The Lift Case: Reducing Wait Times at a Hotel

The XYZ Hotel is a 42-story building with 12 rooms on each floor, totaling 504 rooms. It has 4 lifts, each moving at 20 feet per second. During peak hours, guests experience an average wait time of 15 minutes for a lift, which management wants to reduce to 3 minutes.

At first glance, the solution might seem straightforward: make the lifts faster or add more lifts. But this is a classic case where the problem as stated is not the real problem. The actual job is to improve the guest experience by reducing wait times — or at least the perception of wait times — in a cost-effective and feasible manner.

This lesson walks you through how to approach such a problem systematically, from problem framing through solution evaluation to implementation and monitoring.

Reframing the problem to focus on user perception, not just technical speed

When residents complain that "the elevator is too slow," the immediate impulse is to make it physically faster. Upgrading motors or improving lift algorithms are expensive and time-consuming options. The trap is to live in the solution space too early.

Instead, reframe the problem: users feel the elevator is slow because the waiting period feels long. The question becomes: How can we reduce the perceived wait time?

This reframing opens up lower-cost, more user-centered solutions. For example, installing mirrors in the elevator lobby, playing music, or providing hand sanitizers distract guests and make the wait feel shorter.

This is a classic example of living in the problem space rather than jumping immediately to technical solutions. It’s about understanding the root cause of the complaint and addressing it creatively.

Using data and math to understand and measure the problem

A good product manager converts vague problems into measurable ones. To reduce elevator wait times, start with data:

• How many lift rides occur during peak hours?
• What is the average wait time per ride?
• What is the distribution of wait times?

You can calculate average wait time using this formula:

Average Wait Time = Total Time Spent Waiting for Lifts / Total Number of Lift Rides

This requires lift usage tracking — timestamps of ride start and end times, number of passengers, and floor stops.

Having this data lets you baseline the problem and measure improvements objectively.

Generating and evaluating potential solutions

Brainstorm a range of solutions. These might include:

• Improving lift routing algorithms to reduce idle time
• Increasing the number of lifts
• Increasing lift speed by upgrading motors
• Reducing the number of rooms per floor (long-term structural change)
• Improving user experience to reduce perceived wait time

Each option has trade-offs:

The most feasible and effective solutions often combine operational changes with user experience improvements. For example, a hotel in Mumbai might find adding mirrors and ambient music low-cost and culturally appropriate ways to improve perceived wait times while simultaneously optimizing lift scheduling.

Implementing and monitoring the chosen solution

Once the solution is selected, implementation must be planned with clear milestones, resource allocation, and risk management.

For example, if you choose to improve lift routing:

• Collaborate with lift maintenance vendors to upgrade software
• Pilot the new algorithm on one lift
• Collect data on wait times pre- and post-implementation
• Iterate based on feedback

If you choose user experience improvements:

• Install mirrors and music systems in the lobby
• Survey guests about perceived wait times before and after
• Measure actual wait times to ensure no negative impact

Regular monitoring is critical. Use the same data sources to track average wait times and guest satisfaction scores. If targets are not met, revisit assumptions and explore alternative solutions.

The role of critical thinking in operational problem solving

This case illustrates the importance of critical thinking:

• Define the real problem, not the symptom. Guests say "lift is slow," but the real problem is wait times and user perception.
• Gather relevant data before deciding on solutions.
• Consider cost, feasibility, and impact of each option.
• Prioritize solutions that deliver value quickly and can be iterated upon.
• Monitor and adjust based on real-world outcomes.

Talvinder often emphasizes: The actual job is to figure out what matters most to the user and make a call. This means resisting the temptation to do everything at once or to chase flashy technical solutions without evidence.

Test yourself: Elevator wait time decision

From the field: Lessons from Indian real estate and hospitality

Where to go next

• If you want to master problem framing and root cause analysis: Critical Thinking Fundamentals
• If you want to learn how to design experiments and measure user perception: User Research and Metrics
• If you want to explore operational excellence in product roles: Operations as a Product Skill
• If you want to practice more case studies: Product Case Interview Practice