Forecasting-based SKU classification

Heinecke, Georg; Syntetos, Aris A.; Wang, W.

doi:10.1016/j.ijpe.2011.11.020

Cited by 24 publications

(16 citation statements)

References 15 publications

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“…This scheme was later revised by Kostenko and Hyndman (2006). An empirical analysis of the original and the revised schemes suggested that, despite of the former being simpler, the latter results in an overall superior forecasting performance (Heinecke et al, 2013). Hereafter, the revised classification scheme will be considered and is referred to as SBC-KH.…”

Section: Forecasting and Combiningmentioning

confidence: 99%

Forecast combinations for intermittent demand

Petropoulos

Kourentzes

2015

Journal of the Operational Research Society

104

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Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time-series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice.

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Section: Forecasting and Combiningmentioning

confidence: 99%

Forecast combinations for intermittent demand

Petropoulos

Kourentzes

2015

Journal of the Operational Research Society

104

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“…The empirical utility of the schemes discussed above has been confirmed in many studies (e.g., Ghobbar and Friend 2002, Regattieri et al 2005. Further refinements of these schemes (and empirical evidence on their performance) are offered by Kostenko and Hyndman (2006) and Heinecke et al (2013). So far, we have juxtaposed intermittent and non-intermittent demand time series.…”

Section: (Note 7)mentioning

confidence: 76%

Supply chain forecasting: Theory, practice, their gap and the future

Syntetos

Babai

Boylan

et al. 2016

European Journal of Operational Research

234

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HIGHLIGHTSThe literature on supply chain forecasting is critically reviewed;The process of involving the forecasting community towards that task is described;Gaps between theory and practice are identified; Data and software related issues are explicitly considered; Challenges are summarised followed by suggestions for further research. ABSTRACTSupply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).This paper provides a comprehensive review of the literature and aims at bridging the gap between the theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

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“…In addition to the in-sample model selection schemes, as a benchmark, the item classification proposed by as refined by Kostenko and Hyndman (2006) is used. The refined classification was shown to perform better than the original (Heinecke et al, 2011). The classification is based on the average inter-demand interval p of each time series and the squared coefficient of variation of the demand v. Although the original classification was identifying four regions, the refined version separates the space in only two regions.…”

Section: Model Selection Resultsmentioning

confidence: 99%

“…Kostenko and Hyndman (2006) refined this framework, but also recognised its limitations due to the weaknesses in Croston's method and its variants. Nonetheless, Heinecke et al (2011) found that the proposed refinements provided improved forecasting performance over the original classification. However, this framework is unable to support the different non-Croston based methods that have been proposed in the literature, thus limiting its power further.…”

Section: Crostonmentioning

confidence: 96%

“…Assuming that for each method optimal parameters can be identified, an important problem is which model to use. Although there have been some theoretical advancements for selecting between a subset of intermittent demand methods, depending on the time series characteristics Kostenko and Hyndman, 2006;Heinecke et al, 2011), these cannot provide generic model selection guidance. This paper investigates whether the different error metrics explored here can facilitate and automate model selection for intermittent demand data.…”

Section: Introductionmentioning

confidence: 99%

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On intermittent demand model optimisation and selection

Kourentzes

2014

International Journal of Production Economics

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Intermittent demand time series involve items that are requested infrequently, resulting in sporadic demand. Croston's method and its variants have been proposed in the literature to address this forecasting problem. Recently other novel methods have appeared. Although the literature provides guidance on the suggested range for model parameters, a consistent and valid optimisation methodology is lacking. Growing evidence in the literature points against the use of conventional accuracy error metrics for model evaluation for intermittent demand time series. Consequently these may be inappropriate for parameter or model selection. This paper contributes to the discussion by evaluating a series of conventional time series error metrics, along with two novel ones for parameter optimisation for intermittent demand methods. The proposed metrics are found to not only perform best, but also provide consistent parameters with the literature, in contrast to conventional metrics. Furthermore, this work validates that employing different parameters for smoothing the non-zero demand and the inter-demand intervals of Croston's method and its variants is beneficial. The evaluated error metrics are considered for automatic model selection for each time series. Although they are found to perform similarly to theory driven model selection schemes, they fail to outperform single models substantially. These findings are validated using both out-of-sample forecast evaluation and inventory simulations.

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Forecasting-based SKU classification

Cited by 24 publications

References 15 publications

Forecast combinations for intermittent demand

Forecast combinations for intermittent demand

Supply chain forecasting: Theory, practice, their gap and the future

On intermittent demand model optimisation and selection

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