Judging the judges through accuracy-implication metrics: The case of inventory forecasting

By A. Syntetos, Konstantinos Nikolopoulos, John E. Boylan. 2010

117 citations recorded by the source corpus.

A number of research projects have demonstrated that the efficiency of inventory systems does not relate directly to demand forecasting performance, as measured by standard forecasting accuracy measures. When a forecasting method is used as an input to an inventory system, it should therefore always be evaluated with respect to its consequences for stock control through accuracy implications metrics, in addition to its performance on the standard accuracy measures. In this paper we address the issue of judgementally adjusting statistical forecasts for ‘fast’ demand items, and the implications of such interventions in terms of both forecast accuracy and stock control, with the latter being measured through inventory volumes and service levels achieved. We do so using an empirical dataset from the pharmaceutical industry. Our study allows insights to be gained into the combined forecasting and inventory performance of judgemental estimates. It also aims to advance the practice of forecasting competitions by arguing for the consideration of additional (stock control) metrics when such exercises take place in an inventory context.

Subfields: Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, background): “Syntetos et al. (2010) also demonstrated that, when fast demand is considered, small improvements in terms of accuracy as a result of judgmental revision of the statistical output could mean large gains in terms of inventory reductions.”
Intermittent demand forecasts with neural networks (2013, background): “Gardner (1990), Teunter and Duncan (2009) and Syntetos et al. (2010) dispute this and argue in favour of stock control metrics, which are more directly related to the decision making problems that an organisation faces.”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, background): “Syntetos et al. (2010) ﬁnd out that small gains in forecasting accuracy may lead to considerable inventory reductions, Jakubovskis (2017) stresses the importance of robustness for strategic facility location, capacity acquisition and technology choice decisions, while Ma & Fildes (2017) suggest…”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, unknown): “Consequently, valid competitions have developed a rigorous design, including the use of a representative number of time series (and, where possible, a rolling origin design), the use of multiple robust error metrics, a comparison with established (statistical) benchmark algorithms, and the analysis ”