We need to talk about intermittent demand forecasting

Abstract

In this research essay we propose a methodological innovation through: a) advocating for the broader use of OR forecasting tools and in specific intermittent demand estimators for forecasting black and grey swans, as a simpler, faster and quite robust alternative to econometric probabilistic methods like EVT; b) demonstrating the use in such a context of a rather popular forecasting paradigm: the Naive method (forecasting short horizons) and the SBA method (foresight long horizons) through the ADIDA non-overlapping temporal aggregation method-improving framework, and c) arguing for a new way for deciding the strategic planning horizon for phenomena prone to the appearance of black and grey swans.

Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions

“…We make the implicit assumption that the products we are looking at follow a relatively stable average demand in the long-run. Since we are focusing on the impact of the COVID-19 on the supply chains of these products we assume that the pandemic leads to an intermittent demand pattern over and above the mainstream ( Nikolopoulos, 2020 ).…”

“…For instance, there are several, and at times unknown, factors that affect the contagiousness and the severity of the disease. To that end, forecasting in real time and while new data becomes available is a complex exercise for both government and supply chain managers ( Beliën & Forcé, 2012 ; Nikolopoulos, 2020 ).…”

Forecasting in humanitarian operations: Literature review and research needs

“…Therefore, just like in the for-profit world, better forecasting has the potential to lower inventory costs for humanitarian organizations. For smaller, local organizations, however, forecasting could be a challenge since in their case, catastrophic events are intermittent ( Nikolopoulos, 2020 ). Intermittent demand forecasting is difficult due to the zero demand periods in time series.…”

Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

“…Nikolopoulos [15] also highlights the existing research gap in the context of studying demand forecasting models in the field of intermittent and lumpy time series. At the same time, the methods in Information Systems are rapidly developing.…”

“…However, it remains unclear which meta-level method or specific model is most suitable for forecasting intermittent and lumpy time series. This is because intermittent and lumpy time series have not yet been sufficiently researched [15] and also as a result of the historical lack of appropriate metrics, which were deliberately developed for assessing demand forecasts of this time series pattern [7].However, in saying this, the research field of machine learning and deep learning has also evolved rapidly.…”