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How to stand out in a company’s global manufacturing network
No full textGlobal manufacturers are constantly revamping their network of plants. Surviving over the long term for a factory—especially one located in a high-cost country—can be extremely difficult. But an exception to the rule—a Pfizer plant in Belgium that ended up producing its mRNA Covid-19 vaccine—demonstrated that it can be done. An extensive study of the steps that the plant’s leaders took beginning in the mid-2000s—revealed how a plant can become indispensable. They took the facility through five stages: 1) improve operational excellence, 2) improve capabilities for making new products, 3) specialize, 4) increase responsiveness, and 5) build a knowledge network
Institutions and the real effects of private equity buyouts: A meta‐analysis
No full textAbstract Research Summary This study reviews four decades of fragmented and contradictory empirical literature on the real effects of private equity (PE) buyouts on portfolio companies, differentiating between efficiency and growth outcomes. We hypothesize how institutional forces, including regulatory, cognitive, and normative institutions explain heterogeneity in post‐buyout efficiency and growth across time and countries. We argue that competition and population‐level learning have shifted the cognitive frame underlying value creation in buyouts from financial engineering toward operational engineering and strategic entrepreneurship. Using meta‐analysis, we find support for several of our hypotheses using samples from 66 empirical studies across the finance, management, economics, and entrepreneurship disciplines. Managerial Summary This study delves into four decades of fragmented private equity (PE) literature to unravel the practical implications for post‐buyout efficiency and growth. Using meta‐analysis, we explore the role of institutional forces—regulatory, cognitive, and normative—in shaping outcomes across diverse temporal and geographical contexts. We observe an overall paradigm shift in PE value creation over time, transitioning from focusing on financial engineering to operational value creation and strategic entrepreneurship. This transformation is driven by heightened competition and widespread population‐level learning. Validating our hypotheses through a thorough examination of 66 empirical studies spanning finance, management, economics, and entrepreneurship disciplines, our findings offer insights for policymakers and practitioners navigating the nuanced landscape of PE buyouts
A dynamic learning-based genetic algorithm for scheduling resource-constrained projects with alternative subgraphs
No full textGenetic algorithms (GAs) are population-based algorithms widely applied for solving complex scheduling problems and such the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS) in which alternatives for work packages should be selected prior to project scheduling. The objective of this research is twofold. First, we develop a dynamic GA based on a new hybridisation of initialisation procedures, local searches based on learning approaches and restart schemes for scheduling problems in general. Second, we improve existing benchmark solutions for a large artificial dataset for the RCPSP-AS in particular. Our dynamic GA leverages existing constructive heuristics and priority rules to create a pool of high-quality initial solutions. Subsequently, these solutions are further improved by means of learning approaches that are designed as weight- or population-based local searches. In order to avoid getting stuck in a local optimum, various restart schemes are implemented. Based on our results, gradual learning and learning based on the population outperform other approaches for high-complex problem instances. Since metaheuristics — such as GAs — are mainly beneficial in complex problem settings, we are convinced that these research findings can inspire researcher when solving similar or other scheduling problems.(Research Foundation - Flanders
Year Report 2024 (research in the energy sector)
No full textThe 2024 Year Report for the PhD project between an energy business partner and Vlerick Business School highlights the research activities executed in 2024. Early in the year, a large dataset of customer service interactions was developed, linking conversation transcripts to customer satisfaction and recommendation scores. Using advanced Natural Language Processing (NLP) techniques, the study extracted emotional and linguistic features to analyze their impact on customer perceptions. A key focus was the application of machine learning models for Emotion Recognition in Conversations (ERC) and the integration of multimodal approaches combining textual and audio features.
Mid-year, research expanded into assessing service quality through Large Language Models (LLMs) and conducting statistical analyses on the effects of emotional alignment in service interactions. The findings were consolidated into an academic manuscript, which underwent rigorous peer review and was ultimately accepted for publication in a high-impact journal. Toward the end of the year, work progressed on refining multimodal emotion recognition models and developing predictive frameworks for customer satisfaction and recommendation outcomes. Future research will explore the trade-offs between different modeling approaches and their applications in real-world service environments
Workplace youngism: a scoping review of agism toward younger workers
No full textAbstract Workplace agism research has primarily focused on older workers, in spite of the fact that agism can also target younger workers. Given the growing body of research on workplace youngism (agism toward younger workers) and its need for assessment and synthesis, we conducted a scoping review aimed at mapping the range and characteristics of the workplace youngism literature. Unlike previous reviews, we focused exclusively on younger individuals in the workplace. A search of peer-reviewed literature identified 108 articles published between 1976 and 2022, containing 143 empirical studies from 21 countries, including 58,158 participants in total. The review is guided by three broad research questions: (1) How has workplace youngism been assessed?, (2) What are the main theories and conceptual approaches used to investigate workplace youngism?, and (3) What are the main antecedents and consequences of workplace youngism? For each research question, we summarize what is known and well-supported by evidence, identify disagreements and gaps in the literature, and propose directions for future research. This scoping review highlights the need for research on workplace youngism to broaden the range of methodological designs used, develop instruments specifically assessing agism toward younger workers, and extend the research to a wider range of national and cultural contexts. In addition, future research should investigate prescriptive age stereotypes toward younger workers, consider the perspective of middle-aged workers, pay greater attention to consequences of workplace youngism for the enablers, and extend the intersectional identities investigated
Comparison of two problem transformation-based methods in detecting the best performing branch-and-bound procedures for the RCPSP
No full textThe branch-and-bound (B&B) procedure is one of the most frequently used methods for solving the resource-constrained project scheduling problem (RCPSP) to obtain optimal solutions and has a rich history in the academic literature. Over the past decades, various variants of this procedure have been proposed, each using slightly different configurations to search for the optimal solution. While most of the configurations perform relatively well for many problem instances, there is, however, no known universal best B&B configuration that works well for all problem instances. In this work, we propose two problem transformation-based machine learning classification methods (binary relevance and classifier chains) to automatically detect the best-performing branch-and-bound configuration for the resource-constrained project scheduling problem. The proposed novel learning models aim to find the relationship between the project characteristics and the performance of a specific B&B configuration. With this obtained knowledge, the best-performing B&B configurations can be predicted, resulting in a better solution. A comprehensive computational experiment is conducted to demonstrate the effectiveness of the proposed classification models and the performance improvements over three categories of methods from the literature, including the latest branch-and-bound configurations, the state-of-the-art classification models in project scheduling, and commonly used clustering algorithms in machine learning. The results show that the proposed classification models can enhance solution quality for the RCPSP without changing the core components of existing algorithms. More specifically, the classifier chains method, when combined with the Back-Propagation Neural Network algorithm, achieves the best performance, outperforming binary relevance, which demonstrates the impact of label correlation on the performance. The experiments also demonstrate the merits of the proposed model in improving the robustness of the solutions. Furthermore, these findings not only highlight the potential of the classification models in detecting best-performing B&B configurations, but also emphasize the need for future work and development to further improve the performance and applicability of these models.(China Scholarship Council
A comparison of different clustering algorithms for the project time buffering problem
No full textInvestigating a decentralised approach to allocate time buffers to activity groups. Four clustering approaches are compared to group together similar activities. Two buffer allocation strategies are tested to size the buffers for activities. Multi-population multi-factorial evolutionary algorithm (MPMFEA) is developed. MPMFEA is proven to be effective, especially in combination with network clustering.This paper studies the decentralised time buffering problem (TBP) to absorb project risk by building sufficient buffers with the aim of obtaining a stable project schedule. First, the position of the buffers in the project network should be determined and, subsequently, each buffer must be optimally sized. We investigate different activity clustering methods (K-means, rank order, criticality-based and network clustering) to determine the ideal groups of activities to be clustered together and protected by an allocated buffer. The obtained clusters of activities are then inputted in a multi-population multi-factorial evolutionary algorithm (MPMFEA) for creating buffers based on the characteristics of the activities in each cluster. To the best of our knowledge, this is the first study to integrate existing clustering methods into a buffering algorithm in order to optimise the project stability. Previous studies hybridising both methods use a single clustering algorithm (e.g. K-means) that does not use the same information than the buffering algorithm or require more complex (simulation-based) buffering methods. The computational experiments on a large set of artificial instances validate the effectiveness of the proposed MPMFEA for solving the TBP, especially in combination with the network clustering method. Although the generic K-Means method is still considered a viable option for clustering, the more pragmatic clustering methods are more effective. We inform project managers that considering precedence relations between activities during clustering is crucial, but mimicking this behaviour in all clustering methods does not guarantee successful protection of their projects.This work was supported by the National Natural Science Foundation of China under Grants No. 72371195 and the Fonds of Wetenschappelijk Onderzoek (FWO) under Grant No. 12A4222N. The authors would like to acknowledge the China Scholarship Council for the financial support under Grant No. 202106280089
Bringing microaggressions from the shadows to the spotlight: Unveiling silencing mechanisms and distinct patterns in coping
No full textNegotiation intelligence: Onderhandelen omdenken
No full textOnderhandelingen bepalen de toekomst van partnerships, regeringen, akkoorden en leiderschap. In een snel veranderende wereld vallen we vaak terug op compromissen, machtsstrijd en kortetermijndenken. Om de toekomst vorm te geven, moeten we onderhandelen herdenken en onderhandelingsintelligentie (NQ®) ontwikkelen voor transformatie van onszelf, onze relaties en resultaten. Negotiation Intelligence biedt een grensverleggende onderhandelingsfilosofie, een beproefd samenwerkingsmodel en een groeipad naar duurzame gedragsverandering voor elke onderhandelaar. In dialoog met 24 maatschappelijke koplopers en aan de hand van krachtige beelden, presenteert dit boek een nieuwe kaart voor onderhandelen, die ieder van ons in staat stelt een verschil te maken als gamechangers en impactondernemers: Peter Adriaenssens, Bas Beerens, Peter Blom, Hans Bourlon, Edward Boute, Hanan Challouki, Wim Dejonghe, Jos Delbeke, Petra De Sutter, Frans de Waal, Dirk De Wachter, Dirk Frimout, Katleen Gabriels, Marc Herremans, Louis Jonckheere, Stefaan Lauwers, Roberto Martinez, Kim Swyngedouw, Sophie Vandebroek, Herman Van Rompuy, Saskia Van Uffelen, Koen Vanmechelen, Annelies Verlinden en Ann Wauters
