Raising Canes Project Report
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School
University of Pittsburgh *
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Course
1090
Subject
Industrial Engineering
Date
Feb 20, 2024
Type
docx
Pages
9
Uploaded by KidBee2279
Zack Lesher, Jake Le, Kaija Adams, Chris Gianchetti
Professor Rokou
Applied Optimization and Simulation
December 14, 2023
Raising Canes on Fifth Avenue
Business Problem: Raising Canes opened a new location on campus this semester. Which has been very busy due to its popularity and because of its high demand, there are long wait times, especially during the lunch and dinner rush. Rasing Canes believe that they are losing significant profit and customers, especially returning customers because who wants to return to a restaurant where they
had an absurd wait time. Raising Canes has hired our group to determine different ways in which
we can reduce the wait time for orders. The line to order is sometimes out the door, and in some cases down the street, this is caused by long back up times to make food, since they don’t start preparing food until an order is placed. Long wait times occur before and after ordering the food,
which is an issue we can optimize. In the current model (Appendix A), customers can either order a Tender Combo Box or a sandwich combo box, customers also have the option to mobile order or order once they arrive that the restaurant. The tender combo box is more popular, so we added more weight to it. Once the customer places their order, it moves into the prep server, once the order is completed it moves into the call out server where the customer receives their order. In the current model it takes around 33 minutes per customer who orders the tender combo and 32 minutes per customer
who orders the sandwich combo. Our goal is to shorten the time in the system for customers, as well as wait times. To achieve our goal of shorter time in the system, we have developed 3 improved models.
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SIMIO Set Up and Results:
SOLUTION 1; Two Stations/Lanes: For our first experiment we decided to use a separate 2-lane or work-station system (Appendix B). A precedent and successful model of this system is Chipotle, and we want to mimic a similar system due to its proven efficiency. We want to have a workstation that packages in-store orders only and another packaging mobile orders only with a separate team running both. This is similar to our initial model but instead of having just 1 single processing line we doubled the processing lines to form 2 separate ones. This helps prevent a large backup in either system and to help keep up with both mobile orders and cashier orders, as both will be continuously flowing. In the model the order is placed via mobile or cashier order. Cashier order then goes to normal prep and callout while mobile order goes to prep mobile and callout mobile. They both exit at the same sink leave. This is shown in ‘Appendix B’. A key assumption of this model is that all orders are made-to-order so we cannot pre-prep orders. In terms of cost, this model is pretty feasible as the only additional cost is another workstation/lane. Canes should have the space needed for the additional station if they rearranged the kitchen. If Canes needs to purchase an additional workstation, this can range from $5,000-$7,500USD depending on the quality and features of the station. We believe they would opt towards the higher end due to their
commitment of quality.
The results we received were in fact better than the initial model by a whole 12 minutes. The tender and sandwich TIS are reduced to 21.4 minutes and 20.3 minutes from the original 33.2 and 32.1 minutes. This is shown in ‘Appendix E and F’. For the model we set weights so that 80% of orders are via cashier and 20% are via mobile. We went to Raising Canes, evaluated their process, and determined this was a fair estimate for the proportion. For cashier order 2
processing time we used random triangular (1,2,3) and random exponential (2) for mobile order. For inputting processing system in prep, we used random triangular (4,5,6) for both prep and prep mobile. Both callouts were random exponential (1).
SOLUTION 2; Serve Yourself: For the serve-yourself model, we decided to implement a cafeteria-style method of ordering your food where customers will simply pick up the food items that they desire, so they can quickly check-out (Appendix C). This captures everything on their menu, including tenders, sandwiches, fries, and drinks. We thought this would be a unique strategy, and very effective in terms of reducing wait times. This model has the same allocated weights for tender arrivals and sandwich arrivals as the initial model, being 80% for tenders or sandwiches and 20% for mobile orders. Additionally, this model is very different than the others, considering there is a station for each type of food item. In terms of the processing time for tenders, sandwiches, and mobile orders, we allocated random exponential of (3), (4), and (2), respectively. So, this means there are individuals prep times for each station, being random triangular (1,2,3) for each station thereafter, which is reflective in shortening the prep time. For the time in system for the tenders, we received an average wait time of 9.28 minutes per order. For the time-in-system for the sandwiches, we received an average wait time of 9.66 minutes per order. This experiment cut the time in system for the tenders and the sandwich by more than half of the time in system for the two lanes method. Regarding the initial model, it cut more than 2/3 of time in system for tenders and sandwiches.
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For the implemented costs, Canes will only need a commercial buffet bar which can be sourced and bought for approximately $4,800. This can be added to our SIMIO model by simply inputting $4,800 into our cost equation. This additional cost will only be a one-time capital expense, so it is a useful strategy for Canes to pursue. In terms of the workers, this method requires no additional employees. Rather than scheduling more workers, Canes can schedule the existing cashiers to the back of the restaurant to operate the fryers, thus increasing efficiency even further.
SOLUTION 3; Pre-made Combo Boxes:
One of the main issues the customers face is having to wait longer periods of time compared to other fast-food restaurants in Oakland due to the popularity of Raising Canes. One of the many catalysts to this issue is a direct result of the cooks in the back of the restaurant not making food until it is ordered. Our pre-made combo box solution attempts to counteract this issue by having the cooks make multiple combo boxes, both for tender combos and sandwich combos, ready to be given to the customer right after payment is completed. To make this clearer, think about ordering food at a sporting event such as a Pitt Football game. When you order chicken tenders, they are already cooked and prepared and right as you make your payment
the cashier hands you the food and you are done with the process. The SIMIO model is no differently set up than the original model because there will be no changes to the actual running of the store. The only changes that would be realized would be the overall preparation time for the orders, which will drop dramatically because the cooks in the
restaurant are constantly making tenders and sandwich patties to be placed into the pre-made combo boxes. In the model itself, this is reflected by changing the prep time from the initial Random.Triangular(4,5,6) to the refined Random.Triangular(2,3,4). This is reflective of the 4
shortening of preparation time. The results of the experiment show that the time in system for both the pre-made tender combo and the pre-made sandwich combo dropped from the initial 32.11 minutes and 33.21 minutes down to 7.90 minutes and 8.18 minutes respectively. In terms of implemented costs to the restaurant, the only additional cost to implementing this strategy would be the cost of adding a heated food storage shelving unit to place the pre-
made combo boxes on so that by the time the customers get their food, it is not cold and low-
quality. In the model, this is implemented by adding $3,640 to the cost equation which is the average price of a heated shelving unit in the restaurant industry. Employee wages would not change, nor would the number of employees needed to run the restaurant. This would be a one-
time set up cost, and it is not very expensive so it would be easily feasible for Raising Canes to implement this strategy into their current business model. Conclusions/Suggestions
In conclusion, all three of our experiments were able to reduce the wait time and service time of both tender combo orders as well as sandwich combo orders. The two-station strategy was able to reduce the wait times for customers by, on average, more than ten minutes. The additional cost to the store for this method would come in the form of adding an additional workstation to the restaurant that would deal strictly with mobile orders. Although the main problem of reducing customer wait times is solved, the costs to the restaurant would be much higher than the other proposed strategies. The cafeteria-style serve-yourself model also accomplishes the goal of reducing overall wait times for customers, and the overall cost of this business model would not change from the initial set up because there is no change in the number of employees needed to run the restaurant. The only additional cost to this set up would 5
be the purchase of a cafeteria-style service station which would be a one-time cost, and this is reflected in the cost function of the SIMIO model. The final method, pre-made combo boxes, was the most effective method to reduce overall wait times with an additional one-time cost of purchasing a heated food storage shelving system. Our group would recommend the implementation of the pre-made combo boxes because it would implement the least change to the current business model of the restaurant at the lowest cost. This strategy would cost the restaurant more money than sticking with the current business model, but that cost would be counteracted by the added customer satisfaction which results in new customers as well as recurring customers that would increase overall profits of Raising Canes.
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Appendix:
Appendix A: Initial Model
Appendix B: Two Stations:
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Appendix C: Serve Yourself:
Appendix D: Pre-Made:
Appendix E Results and Costs of initial:
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Appendix F, Results of Two Station:
Appendix G Results and Costs of Serve self:
Appendix H: Results and Costs of Pre-Made:
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