Recent Submissions

  • Physical asset management in the fourth industrial revolution: mapping the literature for condition-based maintenance

    Samii, Behzad; Sandu, Georgiana (2021)
    The low-carbon energy transition depends on adopting renewable energy technologies and requires affected industries to adopt best practices for optimized performance, reliable and sustainable operations. Consequently, the legacy energy distribution system faces many challenges in supporting grid stability, reliability, efficiency, and security under this transition setting. Herein, we adopt a quantitative text analysis of 310 articles complemented by a qualitative review to identify how and to what extent the fourth industrial revolution reflects in the energy distribution system. To this end, we map the literature for the technological innovations that support condition-based maintenance of electricity distribution grid under the Industry 4.0 principles. We emphasize how physical asset management enabled by the Industry 4.0 principles, i.e., interconnection, information transparency, automation, decentralized decisions and sustainability, can become a source of competitive advantage for utilities. This study helps place the physical assets portfolio at the core of strategic decisions aimed at attaining a more sustainable state, operational excellence, and economic prosperity.
  • Fluvius drives towards sustainability: A case on rare earth elements (Ree) supply integrity

    Andersen, Stephen; Sandu, Georgiana; Samii, Behzad (2020)
    As Supply Chain Manager at Fluvius, the Belgian distribution system operator, Gunther wants to formulate a plan that can generate incentives to move towards greater energy supply chain sustainability and resilience. The low-carbon energy transition relies on rare earth elements (REEs)-enabled technologies tainted by their harsh mining ecosystem effects and their Chinese policies dependence. Gunther, adopting a holistic approach, analyses the complexity of the global, green energy supply chain. What does a sustainable energy supply chain actually mean? How to create a cascade of sustainable practices that reaches first-tier suppliers? How to couple resilience and sustainability and contribute to sustainable development? This case is designed to be exposed in Business Administration, Energy Management, Supply Chain Management, Operations Management or Technology Management courses. The goal is to develop and practice skills in identifying trends and weaknesses in a dynamic supply chain and to formulate an action plan that can integrate sustainability and resilience across an organization's supply chain.
  • Early alzheimer disease round table project: Preparedness of the Belgian health system

    Van Dyck, Walter; Vandenberghe, Rik; Salmon, Eric; Hanseeuw, Bernard; De Lepeleire, Jan; Govaerts, Laurenz (2022)
    Disease modifying therapies (DMT) in the field of Alzheimer’s disease becoming accessible will require a transformation of Belgian health care practice. Early diagnosis is a crucial first step for these therapies as the maximal benefit outcome is expected if treatment is started as early as possible. This health policy-preparing paper resulting from a Belgian Early AD Round Table, complemented by an anonymized memory clinics survey and a computer simulation, was geared to investigate the Belgian healthcare system infrastructural preparedness to receive a DMT in the field of Alzheimer disease, which represents a high unmet clinical and societal need. Key summary recommendations include; • Conducting an awareness campaign towards the broader public as of a DMT becoming available; • Increasing GP awareness of implementation guidelines of the early-AD care and diagnostic pathway stressing multi-professional collaboration on diagnostic strategies; • To expedite patient diagnosis and treatment by considering reimbursement of CSF analysis, regardless of their use in symptomatic treatment or –even more so– DMT-available contexts; • CSF analysis cost-effectiveness is shown to require transversal budget impact analysis considering societal costs; • In the long run, to redesign the Belgian Memory Clinics Convention to act as the guardian of a national uniform quality AD health service offering; • To organically grow the present memory clinic-based loco-regional approach to AD treatment, which would result into a higher number of memory clinics acting upon a revised DMT-based health service offering; • To invest cost-effectively in the competence and skills of the informal caregiver; • To set up industry-independent societally funded national AD & dementia registries characterized as care registries and diagnosis/syndrome-specific quality of care registries. Please also consult the recommendations following the public presentation of these study results under Chapter 7 – Conclusions and Recommendations.
  • Smart metering interoperability issues and solutions: Taking inspiration from other ecosystems and sectors

    Reif, Valerie; Meeus, Leonardo (Utilities Policy, 2022)
    Interoperability in the context of smart electricity metering is high on the European policy agenda, but its essence has been challenging to capture. This paper looks at experiences in other ecosystems (electromobility and buildings), in other sectors (healthcare and public administration), and at the national level in the Netherlands and the UK. We show that the definition of interoperability depends on the context, that there are common solutions to different issues across sectors and that cross-sectoral factors must be increasingly considered. We recommend adopting a broader view in smart metering beyond the interoperability of devices, considering solutions that have worked in other sectors and exploiting synergies across sectors. Our analysis of experiences provides a comparison that can help move the debate at the EU level forward.
  • A reduction tree approach for the discrete time/cost trade-off problem

    Van Eynde, Rob; Vanhoucke, Mario (Computers & Operations Research, 2022)
    The Discrete Time/Cost Trade-Off Problem is a well studied problem in the project scheduling literature. Each activity has multiple execution modes, a solution is obtained by selecting a mode for each activity. In this manuscript we propose an exact algorithm to obtain the complete curve of non-dominated time/cost alternatives for the project. Our algorithm is based on the network reduction approach in which the project is reduced to a singular activity. We develop the reduction tree, a new datastructure that tracks the modular decomposition structure of an instance at each iteration of the reduction sequence. We show how it is related to the complexity graph of the instance. Several exact and heuristic algorithms to construct a good reduction tree are proposed. Our computational experiments show that the use of the reduction tree provides significant speedups when compared to the existing reduction plan approach. Although the new approach does not outperform the best performing branch-and-bound procedure from the literature, the experiments show that incorporating modular decomposition can provide significant performance improvements for solution algorithms, showing potential for developing improved hybridized procedures to solve this challenging problem type.
  • Lending when relationships are scarce: The role of information spread via bank networks

    Alperovych, Yan; Divakaruni, Anantha; Manigart, Sophie (Journal of Corporate Finance, 2022)
    We investigate how information flows within bank networks facilitate syndicate formation and lending in the leveraged buyout (LBO) market, where relationships between banks and borrowers are scarce and borrower opacity is high. Using novel measures that characterize a bank’s ability to source and disseminate information within its loan syndication network, we show that the extent of this capability influences which banks join the syndicate, the share the lead bank holds, and LBO borrowing terms. Banks’ ability to source and disseminate network-based information is particularly useful when ties to prospective borrowers are lacking, with the information flows extending beyond knowledge on PE firms and LBO targets.
  • A joint replenishment production-inventory model as an MMAP[K]/PH[K]/1 queue

    Noblesse, Ann M.; Sonenberg, Nikki; Boute, Robert; Lambrecht, Marc R.; Van Houdt, Benny (Stochastic Models, 2022)
    In this paper we analyse a continuous review finite capacity production-inventory system with two products in inventory. With stochastic order quantities and time between orders, the model reflects a supply chain that operates in an environment with high levels of volatility. The inventory is replenished using an independent order-up-to (s, S) policy or a can-order (s, c, S) joint replenishment policy in which the endogenously determined lead times drive the parameters of the replenishment policy. The production facility is modelled as a multi-type MMAP[K]/PH[K]/1 queue in which there are K possible inventory positions when the order is placed and the age process of the busy queue has matrix-exponential distribution. We characterize the system and determine the steady state distribution using matrix analytic methods. Using numerical methods we obtain the inventory parameters that minimize the total ordering and inventory related costs. We present numerical comparisons of independent and joint replenishment policies with varying lead times, order quantities, and cost reductions. We further demonstrate the interplay between the two products in terms of lead times, order quantities and costs.
  • A resampling method to improve the prognostic model of end-stage kidney disease: A better strategy for imbalanced data

    Shi, Xi; Qu, Tingyu; Van Pottelbergh, Gijs; van den Akker, Marjan; De Moor, Bart (Frontiers in Medicine, 2022)
    Background: Prognostic models can help to identify patients at risk for end-stage kidney disease (ESKD) at an earlier stage to provide preventive medical interventions. Previous studies mostly applied the Cox proportional hazards model. The aim of this study is to present a resampling method, which can deal with imbalanced data structure for the prognostic model and help to improve predictive performance. Methods: The electronic health records of patients with chronic kidney disease (CKD) older than 50 years during 2005–2015 collected from primary care in Belgium were used (n = 11,645). Both the Cox proportional hazards model and the logistic regression analysis were applied as reference model. Then, the resampling method, the Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbor (SMOTE-ENN), was applied as a preprocessing procedure followed by the logistic regression analysis. The performance was evaluated by accuracy, the area under the curve (AUC), confusion matrix, and F3 score. Results: The C statistics for the Cox proportional hazards model was 0.807, while the AUC for the logistic regression analysis was 0.700, both on a comparable level to previous studies. With the model trained on the resampled set, 86.3% of patients with ESKD were correctly identified, although it was at the cost of the high misclassification rate of negative cases. The F3 score was 0.245, much higher than 0.043 for the logistic regression analysis and 0.022 for the Cox proportional hazards model. Conclusion: This study pointed out the imbalanced data structure and its effects on prediction accuracy, which were not thoroughly discussed in previous studies. We were able to identify patients with high risk for ESKD better from a clinical perspective by using the resampling method. But, it has the limitation of the high misclassification of negative cases. The technique can be widely used in other clinical topics when imbalanced data structure should be considered.
  • Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems

    Gijsbrechts, Joren; Boute, Robert; Van Mieghem, Jan A.; Zhang, Dennis J. (Manufacturing & Service Operations Management, 2022)
    Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory problems? Academic/practical relevance: Given that DRL has successfully been applied in computer games and robotics, supply chain researchers and companies are interested in its potential in inventory management. We provide a rigorous performance evaluation of DRL in three classic and intractable inventory problems: lost sales, dual sourcing, and multi-echelon inventory management. Methodology: We model each inventory problem as a Markov decision process and apply and tune the Asynchronous Advantage Actor-Critic (A3C) DRL algorithm for a variety of parameter settings. Results: We demonstrate that the A3C algorithm can match the performance of the state-of-the-art heuristics and other approximate dynamic programming methods. Although the initial tuning was computationally demanding and time demanding, only small changes to the tuning parameters were needed for the other studied problems. Managerial implications: Our study provides evidence that DRL can effectively solve stationary inventory problems. This is especially promising when problem-dependent heuristics are lacking. Yet, generating structural policy insight or designing specialized policies that are (ideally provably) near optimal remains desirable.
  • Volume flexibility at responsive suppliers in reshoring decisions: Analysis of a dual sourcing inventory model

    Gijsbrechts, Joren; Boute, Robert; Disney, Stephen M.; Van Mieghem, Jan A. (Production and Operations Management, 2022)
    We investigate how volume exibility, de ned by a sourcing cost premium beyond a base capacity, at a local responsive supplier impacts the decision to reshore supply. The buyer also has access to a remote supplier that is cheaper with no restrictions on volume exibility. We show that with unit lead time difference between both suppliers, the optimal dual sourcing policy is a modi ed dual base-stock policy with three base-stock levels Sf2 , Sf1 , and Ss. The replenishment orders are generated by rst placing a base order from the fast supplier of at most k units to raise the inventory position to Sf 1 , if that is possible. After this base order, if the adjusted inventory position is still below Sf 2 , additional units are ordered from the fast supplier at an overtime premium to reach Sf 2 . Finally, if the adjusted inventory position is below Ss, an order from the slow supplier is placed to bring the nal inventory position to Ss. Surprisingly, in contrast to single sourcing with limited volume exibility, a more complex dual sourcing model often results in a \simpler" policy that replaces demand in each period. The latter allows analytical insights into the sourcing split between the responsive and the remote supplier. Our analysis shows how increased volume exibility at the responsive supplier promotes the decision to reshore operations and effectively serves as a cost bene t. It also shows how investing in base capacity or additional volume exibility act as strategic substitutes.
  • Helping organizations and individuals develop conflict wisdom

    Jordaan, Barney (Conflict Resolution Quarterly, 2022)
    Burgess et al. (BBK) propose to address hyper-polarized, society-wide conflicts through what they call a “massively parallel” approach “seeking to cultivate large numbers of independent but mutually reinforcing projects each addressing particular aspects of hyper-polarization in specific contexts.” The authors propose that these goals be pursued in two mutually reinforcing activity streams. The first involves traditional multiparty conflict resolution processes (e.g., community dialogues and multiparty negotiations) conducted under the guidance of third-party interveners. The second addresses the causes of hyperpolarized conflicts, for example, by instituting changes to electoral systems to try to minimize opportunities for hyperpolarization to occur. This commentary focuses on particular aspects of the second stream, that is, addressing what BBK call “the real energy behind hyperpolarized politics.” Chief among these are the emotional triggers that typically fuel conflicts such as anger, fear, and desperation. Initiatives such as showing people how a better understanding of conflict dynamics can help them defend their legitimate interests, while also pointing out the dangers of allowing conflict to escalate and cause polarization.
  • New summary measures and datasets for the multi-project scheduling problem

    Van Eynde, Rob; Vanhoucke, Mario (European Journal of Operational Research, 2022)
    In recent years, more researchers have devoted their attention to the resource-constrained multi-project scheduling problem, resulting in a growing body of knowledge on solution procedures. A key factor in the comparison of these procedures is the availability of benchmark datasets that cover a large part of the feature space. Otherwise, one risks that the conclusions from experiments on these sets do not hold when they are repeated on a different set. In this paper we propose new multi-project datasets that contain instances with a wide variety of characteristics. We first develop several new summary measures that describe three types of portfolio characteristics, two of the three types are not present in any of the existing datasets. Second, an algorithm is developed that can generate instances with the desired parameter values in a controlled manner. With this procedure, we create three datasets that each focus on one of the characteristics and a fourth dataset that contains all combinations. The computational results show (a) that these sets cover a significantly larger part of the feature space than existing benchmark libraries and (b) that they are more challenging for advanced algorithms.
  • An energy system model to study the impact of combining renewable electricity and gas policies

    Roach, Martin; Meeus, Leonardo (2021)
    Energy system models are needed to help policy makers design renewable energy policies that combine support for renewable electricity with support for renewable gas. In this paper, we advance a stylized model that includes demand for electricity, heating, and hydrogen in industry that is supplied by competing technologies. We first show that the status quo in most countries, which is a combination of carbon pricing with support for renewable electricity, only supports green gases indirectly and in a limited way. When we then add direct support for renewable gas to the model, we have two main findings. First, a Renewable Energy Sources - Gas (RES-G) target is more effective in supporting biomethane than in supporting green hydrogen. Second, there are strong interaction effects between a RES-E target and a RES-G target that can be both complementary and substitutive.
  • The digital future of internal staffing: A vision for transformational electronic human resource management

    Rogiers, Philip; Viaene, Stijn; Leysen, Jan (Intelligent Systems in Accounting Finance & Management, 2020)
    Through an international Delphi study, this article explores the new electronic human resource management regimes that are expected to transform internal staffing. Our focus is on three types of information systems: human resource management systems, job portals, and talent marketplaces. We explore the future potential of these new systems and identify the key challenges for their implementation in governments, such as inadequate regulations and funding priorities, a lack of leadership and strategic vision, together with rigid work policies and practices and a change-resistant culture. Tied to this vision, we identify several areas of future inquiry that bridge the divide between theory and practice.
  • Identifying digital transformation paradoxes: A design perspective

    Danneels, Lieselot; Viaene, Stijn (Business & Information Systems Engineering, 2022)
    In turbulent contexts, organizations face contradictory challenges which give rise to management tensions and paradoxes. Digital transformation is one such context where the disruptive potential of digital technologies demands radical responses from existing organizations. While prior research has recognized the importance of coping with organizational paradoxes, little is known about how to identify them. Although it may be apparent in some settings which paradoxes are at play, other more ambivalent contexts require explicit identification. This study takes a design perspective to identify the relevant paradoxes in a digital transformation context. It presents the results of a 2-year action design research study in collaboration with an organization that chose to explicitly focus on paradoxical tensions for managing its digital transformation. The study's main contribution is twofold: (1) it presents design knowledge to identify organizational paradoxes; (2) it provides a better understanding of the organizational paradoxes involved in digital transformation. The design knowledge will help others to identify paradoxes when working with an organization and highlights dynamic and collaborative aspects of the identification process. The study also enhances the descriptive understanding of digital transformation paradoxes by showing the importance of learning and belonging tensions and by expressing a different view on what knowledge about paradoxes is, and how it is created and used.
  • The DNA of a digital financial leader. How to develop a digital transformation strategy for the finance function and what are the main characteristics of a digital finance leader

    Stouthuysen, Kristof; Decorte, Thomas; Heyvaert, Carl-Erik (2022)
    With the continuous development and adoption of new technologies and trends – such as cloud computing, robotics, blockchain and Artificial Intelligence (AI) – and the availability of vast amounts of data, people’s roles in the finance function are undeniably changing. Furthermore, the COVID-19 pandemic has served as a wake-up call for many finance leaders to start investing in the digitalisation of their department. Indeed, today’s finance leaders have the opportunity to act as a catalyst for reshaping the business strategy and deploying AI technologies to digitally revolutionise the finance function and enhance their value creation and value protection role. The digital journey, however, encompasses more than the mere adoption of several technologies: it involves many challenges that go beyond the obvious technical aspects, such as organisational and cultural difficulties. As the finance function must not lag behind in the digital era, a crucial task awaits finance leaders: they must reinvent themselves and their department in order to successfully embark on this digitalisation journey. We believe our synthesis of how to formulate a digital transformation strategy, along with the 9 must-have characteristics of a true digital finance leader, can equip these leaders to successfully tackle the upcoming challenges of digitalisation! Our Centre for Financial Leadership and Digital Transformation has the ambition to reach out to all finance leaders to help them embark on a successful digital transformation journey and become true digital finance leaders. In our first workshop, the Centre tackled the question: ‘What should the finance leader of the future look like?’ The insights of that workshop were gathered and analysed and resulted in this white paper. A must-read for each finance leader whose aim is to inspire, support, and empower his/her department to embrace the digitalisation of the finance function.
  • The joint replenishment problem: Optimal policy and exact evaluation methody

    Creemers, Stefan; Boute, Robert (European Journal of Operational Research, 2022)
    We propose a new method to evaluate any stationary joint replenishment policy under compound Poisson demand. The method makes use of an embedded Markov chain that only considers the state of the system after an order is placed. The resulting state space reduction allows exact analysis of instances that until now could only be evaluated using approximation procedures. In addition, the size of the state space is not affected if we include nonzero lead times, backlog, and lost sales. We characterize the optimal joint replenishment policy, and use these characteristics to develop a greedy-optimal algorithm that generalizes the can-order policy, a well-known family in the class of joint replenishment policies. We numerically show that this generalized can-order policy only marginally improves the best conventional can-order policy. For sizeable systems with multiple items, the latter can now be found using our exact embedded Markov-chain method. Finally, we use our method to improve and extend the well-known decomposition approach.
  • Variability drivers of treatment costs in hospitals: A systematic review

    Jacobs, Karel; Roman, Erin; Lambert, Jo; Moke, Lieven; Scheys, Lennart; Kesteloot, Katrien; Roodhooft, Filip; Cardoen, Brecht (Health Policy, 2022)
    Objectives Studies on variability drivers of treatment costs in hospitals can provide the necessary information for policymakers and healthcare providers seeking to redesign reimbursement schemes and improve the outcomes-over-cost ratio, respectively. This systematic literature review, focusing on the hospital perspective, provides an overview of studies focusing on variability in treatment cost, an outline of their study characteristics and cost drivers, and suggestions on future research methodology. Methods We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane Handbook for Systematic Reviews of Interventions. We searched PubMED/MEDLINE, Web of Science, EMBASE, Scopus, CINAHL, Science direct, OvidSP and Cochrane library. Two investigators extracted and appraised data for citation until October 2020. Results 90 eligible articles were included. Patient, treatment and disease characteristics and, to a lesser extent, outcome and institutional characteristics were identified as significant variables explaining cost variability. In one-third of the studies, the costing method was classified as unclear due to the limited explanation provided by the authors. Conclusion Various patient, treatment and disease characteristics were identified to explain hospital cost variability. The limited transparency on how hospital costs are defined is a remarkable observation for studies wherein cost variability is the main focus. Recommendations relating to variables, costs, and statistical methods to consider when designing and conducting cost variability studies were provided.
  • Nieuwe technologieën en het auditberoep - La profession d'audit et les nouvelles technologies

    Abadjadi, L.; de Bonhome, O.; Kordel, L.; Jans, M.; Laghmouch, M.; Trumpener, J.; Kroes, D.; Stouthuysen, Kristof (2021)
    Onderhavig boek behandelt de nieuwe technologieën en het auditberoep. Het eerste hoofdstuk betreft de cyberveiligheid en tracht de bedrijfsrevisor elementen aan te reiken om cyberbeveiliging te demystificeren en hem aan te moedigen cyberbeveiliging in een mondiale context te benaderen. In het tweede hoofdstuk wordt dieper ingegaan op artificiële intelligentie, waarbij het de ultieme doelstelling is om systemen te laten denken als mensen. Deze nieuwe technologie zal een belangrijke impact hebben op de toekomstige beroepsinhoud van een auditor. Het derde hoofdstuk behandelt process mining, een verzamelnaam voor alle datagedreven procesanalysetechnieken. Er wordt ingegaan hoe de verschillende process mining-analyses auditors kunnen ondersteunen. Blockchain-technologie en de impact ervan op de audit komt aan bod in het vierde hoofdstuk. Het gaat om een samenspel van bestaande methoden en technieken die toelaten op een unieke manier digitale activa te registreren, te beheren en te verhandelen, zonder tussenkomst van een vertrouwde tussenpersoon. Het vijfde hoofdstuk beschrijft de cloud en het informatiebeveiligingsbeheersysteem. Het boek eindigt met de vraag of een auditkantoor belang zou hebben bij de overstap naar de cloud. Le présent ouvrage traite de la profession d’audit et les nouvelles technologies. Le premier chapitre concerne la cybersécurité et tentera d’apporter au réviseur d’entreprises des éléments afin de démystifier la cybersécurité et de l’encourager à aborder la cybersécurité dans un contexte global. Dans le deuxième chapitre l’intelligence artificielle est abordée, dont le but ultime est de faire en sorte que les systèmes pensent comme les humains. Cette nouvelle technologie aura un impact important sur le futur de la profession d’auditeur. Le troisième chapitre traite de process mining, un nom général pour toutes les techniques d’analyse de processus axées sur les données. On aborde la manière dont les différentes analyses de process mining peuvent soutenir les auditeurs. Le quatrième chapitre traite de la technologie blockchain et son impact sur l’audit. Il s’agit d’une combinaison de méthodes et de techniques existantes qui permettent d’enregistrer, de gérer et d’échanger des actifs numériques de manière unique, sans l’intervention d’un intermédiaire de confiance. Le cinquième chapitre décrit le cloud et le système de management de la sécurité de l’information. L’ouvrage se termine avec la question de savoir si un cabinet d’audit aurait un intérêt à passer sur le cloud.
  • A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data

    van Staden, Heletjé E.; Deprez, Laurens; Boute, Robert (European Journal of Operational Research, 2022)
    We investigate whether historical machine failures and maintenance records may be used to derive future machine failure estimates and, in turn, prescribe advancements of scheduled preventive maintenance interventions. We model the problem using a sequential predict, then optimize approach. In our prescriptive optimization model, we use a finite horizon Markov decision process with a variable order Markov chain, in which the chain length varies depending on the time since the last preventive maintenance action was performed. The model therefore captures the dependency of a machine’s failures on both recent failures as well as preventive maintenance actions, via our prediction model. We validate our model using an original equipment manufacturer data set and obtain policies that prescribe when to deviate from the planned periodic maintenance schedule. To improve our predictions for machine failure behavior with limited to no past data, we pool our data set over different machine classes by means of a Poisson generalized linear model. We find that our policies can supplement and improve on those currently applied by 5%, on average.

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