Vlerick Repository
The Vlerick Repository is a searchable open-access publication database, containing the complete archive of research output written by Vlerick Business School faculty and researchers.
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Publication A genetic algorithm for seafood processing with flexible flow shops and sequence-dependent setups(Springer, 2025)This paper studies a variant of the flexible Flow Shop Scheduling Problem as encountered at a large-scale Belgian seafood processing plant. The operations are conducted in two sequential stages as the seafood products are first filleted or prepared on specialised machines and then packaged through parallel machines. Since the packaging is product-specific, sequence-dependent setup times should be considered in the second stage. Improved scheduling of the operations would require fewer setups and thus efficiently planning the operations on the machines at the packaging station will be an important objective of this research. Furthermore, since the end product quality is crucial in the food industry and this is mainly determined by the speed of processing, the makespan will be minimised in this study. However, we further contribute to the existing literature by investigating several objectives that were relevant to the company’s management. The scheduling problem is solved using a single- and multi-pass algorithms that can easily be implemented in the seafood processing plant. Furthermore, a genetic algorithm with a focus on various diversity measures and problem-specific crossover and mutation operators is developed. Although the genetic algorithm is more difficult to implement, it allowed us to solve real world cases with over 100 orders daily within a reasonable computational time, resulting in an improved solution quality.Publication Leveraging machine learning for strategic performance management(KU Leuven, 2025)This dissertation investigates the use of machine learning (ML) in strategic performance management. While ML applications have been widely explored in financial accounting, their use in management accounting remains relatively underexamined. This research aims to fill this gap by demonstrating how ML can provide in different stages of strategic performance management, including identifying strategic groups, performance measurement and resource allocation. The first chapter explores the potential of ML algorithms to mitigate cognitive biases that managers face when analyzing performance data and making strategic resource allocation decisions. Through a computer-simulated business game, this study compares the effectiveness of ML-based budget allocation against human decision-making. The findings indicate that ML algorithms significantly outperform human participants in optimizing budget allocations, leading to improved organizational value creation. However, the results also highlight the complementary nature of ML and human strategic reasoning. While ML efficiently processes large datasets and uncovers complex, nonlinear relationships, human expertise is needed to align the resource allocation with broader strategic objectives. The second chapter applies an unsupervised learning approach to develop a more nuanced classification of business strategies in the airline industry. Existing research typically categorizes airlines into either focused or full-service strategies. However, recent industry trends suggest that some airlines are adopting hybrid strategies that blend elements of both approaches. Using fuzzy clustering, this study identifies such hybrid airlines and evaluates their performance relative to pure strategic positions. The results reveal that hybrid airlines often achieve superior financial performance, but only when they effectively manage their capacity utilization. If they fail to leverage their increased complexity into a better use of their capacity, the benefits dissapear. The third chapter leverages supervised learning techniques to examine the relationship between nonfinancial performance measures and profitability in the airline industry. By applying ML methods, this study takes an exploratory approach to identify key performance indicators that predict airline profitability, taking into account interactions and nonlinearities. The findings suggest that operational efficiency measures, such as load factors, labor productivity, and fuel consumption , are the strongest predictors of financial success. Moreover, the study uncovers interaction effects, such as the moderating impact of capacity utilization on service failures and a U-shaped relationship between customer complaints and profitability. These results highlight the importance of considering both direct and indirect effects of performance metrics in strategic decision-making. By integrating ML techniques into strategic performance management, this dissertation contributes to the management accounting literature by showcasing ML's ability to uncover hidden patterns, enhance decision-making, and optimize resource allocation.Publication Towards implementing new payment models for the reimbursement of high-cost, curative therapies in Europe: insights from semi-structured interviews(Frontiers Media SA, 2025)BACKGROUND: New ways of reimbursement for high-cost, one-shot curative therapies such as advanced therapy medicinal products (ATMPs) are a growing area of interest to stakeholders in market access such as industry representatives, legislative and accounting experts, physicians, hospital managers, hospital pharmacists, patient representatives, policymakers, and sickness funds. Due to the complex nature of ATMPs, new payment models and reimbursement modalities are proposed yet not widely applied across Europe. OBJECTIVES: This study aimed to elicit opinions on and insights into the governance aspect of implementing outcome-based spread payments (OBSP) in Belgium for the reimbursement of innovative therapies. Stakeholders' responsibilities and roles were analysed and proposed solutions or general beliefs were assessed to identify necessary or sufficient conditions to establish outcome-based spread payments. METHODS: Semi-structured interviews (n = 33) were conducted with physicians (n = 2), hospital pharmacists (n = 4), hospital managers (n = 2), Belgian policymakers (n = 6), legislative experts (n = 2), accounting experts (n = 5), representatives of patients (n = 3), of industry (n = 5), and sickness funds (n = 4). The interviews took place between July 2020 and October 2020. The framework method analysis was performed using Nvivo software (version 20.4.1.851). Statements were allocated into six main topics: payment structure, spread payments, outcome-based agreements, governance, transparency, and regulation. RESULTS: Interviews revealed the necessary conditions that, fulfilled together, are seen to be sufficient for the successful implementation of OBSP, including consensus on pricing, payment logistics, robust data infrastructure and financing, clear agreement terms (duration, outcome parameters, payment triggers), long-term patient follow-up solutions, an external multi-stakeholder governance body, and transparency regarding agreement types. CONCLUSION: Despite the interest, the effective implementation of OBSP falls behind due to a lack of consensus on how this new reimbursement method can be a sustainable solution. By stating the necessary conditions that, when fulfilled together, are deemed sufficient for successful OBSP implementation, this study provides a framework towards overcoming implementation barriers and realizing the potential of OBSP in transforming healthcare reimbursement practices.Publication Preparación del plan financiero – Proyecciones(Amazon, 2025)Este capítulo profundiza en los aspectos técnicos de la planificación financiera de una startup. El plan financiero es el punto de partida para cualquier estrategia en esta área. Su primer objetivo es determinar si la empresa requerirá financiamiento externo y, de ser así, qué cantidad de financiamiento necesitará y en qué momento. Además, este plan sirve como base para la valuación de la empresa. Por último, y probablemente lo más importante, brinda al equipo emprendedor y a los inversionistas potenciales la oportunidad de evaluar críticamente y optimizar el modelo de negocio.Publication Understanding ageism in the workplace (Research Summary)(Francisco Manuel dos Santos Foundation, 2024)An aging population has widereaching social, political and economic implications for societies. Changes in the workforce age composition can create social tensions, perceptions of threat and challenging workplace dynamics, especially between younger and older workers. It is crucial to better understand how younger and older workers see each other, and how this affects how they interact. Although the social psychological literature has provided important insights into ageist perceptions, attitudes, and behaviors, the lion’s share of research has focused on how older people are perceived. However, age biases can be directed at both younger and older adults/workers. This research summary presents key findings of a three-year research project on bidirectional ageism in Portugal, supported by the Fundação Francisco Manuel dos Santos. Data were collected from different samples, including a 1,000-person representative sample of the Portuguese population. Over 20 surveys were conducted, and an experiment was run to rigorously show effects of ageism on workplace outcomes. A variety of qualitative and quantitative approaches were used to analyse the data. A thorough review of relevant literature was conducted, and experts within and outside of Portugal were consulted along the way. Recommendations are provided for addressing ageism against both younger and older workers.