apx II-1) contains information about purpose, formula, data source and responsibility division of the performance measure. If process managers want to see the exact data of a column graph he can click on the “details” button next to the heading of the column graph and the measures of all influencing factors are shown. These buttons help process managers to clarify and to go into detail of performance measures if required, but still ensures compact and clear view of the performances. At the button of the second surface a graph visualises on the Dutch map the clusters and the delivery areas. Ideally, the process manager can zoom in on the map to identify
FIGURE 7.2: DOMINATING CLUSTER PER TEAM (CLUSTER WITH THE HIGHEST PROPORTION)
10% 39% 6% 29% 16% Proportion of 0.2 (0) Proportion of 0.3 (3) Proportion of 0.4 (12) Proportion of 0.5 (2) Proportion of 0.6 (9) Proportion of 0.7 (5)
FIGURE 7.3:HIGHEST PROPORTION OF A CLUSTER WITHIN A T EAM
7% 4% 72% 17% Cluster 1 (0) Cluster 2 (0) Cluster 3 (2) Cluster 4 (1) Cluster 5 (21) Cluster 6 (5) Cluster 7 (0) 3% 3% 19% 49% 26% 1 Cluster (1) 2 Cluster (2) 3 Cluster (6) 4 Cluster (15) 5 Cluster (8) 6 Cluster (0) 7 Cluster (0)
95 Quater 2 Cluster 1 Region Delivery area Benchmark Delivery BG- AH BG- AMF BG- ASD- NO BG- ASD- ZW BG- ASN BG- BBT Cost per item (€) 10 9 7 1 1 3
0 2 4 6 8 10 12 C os t pe r 1 0 0 0 it e m s
Cost per item (€)
Top Scorers of the Quarter
Clustering : Delivery
Details ?
more clearly the clusters and related PC5 areas. If the process manager is interested in certain regions, delivery areas, clusters or quarters, he can select it in the field (see top left of the surface). If the process manager wants to compare the performance measure on a lower level than the delivery area, he can click on a column of a graph and a third surface will open. Surface 3 enables a performance comparison within a delivery area (see Figure 7.6). Again each column chart will show the performance of one cluster. Thereby the process manager can choose to distinguish the performance between the different teams, depots or PC4 areas (see fields top left of Surface 2 or 3).
FIGURE 7.4:BENCHMARKING MODEL, SURFACE 1
Implementation
As already discussed at the beginning of Chapter 5, we do not cover all steps which are required for the benchmarking process in this research. Steps 1, 2, 3 and 5 have already been covered in Chapters 1 to 4, which we have briefly discussed at the beginning of Chapter 5.
Steps 4, 6 and 7 are covered as follows. After applying the four perspectives framework of Neely et al. in Section 5.1, we have been able to identify costs, flexibility and quality as main critical success factors (see Figure 5.1, Step 4). With the service efficiency model of Grönroos and Ojasalo (2004) we have derived performance measures and presented the performance measure record sheet of Neely et al. to specify elements of the performance metrics (see Figure 5.1, Step 6). Finally, in Chapter 6, we have defined seven clusters for the performance measure delivery time per mail volume by using APN/km² and means of transportation as cluster attributes (see Figure 5.1, Step 7).
To ensure a successful implementation of the benchmarking model, we specify tasks, stakeholders and time required to cover the remaining steps in the following (see Table 7.1). A detailed description can be found in Appendix XV. For a general explanation of the steps we refer to Section 4.1.
Delivery
Depot Customer
PostNLs Benchmarkmodel for the mail-delivery
You are one of Topic of the
next session
Quater 2
Cluster Cluster 1
Region Central
Delivery areaBG Utrecht
Team Depot PC4 Details Total Mail (small, large) Packages (mailbox, ring) 3401A-3401B 3443E-3443G 3445X-34457 3511M-3511N 3512J-3512k 3513T-3513W Number of points delivered Volume Total Delivery time (in hour) PC5 area Delivery - BG Utrecht 3401A - 3401B 3443E - 3443G 3445X - 34457 3511 M- 3511N 3512J- 3512k 3513T - 3513 W Cost per item (€) 11 9 8 5 3 3
0 2 4 6 8 10 12 C os t pe r 1 0 0 0 it e m s
Cost per item (€)
Top Scorers of the Quarter ? Details
FIGURE 7.6:BENCHMARKING MODEL,SURFACE 3 FIGURE 7.5:BENCHMARKING MODEL,SURFACE 2
96 For implementation PostNL still has to cover the following steps: In Step 7 PostNL has to define clusters for the remaining performance measures and collect the required data in Step 8. Subsequently, the benchmarking model has to be analysed in Step 9 in order to define performance gaps and best practices in Step 10. Subsequently, an action plan has to be defined (Step 11) to implement the best practices (Step 12). Finally, the success of the implementation should be monitored, and continuous improvement should be secured (Step 13). The exact tasks of those steps are defined in Table 7.1.
The main stakeholders required to cover those steps are the senior process manager of optimisation (O), the senior controller (C), the ambassador of delivery and the process managers (P). Looking at the duration of each step, we can see that some involve onetime tasks, for instance the clustering for the remaining performance measures (Step 7), while others are continuous like analysing and comparing once a month individually the performances (Step 9). For high time-consuming task, especially the clustering with 80 hours for each performance measure and the design of the information system with 120 hours, we recommend to set-up a small team managed by the process manager of optimisation or the senior controller to share the work.
Overall, implementing the benchmarking model will be time intensive in the short-term, however once the standards (clusters, information system, action plan template) are established, the time consume will be significant less: around 16h for a process manager, 11h for the ambassador of delivery (as he does not have to analyse each month the performances, but requires around 2h for the preparation of the benchmarking session) and 13h per quarter for the senior controller.
TABLE 7.1:IMPLEMENTATION OF THE REMAINING STEPS OF THE BENCHMARKING PROCESS
(O = SENIOR PROCESS MANAG ER OF OPTIMISATION, C=SENIOR CONTROLLER, A =AMBASSADOR OF DELIVER Y AND P=PROCESS MANAGERS)
Step Tasks Person
Responsible
Duration 7. Define clusters Define clusters for remaining performance
measures (PM) (see Section 5.3 and Chapter 6 as guideline)
O 80h per PM
8. Data collection Collect for the whole Netherlands data on the cluster attributes for cluster assignment
C 16h per PM
Design an automatic information processing system that retrieves the data and calculates the performance measures
C & IT department
120h
9. Analyse and compare
Discuss and define with process managers the expectations (e.g. time investment and tasks for benchmarking)
A 4h
Analyse and compare P, A, C 2h monthly
(individual) 8h quarterly (plenary) 10. Define best practices and performance gaps
Summarise the findings A, P 1h quarterly
11. Action plan Develop an action plan template (incl. goal, task, time-framework, person responsible, methods for motoring)
O 8h
12. Implementation Execute the action plan P (t, pbz.) -
13. Monitor and continuous improvement
Include the monitoring within the benchmarking model or MJ dashboards
C 5h per
inclusion Evaluate the performance improvement and
recalibrate benchmarking model if necessary
Short-term: P, A, C Long-term: C Part of the 2h monthly analysis
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