De Baets, S., & Harvey, N.. (2023). Incorporating external factors into time series forecasts (pg. 265 – 288), In Judgment in Predictive Analytics (Ed. Seifert, M.), International Series in Operations Research & Management Science, Springer NY.
De Baets, S., Abolghasemi, M., Van der Aeweraer, S., Sroginis, A., & Chojnowski, M. (2023). The IIF “Forecasting Impact” podcast. Foresight: the International Journal of Applied Forecasting, 67, 41 – 48.
Harvey, N., & De Baets, S. (2022). Commentary on “Representativeness: A New Criterion for Selecting Forecasts”. Foresight: the International Journal of Applied Forecasting, 65, 13-15.
De Baets, S., Önkal, D., & Ahmed, W. (2022). Do risky scenarios affect forecasts of savings and expenses? Forecasting, 22(1), 307-334. [Selected for cover of new issue]
Many people do not possess the necessary savings to deal with unexpected financial events. People’s biases play a significant role in their ability to forecast future financial shocks: they are typically overoptimistic, present-oriented, and generally underestimate future expenses. The purpose of this study is to investigate how varying risk information influences people’s financial awareness, in order to reduce the chance of a financial downfall. Specifically, we contribute to the literature by exploring the concept of ‘nudging’ and its value for behavioural changes in personal financial management. While of great practical importance, the role of nudging in behavioural financial forecasting research is scarce. Additionally, the study steers away from the standard default choice architecture nudge, and adds originality by focusing on eliciting implementation intentions and precommitment strategies as types of nudges. Our experimental scenarios examined how people change their financial projections in response to nudges in the form of new information on relevant risks. Participants were asked to forecast future expenses and future savings. They then received information on potential events identified as high-risk, low-risk or no-risk. We investigated whether they adjusted their predictions in response to various risk scenarios or not and how such potential adjustments were affected by the information given. Our findings suggest that the provision of risk information alters financial forecasting behaviour. Notably, we found an adjustment effect even in the no-risk category, suggesting that governments and institutions concerned with financial behaviour can increase financial awareness merely by increasing salience about possible financial risks. Another practical implication relates to splitting savings into different categories, and by using different wordings: A financial advisory institution can help people in their financial behaviour by focusing on ‘targets’, and by encouraging (nudging) people to make breakdown forecasts rather than general ones.
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., … & Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871.
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
Hewage, H.C., Perera, N., & De Baets, S. (2022). Forecast adjustments during post-promotional periods. European Journal of Operational Research, 300(2), 461-472..
Sales promotions are a key cause for judgmental adjustments to forecasts, especially in the world of Fast-Moving Consumer Goods (FMCG). Typically, three types of periods are relevant for sales promotions: a normal period as a comparison point, a promotional period, and a post-promotional period. Yet, research on forecasting with sales promotions has focussed specifically on promotional effects and their elevation versus a normal sales period, while generally disregarding adjustments for post-promotional periods. To investigate further into the effects of promotions after their occurrence, we employed an incentivized laboratory experiment focussing specifically on the role of human judgment in sales forecasting after a promotion has occurred. Our study shows that, without guidance, the post-promotional period is considered equal to a normal period in sales numbers. However, a post-promotion dip is commonly observed in reality. We therefore introduced information on the existence of post-promotional dips, in itself (treatment one) or with added average magnitude of such a dip (treatment two). Information provision resulted in increased forecasting accuracy in both treatments, but more so in treatment two. Thus, this study promotes the provision of guiding information. We find that structured support, allows an improvement in judgmental forecasting accuracy after a sales promotion.
De Baets, S., & Vanderheyden, K. (2021). Individual differences in the susceptibility to forecasting biases. Applied Cognitive Psychology, 35(4), 1106-114.
We set out to investigate whether interindividual differences in cognition affect the susceptibility to four forecasting biases: (a) optimism bias, (b) adding noise to forecasts, (c) presuming positive autocorrelation when series are independent, and (d) trend damping. All four biases were prevalent in the results, but we found no consistent relationships with cognition (cognitive style, cognitive reflection). Our sample included both novice and expert forecasters. They did not differ significantly in their susceptibility to biases. The lack of individual differences in bias susceptibility suggests that universal approaches to debiasing are possible.
De Baets, S. (2021). Surveying forecasting: directions for future research. International Journal of Information and Decision Sciences, 13(2), 111-126.
How is forecasting doing in today’s world? It’s a question researchers have been asking for a long time. For half a century, we have been surveying practitioners, conference attendees, other academics, managers and high-level executives. From the introduction of forecasting in organisations onward, we have questioned technique use and familiarity, accuracy and evaluation methods, the place of forecasting within organisations and the hurdles and barriers that prevent forecasting from evolving as fast in practice as it does in academia. This paper summarizes these findings and concludes with a number of recommendations for future surveys, as we will need to continue tracking the state of the art of forecasting practice. Recommendations includes surveying the analysts rather than the forecasting managers, using an international sample, focussing on process-oriented performance measures and looking into the barriers that prevent a more widespread adoption of sophisticated forecasting techniques. .
Önkal, D., & De Baets, S. (2020). Past-Future Synergies: Commentary on Schoemaker 2020. Futures and Foresight Science, 2:e51.
Commentary paper without abstract.
Available as open access at https://onlinelibrary.wiley.com/doi/full/10.1002/ffo2.51
Vereecke, A., Vanderheyden, K., Decroix, I., De Baets, S., & Cabos, A. (2020). In planning we trust: determinants of trust in human/system interaction. EUROMA 2020 conference paper, peer reviewed.
With the advent of sophisticated and automated planning systems in the supply chain environment, planners see parts of their job being lost to automation. This has an impact on the level of trust planners have in the system. Following grounded theory, an empirical model is constructed from interviews with a multinational pharmaceutical company to address the limitations of previous trust in automation research. These were found to be purely theoretically driven and machine-focused in experimental settings. Said model unearths several previous unknown determinants related to the trust formation process with a stronger emphasis on human and context factors.
Available on request via the contact form.
Önkal, D., Ahmed, W. & De Baets, S. (2020). Nudging for improved projections of future expenses and savings. Technical Report, ING Think Forward Initiative.
Many households do not possess the necessary savings to deal with unexpected financial events. People’s biases play a significant role in their ability to forecast future financial shocks: people are typically overoptimistic, present-oriented and generally underestimate future expenses. This project focusses on how scenario-based information can be used to nudge people’s financial awareness. Our scenario experiments examine how people change their financial projections in response to nudges in the form of new information on relevant risks. Participants are asked to forecast future expenses and future savings. They then receive information on potential events identified as high-risk, low-risk or no-risk. We investigate whether predictions are revised in response to various risk scenarios and how such potential adjustments are affected by the information given. Results reveal the important role that scenarios can play as reality-checks, leading to changes in initial forecasts, with different patterns observed for expenses vs savings projections. Our findings suggest that providing risk information via scenarios offers a prolific toolbox in designing nudges towards betterinformed financial forecasts and heightened financial awareness.
De Baets, S., & Harvey, N. (2020). Using judgment to select and adjust forecasts from statistical models. European Journal of Operational Research, 284(3), 882-895.
Forecasting support systems allow users to choose different statistical forecasting methods. But how well do they make this choice? We examine this in two experiments. In the first one (N = 191), people selected the model that they judged to perform the best. Their choice outperformed forecasts made by averaging the model outputs and improved with a larger difference in quality between models and a lower level of noise in the data series. In a second experiment (N = 161), participants were asked to make a forecast and were then offered advice in the form of a model forecast. They could then re-adjust their forecast. Final forecasts were more influenced by models that made better forecasts. As forecasters gained experience, they followed input from high-quality models more readily. Thus, both experiments show that forecasters have ability to use and learn from visual records of past performance to select and adjust model-based forecasts appropriately.
Van den Broeke, M., De Baets, S., Vereecke, A., Baecke, P., & Vanderheyden, K. (2019). Judgmental forecast adjustments over different time horizons. Omega, 87, 34-45.
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.
Önkal, D., Gönül, M. S., & De Baets, S. (2019). Trusting forecasts. Futures & Foresight Science, 1(3-4), e19.
Accurate forecasting is necessary to remain competitive in today’s business environment. Forecast support systems are designed to aid forecasters in achieving high accuracy. However, studies have shown that people are distrustful of automated forecasters. This has recently been dubbed “algorithm aversion.” In this study, we explore the relationship between trust and forecasts, and if trust can be boosted in order to achieve a higher acceptance rate of system forecasts and lessen the occurrence of damaging adjustments. In a survey with 134 executives, we ask them to rate the determinants of trust in forecasts, what trust in forecasting means to them, and how trust in forecasts can be increased. The findings point to four main factors that play a role in trusting forecasts: (a) the forecast bundle, (b) forecaster competence, (c) combination of forecasts, and (d) knowledge. Implications of these factors for designing effective forecast support and future‐focused management processes are discussed.
De Baets, S., & Harvey, N. (2018). Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support. International Journal of Forecasting, 34(2), 163-180.
How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.
Baecke, P., De Baets, S., & Vanderheyden, K. (2017). Investigating the added value of integrating human judgement into statistical demand forecasting systems. International Journal of Production Economics, 191, 85-96.
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.
De Baets, S. (2016). Forecasting: registreer, analyser en verbeter [Forecasting: record, analyse, and improve.]. Value Chain Magazine, March, 27-29.
Only available in Dutch.
In print only.
Vanderheyden, K., & De Baets, S. (2015). Does cognitive style diversity affect performance in dyadic student teams. Learning and Individual Differences,38, 143–150.
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.
De Baets, S., & Warmoes, V. (2012). Learning later in life: the older worker’s perspective. Working and Ageing, 3, 134 – 135.
Given the increasing need to keep older workers in employment longer, this chapter investigates requirements for effectively training older workers. A significant lacuna in research on the training of older workers has been the failure to take account of the views of older workers themselves. Moreover, a study specifically concerned with the training needs of older workers in Belgium is particularly relevant, since Belgium is seriously underperforming in the training participation rates of older workers compared to other north-European countries. An extensive literature review was undertaken, based on an in-depth qualitative research design with seven focus groups, whose participants were over 45 years of age, and 11 individual semi-structured expert interviews. A wide range of internal influencing factors (experience, anxiety and insecurity,
and motivation) were identified which had implications for the design of training for older workers (didactical approach, trainer requirements, etc.). The outcomes of the qualitative study are discussed and set out in the literature review.
Also available as a book chapter: De Baets, S., & Warmoes, V. (2012). Learning later in life: the Older Worker’s Perspective, In Working and Ageing (Vol. 3; pp. 134 – 154). Cedefop publications: Luxembourg.
De Baets, Shari, & Veronique, W. (2011). Oudere werknemers opleiden : een sinecure? [Training older employees: A walk in the park?]. OVER.WERK, 21(2), 43–47.
Only available in Dutch.
In print only.