Labor is the highest variable cost for most organizations, and today’s labor market is arduous for employers. Leveraging analytics to develop robust staffing models will help hotels balance guest demand with labor resources to optimize efficiency. Streamlined staffing and scheduling can result in decreased labor needs while still meeting or even exceeding guest expectations.
This demand-based approach is effective for many areas of hotel operations with transaction-based workloads and some measure of predictable demand, such as the front desk, housekeeping, maintenance, fitness facilities, spas, or food and beverage locations. This article will focus on the front desk as an application of these principles.
- Quantify Guest Demand
How many guests do you expect? How does this vary by time of day, day of week, or time of year?
Always start with understanding the guest and their needs, behavior, and expectations. This means knowing how many people to expect at any given time. While there is always some variability that can be hard to predict, typically, the demand for transaction-based operations can be forecasted to a significant level of accuracy.
How do you develop these guest demand forecasts? First, start by gathering data. This often can be accomplished via a combination of system-based data and in-person observations. Typically, the number of transactions per time period can be used as a proxy for guest demand for POS-based operations when there is minimal wait time. Analyze these demand curves to understand variability and causes, such as the effects of occupancy levels, day of week/time of day, or location-specific operating hours.
In the example of the front desk, the number of check-ins and check-outs by time can likely be pulled directly out of the guest room management system. Additional tasks that are handled by the front desk but not available from system data should be gathered via observing the operation to understand frequency. This could include guest questions about the processing of guests when their room is not yet ready to check-in.
It is important to note that high occupancy does not necessarily equal high demand. For example, a hotel with a 2-night minimum on weekends likely sees high check-in volume on Friday and high check-out volume on Sunday. However, on Saturday, there are very few check-ins or check-outs, and fewer front desk clerks will be needed.
- Measure Workload to Understand Capacity
How long does it take to process each guest or room?
Next, calculate how many resources are needed to meet guest demand. Typically, this is how long it takes to process one person or one room, known as the transaction time. Be sure to analyze both the distribution of transaction times as well as the average. Understanding what drives the variability and long transaction times could uncover an opportunity for process refinement.
For our front desk example, we would want to measure how long it takes to complete a check-in transaction, check-out transaction, guest question, and any other regular, recurring task. Capture these times by observing in person and capturing data with a stopwatch and clipboard or using one of the many apps now available. Observe a wide variety of staff to ensure you’re not just capturing the “all-star” or the new hire. Also, take observations on both a busy and a slow day.
- Align Capacity and Demand Via Efficient Staffing and Scheduling
What business rules must be met? Do the scheduled labor resources match the workload need?
After completing the first two steps, we now know how many guests to expect at any given time as well as how long it takes to process each guest. Apply these expected transaction times to the respective guest volumes to calculate the total workload and thus number of employees.
The chart below shows the total work required per 15-minute interval for our front desk example. To develop this graph, we took the total number of transactions by time period that we gathered when quantifying the guest demand in step 1 and then multiplied this by the average transaction time for each type of interaction calculated in step 2. We can see the peak in work required at check-out and check-in, as well as the significant drop-off throughout the night.
There will also be other business nuances and constraints that may affect staffing levels. This may include the minimum or maximum number of hours an employee can work in a day or a week, certification requirements, or minimum staffing levels. Additionally, be sure resulting staffing level can accommodate natural fluctuations in demand to avoid excessive wait times.
For this location, a minimum of two people was always required for break coverage and to meet the desired service levels. During the busy check-in and check-out times, additional employees are needed to meet the guest demand. By using a combination of full-time and part-time shifts and aligning these shifts to match the demand, in this example we were able to reduce the total number of employees required per day from 11 full-time shifts to 7 full-time and 3 part-time shifts.
- Maximize Operational Efficiency
What pain points in the process can you reduce or eliminate?
Optimize further by maximizing operational efficiency. Not only does this lead to higher employee productivity and reduced costs, but can also result in higher guest satisfaction and lower employee frustration when barriers and inefficiencies are removed.
One way to maximize operational efficiency is by reducing the transaction time to increase the capacity of a single employee. To do this, observe the operation, map out the process, and collect data on transaction times. Identify and measure the transactions that take longer than average, and brainstorm ways to reduce or eliminate these delays. Reduce wasted time and wasted effort by eliminating redundant or unnecessary steps.
Other opportunities include optimizing communication or signage, modifying facility layouts, or incorporating technology.
- Continuously Improve
How will you avoid stagnation and complacency?
Achieving operational excellence doesn’t just happen from a one-time analysis. Operational processes can become out-of-date as guest expectations, regulations, and technology continue to evolve.
Consider creating a workforce management position whose primary role is to develop labor forecasting and staffing models. This workforce analyst should refactor each model every 6-12 months to incorporate any operational changes or major guest behavior changes.
Regularly revisit and observe processes to identify further operational efficiency improvement. Create forums where front-line employees can share their improvement ideas. Reward innovative ideas and recognize employees who are advocates of efficiency changes.
The hourly labor market has undergone numerous changes across the past few years. COVID-19 caused major disruptions to the service industry and changed how businesses operate. Now, the entire industry is experiencing widespread staffing shortages, increasing wages, and extremely high inflation. More than ever, it is important to staff and schedule as efficiently as possible.
About the Author
Susan Dekker, Senior Manager, brings a strong background in operational efficiency and capacity planning in multiple industries.
Prior to joining Integrated Insight, Susan was a Healthcare Systems Engineer at MD Anderson Cancer Center, leading lab optimization efforts to improve quality. During her time there, she implemented initiatives to reduce turnaround time, decrease error rate, and maximize throughput. Previously, Susan spent seven years as part of the Industrial Engineering team at Walt Disney Parks & Resorts where she led the team responsible for developing design requirements, facility sizing, and capacity needs for future development projects in China. Susan also supported Facility and Operations Services and other Disney divisions such as television production, corporate aviation, and travel visa processing where she focused on right-sizing labor, management resources, organizational design, and process opportunities.
Susan has a BS and MS in Management Science & Engineering from Stanford University.