prof.dr. M.E. Iacob (Maria)

Full Professor
BMS-HBE-IEBIS
Ravelijn 3416
m.e.iacob@utwente.nl
+31534894134
Business card

Maria Iacob is a full professor of Enterprise Systems Engineering at the Department of High-tech Business and Entrepreneurship, at the University of Twente. She is also Educational Director of the Business Information Technology Bachelor and Master and of the Master of Risk Management. Previously she worked as a scientific researcher at Telematica Instituut (2000-2006). She has done research in the areas of enterprise and information systems architecture (design and quantitative analysis), service-oriented architectures, model-driven development, model transformations, e-services architectures, supply chain integration, smart logistics, Industry 4.0, e-government, business process (re)engineering and management, business modeling, intelligence amplification and data-driven decision making.

Expertise

  • Computer Science

    • Models
    • Design
    • Enterprise Architecture
    • Service
    • Case Study
    • Reference Architecture
    • Business Models

  • Social Sciences

    • Enterprises

Organisations

Ancillary activities

  • TKI DinalogMember of the "Programmacommisie Topsector Logistiek"

Maria-Eugenia Iacob is currently full professor of Enterprise Systems Engineering at the Department of High-tech Business and Entrepreneurship, at the University of Twente. Previously she worked as scientific researcher at Telematica Instituut (2000-2006). She holds a Ph.D. degree in Discrete Mathematics from the University Babes-Bolyai of Cluj-Napoca, Romania. She has done research in the areas of enterprise and information systems architecture (design and quantitative analysis), service-oriented architectures, model-driven development, model transformations, e-services architectures, supply chain integration, smart logistics, Industry 4.0, e-government, business process (re)engineering and management, business analytics, business modeling, and intelligence amplification.

Publications

2024
Condition-Based Logistics: Internet of Things Solutions for Resilient Supply Chains. University of Twente. Koot, M.https://doi.org/10.3990/1.9789036561075Emergent Behaviors in a Resilient Logistics Supply Chain. University of Twente. Bemthuis, R. H.https://doi.org/10.3990/1.9789036560733Visualization of Digital Transformation Initiatives Elements through ArchiMate Viewpoints (E-pub ahead of print/First online). Bogea Gomes, S., Santoro, F. M., da Silva, M. M. & Iacob, M. E.https://doi.org/10.1007/s10796-023-10469-4Building an Ontological Bridge Between Supply Chain Resilience and IoT ApplicationsIn Enterprise Design, Operations, and Computing: 27th International Conference, EDOC 2023, Groningen, The Netherlands, October 30 – November 3, 2023, Proceedings (pp. 79-96). Springer. Koot, M., Mes, M. R. K. & Iacob, M. E.https://doi.org/10.1007/978-3-031-46587-1_5Exploring the Integration of Agent-Based Modelling, Process Mining, and Business Process Management through a Text Analytics–Based Literature ReviewIn The Oxford Handbook of Agent-based Computational Management Science. Oxford University Press. Bukhsh, F. A., Govers, R., Bemthuis, R. H. & Iacob, M. E.https://doi.org/10.1093/oxfordhb/9780197668122.013.20
2023
Service-oriented business collaboration reference architecture: Findings from a systematic literature review, Article 020024. Firdausy, D. R., Iacob, M. E. & Van Sinderen, M.https://doi.org/10.1063/5.0118972Circularity information platform for the built environment, Article 104933. Yu, Y., Yazan, D. M., van den Berg, M. C., Firdausy, D., Junjan, V. & Iacob, M. E.https://doi.org/10.1016/j.autcon.2023.104933Designing Essential Components For Logistics Data Spaces: Connecting Logistics interfaces, Converters, Knowledge, and Standards . University of Twente. Firdausy, D. R.https://doi.org/10.3990/1.9789036557177Introducing e-health technology to routine cataract care: patient perspectives on web-based eye test for postoperative telemonitoring, 659-665. Claessens, J. L. J., Maats, E. P. E., Iacob, M. E., Wisse, R. P. L. & Jongsma, K. R.https://doi.org/10.1097/j.jcrs.0000000000001189Towards a Reference Architecture for Planning and Control ServicesIn Advances in Enterprise Engineering XVI: 12th Enterprise Engineering Working Conference, EEWC 2022, Revised Selected Papers (pp. 121-138). Springer (E-pub ahead of print/First online). Pourmehdi, M., Iacob, M. E. & Mes, M. R. K.https://doi.org/10.1007/978-3-031-34175-5_8Tutorial: Conceptualizing intelligence amplification in human-centred AI applications using the design canvasIn Human Interaction and Emerging Technologies (IHIET-AI 2023): Artificial Intelligence and Future Applications (pp. 129-138). AHFE Conference. Piest, J. P. S., Iacob, M. E. & Wouterse, M. J. T.https://doi.org/10.54941/ahfe1002937Defining, Designing, and Implementing Rural Smartness. University of Twente. Mukti, I. Y.https://doi.org/10.3990/1.9789036555777Digital Architectures Under Society 5.0: An Enterprise Architecture PerspectiveIn Enterprise Design, Operations, and Computing. EDOC 2022 Workshops. EDOC 2022.: IDAMS, SoEA4EE, TEAR, EDOC Forum, Demonstrations Track and Doctoral Consortium, Bozen-Bolzano, Italy, October 4–7, 2022, Revised Selected Papers (pp. 5-24). Springer Nature. Piest, J. P. S., Masuda, Y. & Iacob, M. E.https://doi.org/10.1007/978-3-031-26886-1_1Architecting rural smartness: A collaborative platform design for rural digital business ecosystem, Article e12236. Mukti, I. Y., Firdausy, D. R., Aldea, A. & Iacob, M. E.https://doi.org/10.1002/isd2.12236Implementing a Service-Oriented Rural Smartness Platform: Lessons Learned from a Technical Action Research in West Java, IndonesiaIn Enterprise Design, Operations, and Computing. EDOC 2022 Workshops (pp. 129-144). Springer Nature. Mukti, I. Y., Setiaji, Dwianti, I., Aldea, A. & Iacob, M. E.https://doi.org/10.1007/978-3-031-26886-1_8Business rule extraction using decision tree machine learning techniques: A case study into smart returnable transport items, 446-455. Bemthuis, R. H., Wang, W., Iacob, M. E. & Havinga, P. J. M.https://doi.org/10.1016/j.procs.2023.03.057Balancing Simplicity and Complexity in Modeling Mined Business Processes: A User PerspectiveIn Enterprise Information Systems. ICEIS 2022 (pp. 3-21). Springer Nature. Maneschijn, D. G. J. C., Bemthuis, R. H., Arachchige, J. J., Bukhsh, F. A. & Iacob, M. E.https://doi.org/10.1007/978-3-031-39386-0_1
2022
Rural smartness: Its determinants and impacts on rural economic welfare, 1943-1970. Mukti, I. Y., Henseler, J., Aldea, A., Govindaraju, R. & Iacob, M. E.https://doi.org/10.1007/s12525-022-00526-2Towards a Reference Enterprise Architecture to enforce Digital Sovereignty in International Data Spaces, 117-125. Firdausy, D., De Alencar Silva, P., van Sinderen, M. J. & Iacob, M. E.https://doi.org/10.1109/CBI54897.2022.00020A Data Connector Store for International Data SpacesIn Cooperative Information Systems: 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4-7, 2022, Proceedings (pp. 242-258). Springer. Firdausy, D., De Alencar Silva, P., van Sinderen, M. J. & Iacob, M. E.https://doi.org/10.1007/978-3-031-17834-4_14Publieke eindrapportage - Reinforcement Learning Platform voor Logistieke MKB. TKI Dinalog. Piest, J. P. S., Ghezelsoflu, A. & Iacob, M. E.Publieke eindrapportage - Industry 4.0 driven Supply Chain Coordination for Small and Medium-sized Enterprises. TKI Dinalog. Piest, J. P. S. & Iacob, M. E.Designing Intelligence Amplification: a Design Canvas for PractitionersIn Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing, Article 8 (pp. 68-76). Piest, J. P. S., Iacob, M. E. & Wouterse, M. J. T.https://doi.org/10.54941/ahfe1002714Designing Intelligence Amplification: Organizing a Design Canvas WorkshopIn Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing, Article 33 (pp. 247-251). Piest, J. P. S., Iacob, M. E. & Wouterse, M. J. T.https://doi.org/10.54941/ahfe1002739A systematic literature review on Circular Economy implementation in the construction industry: a policy-making perspective, Article 106359. Yu, Y., Junjan, V., Yazan, D. M. & Iacob, M. E.https://doi.org/10.1016/j.resconrec.2022.106359

Other contributions

M.E. Iacob, L.O. Meertens, H. Jonkers, D.A.C. Quartel, L.J.M. Nieuwenhuis, M.J. van Sinderen, From Enterprise Architecture to Business Models and back, Software & Systems Modeling, July 2014, Volume 13, Issue 3, pp 1059-1083, Springer.

M.E. Iacob, M.J. van Sinderen, M. Steenwijk, P. Verkroost, Towards a Reference Architecture for Fuel-based Carbon Management Systems in the Logistics Industry, Information Systems Frontiers, November 2013, Volume 15, Issue 5, pp 725-745.

M.E. Iacob, D. Quartel, H. Jonkers, Capturing Business Strategy and Value in Enterprise Architecture to Support Portfolio Valuation, In Proceedings of The 16th IEEE International EDOC Conference (EDOC 2012) "The Enterprise Computing Conference", September 10-14, 2012, Beijing, China, pp. 11-20.

M.E. Iacob, H. Jonkers, M. Lankhorst, E. Proper, Dick A.C. Quartel, ArchiMate 2.0 Specification, The Open Group, Van Haren Publishing, February 2012, 1-937218-00-3.

Moody, D.L., Iacob, M.E. & Amrit, C. (2010). In Search of Paradigms: Identifying the Theoretical Foundations of the IS Field. In T. Alexander, M. Turpin & J.P. van Deventer (Eds.), Proceedings of ECIS 2010.

M.-E. Iacob, H. Jonkers, "Quantitative analysis of service-oriented architectures", International Journal of Enterprise Information Systems, vol. 3, no. 1, Jan.-Mar. 2007, pp. 42-60.

Research profiles

PhD & PDEng supervision:

  1. Lucas O. Meertens, Relating Business Models and Enterprise Architecture, University of Twente, The Netherlands, Oct 2013.
  2. Adina I. Aldea, Enterprise Strategic Alignment Method (ESAM), University of Twente, The Netherlands, Apr 2017.
  3. Fabian Aulkemeier, Pluggable services: a platform architecture for e-commerce, University of Twente, The Netherlands, Apr 2017.
  4. Andrej Dobrkovic, SynchromodalIT Real Time and Big Data Training, In progress. Estimated end date: Dec 2022.
  5. Iqbal Y. Mukti, E-Government Service Platform for Economic Welfare: Rural Smartness in the West Java Province, in Progress, Estimated end date: May 2023.
  6. Martijn Koot, Resilient Logistics: an application of IoT and data analytics, In progress. Estimated end date: Dec 2022.
  7. Rob Bemthuis, Distributed Business Logic for Resilient Supply Chain Logistics. Estimated end date: Dec 2022.  
  8. J.P. Sebastian Piest, Autonomous Logistics Miners for Small-Medium Businesses, PDEng in progress (will continue with a PhD) Estimated end date PDEng: July 2020.
  9. Yifei Yu, Implementation of Circular Economy in the construction industry, in Progress, Signature PhD, Estimated end date: October 2024.
  10. Danniar Reza Firdaussi, Design of an IDS architecture for logistics, in Progress, Estimated end date PDEng: July 2023.
  11. Fabian Akkerman, Development of DynaPlex; an artificial intelligence toolbox containing (deep) reinforcement learning algorithms for dynamic data-driven logistics decision making, in Progress, Estimated end date: April 2025.
  12. Marjolein Velthuijzen, Model-based Systems Engineering @ Thales, in Progress, Estimated end date: January 2026.
  13. Jillis Kors, Application of Blockchain technology in Healthcare, Estimated end date PDEng: July 2023.

Affiliated study programs

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023

Current projects

Big Data for Resilient Logistics

DataRel

The modern supply chain continues to seek more cost savings and greater transparency and efficiency in all processes across the whole chain. The Internet of Things (IoT) is fundamentally transforming the transport industry and logistics. Next- generation smart logistics will optimize the movement of people and goods locally and globally, improving economics, public safety, and sustainability. This requires a multi-tiered intelligent system architecture providing high degree of modularity and autonomy, satisfying demanding and sometimes conflicting requirements of smart logistics and transport in terms of scalability, availability, and security. The wide availability of sensors (e.g., smart packaging, sensing technology, temperature control, board computers, GPS trackers, etc.) in logistics supply chains and the wide-band communication of Internet of Things (IoT) allow the collection of Big Data from sensors placed at key points in the logistic supply chain. This project is a collaboration between several companies and universities, and partially funded by NWO. Within this project we are focusing on the following areas: - Logistics Internet of Things (Pervasive Systems Group) This PhD research will focus on a new approach to logistic processes involving cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed smart sensors and business logic. These entities, called Smart Returnable Transport Items (SRTIs), are capable of collecting data from extant IoT devices and social networks. The main goal is to detect unexpected behavior and do adaptive sampling of data generated by collaborating SRTIs in the region of interest, in order to solve problems such as, lost and damaged perishable goods during transportation, waste reduction, handling, and storage throughout the lifecycle of products based on sensing and location capabilities, etc. - IoT-driven resilient multimodal planning in smart logistics (Industrial Engineering and Business Information Systems Group) This PhD research will focus on a new approach to use this data input provided by cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed IoT devices in the real-time multi-modal planning of orders. More concretely, the main goal is to improve the resilience and performance of planning decisions in terms of sustainability, efficiency, queue delay, costs and quality, by developing Artificial Logistics Intelligence (ALI) capable of detecting and dealing with emergent phenomena (given large volumes of heterogeneous data) and capable of learning from the experience of human planners using machine learning.

Smart Circular Construction Ecosystems

The research objective of this project is twofold: 1. To propose a Circularity Information Platform (CIP) reference-architecture encompassing functionality, such as CDW-information-sharing, collaborative negotiation and decision-making support in the form of agent-based simulation framework, business analytics, circular supply chain design and coordination; 2. To develop and validate a bi-directional regulatory framework that realizes both the translation of high-level policies into actionable business rules (encapsulated in the CIP’s coordination tooling), and the elicitation (using CIP’s analytical tooling) of specific emergent systematic behaviour and industry requirements concerning a tailored Circular Economy policy-making process.

Reinforcement LeArning Platform for SMEs in Logistics (ReAL)

While real time data and sophisticated (deep) RL approaches are emerging, logistic organizations (in most cases SMEs) lack the tools and expertise to effectively identify whether (parts) of their business processes are suitable for reinforcement learning and adopt these state of the art research in their daily practice. Taking as starting point the results we booked in this area in our previous projects (ICCOS, and Autonomous Logistic Miners), the ReAL project combines the ideas behind the hybrid intelligence paradigm and combines RL with a common data model and industry standard (such as the Open Trip Model) in order to achieve data and process interoperability and support a broader range of logistics decision making processes. ReAL aims to (i) Design, develop, test and implement a RL-based decision support platform based on the Open Trip Model (OTM) and accessible to logistic SMEs. (ii) Complement the ReAL platform with teaching material and training to stimulate its dissemination of throughout the logistic SME community. ReAL will cover the following main functional components: a main common OTM-based data model, a tool for testing multiple RL algorithms and perform (hyper)parameter tuning, and an infrastructure for the management, execution and monitoring of RL agents. ). The platform will be complemented by training materials to transfer the platform, knowledge, and practices to the SMEs via Logistics Associations. ReAL contributes to the data-driven logistics theme by combining advanced data analytics, reinforcement learning and OTM as part of the Federated Data Sharing Infrastructure for the Dutch Logistics sector.

CLICKS IDS CONNECTOR STORE AND INTEROPERABILITY SIMULATOR FOR SMEs

This project develops the Logistic Data Space (LDS), which allows logistics companies to participate in digital ecosystems for efficient collaboration and new services. LDS consists of a Connector Store, offering connectors to realize data exchange between heterogeneous ICT environments, and an Interoperability Simulator, to explore collaboration opportunities prior to implementation.

Defining, Designing, and Implementing Rural Smartness. The case of West Java

This project has three main research goals: RG1: To understand the causal mechanisms underlying the realisation of rural smartness and subsequently leading to economic welfare improvement for rural citizens. For this purpose, we develop a theoretical model that depicts the interplay between the determinants of rural smartness and its implications on the economic welfare of rural citizens. Knowledge on the social artefact of rural smartness explained by this model will provide practical guidance for the implementation of initiatives toward rural smartness and allow us to identify the more accurate requirements of the IT artefacts supporting rural smartness. RG2: To design a reference architecture that could guide the provision of rural smartness platform: a digital service platform that facilitates the establishment of a Digital Business Ecosystem (DBE) tailored to improve the rural economic climate. We provide the reference architecture by means of an enterprise architecture design using the ArchiMate modelling language. The reason for using this approach is that enterprise architecture allows for a multi-layered approach to modelling, and ArchiMate is expressive enough to allow the explicit modelling of services and service exchanges. Furthermore, the design of the reference architecture is grounded in the theoretical model mentioned in RG1. RG3: To achieve the intended societal impact, i.e., the improvement of rural citizens’ economic welfare, the reference architecture needs to be implemented in a real setting. To provide guidance on how the reference architecture could be translated into the actual IT artefact, i.e., the rural smartness platform, we follow the technical action research (Wieringa & Morali, 2012) methodology, by working closely with the ICT agency of the provincial government of West Java, Indonesia. Furthermore, to understand how the rural communities (as the intended target group) are willing to use the platform as part of their business activities, we conducted an empirical study by means of a survey with respondents from rural areas of West Java.

Industry 4.0 driven Supply Chain Coordination for Small- & Medium-sized Enterprises (ICCOS)

ICCOS aims at improving the competitiveness of the Dutch logistics sector by increasing the adoption and usage of industry 4.0 related technologies in combination with advanced real-time data analytics. ICCOS proposes a Logistic Industrial Data Space (LIDS) architecture with applications in logistics decision making and planning.

Finished projects

Autonomous Logistics Miners for Small-Medium Businesses

The exponential data growth of the last years, emphasizes the importance for both small and large businesses to effectively process, analyze and act upon data. As a consequence, specialists are required to analyze ever increasing data sets, and transform them into actionable insights using state-of-the-art data mining algorithms. Although, most companies have access to a large volume of publicly available and own generated data, not all can afford to employ fully dedicated data mining teams to provide an advisory role. We aim to increase the competitive power of the Dutch logistics sector, by providing small-medium size businesses with intelligent data mining agents, that can perform the most common data mining functions, and require minimal supervision and domain knowledge from the human employee. Thus, we help businesses that are overwhelmed with data, and have limited resources to analyze it, by providing them with the ability to get the insight on overall performance factors, such as trends in supply/demand, estimating potential disruptions, and identifying the critical factors that cause shipment delays. Finally, we intend to create a symbiotic interaction between humans and intelligent agents, allowing humans to identify conditions of special interest to them, and enable the intelligent agents to do the routine work and continuously monitor data streams and raise awareness to the human operator, if such event is detected or likely to occur.

SynchromodalIT

This project tackles three major challenges: the need for a unified European logistic network, the need to improve the efficiency and sustainability of logistics services, and the ambition to trigger and flip the “mental switch” among shippers and 4PLs, towards a synchromodal approach. Such an approach delays decisions about the mode of transport for each stage of a route until the very last moment, allowing optimum flexibility in routing and improved service and sustainability. The main objective of this project is to facilitate efficient, reliable, and sustainable logistics services and to strengthen the Dutch logistics sector through: • the design of a synchromodal logistics network model and integrated service platform; • the development of related planning and scheduling policies, and of decision support through serious gaming.

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