Minimizing Peak Wait time at the Union Taco Bell

IE 475 – Simulation

Term Project

Vijayendra ViswanathanIndustrial 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 1 Stuffer 1 Expediter 1

Steamer 2 Stuffer 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 :• -0.5 + 74 * 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

Type -1

Type -2

Type -3

Type -4

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: 53.58 s.– Average Wait time of a Customer: 59.95 s.– Average Total time spent by a Customer in the system: 113.54 s– Average Wait time on Line 1: 56.05 s– Average Wait time on Line 2: 56.07 s– Average Wait time on both lines: 51.93 s– Flowtime – Order type -1: 115.01s– Flowtime – Order Type-2: 110.56s– 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: 41.75 Seconds.• Average Wait time of a Customer: 49.6 Seconds.• Average Total time spent by a Customer in the system:

113.54 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?