Canon Production Printing (CPP), formerly Océ, is a leading multinational manufacturer of high-end digital inkjet printing hardware and software for professional printing markets. CPP solutions are used by variety of customers in different markets, having different business models.
In addition to supporting diverse businesses, CPP printing solutions are also used in varied ways and workflows, at different scales and process integrations. Even between two similar types of customers there might be considerable differences in how they organize their work, both at company and at operator level.
To address this diversity, CPP is developing the Pigment Design System as one design system across the whole hardware and software portfolio. The Pigment Design System is the basis for all user interfaces (UI), making them highly flexible and customizable for an optimal and personalized experience. How users configure and interact with these UIs gives CPP an understanding about customer workflows and how CPP products are used in these workflows.
- Through this design research project on the Pigment Design System, CPP aims to better understand customers and customer roles through collecting and analysing interaction flows and contextual data, and
- To help customers get more value from the CPP solutions by means of better workflow integration, AI-supported personalization and advanced analytics.
This project offers a 4-year design-research PhD position. To address the goal of Canon Production Printing, we will, together with the CPP design and R&D teams and a second design research candidate at ID, focus on two clusters of research questions around UI/UX and functionality/service:1) How can data and AI techniques help in getting more insight in how users compose their UI ecosystem based on the applications provided within the Pigment Design System. More specifically, we want to address the following questions:
2) How can data and AI techniques help in getting more insight in how different users utilize printers and software in their workflows. More specifically, we want to address the following questions:
- Which Pigment functionalities are used by which roles?
- How much are the applications active and used per role?
- What are the primary and secondary applications?
- What UI optimizations per specific environment are recommended to boost productivity and improve accessibility of service offerings?
- Which products and (functionalities within the products) are prominently used or entirely absent in each environment?
- Which functionalities are used in combination?
- What bottlenecks can be found in customer workflows?
- Which optimized workflows for each environment are recommended to prevent redundancies and discover missing solution opportunities?
This project aims to generate new knowledge and actionable methodological advances regarding workflow design for smart operator support, workflow-oriented and servitized predictive maintenance, and finally the use of digital twins in the user interface design of high-tech systems. The project will be part of the TU/e EAISI program and therefore share, learn, and disseminate within the EAISI community and through the TU/e master programs Data Science and AI, AI in Engineering Systems. The project aims for open-access publishing of widely applicable methods and is organized to enable close collaboration and colocation of researchers from the project partners. The project leverages ongoing methodological developments in the Industrial Design department with other regional industries (for example, e/MTIC MEDICAID project), and will use and contribute to the 'Data-AI in Design' efforts at TU/e and beyond.Be part of an innovative multi-disciplinary team
In this PhD position you will closely collaborate with a second design research PhD candidate, and you will be working with a multidisciplinary academic and industrial supervision team to tackle the challenges in a timely and effective way. The research will be carried out in three phases: contextual research, data-supported design, and evaluation and iteration.Design Research PhD position on Designing New Workflow Integrations with AI-supported Personalization
In this design research position, you will explore how to design for and evaluate workflow integrations and work with advanced analytics to develop AI-supported personalization tools using data-driven recommender systems, generative AI tools and large language models. During the first phase, your project focuses on generating propositions based on the insights in the design of adaptive, personalized user interfaces in the Pigment Design System. In the second phase, you will design and implement new UIs and workflow support for specific use cases. In the third phase, you will leverage UI instrumentation and other in-situ data collection to evaluate and iterate accordingly.
Methodologically, we will rely on data-supported ethnography, data-enabled design, and research-through-design, and leverage CPP's data platform and analytics expertise. Throughout the project, we see opportunities to work with advanced artificial intelligence to leverage real-time data collection and to use generative AI for product and UI adaptation for better workflow support. In your PhD project, you will explore the research questions by applying the research through design methodology with hands-on designing and prototyping in close contact with the CPP R&D teams, users, and stakeholders of the Pigment Design System such as UX architects, UX designers, Visual UX Designers, Quality Leads and Workflow Architects. You will also collaborate closely with another design research PhD candidate at ID. Together, you will share design research methods, data, AI models, and technological platforms as complementary focus points. These will be closely involved in the co-creation process. Given the multi-disciplinarity of designers and researchers at CPP and TU/e, we will be able to rely on experts in design and design research but also data mining, process mining, federated learning, and hybrid machine-learning approaches to further refine analysis and design propositions.Eindhoven University of Technology (TU
) is one of Europe's leading research universities. The Eindhoven area, in the southern part of the Netherlands, is one of Europe's top 'innovation ecosystems', with many high-tech companies and institutes. TU/e is intertwined with many of these companies and institutes, and research at TU/e is characterized by a combination of academic excellence, industrial relevance and societal interweaving. The Department of Industrial Design (ID)
of the Eindhoven University of Technology (TU/e), founded in 2001, is a maturing department wit over 850 students, both Bachelor and Master, and around 45 research staff members and about 10 lecturers. The mission of the department of Industrial Design at TU/e is Research on and Education in the Design of Systems with Emerging Technologies in a Societal Context.
The PhD will be under the supervision of prof.dr. Lin-Lin Chen and dr. Mathias Funk in the Department of Industrial Design at Eindhoven University of Technology. The project will be executed in collaboration with Canon Production Printing, Venlo; Frederique de Jongh of CPP is closely involved in the supervision of the PhD position. At the TU/e, the position is situated within the research cluster of 'Future Everyday'. The Future Everyday cluster investigates the everyday interactions between individual people and the highly interconnected technology that surrounds them. Researchers in the Future Everyday cluster measure, model and design for the user experience when individuals interact with social-technological networks in various professional settings in the high-tech industry.
Prospective starting date: February 1, 2024.