Minimizing Peak Wait time at the Union Taco Bell IE 475 – Simulation Term Project Vijayendra Viswanathan Industrial and Manufacturing Engineering UW-Milwaukee.

Overview Introduction Detailed System Description Data Collection and Modeling Issues The Model Output Analysis and Conclusion

Introduction Aim to reduce wait time at the Union Taco bell during peak time Simulation used to build a replica of the real world scenario Results Analyzed and an alternate model was proposed Alternate model validated using Simulation Resulted in an 86% reduction in wait time without any additional investment in either personnel or other resources.

System Description Employs 6 people for restaurant operations Two parallel assembly lines 3 people on each line –Steamer –Stuffer –Expediter 2 Grills, one on each line

Schematic of Restaurant Set-up Grill 1 Steamer 1Stuffer 1Expediter 1 Steamer 2Stuffer 2 Expediter 2 Grill 2 Take Orders Deliver Orders

Some Modeling Issues System Boundaries –A customer enters the system, when he/she joins the queue at the restaurant, and exits the system, when he/she leaves the delivery area with his meal. Scope of the Model –This model considers only peak time. i.e; between 12:00 and 1:30 everyday which is when, the restaurant is most busy. –At other times, there is virtually zero waiting time, as the passenger arrivals are slow enough that they can all be served immediately on arrival. –As such, it makes sense to only study and analyze this system through simulation, in peak time.

Data Collection Inter-arrival time: Personally recorded the arrival times of customers during peak-time on two different days, for half hour. –Then, Inter-arrival times were calculated by subtracting each arrival time from the previous one. –raw inter-arrival times fit into a best-fit probability distribution using the Input Analyzer Module in ARENA. [tools>fit>fit all] –best-fit distribution : * BETA (0.25, 0.881) seconds.

Data Collection Preparation time of different items – talked to the Manager of the restaurant, Ms. Lucy who has been running the restaurant for the past 5 years as also some of the employees who have working in the restaurant for sometime now. –Used TRIA to model ranges of values.

Data Collection choice of entrée during lunch-time [Spoke to Manager to obtain this data.] –approx. 30% - “Crunchwrap-Supreme + Taco” –25% - “Chicken/Cheese Quesadilla + Taco”, –“Cheesy gordita crunch” - 20% of orders. –Rest 25% was other miscellaneous orders. Discrete distribution used to model this information, using the preparation times obtained earlier. Miscellaneous orders modeled as “3 soft tacos”, which represents a typical order well, in terms of preparation time

The Model Screenshot

Output Analysis and Conclusions Basic Run –100 Replications –Warm-up time of half hour –Run length = 2 hrs. Results: –Average VA Time: s. –Average Wait time of a Customer: s. –Average Total time spent by a Customer in the system: s –Average Wait time on Line 1: s –Average Wait time on Line 2: s –Average Wait time on both lines: s –Flowtime – Order type -1: s –Flowtime – Order Type-2: s –Flowtime – Order Type-3:115.11s –Flowtime – Order Type-4:113.74s

Modifications Assign all orders that require grilling to line-1 and all orders that don’t to line-2. Put both grills on line-1, since line 2 now only processes orders that don’t require grilling. Expected to reduce wait time and make process more efficient

Results – Modified Model Average VA Time: Seconds. Average Wait time of a Customer: 49.6 Seconds. Average Total time spent by a Customer in the system: Seconds Average Wait time on Line 1: 7.65 s Average Wait time on Line 2: 7.59 s Average Wait time on both lines: 7.20 s Flowtime – Order type -1: 65.86s Flowtime – Order Type-2: 61.99s Flowtime – Order Type-3:31.68s Flowtime – Order Type-4:31.67s

Summary 86.14% reduction in average wait time 4 more customers served in the modified model compared to the base model. Represents an increased profit of $1872 per year, just for the 2 hour peak period. This doesn't take into account the increased rate of arrivals due to reduced wait time No extra investment in either people or capital

The End Questions?