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Do CFOs play a leading role in digitally transforming their SMEs? And if so, what does a successful digital CFO look like?
No full textOver the last years, the business environment has changed rapidly because of the introduction of new technologies, such as Artificial Intelligence (AI), robotics, and the Internet of Things (IoT). Nowadays, digitalisation should be high on the agenda of every organisation. This has only been emphasized by the COVID-19 pandemic. Now is the time to move forward and embrace the numerous opportunities that digitalisation has to offer. What many organisations lack, however, is someone that stands up and takes the lead in the organisation’s digital transformation process. The Centre for Financial Leadership and Digital Transformation puts forward the CFO as the one in an ideal position to take up a pioneering role in this journey. He/she can guide the organisation through this process in a successful way. In this white paper, we focus on three main questions. In a first step, we examine the current level of involvement of CFOs in their organisation’s digital transformation journey. Next, we verify whether the CFO’s involvement affects the organisation’s digital maturity and finally, we investigate how successful digital CFOs distinguish themselves from the others. In other words, we examine what it takes to become a successful digital leader. We believe that the results of our white paper, which are based on a survey among Belgian CFOs active in the SME landscape, are highly relevant for all finance leaders who want to embark on a successful digital transformation journey. The study is part of a master’s thesis by Michaël Caubergs and Juline Raemdonck (Master students, KU Leuven), together with supervisor Kristof Stouthuysen (Professor of Management Accounting & Digital Finance, Vlerick Business School) and coach Tineke Distelmans (doctoral researcher, Vlerick Business School)
Helping organizations and individuals develop conflict wisdom
No full textBurgess 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
Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems
No full textProblem 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
The value-based payment strategy for Antibiotics – Using the quantitative method to guide policy decision making
No full textGender Differences in Hedge Fund Performance Persistence
No full textThis paper analyses gender differences in hedge fund (HF) performance persistence using parametric and non-parametric risk-adjusted-performance persistence indicators. We find evidence consistent with performance persistence, which in relative (risk-adjusted) terms, is more pronounced amongst females, as opposed to male managers, in short to medium-term horizons. We also, observe a complete loss of persistence for the female managers in the long term, which for the male managers prevails and continues throughout all analysed periods. The findings contribute to the debate on the existence of differences in behaviour across males and females
Reskilling, upskilling and outskilling within the context of digital transformation
No full textWith organisations facing continuous change triggered by ongoing digital transformation, reskilling, upskilling and outskilling are turning out to be imperative. The Covid-19 pandemic has accelerated much of what was predicted about the future of work into a considerably shorter time frame. Supporting your employees to move into future opportunities through reskilling, upskilling or outskilling is part of managing a successful talent strategy. Companies worldwide are providing, on average, reskilling and upskilling initiatives to 62% of their workforce. In addition, we see the emergence of a new approach to outplacement — ‘outskilling’ — which is designed to give employees at high risk of layoff the skills and support needed to land another high-growth job. In this white paper, we will share some of the insights gained throughout the research and activities of the Vlerick Centre for Excellence in Strategic Talent Management. We will first zoom in on the definition of, and business case for, reskilling, upskilling and outskilling. Next, we will highlight 8 action steps for HR in developing their re-, up- or outskilling strategy. These 8 action steps provide a roadmap to shape your re-, up- or outskilling initiatives. Each step in the roadmap builds on those that come before. However, as the business landscape is evolving quickly, it is recommended to regularly revisit the steps to verify whether the approach should be adjusted to a new business reality
Lessons from practice: Extensions of current negotiation theory and research,
No full textNegotiation is in essence a practical skill. We identified the need to increase the number of academic contributions related to negotiation practice. The goal of this special issue – edited by Ramirez Marin, Druckman, and Donohue--is to call attention to areas in which research informs the practice, as well as areas in which the practice calls for advances in theory. The five papers included in this issue illustrate different ways in which practice can help academics extend the current theory. For example, describing how the predictions made by current theories can inform the practice, adapting and applying hostage negotiation principles to everyday negotiations, or testing the limits of current theories by adding external constraints and dependencies between and within negotiation issues. These examples can help researchers and teachers to bridge theory with practices as well as improve the way practitioners use evidence to improve their interventions
Allianz: Optimizing Customer Acquisition Strategy using Machine Learning
No full textThis case is suitable for courses at the graduate and executive levels, at the intersection of strategic (management) accounting, data science, and machine learning. Because the problem in the case study is closely related to sales and marketing, the case can also be used for courses in these subject areas. If students have some programming experience, it can enhance the case discussion, although it is not required. This case helps participants become more familiar with the application of data science and machine learning to address real-word business problems. Although the case focuses specifically on an example from the insurance industry, it can be relevant for anyone who wants an enhanced understanding of how a business can move toward a more data-driven decision-making process. Specifically, after completion of this case, students will be able to
o understand how to handle several data sets from scratch, using a general workflow;
o discuss the challenges of working with raw data;
o gain preliminary insights into structured data by making use of exploratory data analysis;
o work with unsupervised learning techniques to cluster customers into segments;
o understand how to train, use, and interpret supervised learning algorithms in a binary classification problem;
o use the insights derived from machine learning analyses to formulate concrete solutions to a business problem; and
o understand how machine learning can add value to the decision-making process.In October 2019, the regional chief data and analytics officer at Allianz AG, Belgium, attended a two-hour strategy meeting with the Allianz Benelux chief executive officer, who had expressed concerns about the company’s digitalization strategy. A few days earlier, the marketing department had found that online sales channel results had fallen unexpectedly. The chief executive officer was worried that the company could lose market share if it did not react accordingly, which would damage the company’s competitive position in the market. Therefore, the regional chief data and analytics officer was asked to gather a team to investigate why online sales were low and to design an effective customer acquisition strategy. In addition to his data office staff, the regional chief data and analytics officer asked for the business transformation unit to provide assistance. He had to consider how best to approach this challenging task