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Kroger Store Simulation Using Arena

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A simulation of Kroger Store in Cincinnati with alternate scenarios to improve the overall efficiency of the store.

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Kroger Store Simulation Using Arena

  1. 1. KROGER STORE SIMULATION USING ARENA DHIVYA RAJPRASAD M10857825 BANA Fall 2016
  2. 2. CHAPTER -1 INTRODUCTION SYNOPSIS This project is done as a final project for the course BANA 6035- Simulation Modeling where the focus lies on understanding the basis of Simulation concepts using a software called “Arena”. The purpose of the project is to prepare a working simulation of the Kroger’s store at 4777 Kenard Avenue. The model in Arena provides an output for many statistical values like total number of customers served in a particular interval of time, time spent in the queues in the store, maximum service times in the counters, resource allocation and utilization levels etc. The model uses the layout of the store, management systems, check-out counters, options of resource allocation etc. in Arena to simulate real life scenarios. The input data for the model consists of inter-arrival times of the customers, the service times at each counter and the shopping times which was collected at the peak hours of student visits at Kroger’s store. The model was also run for about 10 days to analyze the results, changes were made to the existing model and based on the conclusions, suggestions are made to improve the efficiency of the store. PROBLEM STATEMENT The Kroger Company is an American retailer formed in Cincinnati, Ohio in 1883 and has established itself to become the largest supermarket chain by revenue in US and the third largest retailer in the world. The purpose of choosing the Kroger’s store at 4777 Kenard Avenue is that it is the most frequently visited Kroger store by the students of UC as there is a weekend (Friday, Saturday and Sunday) shuttle that runs from the University main campus to Kroger’s in half hour intervals to enable students to complete their weekly grocery shopping. According to the information from the shuttle drivers and the Kroger authorities, the maximum influx of students’/rush hour occurs between 3pm-7pm on Fridays, Saturdays and Sundays. During these rush hours, the waiting time for a customer peaks to almost 16 minutes after shopping to check-out from the store thus effectively wasting the time of the students and lose potential customers for the store as Students might choose to shop less frequently or in nearby stores to avoid the wastage of time.
  3. 3. Thus the need to make the system more efficient is necessary. The simulation project uses the parameters presented in the model to probe into the grocery retail system and how to make the existing system better to decrease the time spent by a customer in the store. ASSUMPTIONS The existing system at Kroger’s has been modeled to the best possible extent and there are some variations due to unscheduled or unforeseen circumstances and natural variations. There are some assumptions which are made on the above basis to exclude such occurrences. 1. The shop is open for about 19 hours every day with the store timings as 6:00 am to 1:00 am. 2. There are no work shifts between the workers. 3. There are no breaks for the workers in the model. 4. Every counter except the self-check-out has one server. 5. The time of the customers who don’t shop at all is not counted in the total customer time. 6. The service time varies for different workers in different counters. 7. The decision module data has been taken from observation and Kroger authorities due to the scope of the data collection. 8. Customer leaves the store means the transaction with the packing department is over and does not count the waiting time for shuttle or additional browsing without a purchase. 9. The above assumptions hold good throughout the modelling period.
  4. 4. CHAPTER -2 DATA COLLECTION, MODEL ELEMENTS AND FITTING OF DISTRIBUTIONS DATA COLLECTION The model which is prepared for the scope of this project considers the resources in the entire shopping process like Self-Check-out Counter, Manual Counter, Express Counter, Packing Counter, Self-Check-Out Loading etc. to keep a sequence of the process. In order to input the service times for these different resources, data was collected at the Kroger store in the form of inter-arrival times of customers and the service times for the resource allocated at the various points with the permission of Kroger authority. The data used here was collected for between 3pm and 7 pm for three days (The days were either Friday, Saturday or Sunday). From the interaction with the authorities and assumptions, nearly none of the customers leave without a purchase. About 30% use self-check-out counters, 20% use express counters and remaining 50% use the manual counters. Out of the 30% who use self-check-out counters, nearly 30% encounter problems, which are resolved by an employee at a counter allocated for the same. MODEL ELEMENTS The entire store simulation process is shown below as a flow chart which will be used as the basis for Arena Model creation and decision for data points to be collected as raw data.
  5. 5. FITTING OF DATA TO DISTRIBUTIONS The Arena model takes the input data to run and analyze through different modules in the model. Arena accepts the data in the form of a distribution which will be best fitted for the raw data. The raw data of inter-arrival times and service times for all the resources are fitted into a distribution which is fed into Arena. Rockwell’s Input Analyzer was used in this project to determine the distributions of the various inter- arrival times and service times. The data was loaded in the form of a text file with raw data which was then fit to a suitable distribution. Please find below the distribution of the inter-arrival times and service times at various points of the model. Customer Inter-Arrival Times: Shopping Time:
  6. 6. Self-Check-Out Counter: Express Counter: Manual Counter:
  7. 7. Resolving Counter: Packing Counter- Self:
  8. 8. Packing Counter- Express and Manual: Payment Counter:
  9. 9. CHAPTER -3 MODELING MODELING THE PROPOSED MODEL IN ARENA The Kroger Store was bifurcated into different pieces / processes to prepare a model in Arena. Various modules have been used Eg: Create, Assign, Process, Decision, Dispose. The procedure has been split into steps according to the flow diagram mentioned above. The different steps are: 1. Customer enters the store 2. Customer proceeds to shop 3. Customer Waits in the Queue for a counter 4. Customer Bills the purchase- Self/Express/Manual 5. The items are packed in the counter 6. Customer pays for the purchase 7. Customer leaves the store There are also some decision modules to check for the path of the customer. Assign and Record modules are used to assign and record the average time of the customer in the store, in the queue and the number of customers who purchased and came out of the store in a specific time interval. The other parameters are Resources and the Queues allocation. Arena Store Action Performed Entity Customer Part Resource 1 Self- Check Out by Customer Seize Delay Release Resource 2 Express- Check Out Person Seize Delay Release Resource 3 Manual- Check Out Person Seize Delay Release Resource 4 Resolving Person Seize Delay Release Queue 1 Self- Check Out Queue FIFO Queue 2 Express- Check Out Queue FIFO Queue 3 Manual- Check Out Queue FIFO Queue 4 Resolving Queue FIFO The Arena model that was done based on the above modules is as below:
  10. 10. 1. Customer enters the store A Create module is used to model the Customer entering the store whose dialog box and the entry parameters are shown below, the expressions used are got from input analyzer. There is an additional expression for the entities per arrival which varies in a uniform distribution between a minimum of 3 and a maximum of 5. An Assign module is then used to record the time of arrival which will be used to calculate total times for different check-outs. TNOW is an inbuilt function which records the time that the entity passes through. 2. Customer proceeds to shop A process module with ‘delay’ action is used with the expression found through input analyzer to model the shopping behavior of the customer with a normal distribution. 3. Customer Waits in the Queue for a counter
  11. 11. A decide module is used to fix probabilities of 30% for Self-Check-out and 20% for Express counters and the remaining 50% leave for Manual counters. 4. Customer Bills the purchase- Self/Express/Manual Depending on the decide module, three process modules with ‘Seize Delay Release’ action are used based on the expressions derived from the input analyzers which are all assigned a resource each with maximum capacities as Self- 12 Manual -16 Express-4 There is a fourth process module with ‘Seize Delay Release’ action called Resolving for resolving the issues faced by 30% of the Self-Check Out customers with a resource and maximum capacity as Resolution-2 The dialog boxes are all given below:
  12. 12. There is also a decision module before resolving module to segregate the 30% who will go resolving the issue with Self Check Out Counters: There are three record modules to count the number of customers which will be recording the number of customers under each check-out category. 5. The items are packed in the counter According to the check-out counters, the time for packing is modeled as the time taken for self-check-out is higher versus the time taken for a person working at the store for Express and Manual Counters and this is modeled using the expressions provided by the input analyzer.
  13. 13. Two Record modules are also used to record the average time taken for the purchase in Self-Checkout Versus the Manual/Express Counters. One additional Record module is used to record the average spent by a customer shopping irrespective of the counter they take. 6. Customer pays for the purchase A process module with ‘Delay’ action with the expression provided by the input analyzer is used to model the payment process. 7. Customer leaves the store A simple Dispose module is used to specify that the customer leaves the store and out of the model created.
  14. 14. The snapshots of the Queues are as given below: Though the Counters have capacity as follows: Self- 12 Manual -16 Express-4 Resolution-2 But at any particular time, the following is the occupation capacity/resource allocation of these counters and will be used for our Base Model in Arena: Self-Check Out Counters- 8 Manual -9 Express-2 Resolution-1
  15. 15. SIMULATING THE MODEL We use the Run Set-up to run for 10 replications simulating 10 days to consider all the variabilities associated with the model and each replication with 4 hours of operation each day from 3-7 pm. Below is the snapshot of the Run Set up option. The snap shot of the run mode when animated with the queues are as follows:
  16. 16. CHAPTER -4 RESULTS AND INTERPRETATION RESULTS The Arena software produces detailed results post running of the model which helps us to view the results by Entity, Queue, Resource and User Defined which are the default settings. There are also additional reports like process reports which can be generated based on needs. The number of Entities out / number of customers’ out is a default key performance indicator in the category overview report. For this model, the number of customers out of a store in a simulation of 4 hours over 10 days is 437. BY ENTITY Entity/ Customer is defined by two parameters, the number of customers (entering, out and in the system) and by the time taken by the customer (queue and total times). Arena gives a very detailed output with the mean, half width or confidence intervals, maximum and minimum for the various times that are observed in the model simulation. In the Kroger Store model, the major output needed was the total time spent by a customer in the system, the service times at each type of checkout and the wait time for each check-out counter. Time Records:
  17. 17. We find that on an average a customer spends about 48 minutes in the grocery store with a half width of +/- 1.32 minutes. The average service time (value added time) for the customer is nearly 11.67 minutes with an average wait time of about 4.43 minutes which adds up to 16.1 minutes of service and wait times which is being addressed to reduce in the problem statement. The shopping time (non value added time) for the customer is about 31.81 minutes on an average. Number of Customers: We also find that on an average around 550 customers arrive in 4 hours in a grocery store with around 99 customers on average inside the store at any point in time. BY QUEUE
  18. 18. We get the time and number parameters by the queue too with average waiting times and average number of waiting persons in the queue. We find that the average waiting time is maximum for the Manual and Self Check Out Counters and we aim to decrease these times which will improve the efficiency of the billing counters and reduce the time spent in queueing and managing the queues. BY RESOURCE Resource Utilization is a very important parameter which will determine the points at which the system gets chocked up and does not allow the free passage of the entities/customers. The Scheduled utilization is the most important parameter of consideration and below is the output. We can observe that the Manual and Self-Checkout Counters have very high utilization level while Resolution has lesser utilization level. BY USER SPECIFIED
  19. 19. Arena gives separate outputs for all the parameters collected by the users. In this model, we have specified to collect the count of three different types of customers during a run along with time intervals for shopping, total time in the system and time taken for different check out modes. We can see that the number of people using manual counters is the highest which is in accordance with the decision module decisions supplied by us. We also find that the total time spent by a customer in the system is around 48 minutes which was found in the entity table and will be used as a parameter to analyze alternate scenarios to reduce the total time spent in the system. INTERPRETATIONS • We find that the Manual and Self Check Out Counters have very high utilization rates along with high waiting times. So by adding additional resources for these two counters we will be able to get the efficiency of the store to increase and also decrease the average waiting time in the system. • We also find that the utilization levels of Resolution counters are very less despite the above average waiting times and can also be considered to get a better scenario.
  20. 20. CHAPTER 05 ALTERNATE SCENARIOS AND IMPROVEMENTS IN THE SYSTEM ALTERNATE SCENARIOS SCENARIO 1- Self-Man As mentioned in the interpretations, we find that the Self and Manual Check Out Counters have very high utilization levels along with waiting times, so in the first scenario, we increase the capacity of these counters to their maximum levels. Self-Check-out – 12 Manual Check Out- 19 while keeping the other counters at the same level. SCENARIO 2- Res-Man We increase the Resolution counters and Manual Check Out Counters to their maximum capacity in the second scenario to reduce the utilization and waiting times. Manual Check Out- 19 Resolution -2 SCENARIO 3-Res-Express We increase the Resolution counters and Express Check Out Counters to their maximum capacity in the third scenario to reduce the utilization and waiting times without changing the number of operational counters of Self and Manual Checkout. Express Check Out- 4 Resolution -2 SCENARIO 4-Res-Self We increase the Resolution counters and Self Check Out Counters to their maximum capacity in the fourth scenario to reduce the utilization and waiting times. Self-Check Out- 12 Resolution -2 SCENARIO 5-Man-Express We increase the Express counters and Manual Check Out Counters to their maximum capacity in the fifth scenario to reduce the utilization and waiting times. Manual Check Out- 19 Express Check Out -4
  21. 21. PROCESS ANALYZER OUTPUTS Below is the screen shot of the entire allocation system along with our original base case in Process Analyzer Average Total time spent by a customer: We find that the total time spent by the customer is reduced by nearly 3 minutes on average by increasing the Self-Checkout and Manual Checkout Counters. The graphs are plotted for all the scenarios as below: As expected, the best scenario is presented by increasing the Self and Manual Check Out Counters due to the number of customers who use them and also the dependency of Resolution on Self Check Out to reduce the build-up of customers. Utilization Levels: We also try to bring in the utilization levels into the picture to determine if the scenario presented above is really the best one. We find that at the above mentioned scenario at least 3 of the 4 processes operate at an efficiency level of about 50%, which doesn’t
  22. 22. entirely allow build-up of a huge waiting queue but at the same time is not under- utilized as with the other scenarios and also has an added advantage of lowest average time taken for the customer to leave the store making it a better scenario. STATISTICAL ANALYSIS We also have the statistics which were used to plot the box plots above from the Process Analyzer: Scenario Min Max Low High 95% CI- Half Width Base 36.11 82.61 46.60 49.24 +/-1.32 Self-Man 36.15 70.87 44.39 45.68 +/-0.65 Res-Man 36.64 87.60 44.91 48.02 +/-1.56 Res-Express 35.78 77.00 46.24 48.68 +/-1.22 Res-Self 36.25 64.69 45.43 47.45 +/-1.01 Man-Express 36.12 81.12 44.13 46.41 +/-1.13 We find that for the best scenario along with the lowest average time spent by a customer in the system, we also have lowest half width. We find that our result is statistically significant at the 95% Confidence level or at a significance level of alpha= 0.05. CHAPTER 06
  23. 23. CONCLUSION The Kroger’s Store at Kenard Avenue was modelled using Arena Simulation Software in an attempt to suggest the best way to reduce the queueing times and average waiting time for the customer to check out their products. Different pieces of software were used to design the inputs and analyze the outputs provided by Arena like input analyzer which fit the distribution of the data and process analyzer which helped in statistical analysis of the outputs. By probing into the outputs provided by Arena, we were able to observe the waiting time in the queue was large in Self Check out and Manual Counters followed by the others. We implemented 5 different scenarios in Process Analyzer for understanding the effects of changes in resource allocation across different processes. We found through the Process Analyzer outputs that utilizing all the installed manual and self-checkout counters with additional man power during the busy hours will increase the efficiency of the store by decreasing the average wait time for the customers while maintaining about 40-50% utilization in all the counters. We also find the statistical significance of our best scenario from the outputs provided while plotting charts in process analyzer. We thus find that employing more man power during the busy hours could reduce the average wait time for the customer and can also increase the business of this particular store. There are many other modifications which can be made to the model to get better results and this is just one part of the best solution taking into consideration our assumptions to build the model. REFERENCES 1. Simulation with Arena- 6th Edition- David Kelton W., Randall P. Sadowski, Nancy B. Zupick, Rockwell Automation 2. Data- Krogers, Kenard Avenue, Cincinnati, Ohio 3. Logo- Trademark of Kroger
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A simulation of Kroger Store in Cincinnati with alternate scenarios to improve the overall efficiency of the store.

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