Vlerick Repository
Not a member yet
7319 research outputs found
Sort by
Thriving in Pfizer's Global Production Network: Post COVID-19 Strategy for the Puurs (Belgium) Site
No full textThe case describes the trajectory of Pfizer's plant in Puurs, Belgium, towards becoming a lead plant within Pfizer's international network. Additionally, it examines how the plant can maintain this position in the future. Having been the exclusive site outside the United States for producing the COVID-19 vaccine, the key question emerges: how should the plant evolve, and what additional capabilities should it cultivate to continue its leadership role? The case delves into the journey of the Belgian plant, navigating through the aftermath of mergers, acquisitions, and restructuring within Pfizer's global production network from 2000 to 2020. It chronicles the development of crucial capabilities that not only ensured the plant's continued existence, but also how it garnered the trust of senior management to undertake in 2020 the pivotal task of producing the COVID-19 vaccine, which it accomplished with phenomenal success. The case ends with Luc Van Steenwinkel, VP Operations and Site Leader, and his team wondering what the best strategy would be for the plant to sustain its successful trajectory. Should it prioritize increasing its scale or broadening its scope? Or could it do both
A Quest for Projects with Scarce Resources, Seeking Schedule Intelligence Through Project Data Discovery
No full textBased on the shared journey of two researchers, this book explores enhancing algorithms for the resource-constrained project scheduling problem. It examines the search for and significance of project data from multiple, distinct perspectives. In the first part, the quest for project data is presented as a continuous exploration of the complexity of the resource-constrained project scheduling problem. This quest is pursued by solving this challenging problem with the aid of state-of-the-art algorithms from the literature, each time gaining a deeper understanding of its challenging nature. To provide insights into the problem’s complexity, project data is created, manipulated, and analyzed in depth to make current projects easier or harder to schedule. This challenging quest for project data has resulted in new project databases for academic research, new ways of testing future algorithms, and insights into how to improve future algorithms to solve this project scheduling problem with limited resources. In turn, the second part discusses the relevance of project data, demonstrating to the reader the importance of the academic research presented in the first part for the professional world. It shows how project data can be used to calibrate real project data, leading to improved decision-making, e.g. for project scheduling, forecasting, and risk analysis. The book extends a warm invitation to academics and practitioners alike, as fellow seekers of knowledge, to enhance their project management skills
Nonstandard Errors
No full textABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.(FWF Austrian Science Fund, Dutch Research Council, Bank of Sweden Tercentenary Foundation, Knut and Alice Wallenberg Foundation
Resource Dependencies and the Legitimatization of Grocery Retailer’s Social Evaluations of Suppliers
No full textMultinational corporations (MNCs) are increasingly judged not only on their own social impacts but also on those of their supply chain partners. To reduce this environmental dependence, many MNCs implement social evaluations and codes of conduct which suppliers must follow. But how do MNCs legitimise and implement social evaluations in their supply chains? To address this, we draw on and augment resource dependence and legitimacy theories, to analyse a multinational grocery retailer’s implementation of labour standards for its fruit and vegetable suppliers. The case study utilises interviews, analysis of a database of audits, internal documents, and observational data. It provides the basis for theorizing corporate reputation as a resource dependency, with social evaluations a distinct means to co-opt external actors to preserve the focal organization’s autonomy while reducing environmental contingencies. The legitimacy of social evaluations of supply chain partners depends on processes that reconcile both moral and pragmatic concerns, allowing the focal organization to mitigate resource dependencies without ceding control over enforcement and enabling actions
How AI can help your company set a budget
No full textAI has been heralded — and put to use — as a groundbreaking new tool that companies can use in the budgeting process. But even companies that have embraced AI are still struggling with aspects of the budgeting process in today’s complex and rapidly changing business environment. Why is that? When does it make sense to rely on AI, and when does it not? In this article, the authors describe experiments they have conducted on the use of AI in the budgeting process — and conclude that AI can and should replace human managers in tactical tasks, where data-driven decision-making leads to faster and more efficient outcomes, but that in the strategic realm, where long-term planning, market adaptability, and business foresight are critical, human involvement and insight remain indispensable
ESG as a driver for strategic rewards. Enhancing ESG strategies through reward management: the case of collective bonuses
No full textThe corporate landscape increasingly focuses on sustainability, also driven by regulatory initiatives such as the Corporate Sustainability Reporting Directive. Collective bonuses, in turn, have been identified as an effective tool for aligning employee efforts with organisational goals, promoting a sense of shared purpose. As a consequence, they can also play a crucial role in the strategic embedding of sustainability. This white paper from the Centre for Excellence in Strategic Rewards explores the integration of ESG (Environmental, Social and Governance) indicators into collective incentive systems in Belgium under the framework provided by CLA 90, which allows tax-friendly collective bonuses when predefined goals are met. For this research, the centre made an inventory of Key Performance Indicators (linked to financial, environmental, social and governance) from CLA 90 agreements with a view of the legal and market dimensions of this incentive system
New product the bankruptcy of General Motors (GM)
No full textOn June 1, 2009, General Motors (GM) filed for bankruptcy under chapter 11, after more than 100 years of existence and 77 years as the number one car manufacturer in the world. Marking the largest bankruptcy in the corporate world, it remains intriguing to understand what caused GM's bankruptcy and whether could it have been avoided. One of the main reasons often proclaimed is the financial crisis, which in 2009 was coinciding. But besides this external shock, had other issues within the automotive industry or within GM itself an even stronger impact? To recover from a crisis or even bankruptcy, as GM attempted with the emergence of 'New GM' after the bankruptcy, it is crucial to have a thorough understanding of the specific issues at hand
A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks
No full textA forecasting model is proposed using Structural Equation Modeling and Bayesian Networks.
The model outperforms most of the static forecasting methods for both time and cost.
For time, the model outperforms dynamic forecasting methods in early and middle stages.
For cost, the model outperforms dynamic forecasting methods in early stages.
The model can be combined with the EVM to improve the overall forecasting accuracy.This paper presents a new hybrid forecasting model to predict the final time and cost of a project using input parameters from the project scheduling and risk analysis literature. The hybrid method integrates two well-known risk models. A Structural Equation Modeling constructs and validates a theoretical risk model to represent known relations between project indicators and the project performance. A Bayesian Networks is used to train the theoretical model using artificial project data from the literature. These two integrated models are then used to predict the final duration and cost of a new unseen project. The accuracy of this integrated model is compared with other well-known forecasting methods from the literature. The computational experiments on a set of 33 empirical projects show that risk models demonstrate a noteworthy advantage for time and cost forecasting. To show the usefulness of this method, it is compared with a set of known machine learning forecasting algorithms. These static predictions of risk models are also compared with some well-known dynamic forecasting methods that continuously update the time/cost predictions along the project progress. These dynamic models make use of predictors from the earned value management and earned duration management literature. The results show that the static risk models offer more precise forecasts than the dynamic methods in the first half of the project progress for time forecasting, but then loose their power in favor of the dynamic forecasts
Great ways to explore career expectations
No full textIt is 2024. Fighting for and retaining top talent is still a hot topic. Companies must continually reinvent themselves and commit to good HR practices to retain their people and attract new ones. Why? Because job candidates and employees are currently setting the ship's course. Strategically managing human capital continues to be fundamental for proactively addressing the demands of an increasingly competitive talent landscape. Can organisations impact their retention rate? And can organisations match their offerings to the expectations of new talent? This is where Great Place To Work and Vlerick Business School have joined forces with the support of our media partners: Références, HTag, Nieuwe Media Group, and HR Magazine. The aim of this study is to assist HR practitioners in navigating how to respond to the rapidly changing needs en expectations of talent. For this study, we sought employees currently working in Belgian organisations. The questionnaire explores career expectations and intentions, as well as the importance Belgian employees attach to the promises made by current or prospective employers. Through this academic approach, we hope to inspire organisations to draft an effective employer branding strategy
Machine learning for fraud detection
No full textDeveloping methodologies that can optimally model and detect fraud is of utmost importance, as undetected/unprevented fraud negatively impacts multiple stakeholders. We aim to address three major issues researchers and decision-makers encounter when developing such models: model overconfidence, bias and inconsistency. (Chapter 1: overconfidence) Many machine learning models make overconfident predictions outside the range of the training data, which severely negatively impacts the deployment and usefulness of these models in real life. This is a major issue in the field of fraud detection when selecting false positives wastes your limited resources. Furthermore, it causes decision-makers to lose confidence in the model as out-of-distribution predictions are not substantiated. In this paper, we develop machine learning models by assigning predetermined non-uniform class probabilities outside the training data, which positively affects the model's behavior and performance. (Chapter 2: bias) Traditional statistical methods and newer machine learning methods are used to identify predictors of financial misconduct periods. However, the partial observability of committed financial misconduct biases these prior findings. That is, it is crucial to not only consider misconduct firms labeled by the labeling mechanism but also account for unlabeled financial misconduct. In this paper, we use machine learning methods incorporating modeling partial observability by exploiting new and existing features to capture the labeling propensity. We show that our methodology improves the detection of future misconduct and identifies the predictors significantly affecting labeling propensity. By modeling partial observability, we aim to model all firms participating in financial misconduct instead of merely focusing on those labeled by the labeling mechanism. (Chapter 3: inconsistency) Fraud investigators under constrained resources cannot thoroughly examine every case. Therefore, such stakeholders should prioritize metrics that capture the benefit of flagging financial misconduct while limiting the cost of falsely accusing legitimate firms among a group of selected cases. However, the employed detection model is often not optimized and validated on these metrics, leading to subpar performance. This paper constructs customized financial misconduct detection models by optimizing suitable cost-sensitive performance metrics rather than relying on an ad-hoc approach. We illustrate that our methodology improves the economic validity of financial misconduct models on various financial misconduct proxies and cost structures