Projects
Current projects
Ontzorgen in de VVT Zorg - Taking care of clients and careworkers in residential care and home care
Problem context
The increasing pressure on the Dutch healthcare sector, caused by the ageing of the population, is leading to a growing gap between demand for care and the available workforce. Policy measures encourage independent living at home, which aligns with clients’ preferences but simultaneously creates new logistical and organisational challenges. The transition from care institutions to home care requires small-scale deliveries of care resources and services to the home, resulting in more transport movements, higher costs and a heavier burden on care providers. There is a need for a system that balances both clients’ preferences and the workload of care providers.
Approach
Within the Ontzorgen project, a regional care system is developed in which care, goods and services are coordinated in an integrated manner. The approach involves mapping client preferences, experiences of care providers and opportunities for task redistribution. Focus lies especially on staff, who take over non-care-related tasks such as the delivery of medical aids. In addition, research is being conducted into how deliveries can be consolidated and routes optimised to reduce transport movements and CO₂ emissions.
Expected outcomes
The project will result in the development of a regional control centre that supports real-time, data-driven decision-making within the healthcare ecosystem. This centre will facilitate the joint management of healthcare, goods and services supply chains, taking into account supply chain constraints and the preferences of those involved.
Concrete expected outcomes are:
Reduced workload for care providers through smart task allocationBetter alignment of care moments with client preferencesReduction in transport movements and emissionsA digital control centre for data-driven decision-making
The solutions will be tested in practice-oriented pilots with project partners, in preparation for wider implementation.
The consortium
The consortium comprises healthcare organisations, suppliers, research institutions and a network organisation. This combination brings together practical experience, data expertise and innovation capacity. Through this multidisciplinary collaboration, a bridge is built between research and implementation, which enhances the practical applicability and acceptance of the solutions developed.
Project partners: Care Invest Group, Carel Lurvink, Draaijer Group, Essity, Huuskes, Lucrum, Lyreco, Mediq, Pinkroccade, Renewi, TNO, Zorgcentrum de Posten, Zorgfederatie Oldenzaal, Zorgorganisatie Norschoten, Universiteit Twente, Windesheim
This project is funded by TKI Dinalog.
The increasing pressure on the Dutch healthcare sector, caused by the ageing of the population, is leading to a growing gap between demand for care and the available workforce. Policy measures encourage independent living at home, which aligns with clients’ preferences but simultaneously creates new logistical and organisational challenges. The transition from care institutions to home care requires small-scale deliveries of care resources and services to the home, resulting in more transport movements, higher costs and a heavier burden on care providers. There is a need for a system that balances both clients’ preferences and the workload of care providers.
Approach
Within the Ontzorgen project, a regional care system is developed in which care, goods and services are coordinated in an integrated manner. The approach involves mapping client preferences, experiences of care providers and opportunities for task redistribution. Focus lies especially on staff, who take over non-care-related tasks such as the delivery of medical aids. In addition, research is being conducted into how deliveries can be consolidated and routes optimised to reduce transport movements and CO₂ emissions.
Expected outcomes
The project will result in the development of a regional control centre that supports real-time, data-driven decision-making within the healthcare ecosystem. This centre will facilitate the joint management of healthcare, goods and services supply chains, taking into account supply chain constraints and the preferences of those involved.
Concrete expected outcomes are:
Reduced workload for care providers through smart task allocationBetter alignment of care moments with client preferencesReduction in transport movements and emissionsA digital control centre for data-driven decision-making
The solutions will be tested in practice-oriented pilots with project partners, in preparation for wider implementation.
The consortium
The consortium comprises healthcare organisations, suppliers, research institutions and a network organisation. This combination brings together practical experience, data expertise and innovation capacity. Through this multidisciplinary collaboration, a bridge is built between research and implementation, which enhances the practical applicability and acceptance of the solutions developed.
Project partners: Care Invest Group, Carel Lurvink, Draaijer Group, Essity, Huuskes, Lucrum, Lyreco, Mediq, Pinkroccade, Renewi, TNO, Zorgcentrum de Posten, Zorgfederatie Oldenzaal, Zorgorganisatie Norschoten, Universiteit Twente, Windesheim
This project is funded by TKI Dinalog.
Industrial Engineering and Management Science | Health
Predictive Workload-Aware Feature-Based Planning for Resilient High-Mix Low-Volume Manufacturing
The project Predictive Workload-Aware Feature-Based Planning for Resilient High-Mix Low-Volume Manufacturing (PREDICTIVE PLAN), is a strategic initiative within the High Tech Systems and Materials (HTSM) program, specifically focused on Systems Engineering for High Tech Systems.
The primary objective of PREDICTIVE PLAN is to develop and validate a predictive, feature-based, and human-centric planning framework designed to manage variability early and throughout the production process. It aims to provide explainable, data-driven digital assistance to planners and engineers by combining artificial intelligence, simulation, and human expertise to enhance the sustainability, resilience, and efficiency of Dutch high-tech manufacturing.
The project addresses the critical challenges faced by High-Mix, Low-Volume (HMLV) manufacturers like Thales NL. Current planning systems (ERP/APS) are designed for standardized products and fail to account for high Variability due to unique products and complex routings lead to unpredictable bottlenecks and resource conflicts, human capital risk, planning heavily relies on the "best-guess" estimations of experienced planners who are nearing retirement, making the function vulnerable and unsustainable
Predictive Plan follows a 4-Step modular research cycle to move from proof-of-concept to industrial implementation. Including Digital Twin (Module 1): Building a discrete-event simulation to model demand, supply, and operational variability, AI Surrogate Models (Module 2): Developing machine learning models for near-instant predictions of workload, waiting times, and throughput, Hybrid AI Planning (Module 3): Integrating surrogate predictions with reinforcement learning and heuristics to optimize job release and material planning, Human-in-the-Loop (Module 4): Implementing a framework that uses Reinforcement Learning from Human Feedback (RLHF) to capture tacit knowledge and ensure planners trust and adopt the system.
The project targets reaching a Technology Readiness Level of 7 (TRL7) with a validated prototype operating in "shadow mode" at Thales, Key performance targets include: 40–50% reduction in waiting times and work-in-progress (WIP), 10–15% reduction in material waste and energy consumption, increased on-time delivery and faster, more consistent planning decisions and digital Intelligence for human workers: Capturing the expertise of retiring planners to support younger staff with explainable AI.
This project is funded by TKI HTSM.
The primary objective of PREDICTIVE PLAN is to develop and validate a predictive, feature-based, and human-centric planning framework designed to manage variability early and throughout the production process. It aims to provide explainable, data-driven digital assistance to planners and engineers by combining artificial intelligence, simulation, and human expertise to enhance the sustainability, resilience, and efficiency of Dutch high-tech manufacturing.
The project addresses the critical challenges faced by High-Mix, Low-Volume (HMLV) manufacturers like Thales NL. Current planning systems (ERP/APS) are designed for standardized products and fail to account for high Variability due to unique products and complex routings lead to unpredictable bottlenecks and resource conflicts, human capital risk, planning heavily relies on the "best-guess" estimations of experienced planners who are nearing retirement, making the function vulnerable and unsustainable
Predictive Plan follows a 4-Step modular research cycle to move from proof-of-concept to industrial implementation. Including Digital Twin (Module 1): Building a discrete-event simulation to model demand, supply, and operational variability, AI Surrogate Models (Module 2): Developing machine learning models for near-instant predictions of workload, waiting times, and throughput, Hybrid AI Planning (Module 3): Integrating surrogate predictions with reinforcement learning and heuristics to optimize job release and material planning, Human-in-the-Loop (Module 4): Implementing a framework that uses Reinforcement Learning from Human Feedback (RLHF) to capture tacit knowledge and ensure planners trust and adopt the system.
The project targets reaching a Technology Readiness Level of 7 (TRL7) with a validated prototype operating in "shadow mode" at Thales, Key performance targets include: 40–50% reduction in waiting times and work-in-progress (WIP), 10–15% reduction in material waste and energy consumption, increased on-time delivery and faster, more consistent planning decisions and digital Intelligence for human workers: Capturing the expertise of retiring planners to support younger staff with explainable AI.
This project is funded by TKI HTSM.
Industrial Engineering and Management Science | Safety & Security | Smart Industry