The research focus on analyze different scenarios when an unplanned event occurs and affects the production planning in a manufacturing company. The simulations were running 30 times each, with a production quantity size of 600 units from week 6 to week 52.
This chapter present result of each scenario based on the seven performance metrics mentioned in the last part of Chapter 4. The totals and weekly results shown in the next figures represent the average results of the 30 runs in each simulation.
Inventory Recovery Time Results
Result of a disruption, no production during week 1 to 5, levels of inventory at each stage will drop. After running the simulations, the data collected in each run will be used to calculate the time when the company recovered their normal performance.
The simulations present two types of inventories, the total system inventory which is the sum of the products that are stock in the manufacturing plant, regional warehouse and retailer’s warehouse and the Inventory which only includes the products that are stock in plant and regional warehouses.
As can be seen in all scenarios (Figure 7 to Figure 14) the inventory and system inventory are significantly decreasing from week 1 to week 5 because those weeks were no production due to an unexpected event, for this reason the current inventory that the company keeps is used to supply the demand during the period without production. After this period each simulation presents a different behavior in the process to recover the starting level of system inventory.
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In the case of Scenario 1, the inventory level shown in Figure 7 at the end of the year in average remains under the starting level approximately 10%; while Figure 8 shows that the system inventory level after disruption is increasing as the weeks go by also cannot recover from the disruption within the analysis period, at the end of the that period (52 weeks) the system inventory level has in average 4% less inventory than initially has.
Figure 7. Scenario 1. Inventory Recovery Time Source: Made by author
Figure 8. Scenario 1. Recovery Time of System Inventory.
Source: Made by author
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Since the research analysis cover until week 52, is not prove in which week the scenario 1 will recover from disruption. But analyzing the data it can be said that around 1 to 3 more weeks (between weeks 53 to 55) could be regaining the system inventory level because in average at the end of week 52, the inventory level is under the starting system inventory level with 497 units.
While in Scenario 1 both inventories levels behave in the same manner, after disruption increase gradually; with the Figure 9 and Figure 10 is observed that in Scenario 2 the inventory and system inventory curves are different. In the Inventory level depict in Figure 9 the curve after disruption continues to decline then remains almost the same level until week 42 and finally increase slightly getting a 57% less inventory than at the beginning of the simulation.
Figure 9. Scenario 2. Inventory Recovery Time Source: Made by author
Instead, in Figure 10 the system inventory will increase from week 6 reaching up the initial inventory level at week 26. The following weeks the system inventory level completes the period under consideration with an average of 25% more inventory. From the data presented at the end of this Chapter, the average recovery time of the system is 26.93 weeks with a standard deviation of 3.18.
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Figure 10. Scenario 2. Recovery Time of System Inventory Source: Made by author
For Scenario 3 which represents a Push-Pull system, can be observed in Figure 11 in average at week 44 is recovered the initial inventory level. At the end of the year, the inventory level remains 15% over the initial level. Based on Figure 12 the system inventory is recovered in average at week 41, with an 8% more inventory at the end of the simulation run. This push-pull scenario enables recover the initial inventory levels before the analysis period ends. The initial inventory level in Scenario 3 is 3849 units and the system inventory level 11,153 pieces.
Figure 11. Scenario 3. Inventory Recovery Time Source: Made by author
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Figure 12. Scenario 3. Recovery Time of System Inventory Source: Made by author
Figure 13 and Figure 14 presents the behavior of the Pull System in the Scenario 4. As noted in the previous Simulation graphics (Figure 11 and 12 from Scenario 3) both recovery inventory graphics presents the same behavior and also regain their initial inventory levels in a period less than 52 weeks. It can be observed in the following graphs (Figure 13 and 14) that the inventory levels go down until week 5 but on week 6 both will increase until the analysis in complete at week 52.
The difference between Figure 13 and 14 remains in the week in which the inventory level equals to the starting level. For Figure 13, on average at week 40 the inventory of Scenario 4 is recovered and based on Figure 14 the system inventory is recovered in average at week 33. At week 52, the inventory level is 23% more than initially and the system inventory level with 24% more.
0 2000 4000 6000 8000 10000 12000 14000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Pieces
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Scenario 3
Recovery Time of System Inventory
Scenario 3
Starting System Inventory
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Figure 13. Scenario 4. Inventory Recovery Time Source: Made by author
Figure 14. Scenario 4. Recovery Time of System Inventory Source: Made by author
With the results of recovery time, Scenario 2 present the shortest recovery system time (in week 26) but for inventory level within the 52 weeks of analysis cannot reach the initial level. Instead Scenario 4 which represents a Pull system has better performance after a disruption, and has the capacity to recover the starting inventory and system inventory after an event in a period less than 52 weeks.
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32 Missed to Clients Results
The missed to Clients quantities represent the amount of product from retailer’s orders at the end of the year that cannot be sent. This parameter is important to analyze because reflects not only the total amount at the end of one year as presented in Figure 15, but in Figure 16 also we can observe the behavior along 52 weeks. After 52 weeks, the scenario 2 presents a huge amount of total missed to clients, this quantity of products increase over all the year, unlike other simulations where during 10 to 20 weeks presents missed to clients but the following weeks until the end of the simulation period this quantity do not increase.
Figure 15. Missed to Clients Total Results Source: Made by author
3772
108373
5374 3126
0 20000 40000 60000 80000 100000 120000
Pieces
Missed to Clients
Scenario 1 Scenario 2 Scenario 3 Scenario 4
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Figure 16. Missed to Clients Weekly Totals Source: Made by author
Missed to Market Results
The sales opportunities that are lost due to stock out are called missed to market. In these simulations, the scenario who presents more loses on sales of end products to final consumers was Scenario 2 (See Figure 17). Also not only the total missed to market amount on Scenario 2 is higher but on Figure 18 it can be observed the same pattern that Scenario 2 presents in missed to clients graphics, the amount of missed to market pieces during the year will increase until week 52. Although the recovery time parameter report that around week 26 that scenario recover the initial inventory system level, over all year still have lost in sales due to lack of product, indicating that despite the system has lots of product to meet the demand, those products are not placed in the correct region because Scenario 2 is a push-pull system, where the plant does not maintain large amount of finished product, those products are sent directly to the regional centers based on the client’s orders.
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Missed to Clients
Scenario 1 Scenario 2 Scenario 3 Scenario 4
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Figure 17. Missed to Market Total Results Source: Made by author
Figure 18. Missed to Market Weekly Totals Source: Made by author
The tendency of the rest of scenarios is to increase in the total amount of missed to market items, but that increment is not significant compared to the presented on Scenario 2. But is necessary emphasizing in week 33, when the Initial System Inventory Level of Scenario 4 is recovered, the amount of missed to market products remains the same until finish the simulation, which means recover the
Scenario 1 Scenario 2 Scenario 3 Scenario 4
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35 Returns
Since the product is finished it has a lifetime of 30 weeks over all simulations.
The value of average returned products is 2,273 pieces for Scenario 1; 2,156 pieces for Scenario 2; Scenario 3 has 1,045 returned pieces; for Scenario 4 is 79 returned pieces (Figure 19).
Figure 19. Total Returned Items Source: Made by author
Scenario 1 and 2 presents higher amount of returned products at the end of the year. That happens because in both simulations the system carry high levels of inventories (6 and 10 weeks of average sales) based on forecasts. Another reason is products need to be distributed to regions because there is no warehouse in the plant, then the products are already sent to regions, but if the market demand changes there is no way to move the products from one region or retailer to another to cover fluctuation on demand. Also with high inventory the product rotation is less, keeping the products in storage for a long time reducing the time when the end consumer can used it.
Scenario 1 Scenario 2 Scenario 3 Scenario 4
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While Figure 20 shows the comparison of all simulations in the increment of returned products among the 52 weeks.
Figure 20. Comparison of Returns during 52 weeks Source: Made by author
Net Profit
The net profit is the earnings a company has after considered all the expenses (fixes, returns, transport and interest on the inventory); with this parameter will be known if the company is or is not profitable. Scenario 1, has an average net profit at the end of analysis on $ -1,109 which is because the level of inventory over all year is less than the starting inventory, and also because is affected by the high quantity of returned products, compared with total sales the costs incurred by the company are higher than the quantity of products sold to market.
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For Scenario 2, the Net Profit amount after 52 weeks is $50,081. Scenario 3 presents $23,900, while Scenario 4 had $52,919 (See Figure 21).
Figure 21. Net Profit Results Source: Made by author
As can be observed in Figure 22, the behavior of Scenario 2 was increase the net profit over the year but then around week 40 their net profit slightly decreases.
That reduction in the Net Profit since week 40 to 52 may be due to the significant increment in the returned products during the same time frame.
Figure 22. Comparison of Net Profit during 52 weeks Source: Made by author
Scenario 1 Scenario 2 Scenario 3 Scenario 4
-20000
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Scenario 3 and 4 presents the same behavior; they increase their net profit along the year, with a difference that at week 52 (end of simulation) the average net profit in Scenario 4 is the almost the double as Scenario 3.
In Table 6 Comparison between Scenarios is shown the results of all the parameters analyzed between the scenarios. For Scenario 1, during 52 weeks of performance analysis cannot recover the total system inventory and also at the end of the year the net profit shows that is under $0, that is to say is in bankruptcy. This means that for this company structure, the push system is not considered as a good option to run the company in order to have the capacity of overcome a disruption of 5 weeks which affects the production in the plant.
Table 6. Comparison between Scenarios
Scenario 2 has a recovery system time in week 26, the shortest recovery time between simulations and also has the second higher net profit among simulations.
Scenario 3 in average takes more time to recover from a disruption, around week 41 to back to the system starting level and for the inventory level on week 44.
The profits made in a year by Scenario 3 is little more than the half of profits made by scenario 4.
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Scenario 4 instead is the scenario which had higher average net profit ($52,919) also both inventories recovers their initial levels before week 52. Due to the structure of the supply chain the amount of missed to clients, missed to market and return products is not that higher than other simulations. With these results it can be proved that the pull supply chain system design is the best option to run a company in order to have the capability to handle small disruptions.
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