Elucidate structure in intermittent demand series

Kourentzes, Nikolaos; Athanasopoulos, George

doi:10.1016/j.ejor.2020.05.046

Cited by 41 publications

(15 citation statements)

References 55 publications

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“…Much of the literature focuses on ADIs in the range of 1-10 and CV 2 values of 0-2.25 (Kourentzes and Athanasopoulos, 2021;Syntetos and Boylan, 2001;Petropoulos et al, 2016). As an example, we have observed military spare part ordering data with ADIs ranging from 1 to 2.53 and CV 2 values from 0.3 to 3 (McDermott, 2020; Appendix A).…”

Section: Approachmentioning

confidence: 91%

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

McDermott

Winz

Hodgson

et al. 2021

JDAL

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PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

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Section: Approachmentioning

confidence: 91%

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

McDermott

Winz

Hodgson

et al. 2021

JDAL

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“…We start by (i) re-assessing the results found by Hollyman et al (2021) using the forecast accuracy evaluation approach recommended by Davydenko and Fildes (2013), and (ii) considering some non-negativity issues that emerge during both the base forecasting and the reconciliation phases of the analysis. On this latter point we note that, with the notably exceptions of Wickramasuriya et al (2020) and Kourentzes and Athanasopoulos (2021), this issue is generally overlooked in the forecast reconciliation literature, even though it has not irrilevant implications as for the interpretation of the results (e.g., a negative forecast touristic demand makes no sense). Possible, though not fully convincing, motivations for this are that, on the practical side, adopting non-negative forecast reconciliation procedures is perceived as computation burdensome, and on the theorethical side, assuring non-negativity does not preserve unbiasedness in the final non-negative reconciled forecasts (on this point, see Ben Taieb and Koo, 2019, Wickramasuriya et al, 2020, Wickramasuriya, 2021b.…”

Section: Empirical Applicationmentioning

confidence: 91%

Forecast combination based forecast reconciliation: insights and extensions

Fonzo¹,

Girolimetto²

2021

Preprint

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In a recent paper, while elucidating the links between forecast combination and cross-sectional forecast reconciliation, Hollyman et al. (2021) have proposed a forecast combination-based approach to the reconciliation of a simple hierarchy. A new Level Conditional Coherent (LCC) point forecast reconciliation procedure was developed, and it was shown that the simple average of a set of LCC, and bottom-up reconciled forecasts (called Combined Conditional Coherent, CCC) results in good performance as compared to those obtained through the state-of-the-art cross-sectional reconciliation procedures. In this paper, we build upon and extend this proposal along some new directions. (1) We shed light on the nature and the mathematical derivation of the LCC reconciliation formula, showing that it is the result of an exogenously linearly constrained minimization of a quadratic loss function in the differences between the target and the base forecasts with a diagonal associated matrix. (2) Endogenous constraints may be considered as well, resulting in level conditional reconciled forecasts of all the involved series, where both the upper and the bottom time series are coherently revised. We show that even in this framework it is still valid the interesting interpretation given by Hollyman et al. ( 2021) of the reconciliation formula as the combination of direct (base) and indirect forecasts, the latter ones depending on the accounting relationships linking upper and bottom series. (3) As the LCC procedure (i.e., with exogenous constraints, but the result holds in the endogenous case as well)does not guarantee the non-negativity of the reconciled forecasts, we argue that -when non-negativity is a natural attribute of the variables to be forecast -its interpretation as an 'unbiased top-down reconciliation procedure' leaves room for some doubts. (4) The new procedures are used in a forecasting experiment on the classical Australian Tourism Demand (Visitor Nights) dataset. Due to the crucial role played by the (possibly different) models used to compute the base forecasts, we re-interpret the CCC reconciliation of Hollyman et al. (2021) as a forecast pooling approach, showing that accuracy improvement may be gained by adopting a simple forecast averaging strategy.

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“…Use Theory(T) and Methods(M) Kourentzes & Athanasopoulos, 2016;Syntetos et al, 2016) as a way to derive low frequency time series from high frequency observations. Although aggregation across time has been validated as a useful approach for tackling nonsmooth demand patterns widely seen in various industry sectors (Nikolopoulos, Syntetos, Boylan, Petropoulos, & Assimakopoulos, 2011), related studies almost exclusively identify patterns from temporally aggregated univariate series via distribution or function fitting.…”

Section: Learn From Usementioning

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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Elucidate structure in intermittent demand series

Cited by 41 publications

References 55 publications

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

Forecast combination based forecast reconciliation: insights and extensions

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

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