To deal with an increasingly competitive environment, organizations are combining continuous improvement (CI) practices with digitalization to accrue their benefits on operational performance and achieve operational excellence. Using a mixed-methods approach consisting of an online survey and semi-structured interviews, we examined how digitalization technologies have been incorporated in CI projects by organizations. Besides significant relationships between the nature of CI initiative and the use of digitalization tools, we found key enablers for organizations to be system compatibility, room to experiment, data-driven decision-making and step-by-step introduction with involvement of stakeholders. These enablers were found to be interlinked through knowledge of digitalization.
In pursuit of competitive advantage in an increasingly globalized and complex environment, organizations are turning to continuous improvement and digitalization to achieve operational excellence. Viewed through the lens of Dynamic Capabilities Theory, the similarities complementarities, and synergies of continuous improvement capability and data analytic capability are examined. Bridging the gap between theory and practice, continuous improvement routines and practices that can be harnessed to accelerate the implementation of data analytical capability are identified. These include Hoshin Kanri to link digitalization projects to organizational strategic, training to develop organizational knowledge of digitalization, problem solving teams to break knowledge silos, and the use of PDCA-type processes for adopting and monitoring the performance of digital technologies.
LINK
Introduction: The implementation of oncology care pathways that standardize organizational procedures has improved cancer care in recent years. However, the involvement of “authentic” patients and caregivers in quality improvement of these predetermined pathways is in its infancy, especially the scholarly reflection on this process. We, therefore, aim to explore the multidisciplinary challenges both in practice, when cancer patients, their caregivers, and a multidisciplinary team of professionals work together on quality improvement, as well as in our research team, in which a social scientist, health care professionals, health care researchers, and experience experts design a research project together. Methods and design: Experience-based co-design will be used to involve cancer patients and their caregivers in a qualitative research design. In-depth open discovery interviews with 12 colorectal cancer patients, 12 breast cancer patients, and seven patients with cancer-associated thrombosis and their caregivers, and focus group discussions with professionals from various disciplines will be conducted. During the subsequent prioritization events and various co-design quality improvement meetings, observational field notes will be made on the multidisciplinary challenges these participants face in the process of co-design, and evaluation interviews will be done afterwards. Similar data will be collected during the monthly meetings of our multidisciplinary research team. The data will be analyzed according to the constant comparative method. Discussion: This study may facilitate quality improvement programs in oncologic care pathways, by increasing our real-world knowledge about the challenges of involving “experience experts” together with a team of multidisciplinary professionals in the implementation process of quality improvement. Such co-creation might be challenging due to the traditional paternalistic relationship, actual disease-/treatment-related constraints, and a lack of shared language and culture between patients, caregivers, and professionals and between professionals from various disciplines. These challenges have to be met in order to establish equality, respect, team spirit, and eventual meaningful participation.
Energy transition is key to achieving a sustainable future. In this transition, an often neglected pillar is raising awareness and educating youth on the benefits, complexities, and urgency of renewable energy supply and energy efficiency. The Master Energy for Society, and particularly the course “Society in Transition”, aims at providing a first overview on the urgency and complexities of the energy transition. However, educating on the energy transition brings challenges: it is a complex topic to understand for students, especially when they have diverse backgrounds. In the last years we have seen a growing interest in the use of gamification approaches in higher institutions. While most practices have been related to digital gaming approaches, there is a new trend: escape rooms. The intended output and proposed innovation is therefore the development and application of an escape room on energy transition to increase knowledge and raise motivation among our students by addressing both hard and soft skills in an innovative and original way. This project is interdisciplinary, multi-disciplinary and transdisciplinary due to the complexity of the topic; it consists of three different stages, including evaluation, and requires the involvement of students and colleagues from the master program. We are confident that this proposed innovation can lead to an improvement, based on relevant literature and previous experiences in other institutions, and has the potential to be successfully implemented in other higher education institutions in The Netherlands.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
