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Master Projects

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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 www.flightmap.com. 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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Real-Time Model Discovery of the Service Order Process Using Stream Process Mining

Kropman Installatietechniek is a Dutch company established in 1934 and has become one of the leading companies of the Dutch installation industry. With about 800 employees, 12 regional locations and an annual turnover of more than 100 million Euro, Kropman is an integral service provider with a multidisciplinary approach. Kropman is mainly active in office buildings, health care and industry. It offers design, construction and maintenance in the field of facility installations. Kropman also has a separate business for process installations and cleanrooms: Kropman Contamination Control. The maintenance (services) is a fast growing business line. The order process is fully supported within an ERP environment. The service order process is not a trouble-free process: the process takes too long and flows more often than necessary. The company aims at increasing the throughput of the SO process and decreasing the amount of process deviations by applying process mining and data mining techniques.

To have a better overview of the process, Kropman is aiming at:

  • Exposing bottlenecks happening in the SO process
  • Detecting deviations from the supposed model in the real time they are happening using stream process mining techniques

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Example completed master projects

Bram in 't Groen

VDSEIR - A graphical layer on top of the Octopus toolset

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Borana Luka

Model merging in the context of configurable process models

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Cosmina Cristina Niculae

Guided configuration of industry reference models

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Dennis Schunselaar

Configurable Declare

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Erik Nooijen

Artifact-Centric Process Analysis, Process discovery in ERP systems

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Irina-Maria Ailenei

Process mining tools: A comparative analysis

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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.