Delivery time

In chapter 4 we discuss the delivery time of warehouse and sales products. We analyse the delivery time of the warehouse and sales products separately.

5.1.1 Delivery time warehouse products

In Section 4.2 we conclude that the delivery process of route blue, red, and white is well organized because the variance of the delivery time is not high and most of the warehouse products are delivered on time. We conclude that there is some delay caused by one or more of the process steps of the order process in route blue, red, and white, because the medians of delivery time increases in order of these routes. We also conclude that the delivery process of route orange is not well organized because we observe a high median and variance and 30% of the warehouse products are not delivered on time. Furthermore, we observe that the medians of the delivery time are high on Monday and we conclude that this is because of the higher number of orderlines on Monday compared to the rest of the weekdays.

Delay in delivery time route blue, red and white

The first effects we analyse are the variance and the medians of the delivery times of route blue, red and white. We observe a delay in order the delivery routes are processed. We conclude this is caused by one or more steps in the order process because the order processes for every route are the same. The delay is not caused by for example an extra process step. The first cause we identify is that the first read out moment of the order board is not matching with the starting time of the logistic employees. The first read out moment is at 7.00 AM and the starting time of the logistics employees is 8.00 AM. The orders of the first read out moments already start with a delay. The second read out moment is at 7.45 AM. Because the orders of the first read out moment are picked first, the orders of the second read out moment also start with a delay.

Another cause that can ensures a delay is one of other process steps. First, we measure the processing time of the picking process per route with the data from Oracle. Table 5.1 shows the results of this measurement. The time is measured with the assumption one person is picking all orderlines. Commonly three warehouse employees are

Monday Tuesday Wednesday Thursday Friday

Orange sterile (HH:mm) 2:29 1:21 1:26 1:18 1:34 Blue sterile (HH:mm) 1:50 0:53 0:49 0:49 0:49 Red sterile (HH:mm) 1:52 0:58 0:59 0:58 1:04 White sterile (HH:mm) 0:44 0:24 0:19 0:25 0:25 Orange unsterile (HH:mm) 4:33 2:36 2:40 2:29 2:55 Blue unsterile (HH:mm) 6:21 3:14 2:48 3:13 2:56 Red unsterile (HH:mm) 4:48 2:20 2:22 2:26 2:43 White unsterile (HH:mm) 2:38 1:38 1:17 1:26 1:24

38 picking the unsterile orderlines and two warehouse employees are picking the sterile orderlines. We use this information to make a conclusion.

We observe that on Monday the picking process of route orange, blue, and red takes more time than the time interval between the read out moments. We conclude that this ensures a delay in the delivery time. On the other weekdays we observe that the picking process scarcely takes more time than the time interval between the read out moments. From this we conclude that on these weekdays the delay in the delivery time is not caused by the picking process.

The next step is to transport the orders. In is difficult to measure the throughput time of a transporter during the day. We assume that the transportation time from the central warehouse into the hospital takes 17 minutes, which is the average of the transportation times measured by several employees. The transporters can only transport six carts at a time. The number of carts depends on what products are ordered, for example large or small products. Because of that it is difficult to measure how many carts are needed for one route. We assume a maximum of 12 carts for one route, and based on that we conclude that the maximum transportation time of one route takes 34 minutes. This is lower that the time intervals between read out moments, we can conclude that the transportation process does not lead to a delay in delivery time. In this conclusion we assume that the transport employees give priority to the products ordered by the order boards instead of the sales products.

The last process is the delivery process. We measure the average delivery time of route blue, red, and white with the data from Oracle. During the reference period commonly three logistic employees were delivering the orders, however the data given in Table 5.2 is calculated for the delivery by only one employee to account for a varying number of delivery employees. To obtain the average delivery time per route the calculated times are divided by the number of employees delivering that route. We observe that on Monday the average delivery times of route blue and red are twice as long as the interval time between the read out moments when delivery is done by three employees. We observe route white exceeds the interval time between the read out moments on Monday but not on the other weekdays. We conclude this is caused by the high number of orderlines because of the weekend. In the weekend no products are ordered and delivered. On the other weekdays, the average delivery time of route blue and red is higher than the time interval between the read out moments. We conclude this is caused by the higher number of orderlines requiring more walking time between the departments and the basement. According to our measurements 40% of the delivery time is walking time. We conclude the walking time is not preventable, but we think the walking time can be decreased. We observed that the walking time is caused by the fact the employees need to walk from the basement to the departments, from department to department and from the last department to the basement. We think that the walking time between the departments can be decreased because now the sales, sterile and unsterile products are delivered separately.

Table 5.2 Average orderlines and deliver time per route and per weekday calculated for one person

Variance route blue, red, and white

Secondly, we discuss the variance in delivery time. In Section 4.2 we show the variance in delivery time per route and per day. We observe some variance in the three routes, but the variance is not remarkably high. On Monday the variance is higher in comparison with the other weekdays, the variances on the other weekdays are comparable

Monday Tuesday Wednesday Thursday Friday

Average orderlines blue (#) 485 242 215 236 222

Average deliver time blue (h) 8:10 4:04 3:37 3:59 3:44

Average orderlines red (#) 413 206 209 210 236

Average deliver time red (h) 6:58 3:27 3:31 3:32 3:58

Average orderlines white (#) 198 118 92 110 109

39 to each other. We conclude that a stable variance is caused by two aspects: the number of employees working per process and the number of ordered orderlines. In Section 4.2, we observe a correlation between the average total number of orderlines and the number of orders that are delivered on time. We conclude that when the number of orderlines is higher, the variance of the delivery time is also higher. Furthermore, we conclude that the variance is higher when the number of employees working on the delivery process is lower than 3 and the number of orderliness of the route stays the same.

Median and variance delivery time route orange

Above, we observe the results of route blue, red, and white. We analyse route orange separately because this route is only picked and transported by the logistic department. Delivery to the department warehouses is done by a different department, the Transferium. We show the results of the delivery time of route orange in Section 4.2. The delivery time medians and variance per day are much higher in comparison with the other routes. We conclude above that the first read out moment does not match with the starting time of the warehouse employees. The first read out moment is for the orders of route orange. Because of that we conclude that the delivery time of route orange is delayed because of the read out moment. The delay is not influenced by the other order route because the orders of route orange are picked first. Furthermore, we observe three other causes for the results: the number of orderlines, the route plan, and the number of delivery employees.

First we analyse the average number of orderlines per day of route orange and compare these with the other routes using the data from Oracle. Table 5.3 shows that the average number of orderlines for route orange are only slightly higher than for the other routes. The difference is too small to account for the increased delivery times. From this we conclude that the number of orderlines is not the cause of the difference in performance of route orange compared to the other routes.

Besides the number of orderlines, we assess the number of employees working on the delivery process and the delivery plan for route orange. We find that often only one delivery employee per day is delivering the products to

Orange Blue Red White

Monday 460 485 413 198

Tuesday 257 242 206 118

Wednesday 268 215 209 92

Thursday 247 236 210 110

Friday 292 222 236 109

Table 5.3 The average number of orderliness per route per day

40 the department warehouses on route orange. Besides that, delivery of route orange is done horizontally. We can image both aspects can have influence on the walking times between the department warehouses and thus have influence on the delivery time. We analyse these aspects by comparing the delivery time box plots per department of route orange and blue. Figure 5.1 shows the results of this measurement. The first 5 departments are linked to route orange and the other 9 departments are linked to route blue. We observe a significant difference in variance between the departments of route orange and route blue. The delivery times of the departments of route blue all have the same median and variance and while a remarkable difference in medians and variances between the departments of route orange is observed. Route blue is delivered by three employees and route orange by one employee. Furthermore the route plan is different, but all other factors are the same. We conclude that the number of employees have influence on the median and variance of the delivery times because of the difference in the results. We also expect that the route plan has an influence in the results because horizontal delivery causes more walking time. We conclude that the difference is high and we think this can be improved.

5.1.2 Delivery time of sales products

In Section 4.2 we conclude that that the average delivery time of the sales products on Monday, Tuesday, and Wednesday is 54 hours. At the end of the week the delivery time increases because of the weekend. We conclude that 61% of the sales products are delivered within 5 days. We conclude that many external suppliers have a high delivery time and that ensures a low rate of sales products that are delivered on time.

We observe that the delivery time strongly depends on the delivery time of the external supplier. Every external supplier has their own stock system, for example the external supplier may have a high stock level of a product or has no stock of the product, but manufactures the product when it is ordered. Therefore we consider it is important to analyse the delivery time per product. We measure this with the data from Alltrack. We observe 586 orderlines and 249 sales products that are ordered in the analysed time interval. We show the results in Appendix XIV. Firstly, we conclude that the sales products are not ordered often during the time interval, the highest number is 11 orders for a product in the three months. We observe 105 sales products are ordered once during the time interval. We observe 142 of the 249 products have an average delivery time below 120 hours, so five days. The other 107 products have an average delivery time over 120 hours. The delivery times varies between 120 and 652 hours, meaning waiting times up to one month are observed. We conclude that delivery times of external suppliers are high and we conclude the central warehouse cannot guarantee that the sales products are delivered on time.