Workforce management is about having the right number of people to handle customer requests efficiently without overstaffing. If you're new to planning for call centers or customer service teams, you might have heard about Erlang C or simulation. Let’s explore these tools step by step so anyone can grasp them. Along the way, we’ll dive into real-world applications, challenges, and exciting advancements.
Where It All Began: A Quick History
The math behind workforce management started with queueing theory, a way to study waiting lines. Over 100 years ago, A.K. Erlang, a Danish engineer, created formulas to figure out how many people were needed to handle phone calls. His ideas revolutionized telecommunications and laid the groundwork for modern workforce planning. Over time, these formulas were adapted to handle complex systems, like multi-channel customer service centers and digital environments.
For more about queueing theory, visit Queueing Theory Explained.
Key Terms You Should Know
Before diving in, let’s cover some important terms you’ll encounter in workforce management:
- Service Level: The percentage of customer interactions resolved within a specific time frame, ensuring consistent customer experience (e.g., 80% of calls answered in 20 seconds).
- Occupancy Rate: The percentage of time agents spend helping customers versus waiting. High occupancy can lead to burnout if too high.
- Average Handle Time (AHT): The average time spent helping a customer, including wrap-up tasks like documentation.
- Arrival Rate: The number of customer interactions reaching out in a set period (minute, hour, or day). Predicting this accurately is vital for effective planning.
To calculate these terms yourself, try the Call Center Helper Calculator.
Erlang C: The Old Reliable Tool
What Is Erlang C?
Erlang C is a formula used to calculate how many agents are needed to handle incoming calls. It assumes calls arrive randomly, agents work steadily, and everyone waits in line until their turn. However, it doesn’t consider customers who might hang up if the wait gets too long.
When It Works Best
Erlang C works well in simple setups, like a small call center with steady demand. It’s quick and efficient but less reliable in situations where abandonment rates are high or demand fluctuates significantly.
Never heard of Erlang? Read more in our article: Erlang-C Traffic Model for Non-Mathematicians
Erlang A: The Smarter Upgrade
What Is Erlang A?
Erlang A enhances Erlang C by considering customer impatience—some people hang up if the wait is too long. This makes it more realistic for today’s call centers, where patience is often limited.
Why Use It?
If your operation sees customers frequently abandoning calls, Erlang A provides a better staffing estimate. It’s especially helpful in industries like healthcare or retail, where retaining customers during interactions is critical.
Simulations: The Modern Powerhouse
What Are Simulations?
Simulations create virtual models of real-world situations. They allow you to test staffing setups without making real-life changes. Unlike Erlang models, simulations can handle varying demand, agent skills, and customer patience levels.
Types of Simulations
- Monte Carlo Simulation:
- Randomly tests different possibilities to explore likely outcomes.
- Ideal for unpredictable scenarios or exploring probabilities in-depth.
- For an introduction, check out Monte Carlo Method Basics.
- Discrete Event Simulation:
- Tracks events like calls arriving or being answered step by step.
- Useful for analyzing bottlenecks in systems with high variability.
- Learn more in this guide to Discrete Event Simulation.
- Agent-Based Simulation:
- Models customers and agents individually to see how interactions affect the system.
- Perfect for studying how individual behaviors impact efficiency.
- Read about agent-based modeling.
- Queueing Theory-Based Simulations:
- Uses queueing math and dynamic setups to simulate changing conditions.
- Great for deep insights into multi-channel environments.
Simulations are invaluable for “what-if” scenarios, like predicting how service levels might change during a major event or holiday season.
Chat as a Channel: It’s More Than Just Text
Beyond In-App Chat
Chat isn’t just about website service windows anymore. It includes direct messages (DMs) on platforms like Facebook, Instagram, and X. Customers expect fast responses everywhere, and businesses must juggle these platforms to provide consistent service.
The Challenges
- Handling Multiple Conversations: Agents often manage several chats at once, requiring strong prioritization skills.
- Platform-Specific Expectations: Tone and response time expectations differ between platforms, such as a casual Instagram DM versus a formal customer support chat.
- Variable Patience: Some customers expect instant replies, while others are okay with waiting, depending on the channel.
How Simulations Help
Simulations model agent behavior across multiple chat platforms, helping predict peak times and resource needs. For example, during a viral campaign, simulations can show how message surges might affect response times, enabling better planning.
Strengths and Weaknesses of Each Approach
Where Erlang Models Fall Short
- Assume steady conditions, which isn’t always realistic.
- Struggle with routing complexities and multi-channel systems.
Where Simulations Struggle
- Take more time and expertise to set up.
- Require significant computing power for large systems.
Real-Life Examples
Healthcare
A hospital used Erlang A during flu season to optimize staffing, ensuring quick responses without overstaffing. Accounting for abandonment rates helped balance resources effectively.
Retail
A major retailer relied on simulations for Black Friday. Forecasting chat volume surges helped maintain fast response times and customer satisfaction during this high-stakes period.
Wrapping It Up
Erlang models and simulations both have unique strengths. Erlang C and A are simple, reliable tools for straightforward needs, while simulations provide the flexibility to handle complex, ever-changing systems. By choosing the right approach—or combining them—you can ensure your team meets customer demands efficiently. With the right mindset and tools, workforce management becomes a strategic advantage for any business.
