Tourism

Forecasting the Growth of Overseas Travellers in UK for 2019

Application Area: 

Project Details

Term: 

2019

Students: 

Nirali Joshi, Dreamy Agrawal, Shruti Shah, Rashmi Agarwal, Shubham Vijay, Honey Tibrewal

University: 

ISB

Presentation: 

Report: 

Demand forecast in tourism is of great economic value both for the public and private sector. Any information concerning the future evolution of tourism flows is of great importance to hoteliers, tour operators and other industries concerned with tourism.

Our Client is a Tours and Travel agency who wants to efficiently plan its offerings according to different segments for the upcoming year and ensure adequate capacity and infrastructure.

Problem Definition:

Forecasting AsiaYo’s One Month Ahead Daily Room Occupancy in Different Cities for Supply Preparation

Application Area: 

Project Details

Term: 

Fall 2018

Students: 

Sz Wei Wu (Sandy), Cheng Che Liao (John), Min Sheng Wu (Akira), Kai Wei Pai (Kelvin)

University: 

NTHU

Presentation: 

Report: 

VIDEO

In this project, we collaborate with AsiaYo, an online B&B booking platform company headquartered in Taiwan, to work together on solving their business problem by using forecasting methods. One challenge facing AsiaYo is the revenue lost when they are lack of available rooms on holidays or special peak periods. Considering the enterprise level and resource, we find that it will be more affordable and understandable to focus our solution of this business problem on certain popular areas.

Forecasting Tourist Flows in New Zealand to Support Product Development and Pricing Decisions for Expedia

Application Area: 

Project Details

Term: 

2017

Students: 

Akshayaa Pasupathy, Ashwath Bhat, Anil Pujari, Prerna Lnu, Rishi Chakravarti, Sathyanarayanan Sridhar

University: 

ISB

Presentation: 

Report: 

Problem description:
Expedia is in the process of setting up its branch in the New Zealand market, called
Expedia.NZ, to compete with the existing big guns such as Black Sheep, ExperienceNZ, and
Kiwiway.
It is looking for a niche edge over the others in the already crowded travel space and wants to
capitalize on the open data on the tourists’ and visitors’ pattern to come up with key insights
on total addressable market.
It has past data for over 8 years and has information on what its competitors are doing; it has

Forecasting daily maximum Carbon Monoxide level for an event management firm

Application Area: 

Project Details

Term: 

2017

Students: 

Ankush Chetwani, Bharathi Rajan Muthu Krishnan, Surya Ramkumar, Shravanan Rudrapathy, Vaishnavi Gurusamy

University: 

ISB

Presentation: 

Report: 

Business Objective
Considering the increasing pollution levels in the city and its harmful effects on kid’s health, an event
management firm has decided to conduct outdoor events only when Carbon monoxide levels are within
3ppm to 9ppm. For this, they need a model to know the expected daily maximum level of Carbon
Monoxide (CO) one week in advance.

Forecasting Tourist Volume in Northeast and Yilan Coast National Scenic Area to better allocate Human Resources

Application Area: 

Project Details

Term: 

Fall 2014

Students: 

AnYu Luo, ChienMin Kao, Xi Wang, Daisin Li

University: 

NTHU

Presentation: 

Report: 

Our business goal is try to better allocate human resource in Northeast and Yilan Coast National Scenic Area Due to the tourism industry booming in Taiwan the past five years, the domestic and foreign tourists are increasing. we select ocean park, hiking trail, rock climbing area, diving space and camp site. These five locations are directly under different units. Try to organize the staffs between these five locations.

Forecasting Monthly Tourism to Sikkim, India

Application Area: 

Project Details

Term: 

2012 (Feb)

Students: 

Palash Borah, Saurabh Agarwal, Varun Sayal, Dipayan Dey, Abhishek Kumar

University: 

ISB

Presentation: 

Report: 

The objective of this project is to enable Sikkim Government (and other stakeholders) to generate forecasts for the next 12 months for state of Sikkim, month after month.

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