Disaggregation methods to expedite product line forecasting

Gross, Charles W.; Sohl, Jeffrey

doi:10.1002/for.3980090304

Cited by 104 publications

(78 citation statements)

References 14 publications

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“…The top-down approach requires forecasts for only one time series at the very aggregate level. However aggregation implies a large loss of information and it is challenging to disaggregate the forecasts down the hierarchy (see Gross andSohl 1990 andAthanasopoulos et al 2009 for a summary of top-down approaches). In contrast bottom-up implies no loss of information but it requires many and possibly very noisy time series to be forecast.…”

Section: Introductionmentioning

confidence: 99%

Forecasting with temporal hierarchies

Athanasopoulos

Kourentzes

Petropoulos

2017

European Journal of Operational Research

176

163

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This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from shortterm operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.

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Section: Introductionmentioning

confidence: 99%

Forecasting with temporal hierarchies

Athanasopoulos

Kourentzes

Petropoulos

2017

European Journal of Operational Research

176

163

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“…For example, one could multiply the aggregate demand forecast by the (forecast of the) ratio of the respective individual item demand to aggregate demand (called proration in the literature, see, e.g., Fliedner, 1999;Strijbosch et al, 2008) in order to estimate individual SKU requirements. Gross and Sohl (1990) demonstrated, in a large empirical study, that simple sample averages for the proration factor are very effective (NOTE 5) . Obviously, if a forecast is required at the aggregate level only, then disaggregation becomes redundant.…”

Section: Hierarchical Product Aggregationmentioning

confidence: 99%

“…Some scholars advocate the top down approach (Grunfeld and Griliches, 1960;Barnea and Lakonishok, 1980;Gross and Sohl, 1990;Fliedner, 1999), whereas others disagree (Orcutt et al, 1968;Edwards and Orcutt, 1969;Dangerfield and Morris, 1992;Gordon et al, 1997;Weatherford et al, 2001) and, among other reasons, argue that the outperformance of bottom-up is due to the fact that information loss is substantial when aggregating time series; that is, the data is too aggregated to represent diverse demands (NOTE 6) . Some additional arguments that are often put forward by practitioners against topdown approaches have been summarised by Dawson (2013):…”

Section: Hierarchical Product Aggregationmentioning

confidence: 99%

“…NOTE 5 Gross and Sohl (1990) found that two simple disaggregations may be particularly effective: i) For each period, calculate the proportion that an individual series contributes towards the aggregate series. Then average the proportions over all periods; ii) Calculate the total, over all periods, for the indivudual series, and then do the same for the aggregate series.…”

Section: Notementioning

confidence: 99%

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

Syntetos

Babai

Boylan

et al. 2016

European Journal of Operational Research

226

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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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“…A top-down method predicts the aggregated series at the top level and then disaggregates the forecasts based on historical or forecast proportions (see for example, Gross and Sohl, 1990). The bottom-up method involves forecasting each of the disaggregated series at the lowest level of the hierarchy and then using simple aggregation to obtain forecasts at the higher levels of the hierarchy (see for example, Kahn, 1998).…”

Section: Introductionmentioning

confidence: 99%

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Shang

2016

Popul Res Policy Rev

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Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion or ethnicity. Forecasting mortality rates at the national and subnational levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman, Ahmed, Athanasopoulos and Shang, 2011).A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.

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Disaggregation methods to expedite product line forecasting

Cited by 104 publications

References 14 publications

Forecasting with temporal hierarchies

Forecasting with temporal hierarchies

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

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

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