Boute, Robert; Isik, Öykü; Kleer, Robin; Muylle, Steve; Vanderheyden, Karlien; Vereecke, Ann (2019)
Everyone is talking about Industry 4.0 – from the Internet of Things, additive manufacturing and the cloud through to artificial intelligence, augmented reality and blockchain. But what does it all mean in practice? It’s not a question of if Industry 4.0 will drastically change the way we do business. It’s a question of when and how quickly. So how can you apply this technology in your business – not just to improve production and performance, but to make a difference for customers? This white paper gives you the insight you need to get ahead of the game and prepare your organisation for the fourth industrial revolution.
Van den Broeke, Maud; De Baets, Shari; Vereecke, Ann; Baecke, Philippe; Vanderheyden, Karlien (Omega: the international Journal of Management Science, 2019)
Accurate demand forecasting is the cornerstone of a firm’s operations. The statistical system forecasts are often judgmentally adjusted by forecasters who believe their knowledge can improve the final forecasts. While empirical research on judgmental forecast adjustments has been increasing, an important aspect is under-studied: the impact of these adjustments over different time horizons. Collecting data from 8 business cases, retrieving over 307,200 forecast adjustments, this work assesses how the characteristics (e.g., size and direction) and accuracy of consecutive adjustments change over different time horizons. We find that closer to the sales point, the number of adjustments increases and adjustments become larger and more positive; and that adjustments, both close and distant from the sales point, can deteriorate the final forecast accuracy. We discuss how these insights impact operational activities, such as production planning.
Vereecke, Ann; Vanderheyden, Karlien; Baecke, Philippe; Van Steendam, Tom (International Journal of Operations and Production Management, 2018)
The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations. The authors developed a maturity assessment model for demand planning through iterations of theoretical and empirical work, combining insights from literature and practitioners. An online survey is developed to validate the model using data from different industries. The authors identify six dimensions of demand planning maturity: data management, the use of forecasting methods, the forecasting system, performance management, the organisation and people management. The empirical study indicates that demand data are well managed and organisation readiness is high, yet improvements in the forecasting system and the management of forecast performance are needed. The results show a positive relationship between the size of an organisation and its demand planning maturity.
Baecke, Philippe; De Baets, Shari; Vanderheyden, Karlien (International Journal of Production Economics, 2017)
Whilst the research literature points towards the benefits of a statistical approach, business practice continues in many cases to rely on judgmental approaches for demand forecasting. In today's dynamic environment, it is especially relevant to consider a combination of both approaches. However, the question remains as to how this combination should occur. This study compares two different ways of combining statistical and judgmental forecasting, employing real-life data from an international publishing company that produces weekly forecasts on regular and exceptional products. Two forecasting methodologies that are able to include human judgment are compared. In a 'restrictive judgement' model, expert predictions are incorporated as restrictions on the forecasting model. In an 'integrative judgment' model, this information is taken into account as a predictive variable in the demand forecasting process. The proposed models are compared on error metrics and analysed with regard to the properties of the adjustments (direction, size) and of the forecast itself (volatility, periodicity). The integrative approach has a positive effect on accuracy in all scenarios. However, in those cases where the restrictive approach proved to be beneficial, the integrative approach limited these beneficial effects. The study links with demand planning by using the forecasts as input for an optimization model to determine the ideal number of SKUs per Point of Sale (PoS), making a distinction between SKU forecasts and SKU per PoS forecasts. Importantly, this enables performance to be expressed as a measure of profitability, which proves to be higher for the integrative approach than for the restrictive approach.
Bouckenooghe, Dave; Cools, Eva; De Clercq, Dirk; Vanderheyden, Karlien; Fatima, T. (Learning and Individual Differences, 2016)
This study aims to clarify whether and how various configurations of three cognitive style dimensions (creating, knowing, and planning) emerge among graduate business students, with differential impacts on their learning approaches. With a person-centered, latent transition analysis of cognitive styles, the authors identify several distinct cognitive style profiles: a moderate cognitive style profile, a dominant creating and knowing style profile, a dominant creating and low planning style profile, and a dominant planning and low creating style profile. The analysis also offers evidence of the trait-like character of these cognitive style profiles, by demonstrating their temporal stability. Furthermore, significant differences arise across profiles in terms of how they relate to different learning approaches (strategic, deep, and surface learning).
Vanderheyden, Karlien; De Baets, Shari (Learning and Individual Differences, 2015)
This study seeks to investigate the effect of diversity in cognitive styles (deep-level variable) and gender and age (surface-level variables) in small teams (dyads), on satisfaction with the team and performance. A multisource study was conducted using 318 business school students, who were tested during a two-month, in-company project. Variables were measured at different time intervals, and performance was rated by an academic jury. Dyadic relationships proved to depend on the specific cognitive styles used — providing evidence for the complexity and multidimensionality of the concept. More specifically, diversity in knowing style led to less satisfaction, while diversity in planning style led to more satisfaction, and diversity in creating style had no effect. Satisfaction with the team in turn was positively linked to the performance of the team. Neither age diversity nor gender diversity had an effect on team satisfaction or performance.
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