Student Name: Ozan Uslan
Course: Modeling and Simulation
Date: January 8, 2026
The primary objective of this project is to model the service processes of "HelloWork Coffee Shop" using Rockwell Arena Simulation Software. The study aims to:
- Analyze the current system performance and throughput.
- Identify bottlenecks causing excessive customer wait times.
- Propose and validate a resource optimization plan to maximize efficiency without increasing operational costs.
The system models a typical coffee shop flow consisting of four main stages: Arrival, Ordering (Cashier), Preparation (Barista), and Consumption (Tables). Since historical data was unavailable, synthetic data based on industry standards was utilized.
The following statistical distributions were applied to model the stochastic nature of the system:
| Process / Component | Distribution Model | Parameters |
|---|---|---|
| Customer Arrivals | Exponential | Mean: 3 minutes |
| Cashier Service | Normal | Mean: 1.5 min, Std Dev: 0.5 min |
| Barista Service | Triangular | Varies by Coffee Type |
| Consumption Time | Uniform | Min: 30 min, Max: 90 min |
| Customer Behavior | Probability | 90% Dine-in, 10% Take-away |
The Barista's processing time is dynamic, dependent on the complexity of the order:
| Coffee Category | Examples | Probability | Complexity | Process Time (Min-Mean-Max) | Arena Expression |
|---|---|---|---|---|---|
| Type 1: Practical | Tea, Filter Coffee | 40% | Low | 0.5 - 1 - 1.5 | TRIA(0.5, 1, 1.5) |
| Type 2: Standard | Latte, Americano | 40% | Medium | 2 - 3 - 4 | TRIA(2, 3, 4) |
| Type 3: Special | Mocha, Frozen | 20% | High | 3 - 5 - 7 | TRIA(3, 5, 7) |
- Entities: Customers.
- Resources (Initial): 2 Cashiers, 1 Barista.
- Queues: Checkout Queue (FIFO), Barista/Pickup Queue (FIFO).
- Simulation Parameters: 10 Replications (95% Confidence Interval).
Figure 1: General Arena Flowchart Model of HelloWork Coffee Shop
- Arrival and Ordering: Customers arrive and seize a Cashier resource.
- Product Classification: Customers are routed to assign modules (Simple, Standard, Special) to determine service time.
- Preparation: All branches merge at the "Barista Preparation" process.
- Consumption Decision: - True Path: Customer seizes a Table (30-90 mins).
- False Path: Customer exits (Take-away).
- Departure: Entities terminate at "Customer Exit".
The initial model simulated the current operational state with 2 Cashiers and 1 Barista, assuming infinite seating capacity.
Figure 2: Resources of First Model
Key Findings:
- Bottleneck Identification: The Barista station was identified as the severe bottleneck with an 86% utilization rate.
- Resource Inefficiency: Cashiers were underutilized (23%), indicating wasted labor cost.
- Customer Experience: Average wait time was 17.7 minutes, peaking at 19 people in the queue.
A resource reallocation strategy was implemented without hiring new staff.
- Resource Optimization:
- Cashier: Reduced from 2 to 1 (Cost saving).
- Barista: Increased from 1 to 2 (Bottleneck elimination).
- Constraint Analysis: A constraint of 70 Tables was introduced.
Figure 3: Resources of Optimized Model
The optimization resulted in a dramatic improvement in system performance:
| KPI | Baseline (As-Is) | Optimized (To-Be) | Improvement Rate |
|---|---|---|---|
| Avg. Total Wait Time | 17.73 Minutes | 0.88 Minutes | ~95% Reduction |
| Barista Queue Time | 17.09 Minutes | 0.25 Minutes | Problem Solved |
| Barista Utilization | 86% (Overloaded) | 43% (Balanced) | Optimized |
| Cashier Utilization | 23% (Idle) | 47% (Efficient) | +104% Efficiency |
| Max Queue Length | 19 Customers | 3 Customers | Stable Flow |
In the optimized model, consumption time was increased to a realistic average of 60 minutes (Uniform 30-90).
- Observation: Simulation showed a maximum occupancy of 26 tables.
- Critical Finding: Previous estimates of 15-20 tables are insufficient.
- Recommendation: A capacity of 30-35 tables is recommended to cover peak demand plus a safety margin, saving 50% space compared to the initial 70 tables.
The simulation proved that operational challenges were due to misallocation of resources and unoptimized facility planning, not excessive demand.
By shifting one staff member from Cashier to Barista:
- Throughput Maximized: Wait times reduced to <1 minute.
- Resource Efficiency: Workload balanced.
- Space Optimization: Table requirement optimized to 30-35 units.
This project demonstrates that operational efficiency can be achieved through process optimization rather than costly investments.