A cross-temporal hierarchical framework and deep learning for supply chain forecasting

Abstract Organizations require short-term up to long-run aggregated forecasts for making strategic, tactical, and operational decisions for their supply chain management. In supply chain forecasting, the Tt emphasis is primarily on the accuracy while coherency of forecasts often gets ignored. This paper proposes a novel cross-temporal forecasting framework (CTFF) to generate coherent forecasts at all levels of a retail supply chain. A deep learning method, the long-short-term-memory network, is used as the base forecasting method in the CTFF. The performance of the CTFF is evaluated on point-of-sales data from a large multi-channel retail supply chain. Through several performance metrics and statistical tests, we conclude that forecasts from the CTFF are significantly better than the direct forecasts. In addition, improvements are significant and consistent across cross-sectional and temporal levels of a supply chain. Further, it has been observed that bottom-up forecasts are more accurate than top-down forecasts when point-of-sales data is used for forecasting in online and offline retail supply chain.

[1]  Byron J. Dangerfield,et al.  Top-down or bottom-up: Aggregate versus disaggregate extrapolations , 1992 .

[2]  Nikolaos Kourentzes,et al.  Cross-temporal coherent forecasts for Australian tourism , 2019, Annals of Tourism Research.

[3]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[4]  Fotios Petropoulos,et al.  Improving forecasting via multiple temporal aggregation , 2014 .

[5]  Zoubin Ghahramani,et al.  A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.

[6]  M. Qi,et al.  Forecasting Aggregate Retail Sales: a Comparison of Arti"cial Neural Networks and Traditional Methods , 2001 .

[7]  Ran Li,et al.  Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.

[8]  Rob J. Hyndman,et al.  Forecasting hierarchical and grouped time series through trace minimization , 2015 .

[9]  P. Vigneswara Ilavarasan,et al.  Forecasting container throughput with long short-term memory networks , 2019, Ind. Manag. Data Syst..

[10]  Rajesh Piplani,et al.  Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework , 2009 .

[11]  Fotios Petropoulos,et al.  'Horses for Courses' in demand forecasting , 2014, Eur. J. Oper. Res..

[12]  William Remus,et al.  Does updating judgmental forecasts improve forecast accuracy , 2000 .

[13]  Marwan Khammash,et al.  Forecasting branded and generic pharmaceuticals , 2016 .

[14]  Heiner Stuckenschmidt,et al.  Cluster-based hierarchical demand forecasting for perishable goods , 2017, Expert Syst. Appl..

[15]  Amir F. Atiya,et al.  Combination of long term and short term forecasts, with application to tourism demand forecasting , 2011 .

[16]  Marco A. Villegas,et al.  Supply chain decision support systems based on a novel hierarchical forecasting approach , 2018, Decis. Support Syst..

[17]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[18]  Robert Fildes,et al.  Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information , 2016, Eur. J. Oper. Res..

[19]  Kenneth B. Kahn.  Revisiting Top-Down versus Bottom-Up Forecasting , 1998 .

[20]  Yossi Aviv,et al.  On the Benefits of Collaborative Forecasting Partnerships Between Retailers and Manufacturers , 2007, Manag. Sci..

[21]  Giulio Zotteri,et al.  The impact of aggregation level on forecasting performance , 2005 .

[22]  Konstantinos Nikolopoulos,et al.  Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions , 2020, European Journal of Operational Research.

[23]  Rob J. Hyndman,et al.  Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization , 2018, Journal of the American Statistical Association.

[24]  Konstantinos Nikolopoulos,et al.  Supply chain forecasting: Theory, practice, their gap and the future , 2016, Eur. J. Oper. Res..

[25]  John J. Seater,et al.  Temporal Aggregation and Economic Time Series , 1995 .

[26]  Brent D. Williams,et al.  Top‐Down Versus Bottom‐Up Demand Forecasts: The Value of Shared Point‐of‐Sale Data in the Retail Supply Chain , 2011 .

[27]  Fotios Petropoulos,et al.  Forecasting with temporal hierarchies , 2017, Eur. J. Oper. Res..

[28]  SchmidhuberJürgen,et al.  2005 Special Issue , 2005 .

[29]  Surya Prakash Singh,et al.  From predictive to prescriptive analytics: A data-driven multi-item newsvendor model , 2020, Decis. Support Syst..

[30]  John S. Morris,et al.  Top-down versus bottom-up forecasting strategies , 1988 .

[31]  Fotios Petropoulos,et al.  Forecasting with multivariate temporal aggregation: the case of promotional modelling , 2016 .

[32]  Konstantinos Nikolopoulos,et al.  Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail , 2020, Int. J. Prod. Res..

[33]  George Athanasopoulos,et al.  Hierarchical forecasts for Australian domestic tourism , 2009 .

[34]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[35]  Rob J. Hyndman,et al.  Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..

[36]  Daria Dzyabura,et al.  Offline Assortment Optimization in the Presence of an Online Channel , 2018, Manag. Sci..

[37]  F. Petropoulos,et al.  Improving forecasting by estimating time series structural components across multiple frequencies , 2014 .

[38]  Siddhartha S. Syam,et al.  Allocating a hybrid retailer's assortment across retail stores: Bricks-and-mortar vs online , 2014 .

[39]  Jeffrey Sohl,et al.  Disaggregation methods to expedite product line forecasting , 1990 .

[40]  M. Z. Babai,et al.  Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis , 2012 .

[41]  Kai Hoberg,et al.  The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores , 2020 .