User:Rysc00

Problem Definition

Topic

Simulation of the customer check-in process at the checkout, including incoming customers, their checkout selection (including self-service options), queuing, and service. The model reflects different service speeds, customer behavior (e.g., transitioning between checkouts), and satisfaction levels.

Problem

The supermarket lacks rules for opening and closing checkouts, resulting in either excessive staff costs or low customer satisfaction. Data-based rules for checkout operations can optimize efficiency and satisfaction.

Goal

Establish optimal rules for opening and closing checkouts to maintain customer satisfaction above 70%.

Simulation Stakeholders

• HR Managers: Determine employee requirements per shift.
• Shift Leaders: Establish rules for opening new checkouts.
• Financial Manager: Optimize employee costs.

Method

• Platform: NetLogo
• Method: Agent-based simulation

Requirements

Reality Abstraction Plan

Customers

• Number of Items in Purchase: Lognormal distribution (µ=3, σ=0.3).
• Checkout Type Preference:
  • Items > 20: Preference = 1 (regular checkouts only).
  • Items > 10: Lognormal distribution on scale 1-100 (µ=2.8, σ=0.7).
  • Items ≤ 10: 100 - (Lognormal distribution on scale 1-100, µ=2.8, σ=0.7).
• Willingness to Switch Checkouts: Lognormal distribution on scale 1-100 (µ=2.9, σ=0.4). Drops to half after switching.
• Satisfaction: Depends on waiting time, switching, and checkout type.
  • Initial value: Normal distribution (mean=95, std=5).
  • Waiting Time:
    • ≤ 3 min: No change.
    • > 3 min: Decreases logarithmically: S(t) = 100 − 10 * ln(1 + (t − 3)).
  • Switching Checkouts:
    • Willingness > 80: No change.
    • Willingness ≤ 80: Decreases by a range (e.g., 1-5 points for willingness ≤ 80 & > 60).
  • Checkout Type: Satisfaction changes based on preference and actual service type (e.g., increases by 5-10 points if preference > 60 and served at self-checkout).
  • Satisfaction cannot be negative.
• Goal: Leave the supermarket as satisfied as possible.

Checkouts

• Regular Checkouts (Count: 6):
  • Service Speed:
    • Payment: Normal distribution (mean=1 min, std=0.2).
    • Marking Items: 80% EAN-coded items (mean=0.02 min, std=0.01); 20% non-EAN (mean=0.03 min, std=0.01).
  • Checkout Error: 0.1% probability; solving time = Normal distribution (mean=0.5 min, std=0.1).
• Self-Service Checkouts (Count: 12):
  • Service Speed:
    • Payment: Normal distribution (mean=1.2 min, std=0.3).
    • Marking Items: 80% EAN-coded items (mean=0.03 min, std=0.01); 20% non-EAN (mean=0.04 min, std=0.01).
  • Checkout Error: 1% probability; solving time = Normal distribution (mean=0.5 min, std=0.1).

Simulation Rules

• Operating Hours: 8:00-20:00 (1 average day).
• Customer Arrivals:
  • Peak Hours (8:00-10:00 & 16:00-18:00): Normal distribution (mean=4, std=1).
  • Non-Peak Hours (10:00-16:00 & 18:00-20:00): Normal distribution (mean=2, std=1).
• Goal of Checkouts: Maintain customer satisfaction above 70% with minimal open regular checkouts.
• The simulation will not address scenarios outside the checkout area.
• One customer corresponds to one purchase.

Rysc00 (talk) 19:37, 30 November 2024 (CET)