# BUS 440 REPORT (FINAL) 1.pdf

July 15, 2017 | Author: Anonymous YrtGnvE | Category: Van, Simulation, Airport, Microsoft Excel, Profit (Accounting)

#### Description

SM RENTALS REPORT

JLLY CONSULTING

SM RENTALS REPORT Team 6

SM RENTALS REPORT

EXECUTIVE SUMMARY In order to aid the fictional organization SM Rentals, a budget car rental service, we simulated the operation of a midsize airport location. Using data provided that pertained to the arrivals and departures of customers, we developed a simulation model that will process the route taken by customers as they arrive in order to discover the total amount of time that they spent in the system. SM Rentals set a threshold of less than 18 minutes spent waiting on departure and 20 minutes upon arrival to determine satisfaction. The purpose of this case is to determine the required resources to achieve satisfaction rates of 85% and 90%. Upon analyzing the case, we came to the conclusion that the three significant variables determining the time that customers spent in the system were: 1. Van Size 2. Number of Rental Counter Agents 3. Number of Drivers Upon running multiple regression on the van size, we found that the large, 30 seat van was the least significant and therefore removed it as an option for this model. This multiple regression also found that the optimal number of agents and drivers for reducing the time customers spent in the system was 18 and 16, respectively. However considering the satisfaction threshold set by SM Rentals of 18 minutes for arrivals and 20 minutes for departures, it was possible to analyze a smaller subset of options. 24 combinations were simulated using Arena software and their data recorded and analyzed in Excel. The results processed in Excel revealed that in order to meet the 85% satisfaction threshold, SM Rentals was best served with 12 agents and drivers, while using small sized vans. The total cost

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SM RENTALS REPORT of these resources is \$1,743.31. This arrangement allowed for a steady flow of customers into the rental counter, which prevented any bottlenecks that could cause customers to exceed their limit. In order to reach the 90% threshold, 2 additional drivers were required, bringing the total number of drivers and small vans to 14, with 12 rental counter agents. The total cost of the inputs required is \$1,932.96. The improvements yielded by the additional 2 drivers and vans were primarily in increasing the number of drivers available at peak hours, preventing a bottleneck from forming and allowing for more regular van service. By using these combinations of resources, SM Rentals will be able to maintain their low cost service while at the same time maintaining high levels of customer satisfaction. This will serve them well as more competitors enter the markets that SM Rentals currently occupies, or if SM Rentals intends to expand in the future. Utilizing these recommendations will allow for SM Rentals to retain their customers and grow their customer base, which will allow for greater revenue, and consequently more profit, in both the short- and long-term.

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SM RENTALS REPORT

TABLE OF CONTENTS Introduction: ................................................................................................................................................. 1 Case Background ....................................................................................................................................... 1 Challenges ................................................................................................................................................. 2 Objectives ................................................................................................................................................. 2 Limitations ................................................................................................................................................ 3 METHODS ...................................................................................................................................................... 4 Data used .................................................................................................................................................. 4 Flow of model ........................................................................................................................................... 5 Actual Model ............................................................................................................................................. 6 Metrics tracked ......................................................................................................................................... 9 Wait time ............................................................................................................................................ 10 Average Flow Time .............................................................................................................................. 10 Multi Regression Analysis ....................................................................................................................... 10 Writing into Excel .................................................................................................................................... 12 RESULTS ...................................................................................................................................................... 13 DISCUSSION................................................................................................................................................. 15 INNOVATION IN APPROACH AND METHODOLOGY .................................................................................... 16 FURTHER CONSIDERATIONS ....................................................................................................................... 19 CONCLUSION............................................................................................................................................... 19 APPENDIX .................................................................................................................................................... 21

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SM RENTALS REPORT

LIST OF FIGURES Figure 1: Van Route ........................................................................................................................ 5 Figure 2: Create Customers ............................................................................................................ 6 Figure 3: Create Van Drivers .......................................................................................................... 7 Figure 4: Retail Counter Loading ................................................................................................... 7 Figure 5: Drop-off Station .............................................................................................................. 7 Figure 6: Terminal 1 and Terminal 2 Loading................................................................................ 8 Figure 7: Retail Counter 1............................................................................................................... 9 Figure 8: Retail Counter 2............................................................................................................... 9 Figure 9: Small Van Output .......................................................................................................... 10 Figure 10: Medium Van Output .................................................................................................... 11 Figure 11: Large Van Output ........................................................................................................ 11 Figure 12: Excel Data Summary ................................................................................................... 13 Figure 13: Simulation Results....................................................................................................... 14 Figure 14: Drop-off Queue at the Rental Counter ........................................................................ 16

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SM RENTALS REPORT

INTRODUCTION Case Background This project is based on Case Competition 5 provided by Dr. Payman Jula from the IIE/RA Contest Problems. The scenario pertains to a fictional budget car company that is facing an increasingly challenging environment and is aiming to increase their efficiency. In order to do so they have hired JLLY Consulting, who will simulate the processes involved in their business. They have provided data pertaining to the arrivals from two separate terminals along with those who are returning to the airport. The basic premise of the model is to simulate the route taken by the vans as they travel between the Rental Counter to the Drop-off Point, to Terminal 1, Terminal 2, and then back to the Rental Counter. The end goal is to have a minimum of 85% satisfaction rate from the customers, which is determined by the amount of time they spend in the system. For those arriving, this benchmark is 20 minutes, and for those departing it is 18 minutes. The reason we will aim for an 85% satisfaction is that there will be those customers who, for various reasons, will require an extra amount of time. We will also find what combination of variables will be necessary to maintain a 90% satisfaction rating, and what the end cost will be for both the 85% and 90% rate. The variables available to find the optimal model are: 1. The number of customer service agents at the Rental Counter, 2. The number of vans and corresponding van drivers, 3. The size of the vans. The hourly cost for the customer service agents and van drivers are \$11.50 and \$12.50 respectively. The cost for a large, 30 seat van is \$0.92/mile, a midsize, 18 seat van is \$0.73/mile 1

SM RENTALS REPORT and a small, 12 seat van is \$0.48/mile. We will assume that the size of vans will be constant for the simulation period, which is from 4:00pm to 8:30pm as outlined in the case.

Challenges This model will face a number of challenges as we work to simulate a real world scenario which will have a variety of different factors. One of these challenges is determining the appropriate base set of variables to use in the simulation. Ideally, we would have some idea from the existing business of what has worked for the business. However, this is not available. With a wide variety and combination of variables, we will need to narrow this field. Another challenge is the analysis of the data. Due to the structure of the model, it may be necessary to analyze the output in another program for ease of use.

Objectives The end goal of this simulation is to manipulate the customer service agent, number of vans and van size variables to uncover the optimal combination that will maximize customer satisfaction and minimise the overall costs. The benchmark for this goal is to have a minimum of 85% satisfaction by the customers, which is determined by the amount of time they spend in the system. For those arriving, this benchmark is 18 minutes, and for those departing it is 20 minutes. The reason we will settle for an 85% satisfaction is that there will be those customers who for various reasons will require an extra amount of time. We will also find what combination of variables will be necessary to maintain a 90% satisfaction rating, and what the end cost is for both the 85% and 90% satisfaction. 2

SM RENTALS REPORT

Limitations Since we are using a software program to simulate events that would happen in the real world, we must address the limitations of Arena for real world events, as well as the challenges presented by the information presented, and not presented, in the case Among the information deficits is the information regarding the luggage. Theoretically, a customer with more luggage would require more time to load and disembark from the van, and may restrict the amount of space available for other customers. However, this information was not provided for the case, and we therefore must consider it something that will be dealt with in future models. Another limitation is more specific to the real world; more precisely, traffic and pedestrians. While the model will assume the vans will travel unimpeded, this will not always be the case as airports can be very busy. Since there is no information regarding the traffic cycles and delays, we will assume ideal situations for the car rental vans, although this is highly unlikely. We also must consider the role of human error in a real world situation. Since there is no way to build this completely random element into the model, it will have to be a limitation that is considered when implementing any findings. Another consideration is that of back-up plans. When working with machinery, it is highly likely that there will be some issue at various points of the day, such as the need to refuel, low air pressure in the tires, or low oil. However, this goes beyond the scope of our model in this iteration, and therefore can be dealt with in future models.

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SM RENTALS REPORT

METHODS Data used In the appendix we have provided the data set pertaining to the arrival times at the various entry points to the model (Table 1). This data has been entered into Arena in the create entities section. The data is focussed on the peak business period for the car rental, and therefore we will only simulate for the peak period of 4:00pm until 8:30pm. The data is presented in intervals, with the number of arrivals per 15 minutes. Also included in the create entities is the likelihood that they will have a passenger with them. 60% have no passenger, 20% have 1 passenger, 15% have 2 passengers, and 5% have three passengers. The table containing this information can be accessed in the appendix (Table 2). In order to properly model this scenario, we needed to include the distances between the various points that the vans would travel in the model. These distances were: 

Rental Counter to Terminal 1: 1.5 miles

Rental Counter to Drop-off Point: 1.7 miles

Drop-off Point to Terminal 1: 0.5 miles

Terminal 1 to Terminal 2: 0.3 miles

Terminal 2 to Rental Counter: 2.0 miles.

Figure 1 below illustrates the route for the model:

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SM RENTALS REPORT

Figure 1: Van Route

This data is important as the cost of the vans is based on the distance travelled, and therefore an important component of the model. The average speed of the vans that we will assume for the purposes of this model is 20 miles per hour.

Flow of model The flow of model consists of customers who are returning their rented cars to the Rental Counter arriving at the Rental Counter and meeting with the first available rental agent then queuing for the next available van. The van will arrive, pick up all the passengers, or as many as will fit, and then move onto the Drop-off Point where all the customers will disembark. The van will then move to Terminal 1, pick up the customers and their passengers that are awaiting the van, then repeat the process at Terminal 2 until all the customers are picked up, or there are no more available seats. From here, the van will return to the Rental Counter where the passengers will disembark and the van will collect the next group of passengers and repeat the process, beginning with the Drop-off

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SM RENTALS REPORT Point (unless there are no customers waiting, in which case it will proceed directly to Terminal 1). Those who have arrived with the van to the Rental Counter will proceed inside, and will queue up for the next available agent. When they have been served by the agents present, they will then move on and out of the system with their rental car. These processes will repeat for the time period of 4:30 until 8:30.

Actual Model The model will begin with the creation of entities for the three entry points in the model. They will immediately be assigned the required attributes, and then they will move to the required portion of the van movement model.

Figure 2: Create Customers

The van drivers are created separately and are released into the system within the completion of one cycle by the first van, and in intervals that allow initially for the equal spacing of vans around the airport route. In order to simulate real world events, the vans are set to begin at the 6

SM RENTALS REPORT Rental Counter, determining if there are any passengers present, then if any fit the criteria matching the number of available seats. This process will continue as a loop until there are no more passengers that can fit into the van, or if there are no passengers still waiting.

Figure 3: Create Van Drivers

From this point, the van will travel onto the Drop-off Point, where the entities will exit the system, and the van will reset its number of available seats before moving onto the next station, Terminal 1.

Figure 5: Drop-off Station

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SM RENTALS REPORT At Terminal 1, it will repeat the same procedure that it did with the Drop-off Point, determining if there are passengers waiting, and then if there are any that will fit into the remaining seats. Upon satisfying this procedure, it will move on to Terminal 2 and repeat the procedure once more. Then it will return to the Rental Counter.

At the Rental Counter, the van will empty and reset its seats, then load up passengers waiting to be dropped back off at the airport, and repeat its path. For those who have just disembarked from the van, they will proceed to the Rental Counter, where they will queue up at the rental counter alongside passengers who are returning their cars and dropping off their keys. After getting processed by the agent at the rental counter, all the customer entities will pass through a decision that will distinguish them from those who have picked up their car (arrived at T1 and T2), and those who have just dropped off their car (needs to be dropped off). Those who have picked up their car will proceed to leave the rental counter and depart from the system.

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SM RENTALS REPORT Customers who have dropped off their cars and needs to be dropped back off at the airport will queue up for a van, which will take them back to the airport to be dropped off and disposed off.

Figure 7: Retail Counter 1

Figure 8: Retail Counter 2

If there are no customers lined up at the rental counter to get dropped, the van will proceed from the rental counter directly to T1, bypassing the Drop-off Area. It is important to note that if there are no passengers waiting at a station, the van will proceed straight through, as they would in a real world situation.

Metrics tracked In order to gain an understanding of what the model illustrates, there was a variety of data captured from the simulations.

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SM RENTALS REPORT Wait time Since wait time is a key portion of the model’s end goals, it was important to record these statistics for analysis. The wait time consists of the time that the customer spent within the system, from arrival to departure. This was collected under a variety of different scenarios, using a different combination of agents, van sizes, and number of drivers each time. Average Flow Time Average flow time was captured in order to illustrate the amount of time the entity spent in the system from creation to disposal.

Multi Regression Analysis Running this module, it was found that the 30-seat van was negligible, with results that were highly similar to the 18-seat van. Therefore, the simulation will be limited to testing variations of inputs with the small and medium sized vans.

Figure 9: Small Van Output

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SM RENTALS REPORT

Figure 10: Medium Van Output

Figure 11: Large Van Output

Using this same output, we found that the number of agents plateaus in the range from 12 to 14, as illustrated by the blue box in Figures 9-11 above. This means that there are decreasing marginal returns for additional personnel, and our resources are better focussed on the 12-14 agent range. 11

SM RENTALS REPORT The drivers’ output shows that the number of drivers that will minimize time is 13, as illustrated in Figures 9-11 above in the green box. However, we will analyze more options around this number in order to ascertain the best option. It is important to note that the goal of this model is not solely minimizing time however; we must also consider the cost of the inputs required to reduce the time. Based off this information, we will run simulations for 24 different variations of the small/medium size vans, number of drivers, and number of agents. The goal of the combinations will be to satisfy the 85%, and 95%, satisfaction rates while minimizing the costs. In order to ensure accuracy, we will run each simulation for 5 days, and take the average from this set of 5.

Writing into Excel In order to summarize the information produced by the model, we utilized the Write module in Arena to copy the data into Excel. In particular, we focussed on the total time the customers spent within the system in order to test the satisfaction levels with each combination of variables. From Excel, we will then average the data points collected from each run of the model, and bin them in order to determine the distribution and test their satisfaction against the standards we have set. Figure 12 below illustrates the summary of data for one combination of variables.

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SM RENTALS REPORT Arrival

Dropoff

Avg. time in system: 15.54439 Bins

# 0 5 10 15 20 25 30

Count Sat. Rate

12.49364 Bins

0 0 108 755 494 253 45 1656 0.819444

# 0 5 10 15 18 25 30

0 0 501 573 208 183 0 1465 0.875085

Figure 12: Excel Data Summary

As shown above, the time is averaged, and the distribution is shown in the bins below, along with the total number of customers that were in the system for the simulation. The satisfaction rate is found by summing the total number customers that were under the time limit, and then divided by the total count. For instance, the arrival bins 0, 5, 10, 15, and 20 have values that sum to 1,357. Divide this value by the total count, 1656, and the satisfaction rate is 81.9%.

RESULTS Referring to Figure 13 below, we can see the outcome for the 24 simulated scenarios. It is immediately apparent from looking at the satisfaction section that the least expensive combination for a minimum of 85% satisfaction is combination 3, with 87.9% satisfaction for arrivals and 91.72% for departures. This entails:

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SM RENTALS REPORT 

Van = Small (Fuel Cost = \$447.31)

12 Drivers

(Wages = \$675)

12 Agents

(Wages = \$621)

Total cost

\$1,743.31

In order to satisfy the 90% satisfaction goal, combination 11 is the least expensive option. This provides a satisfaction rate of 95.17% for arrivals and 95.96% for departures. The variables are: 

Van = Small (Fuel Cost = \$524.46)

14 Drivers

(Wages = \$787.50)

12 Agents

(Wages = \$621.00)

Total Cost = \$1,932.96 Outcomes

Comb. # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Car Types Small (12) Small (12) Small (12) Small (12) Small (12) Small (12) Small (12) Medium (18) Small (12) Medium (18) Small (12) Small (12) Medium (18) Medium (18) Small (12) Medium (18) Small (12) Medium (18) Medium (18) Medium (18) Medium (18) Medium (18) Medium (18) Medium (18)

Fuel Costs \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$

403.82 403.82 447.31 403.82 447.31 490.00 447.31 612.45 490.00 612.45 524.46 490.00 612.45 683.55 524.46 683.55 524.46 683.55 738.64 738.64 738.64 796.92 796.92 796.92

#s of Drivers 11 11 12 11 12 13 12 11 13 11 14 13 11 12 14 12 14 12 13 13 13 14 14 14

Cost \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$

#s of Agents Cost 618.75 618.75 675.00 618.75 675.00 731.25 675.00 618.75 731.25 618.75 787.50 731.25 618.75 675.00 787.50 675.00 787.50 675.00 731.25 731.25 731.25 787.50 787.50 787.50

12 13 12 14 13 12 14 12 13 13 12 14 14 12 13 13 14 14 12 13 14 12 13 14

Figure 13: Simulation Results

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\$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$

621.00 672.75 621.00 724.50 672.75 621.00 724.50 621.00 672.75 672.75 621.00 724.50 724.50 621.00 672.75 672.75 724.50 724.50 621.00 672.75 724.50 621.00 672.75 724.50

Total Cost \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$ \$

1,643.57 1,695.32 1,743.31 1,747.07 1,795.06 1,842.25 1,846.81 1,852.20 1,894.00 1,903.95 1,932.96 1,945.75 1,955.70 1,979.55 1,984.71 2,031.30 2,036.46 2,083.05 2,090.89 2,142.64 2,194.39 2,205.42 2,257.17 2,308.92

0), asking if the length of Drop Off Hold.Queue is greater than 0 (is there someone in the queue?) and if the van-entity’s seat-attribute is greater than 0 (does my van have at least 1 empty seat?). If both conditions are satisfied, the van-entity will proceed to assign a variable, in this case DO Seats Var, to be equal to its current Seats-attribute. This step is necessary because the search module following it requires that a variable be used in its search condition.

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SM RENTALS REPORT The van-entity then reaches the search module, where we have specified that it is to search through the entire Drop Off Hold.Queue (NQ = length of queue), and to assign the letter-variable J to the first entity in the queue that satisfies the search condition (Number of Passengers