Hotel occupancy and staff workloads are affected by various factors such as seasonality, holidays, social events, marketing campaigns, weather, market demands, etc. Currently, the process of staff planning is handled by staff managers. It is a time-consuming process shaped by personal experiences and management skills, susceptible to sudden changes and unevenly distributed staff workloads. Our ML based, automated staff planning and optimization solution is able to provide hotel managers with forecasts of staff requirements on a daily, weekly and monthly basis. The solution is built on top of open source ML platforms with focus on time-series forecasting. Once integrated within the hotel HR and RM systems this automated solution can increase hotel revenues, optimize resource management, reduce operating costs, and optimize the staff planning as well as staff workloads.

1. DSC Zagreb
May 2022
Nebojsa Mitric, IT Director Maistra
Goran Gvozden, Senior Data Science Consultant, Poslovna Inteligencija
Automated ML Based Staff Planning
and Optimization Solution for Hospitality
Industry

2. Business challenge
„Human resources management in the hotel industry
marks a fundamental difference in the positioning of
hotel companies”
“The hotel industry is a labor-intensive business activity.
About 40% of operating costs are labor costs. In the
hospitality sector, that percentage rises to over 50%

3. Maistra is one of the leading tourist companies in Croatia
 Total capacities over 12k accommodation units, 35k guests - 10 hotels,
8 settlements, 6 camps
 The facilities are mainly located around Istra and its destinations -
Rovinj, Vrsar, 1 hotel in Dubrovnik
 We manage the portfolio of HUP d.d. Zagreb
 6 hotels in Zagreb
 5 in Dubrovnik
 During a stable year of operation we usually achieve about 8% of the
annual volume of overnight stays in the Republic of Croatia

4. Maistra follows latest technological trends
 The natural sequence of our evolutionary process is a result of continuity in our investments in the
development of the information system.
 In our business culture it is deeply incorporated the awareness in the overall importance of data and in the
managing of its results based on objective and transparent indicators.
 A key element in the implementation of modern models is the existence of a reliable, thorough and
consistent data sources
 All the data is structured and consistent with the databases which we have developed and kept
since 2008
 Information on all important business segments
 All of the prerequisites for the transition from automatic reporting to decision automation have been met
 Labor cost management is the key to the efficiency of the hotel industry
 Hours and labor costs are monitored on a daily basis, per lowest business unit, per individual worker,
workplace, and sorted by type of working hour

5. Family hotel Amarin
Family Hotel Amarin
 280 units
 Up to 850 guests
 Year-round business
 Guest profile - families with small children
The main criteria for choosing the initial model
 The relative simplicity of the business model
 Significance of the hotel due to its size and the share of performance in the overall results of the company
 Model tested at the main restaurant for boarding hotel guests

6. What do we want to achieve?
 Optimize labor costs and maximize profitability
 Savings in terms of work hours dedicated by the
management - focus is then switched to the creation of new
business opportunities
 Simplified control and objectified validation of business
performance and implemented strategies
 Simpler and automated planning, and long-term modeling
 Balanced workload of workers
 Objective basis for rewarding

7. Business solution
Web application based on time-series machine learning model

8. Approach and Methodology
Business
Understanding
Data
Understanding
Data
Preparation
Model
Selection
Model
Evaluation
Model
Deployment
CRISP-DM
TDSP

9. Time series toolkits
 Manipulation, analysis and forecasting of time series
 From classics such as ARIMA to deep neural networks
 Univariate and multivariate time series and models
 Past and future covariates support
 Time series classification, regression and clustering
 Utilities for simulating historical forecasts, using
moving time windows

10. Architecture

11. Model Performance Evaluation
 Mean Absolute Percentage Error – MAPE
 Overall Percentage Error – OPE
 Mean Absolute Error - MAE

12. Predictive model effects
 Increased planning transparency
 Better understanding of planning process
 Reduced human errors
 Reduced time to onboard new employees
 Lower costs of staff planning, management and training
 Optimized allocation of hotel resources related to staff

13.  On-premise, cloud, hybrid environment
 Provide forecasts and schedules through API
 SaaS availability
 Optimized allocation of other hotel resources
Options & future work

14. Nebojsa.Mitric@maistra.hr
Goran.Gvozden@inteligencija.com
Contact