Lancaster University Management School Working Paper 2007 / 005 Against your better judgment ? How organizations can improve their use of management judgment in forecasting

By Robert Fildes, Paul Goodwin. 2007

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Citations in the corpus

Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them (2018, background): “Fildes and Goodwin (2007) conducted a survey of 149 professional forecasters from a wide variety of domains (e.g., cosmetics, banking, manufacturing) and found that many professionals either did not use algorithms in their forecasting process or failed to give them sufficient weight.”
Judging the judges through accuracy-implication metrics: The case of inventory forecasting (2010, background): “However, in many cases these statistical forecasts will be subject to judgemental adjustments by managers (Fildes & Goodwin, 2007; Sanders & Manrodt, 1994).”
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, methodology): “Fildes and Goodwin (2007) cautioned that inflating predicted values would distort this normalization type.”
Golden Rule of Forecasting: Be conservative (2015, unknown): “Most forecasting practitioners expect that judgmental adjustments will lead to error reductions of between five and 10 percent (Fildes and Goodwin, 2007).”
Forecasting and operational research: a review (2008, methodology): “For some software we examined (Fildes et al, 2008) the statistical forecasts available in the company FSS proved worse than a naı̈ve random walk! In most operational applications, the (usually inadequate) statistical forecasts are combined with judgement (Fildes and Goodwin, 2007).”
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).”
Making and Evaluating Point Forecasts (2009, methodology): “Our plea for the use of consistent scoring functions supplements and qualifies, but does not contradict, extant recommendations in the forecasting literature, such as those of West and Harrison (1997), Armstrong (2001), Jolliffe and Stephenson (2003), and Fildes and Goodwin (2007).”
On the identification of sales forecasting models in the presence of promotions (2015, background): “Fildes and Goodwin (2007) and Lawrence et al (2000) suggested that the reliance on expert interventions is more widespread.”
Impact of Information Exchange on Supplier Forecasting Performance (2012, unknown): “Email addresses: j.traperoarenas@lancaster.ac.uk (Juan R. Trapero), n.kourentzes@lancaster.ac.uk (N. Kourentzes), r.fildes@lancaster.ac.uk (R. Fildes) Preprint submitted to Omega August 1, 2011”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “The RMSE, MAE, and MAPE are the most commonly used forecast error measures among both academics and practitioners, and the retail forecasting literature is no exception [32].”