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Master Projects


Possible assignments

Detecting root causes of complaints and investigating the continuation within the customer journey

In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most important KPI. Complaints are related to customer satisfaction, because they have a highly negative effect on customer satisfaction. Therefore, it is crucial to prevent customers from having any complaints. Customers do not submit a complaint out of the blue: a complaint is a reaction to one or more occurrences in the past. The best way to understand the triggers, or root causes, it to understand the touchpoints prior to a complaint.

CZ aims for a method to prevent complaints (in order to increase customer satisfaction and lower churn). When taking the touchpoints of the customer journey into account, complaint root causes can be detected and CZ can prevent future complaints. Moreover, the touchpoints after the complaint give insights about an optimal follow up after the complaint.

Prior research development

Underlined works together with several companies like CZ, Aegon and SNS to build a generic framework in which all customer contacts are brought together as a unique dataset which is further enriched by Underlined with relevant analyses that can be linked to customer events. Underlined co-created customer journey mining algorithms, together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group). This research showed that it is possible to distinguish the different journeys per customer. Moreover, in collaboration with the TiU econometrist department, a driver model has recently developed to distinguish relevant drivers and to predict the NPS-score.

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Philips HUE Product Evolution Using Stream Mining of Customer Journey

Philips HUE is a connected personal lighting system. It is controlled by a range of apps and smart home devices.

To acquire Philips HUE, one starts with a starter kit that consists of a few lamps and a bridge. Subsequently, consumers decide to expand their system with additional lamps or/and physical sensors. About 50 lamps can be controlled in one system.

An example of HUE system is seen in the Figure below. It consists of a bridge, three color lamps and a dimmer switch.

Every consumer chooses a device to interact with the lights. By default, all consumers start with HUE or third-party app. There are also options in the HUE app to create routines to be able to control the lights automatically. For instance, one can create a wakeup routine so that the lights in the bedroom go on naturally in the morning. HUE can also be controlled by Philips physical sensors (motion sensor, tap, dimmer switch), voice control and other third party smart home devices. Amazon Echo, Google Home, Eneco Toon, Homekit are some of smart home devices that are linked to HUE. In addition, traditional wall switch can also be used to control the lights. For further information see Philips Lighting website.

The master thesis focusses on developing a process mining model to understand product evolution of consumers in their journey and identify the paths that contribute to a high Customer Lifetime Value (CLV). Moreover, the thesis will also explore data mining techniques that can be applied on the outcome of process mining model.

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Process mining in Logistics - 3D Visualization and Scalable Process Mining on Big Event Data (2 Topics)

Vanderlande is the global market leader for value-added logistic process automation at airports and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Some figures:

  • Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year.
  • Our systems are active in 600 airports including 13 of the world’s top 20.
  • More than 39 million parcels are sorted by its systems every day, which have been installed for the world’s leading parcel companies.
  • Many of the largest global e-commerce players and distribution firms have confidence in Vanderlande’s efficient and reliable solutions.

Vanderlande focuses on the optimization of its customers’ business processes and competitive positions. Through close cooperation, we strive for the improvement of our customers’ operational activities and the expansion of their logistical achievements.

For Vanderlande, it is critical that we have state-of-the-art techniques to analyze and optimize our customers’ logistics processes. Reasons are (a) the constant increasing size and complexity of our material handling solutions, (b) growing complexity of our software solutions, covering larger parts of our customers’ business processes, and © the demand for more advanced service offerings, covering logistics and business services. We believe that process mining is of high value for Vanderlande. Therefore, we work with the Eindhoven University of Technology on making process mining fit for analyzing logistics processes. In this context, we offer graduation projects on process mining and the application in our business.

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Want to win?

Win the Process Discovery Contest (PDC) 2018!

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When Portfolio Management meets Process Mining Challenges and Opportunities

FLIGHTMAP is Bicore’s flagship software solution for portfolio management. Since its launch in 2010, a growing group of international clients, such as DAF, Océ, and Fokker, have implemented FLIGHTMAP. With this tooling, they can perform roadmapping, budget and resource planning, scenario analysis, planning and tracking, and more. More information about FLIGHTMAP is available via The figure above shows a screenshot of the tool obtained after the portfolio analysis

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Data Science: Developing a Self-Standing Dynamic reporting tool

A huge amount of (transaction) data is generated on a daily basis in ASML Development and Engineering department. The data is scattered in different sources. The challenge would be extracting data from relevant sources and creating a self-standing dynamic reporting tool (dashboard) demonstrating performance of (Supplier Quality) Engineers in different granularity levels (Department, Section, Individual) based on a set of pre-defined KPIs.

Are you a master student in Software Engineering (Data Science) with a passion on real-life data challenges? Then we are looking for you!

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Erasmus Mundus Joint Master in the field of Big Data Management and Analytics

The department of Computer Science at Technische Universiteit Eindhoven (TU/e) in a consortium of five European Universities is awarded EU co-funding to run a Erasmus Mundus Join Master Degree (EMJMDs) in the field of Big Data Management and Analytics (BDMA).

The programme curriculum is jointly delivered by Université libre de Bruxelles (ULB), Belgium, Universitat Politècnica de Catalunya (UPC), Spain, Technische Universität Berlin (TUB), Germany, Technische Universiteit Eindhoven (TU/e), the Netherlands, and Université François Rabelais Tours (UFRT), France. Academic partners around the world and partners from leading industries in BI and BD, private R&D companies, excellence centres, service companies, start-up incubators, public research institutes, and public authorities will contribute to the programme by giving lectures, training students, providing software, course material, and internships or job placements. The EMJMD in “Big Data Management and Analytics” (BDMA) is designed to provide understanding, knowledge, and skills in the broad scope of fields underlying Business Intelligence (BI) and Big Data (BD). Its main objective is to train computer scientists who have an in-depth understanding of the stakes, challenges, and open issues of gathering and analysing large amounts of heterogeneous data for decision-making purposes. The programme will prepare the graduates to answer the professional challenges of our data-driven society through a strong connection with industry, but also to pursue their studies into doctorate programmes through a strong connection with research and innovation

BDMA is a 2-year (120 ECTS) programme. The first two semesters are devoted to fundamentals on BI and BD delivered by ULB and UPC. Then, all students participate to the European Business Intelligence and Big Data Summer School (eBISS). In the third semester, students chose one of the three specialisations delivered by TUB, TU/e, and UFRT. The fourth semester is dedicated to the master's thesis and can be carried out as an internship in industry or in a research laboratory in any full or associated partner. Eventually, all students attend the Final Event devoted to master's theses defences and the graduation ceremony. The tuition language is English. The program targets students with a Bachelor of Science (or a level equivalent to 180 ETCS) with major in Computer Science, as well as an English proficiency corresponding to level B2 of CEFR. The programme will deliver a joint degree to graduates following the mobility ULB, UPC, and UFRT, and three degrees from ULB, UPC, and the university of the specialisation (UFRT, TUB or TU/e) to graduates following the other mobilities.

More information can be found on the UFRT BDMA site, which will open soon.

If you have a master project item for this page, which may include possible master project assignments, on-going master projects, and completed master projects, please send it to Eric Verbeek.