Forecasting - Inventory

[Case Marriott] What is the core challenge faced by Linda Snow as the Reservation Manager?

• Balancing customer satisfaction with maximizing room occupancy

• Addressing the high costs associated with unoccupied rooms

• Managing fluctuating customer preferences for room types

• Ensuring timely reporting of occupancy rates to corporate headquarters

[Case Marriott] What is the most critical consequence of inaccurate forecasting in hotel operations?

• A potential decrease in repeat customers due to overbooking incidents

• The inability to maintain operational budgets for other departments

• Suboptimal allocation of hotel resources, such as housekeeping staff

• Increased dependency on third-party booking platforms

[Case Marriott] What is the primary root cause behind difficulties in creating accurate forecasts?

• The unpredictability of weekend occupancy compared to weekdays

• Variability in customer behaviors, such as cancellations and no-shows

• Lack of sufficient training for reservation staff on forecast tools

• Over-reliance on historical data without accounting for seasonal changes

[Case Marriott] What is the main trade-off that Linda Snow must consider in capacity management?

• Prioritizing revenue generation over reducing operational costs

• Ensuring full occupancy versus maintaining goodwill with loyal customers

• Balancing staffing efficiency with customer service quality

• Allocating rooms for large groups versus individual customers

[Case Marriott] Which factor most significantly influences room demand on specific days?

• Day of the week (DOW) patterns in customer behavior

• Marketing campaigns run by competing hotels

• Weather conditions affecting tourist visits

• Hotel location in proximity to business districts

[Case Marriott] What methodology did Linda Snow apply to adjust the forecast pickup ratio?

• Weighting historical data based on customer feedback

• Incorporating seasonal trends into the adjusted ratios

• Dividing the pickup ratio by the Day of the Week Index

• Using rolling averages to smooth demand fluctuations

[Case Marriott] How is the pickup ratio used to forecast the number of occupied rooms?

• By multiplying the pickup ratio by the number of rooms booked as of Tuesday

• By averaging demand data for the corresponding days in previous years

• By calculating the difference between booked and available rooms

• By dividing the total demand by the Day of the Week Index

[Case Marriott] Why is the Day of the Week Index critical to demand forecasting?

• It helps adjust forecasts for seasonal variations in demand

• It standardizes demand data across different periods

• It accounts for occupancy trends that vary by day of the week

• It reflects customer preferences for specific types of accommodations

[Case Marriott] What is the best approach to handle the request for 60 additional rooms from the tour company?

• Accept all 60 reservations without conducting additional analysis

• Decline the request entirely to avoid potential overbooking issues

• Use the forecast pickup ratio to determine how many rooms can be allocated

• Prioritize reservations for loyal customers to minimize risks

[Case Marriott] What innovative solution could improve forecasting accuracy in the future?

• Implementing AI-based predictive models for pickup ratio adjustments

• Outsourcing reservation management to third-party agencies

• Introducing stricter cancellation policies for high-demand days

• Investing in customer relationship management tools to predict behavior