Implementation potential of the replenishment policy matrix

In order to illustrate the benefits and saving potential of the replenishment policy matrix, inventory level simulations were conducted for the stock transport items by using the new replenishment policies and lot sizes for the different item classes.

The data that was used in the simulation of the items was gathered from the case company’s SAP ERP system and it was based on 12 months transaction history. As the purpose of the simulation was to illustrate the benefits and savings of the re-plenishment policy matrix so that the practitioners can easily understand them and the number of sporadic demand items was relatively low, assumption of normality was used for the distribution of demand. In addition, as there was no performance gaps in the supply capability and accuracy of the supplying facilities, the replenish-ment lead-time was considered to be constant. Hence, the results of the simulation were calculated by using the equations 1, 4 and 6 that were presented in the theory chapter 4.3.1, and the impact of the replenishment policy matrix on the average inventory values of different material types is presented in the figure 24.

Figure 24. Impact of the replenishment policy matrix on average inventory values.

As can be noticed from the figure 24, the most significant changes in relation to the baseline situation have been achieved with the component and semi-finished prod-uct types as they had the most significant performance gaps. As a whole, the impact

on the inventory value is -781 721 € which means 45 percent decrease compared to the baseline situation, if the guidelines of the replenishment policy matrix are uti-lized for the items. In addition, the overall service level has also increased 1,1 per-cent at the same time as should be as the order frequencies have been increased which means that there is more replenishment cycles compared to the baseline sit-uation. Hence, the service levels have to be higher in order to prevent the stock out situations, and the impact on the service levels is illustrated in the table 7 below.

Table 7. Impact of the replenishment policy matrix on average service levels.

Material type

Current average service level (%)

Optimized average service level (%)

Change (%)

COMP 87,0 % 88,2 % 1,2 %

SEMI 87,4 % 88,5 % 1,1 %

FINI 88,3 % 88,3 % 0,0 %

Total 87,2 % 88,2 % 1,0 %

In addition, further inventory reduction potential of 94 628 € can be achieved if the inventory centralization and pooling of the A sporadic demand class items is uti-lized. However, exact inventory reduction for these items is hard to estimate as the items might be critical for the devices and customers’ processes and hence every item’s stocking policy needs to be reviewed in cooperation with the product man-agers in order to clarify the exact values. The impact of the replenishment policy matrix on inventory turnover and target values in turn is illustrated in the figure 25.

Figure 25. Impact of the replenishment policy matrix on inventory turnovers and target values.

As can be noticed from the figure 25, all the material types exceeds the set target values, if the guidelines of the replenishment policy matrix are utilized for the stock transport items. In addition, the items belonging to the semi-finished product type have now actually the best inventory turnover. This is due the reason that there were seven items with very high VAU that represent 53 percent from the total VAU of the semi-finished products, and they were classified to the A stable demand class where the demand behavior of the items makes the strict inventory control and in-ventory value reduction possible. The impact of the replenishment policy matrix on the inventory coverage in turn is presented in the table 8 below where the average days to sell the inventory and changes to the baseline situation are illustrated.

Table 8. Impact of the replenishment policy matrix on inventory coverage

Material type

Baseline average days to sell the inventory

New average days to sell the inventory

As can be noticed from the table 8, the biggest change has occurred with the items under the component material type as they had the biggest performance gaps based on the findings of the process analysis. In total, the inventory coverage has de-creased 25 days which means that the cash conversion cycle (CCC) of the stock transport items has improved significantly. This in turn would allow the case com-pany to turn the working capital tied up to the order delivery process of stock transport items into cash flows more rapidly which in turn would improve the fi-nancial profitability of the case company. In addition, the potential working capital that could be released by reducing the inventory coverage of stock transport items could be used instead to fund the strategic investments and growth of the case com-pany in order to create new cash flows and more value for the customers of the case company. However, in order to illustrate the cost of the potential working capital release, the increase in total order rows needs to be compared, and the impact of the replenishment policy matrix on the total number of yearly order rows is presented in the figure 26 on the next page.

Figure 26. Impact of the replenishment policy matrix on yearly order rows.

As can be noticed from the figure 26, the total number of yearly order rows has increased 14 224 order rows and the biggest change (13 563) has occurred with the X class items where the characteristics of the items allows the automated processing of the orders. By using the automated order processing, resources could be in fact released and further used in the manual order processing at the purchasing depart-ment. In addition, the manual order processing times could also be reduced as the lot sizes that are used in the replenishment policy matrix are now better align with the supply capability and demand of the items which would reduce the rework and inspections in the STO processing. Hence, it was estimated that the order processing time of the manual order rows could be reduced from 2 minutes to 1 minute per order row, and the impact on the buyers’ working time is illustrated in the table 9.

Table 9. Impact of the replenishment policy matrix on the buyers’ working time.

Scenario Processing time

per order row

No. of manual order rows

Working time (h)

Working time in days (7,5 h / day)

Baseline 2 min / order row 8426 281 h 37 days

After implementation 1 min / order row 9087 151 h 20 days Total Change -1 min / order row 661 rows -130 h -17 days

As can be noticed from the table 9, the improved lot size alignment of the replen-ishment policy matrix and the utilization of automated order processing could save up to 17 working days of the buyers’ working time. This saved working time in turn could be further used to conduct the inventory management actions, and manage the suppliers and different distribution chains which would create more value for the case company and customers than the wasteful human-computer interactions.

However, as can be noticed from the figure 26, the total number of the yearly order rows has increased compared to the baseline situation which means that amount of the replenishment costs will increase. Hence, it is important to find the right balance between the inventory holding costs and the replenishment costs when the working capital is released in the case company. At the moment, the inventory holding cost is 18 percent from the average inventory value in the case company, and the yearly savings that could be achieved on the inventory holding costs by utilizing the pre-viously mentioned inventory reduction actions are illustrated in the table 10 below.

Table 10. Impact of the inventory reduction actions on inventory holding costs.

Inventory reduction actions Impact on average inventory value (€)

Impact on inventory holding costs in a year (18 %)

Implementation of replenishment policies and lot sizes of the replenishment policy matrix

-781 721 € -140 710 €

Inventory pooling and centralization of A spo-radic demand class items

-94 628 € -17 033 €

Total change -876 349 € -157 743 €

As can be noticed from the table 10, total yearly saving potential of 157 743 € could be achieved if the above mentioned inventory reduction actions are utilized for the stock transport items. However, if the fixed replenishment cost is more than ~11 € per order row, the amount of total costs will exceed the savings that are achieved on the inventory holding costs based on the increase in the order rows. In this case, the more frequent ordering will generate additional costs for the case company and the releasing of the working capital cannot be done without additional costs. How-ever, at the time this study was conducted, the case company was looking for new third party logistic partner and hence the fixed replenishment cost of 11 € per order row can be used as a reference cost when the new agreements are being negotiated.

In addition, if the number of order rows needs to be reduced, the most cost-effective results can be achieved by increasing the lot sizes in the CX class which includes inexpensive items that forms 44 percent from the total number of the order rows.

On the other hand, it is important to understand that the increased order frequency of the replenishment policy matrix will enable many other benefits and cost saving opportunities that are much harder to estimate than the impact on the inventory

value and inventory holding costs, and which are only visible after the implemen-tation. One of the most important benefits of the increased order frequency is the reduction of demand variability in the supplying facilities as it lowers the standard deviation of demand which in turn would allow to reduce the amount of needed safety stock, and thus the average inventory value and inventory holding costs also in the supplying facilities. In addition, the increased order frequency would also benefit the case company as it would help the supplying facilities to increase the predictability and smoothness of the supply which in turn would allow to reduce the supply variability. Hence, the increased order frequency could create significant benefits and cost saving opportunities on the divisional level of the case company.

Another important benefits would be the improved lot processing times, reduced over processing and transportation of materials in the operations of the case com-pany’s warehouse and smoothed truck fill-rates as the lot sizes are smaller. In addi-tion, as the case company is already using the daily “milk runs”, the potential of such transportation system could be better utilized without any additional transpor-tation costs. In fact, the more frequent ordering might even reduce the transportranspor-tation costs, as the probability that all the deliveries can be loaded to the truck increases which in turn would reduce the amount of additional “milk runs”. Hence, when the order frequency and lot size decisions are made in the case company, the impact of the decisions on the total costs and processes of the supply chain should be always considered instead of sub optimizing total costs only in one echelon of the supply chain as it generates sub-optimal results for the whole supply chain.