Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

By Robert Fildes, Paul Goodwin, Michael Lawrence, Konstantinos Nikolopoulos. 2009

456 citations recorded by the source corpus.

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company's demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.

Subfields: General Decision Sciences, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/865401585c7091472934fa61da90fc3991ba6099

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In General Decision Sciences

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Retail forecasting: Research and practice (2019, background): “Judgmental adjustments are common in practice, but expensive at SKU level demand forecasting where the number of adjustments is large and potentially prone to systematic biases and inefficiencies (Fildes and Goodwin, 2007; Fildes et al., 2009).”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “Various research suggests that users—again, primarily experts— may in certain cases systematically over-rely on the outputs of intelligent systems [26, 19], while in others they may systematically distrust systems, despite reassurances as to their accuracy [57, 24].”
Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets (2010, unknown): “It is known from the literature on forecasting SKU level sales data, where experts frequently adjust (or deviate from) model-based forecasts, that expert forecasts are often biased (Fildes et al., 2009, Franses and Legerstee, 2009).”
Forecasting in social settings: The state of the art (2020, background): “, 2013); such upwards adjustments are far less effective (Fildes et al., 2009).”
Correcting Forecasts with Multifactor Neural Attention (2016, background): “For example, humans were found to often have an optimism bias in their projections of the impact of promotions in (Fildes et al., 2009), (Trapero et al.”
Forecasting: theory and practice (2020, background): “Adjustments are also often made for other reasons and these can be detrimental to accuracy (Fildes et al., 2009; Franses and Legerstee, 2009a).”
On the identification of sales forecasting models in the presence of promotions (2015, background): “Previous references found that experts’ negative adjustments were more accurate (Fildes et al, 2009; Trapero et al, 2011); here, we provide a feasible explanation why this is so, at least regarding adjustments due to promotional activity.”