
Introduction
Welcome to the next part of our ongoing series on Business Process Automation. This time, we focus on capacity planning using Woped, a workflow Petri net designer. By leveraging Woped’s features, we can analyze resource utilization and plan for optimal staffing in various workflows. To illustrate, we will use the example of a hairdressing salon, a familiar and relatable scenario for demonstrating these principles.
Modeling the Workflow
In our example, all customers visiting the salon go through the following steps:
- Hair Washing: This is the initial task performed for all customers.
- Waiting and Coffee: While waiting for their haircut, customers are served coffee.
- Haircut: The core service provided by the salon.
- Optional Hair Coloring: 20% of customers opt for hair coloring, which may occasionally require re-washing and re-coloring (10% of cases).
Using Woped, we modeled this process with details such as average service times, required roles, and branching probabilities. These branching probabilities help us account for variations, such as the percentage of customers opting for additional services or requiring rework.
Capacity Planning with Woped
Capacity planning is crucial for determining the number of staff needed to meet customer demands efficiently. For this example, we used Woped’s capacity planning feature with the following inputs:
- Observation Period: 10 hours.
- Customer Arrivals: 100 customers during the observation period.
- Resource Utilization: Targeted at 90% to ensure a balance between efficiency and workload.
The results showed that we would need:
- 6.54 hairdressers.
- 2.06 apprentices.
While two apprentices are feasible, the salon has space for only five hairdressers. This discrepancy raises an important question: will this staffing limitation lead to unacceptable waiting times for customers?
Simulating Resource Constraints
To answer the question about waiting times, we need to simulate the workflow using Woped’s simulation feature. By inputting the actual available resources (e.g., five hairdressers and two apprentices), we can analyze the impact on service times and identify potential bottlenecks.
Simulation allows us to explore different scenarios and make data-driven decisions about staffing, scheduling, and resource allocation. For instance, if waiting times are deemed too long, we might consider adjusting service durations, hiring additional staff, or optimizing task assignments.
Conclusion
Effective capacity planning is a cornerstone of successful Business Process Automation. Tools like Woped empower organizations to model workflows, analyze resource requirements, and simulate scenarios, enabling smarter decisions and better outcomes.