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Predictive maintenance of tourism facilities

Introduction

Tourism industry is one of the largest industries in the world and generates several millions of dollars in revenues. The structures that support this industry include accommodation and recreation facilities such as hotels and resorts, amusement parks and historical sites which are vital infrastructure that need constant attention and repair. Predictive maintenance, which is a technique that employs data mining to determine when a specific component is likely to fail and thus arrange for the necessary maintenance at the right time, is fast being adopted in this particular field. Machine Learning (ML) Artificial Intelligence (AI) has been instrumental in this shift as it offers solutions to hitherto unachievable problems of fault detection, resource management, and customer satisfaction.

Challenges

There are however several hurdles that the tourism industry has to overcome in order to fully realise the potential of predictive maintenance. One of the biggest of these is the fact that there are a great many different and complex assets which need to be maintained. From roller coasters to hotel HVAC systems, the variety of equipment makes it hard to develop a general predictive model that would work for all of them. Also, data integration and analysis is a strenuous task as there is a need to consolidate data from multiple systems and in different structures. In addition, the issue of high investment requirements especially for small and medium enterprises and absence of skilled professionals to oversee and interpret the AI models are some of the challenges that exist. Last but not the least, the seasonal and fluctuating demands and usage of facilities in the tourism industry also pose a challenge to predictive maintenance.

AI Solutions

This paper finds that AI solutions, especially those based on ML algorithms, are giving useful answers to these problems. It can manage the complexity and diversity of tourism sector’s assets as it learns from multivariate data sets and is capable of identifying similarities and trends which may indicate a developing issue. There are many companies such as IBM and Microsoft which provide AI solutions that make use of predictive analytics and by connecting different types of data. These solutions also have a simple and user-friendly interface which does not require much expertise from the users. In addition, the costs of implementing AI are coming down due to the adoption of cloud-based solutions, which means that predictive maintenance is now within the reach of small enterprises. Last but not the least, AI models are capable of adapting to the seasonal nature of the tourism industry as they are trained with historical data to make precise predictions of the maintenance requirements.

Benefits

Applying ML AI in predictive maintenance has the following advantages for the tourism sector. First, it can greatly minimize the down time of facilities and therefore improve the satisfaction of the customers as well as the revenue. It also effectively manages the maintenance resources, and align them to areas that require their attention, thus being more efficient and timely. In addition, the enhancement of the prediction accuracy can also help to increase the lifetime of assets which will lead to cost savings in the long run. Furthermore, the data that is produced by these AI models can also support the process of continuous improvement thus increasing efficiency and customer satisfaction.

Return on Investment

The ROI of applying ML AI in predictive maintenance can be very valuable. For example, a research done by Deloitte revealed that predictive maintenance can cut down on maintenance expenses by 5-10%, enhance the availability of equipment by 10-20%, and increase the lifespan of equipment by several years. In addition, enhanced customer satisfaction due to reduced downtime can lead to higher repeat business and increased sales. Although the costs of establishing such systems may be quite expensive at the initial level, the future costs savings and revenue flows offer a good return on investment.

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