Optimising Forecasting Models for Inventory Planning

Kourentzes, Nikolaos; Trapero, Juan R.; Barrow, Devon K.

doi:10.2139/ssrn.3363117

Cited by 12 publications

(13 citation statements)

References 33 publications

(38 reference statements)

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“…Note that gains in bias are generally considered to be more effective in improving the supported inventory decisions compared to the reduction in RMSE. This has been reported multiple times in the literature (for example, Sanders and Graman, 2009;Kourentzes et al, 2019b).…”

Section: Resultsmentioning

confidence: 73%

Elucidate structure in intermittent demand series

Kourentzes

Athanasopoulos

2021

European Journal of Operational Research

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Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate of producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

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Section: Resultsmentioning

confidence: 73%

Elucidate structure in intermittent demand series

Kourentzes

Athanasopoulos

2021

European Journal of Operational Research

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“…Week-to-week differences in forecast accuracy within the lead time are not considered as crucial for stocking decisions (Bruzda, 2020;Cobb et al, 2015;Trapero et al, 2019b). The cumulative demand and errors over lead time remains the focal issue for up-to-date forecasting research (Kourentzes, Trapero, & Barrow, 2020;Prak & Teunter, 2019;Trapero et al, 2019a). Accordingly, we aggregated demand observations by L weeks and redefine the forecasting goal to generate demand estimates for the full lead time.…”

Section: Problem Reframing-data Aggregationmentioning

confidence: 99%

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

Chuang

Chou

Oliva

2021

J of Ops Management

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Despite its importance to OM, demand forecasting has been perceived as a "problem-solving" exercise; most empirical work in the field has focused on explanatory models but neglected prediction problems that are part of empirical science. The present study, involving one of the leading electronics distributors in the world, aims to improve prediction accuracy under high demand volatility for procurement managers to make better inventory decisions. In response to requests for an integrated forecasting methodology, we undertook an iterative process based on three guiding principles-data pooling, theoryinformed feature engineering, and ensemble-based machine learning. The resulting framework managed to improve forecast accuracy significantly and is applicable to a broad range of situations. We present reflections and insights derived abductively through engagement with managers in this problem situation. This "problem-driven" process corresponds to intervention as a research strategy that can foster theoretical and methodological innovations in OM. Our contribution goes beyond the development of the prediction framework as it elucidates ways OM researchers could leverage theoretical foundations to inform feature derivation and model construction. We posit that this work points to a way forward to the combination of OM principles with the emerging innovations in data science and artificial intelligence.

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“…This existing disconnect between prediction and optimization has been addressed in general terms by Elmachtoub and Grigas (2021). Particularly, the lacking interface between demand forecasting and inventory control has been pointed out by Tratar (2010), Prak et al (2017), andKourentzes et al (2020). Syntetos et al (2010) state that the orders of magnitude of forecasting accuracy and inventory performance may differ wildly.…”

Section: Demand Forecastingmentioning

confidence: 99%

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management

Gammelli¹,

Wang²,

Prak³

et al. 2021

Preprint

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Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is typically done during nighttime. To determine the optimal starting inventory level of a station for a given day, a User Dissatisfaction Function (UDF) models user pickups and returns as non-homogeneous Poisson processes with piece-wise linear rates. In this paper, we devise a deep generative model directly applicable in the UDF by introducing a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA, and show how our model outperforms benchmarks in terms of system efficiency and demand satisfaction. By explicitly focusing on the combination of decision-making algorithms with learning-based forecasting methods, we highlight a number of shortcomings in literature. Crucially, we show how more accurate predictions do not necessarily translate into better inventory decisions. By providing insights into the interplay between forecasts, model assumptions, and decisions, we point out that forecasts and decision models should be carefully evaluated and harmonized to optimally control shared mobility systems.

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Optimising Forecasting Models for Inventory Planning

Cited by 12 publications

References 33 publications

Elucidate structure in intermittent demand series

Elucidate structure in intermittent demand series

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management

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