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Research Article

Demand forecasting in supply chains: a review of aggregation and hierarchical approaches

ORCID Icon, ORCID Icon & ORCID Icon
Pages 324-348 | Received 23 Apr 2021, Accepted 28 Oct 2021, Published online: 06 Dec 2021

References

  • Abolghasemi, M., R. Hyndman, E. Spiliotis, and C. Bergmeir. 2020. “Model Selection in Reconciling Hierarchical Time Series.” arXiv preprint: 2010.10742.
  • Abolghasemi, M., R. J. Hyndman, G. Tarr, and C. Bergmeir 2019. “Machine Learning Applications in Time Series Hierarchical Forecasting.” arXiv preprint arXiv:1912.00370.
  • Altay, N., L. Litteral, and F. Rudisill. 2012. “Effects of Correlation on Intermittent Demand Forecasting and Stock Control.” International Journal of Production Economics 135: 275–283.
  • Amemiya, T., and R. Y. Wu. 1972. “The Effect of Aggregation on Prediction in the Autoregressive Model.” Journal of the American Statistical Association 67 (339): 628–632.
  • Athanasopoulos, G., R. J. Hyndman, N. Kourentzes, and F. Petropoulos. 2017. “Forecasting with Temporal Hierarchies.” European Journal of Operational Research 262 (1): 60–74.
  • Babai, M. Z., M. M. Ali, and K. Nikolopoulos. 2012. “Impact of Temporal Aggregation on Stock Control Performance of Intermittent Demand Estimators: Empirical Analysis.” Omega 40 (6): 713–721.
  • Babai, M. Z., A. Tsadiras, and C. Papadopoulos. 2020. “On the Empirical Performance of Some New Neural Network Methods for Forecasting Intermittent Demand.” IMA Journal of Management Mathematics 31: 281–305.
  • Barrow, D. K., and N. Kourentzes. 2016. “Distributions of Forecasting Errors of Forecast Combinations: Implications for Inventory Management.” International Journal of Production Economics 177: 24–33.
  • Blanc, S. M., and T. Setzer. 2016. “When to Choose the Simple Average in Forecast Combination.” Journal of Business Research 69 (10): 3951–3962.
  • Bookbinder, J. H., and A. E. Lordahl. 1989. “Estimation of Inventory Re-order Levels Using the Bootstrap Statistical Procedure.” IIE Transactions 21: 302–312.
  • Boylan, J. E., and M. Z. Babai. 2016. “On the Performance of Overlapping and Non-overlapping Temporal Demand Aggregation Approaches.” International Journal of Production Economics 181: 136–144.
  • Boylan, J. E., H. Chen, M. Mohammadipour, and A. A. Syntetos. 2014. “Formation of Seasonal Groups and Application of Seasonal Indices.” Journal of the Operational Research Society 65: 227–241.
  • Brewer, K. R. 1973. “Some Consequences of Temporal Aggregation and Systematic Sampling for Arma and Armax Models.” Journal of Econometrics 1 (2): 133–154.
  • Carlstein, E. 1986. “The Use of Subseries Methods for Estimating the Variance of a General Statistic From a Stationary Time Series.” Annals of Statistics 14: 1711–1719.
  • Chen, A., and J. Blue. 2010. “Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands.” International Journal of Production Economics128 (2): 586–602.
  • Chen, H., and J. E. Boylan. 2007. “Use of Individual and Group Seasonal Indices in Subaggregate Demand Forecasting.” Journal of the Operational Research Society 58: 1660–1671.
  • Chen, A., C.-H. Hsu, and J. Blue. 2007. “Demand Planning Approaches to Aggregating and Forecasting Interrelated Demands for Safety Stock and Backup Capacity Planning.” International Journal of Production Research 45 (10): 2269–2294.
  • Dalhart, G. 1974. “Class Seasonality – A New Approach.” In American Production and Inventory Control Society COnference Proceedings, APICS.
  • Dangerfield, B. J., and J. S. Morris. 1992. “Top-down Or Bottom-up: Aggregate Versus Disaggregate Extrapolations.” International Journal of Forecasting 8 (2): 233–241.
  • Dekker, M., K. Van Donselaar, and P. Ouwehand. 2004. “How to Use Aggregation and Combined Forecasting to Improve Seasonal Demand Forecasts.” International Journal of Production Economics 90: 151–167.
  • Edwards, J. B., and G. H. Orcutt. 1969. “Should Aggregation Prior to Estimation Be the Rule?.” The Review of Economics and Statistics 51(4), 409–420.
  • Efron, B. 1979. “Bootstrap Methods. Another Look At the Jackknife.” Annals of Statistics 7: 1–26.
  • Fildes, R., S. Ma, and S. Kolassa. 2019. “Retail Forecasting: Research and Practice.” International Journal of Forecasting.
  • Fildes, R., O. Schaer, and I. Svetunkov. 2018. “Software Survey: Forecasting 2018.” INFORMS ORMS-Today 45 (3): 47–51.
  • Fliedner, G. 1999. “An Investigation of Aggregate Variable Time Series Forecast Strategies with Specific Subaggregate Time Series Statistical Correlation.” Computers & Operations Research 26 (10–11): 1133–1149.
  • Fricker, R., and C. Goodhart. 2000. “Applying a Bootstrap Approach for Setting Reorder Points in Military Supply Systems.” Naval Research Logistics 47: 459–478.
  • Fu, W., and C.-F. Chien. 2019. “Unison Data-driven Intermittent Demand Forecast Framework to Empower Supply Chain Resilience and An Empirical Study in Electronics Distribution.” Computers & Industrial Engineering 135: 940–949.
  • Granger, C. W. J., and M. J. Morris. 1976. “Time Series Modelling and Interpretation.” Journal of the Royal Statistical Society: Series A (General) 139 (2): 246–257.
  • Gross, C. W., and J. E. Sohl. 1990. “Disaggregation Methods to Expedite Product Line Forecasting.” Journal of Forecasting 9 (3): 233–254.
  • Grunfeld, Y., and Z. Griliches. 1960. “Is Aggregation Necessarily Bad?.” The Review of Economics and Statistics 42(1), 1–13.
  • Hall, P. 1985. “Resampling a Coverage Pattern.” Stochastic Processes and Their Applications 20: 231–246.
  • Harvey, A. C. 1993. “Time Series Models”. Harvester Wheatsheaf, 2nd edition, New York.
  • Harwell, J. 2015. “Sales and Operations Planning in the Retail industry.” In Business Forecasting: Practical Problems and Solutions, edited by M. Gilliland, L. Tashman, and U. Sglavo, 363–372. New Jersey.
  • Hasni, M., M. Aguir, M. Babai, and Z. Jemai. 2019. “On the Performance of Adjusted Bootstrapping Methods for Intermittent Demand Forecasting.” International Journal of Production Economics 216: 145–153.
  • Hasni, M., M. Z. Babai, M. Aguir, and Z. Jemai. 2019. “An Investigation on Bootstrapping Forecasting Methods for Intermittent Demands.” International Journal of Production Economics 209: 20–29.
  • Hotta, L. K., P. A. Morettin, and P. L. V. Pereira. 1992. “The Effect of Overlapping Aggregation on Time Series Models: An Application to the Unemployment Rate in Brazil.” Brazilian Review of Econometrics12 (2): 223–241.
  • Huber, J., A. Gossmann, and H. Stuckenschmidt. 2017. “Cluster-based Hierarchical Demand Forecasting for Perishable Goods.” Expert Systems with Applications 76: 140–151.
  • Hyndman, R. J., R. A. Ahmed, G. Athanasopoulos, and H. L. Shang. 2011. “Optimal Combination Forecasts for Hierarchical Time Series.” Computational Statistics & Data Analysis 55 (9): 2579–2589.
  • Hyndman, R. J., and G. Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. Melbourne: OTexts. OTexts.com/fpp3.
  • Hyndman, R., and A. Kostenko. 2007. “Minimum Sample Size Requirements for Seasonal Forecasting Models.” Foresight: The International Journal of Applied Forecasting 6: 12–15.
  • Hyndman, R. J., A. J. Lee, and E. Wang. 2016. “Fast Computation of Reconciled Forecasts for Hierarchical and Grouped Time Series.” Computational Statistics & Data Analysis 97: 16–32.
  • Jeon, J., A. Panagiotelis, and F. Petropoulos. 2019. “Probabilistic Forecast Reconciliation with Applications to Wind Power and Electric Load.” European Journal of Operational Research 279 (2): 364–379.
  • Jin, Y. H., B. D. Williams, T. Tokar, and M. A. Waller. 2015. “Forecasting with Temporally Aggregated Demand Signals in a Retail Supply Chain.” Journal of Business Logistics 36 (2): 199–211.
  • Kolassa, S. 2016. “Evaluating Predictive Count Distributions in Retail Sales Forecasting.” International Journal of Forecasting 32: 788–803.
  • Kourentzes, N., and G. Athanasopoulos. 2019. “Cross-temporal Coherent Forecasts for Australian Tourism.” Annals of Tourism Research 75: 393–409.
  • Kourentzes, N., and G. Athanasopoulos. 2021. “Elucidate Structure in Intermittent Demand Series.” European Journal of Operational Research 288 (1): 141–152.
  • Kourentzes, N., and F. Petropoulos. 2016. “Forecasting with Multivariate Temporal Aggregation: The Case of Promotional Modelling.” International Journal of Production Economics 181: 145–153.
  • Kourentzes, N., F. Petropoulos, and J. R. Trapero. 2014. “Improving Forecasting by Estimating Time Series Structural Components Across Multiple Frequencies.” International Journal of Forecasting 30 (2): 291–302.
  • Kourentzes, N., B. Rostami-Tabar, and D. K. Barrow. 2017. “Demand Forecasting by Temporal Aggregation: Using Optimal Or Multiple Aggregation Levels?.” Journal of Business Research 78: 1–9.
  • Kremer, M., E. Siemsen, and D. J. Thomas. 2016. “The Sum and Its Parts: Judgmental Hierarchical Forecasting.” Management Science 62 (9): 2745–2764.
  • Künsch, H. 1989. “The Jackknife and the Bootstrap for General Stationary Observations.” Annals of Statistics 17: 1217–1241.
  • Lapide, L. 2016. “Retail Omnichannel Needs Better Forecasting & Planning.” The Journal of Business Forecasting 35 (3): 12.
  • Lei, M., S. Li, and Q. Tan. 2016. “Intermittent Demand Forecasting with Fuzzy Markov Chain and Multi Aggregation Prediction Algorithm.” Journal of Intelligent & Fuzzy Systems 31 (6): 2911–2918.
  • Li, C., and A. Lim. 2018. “A Greedy Aggregation–decomposition Method for Intermittent Demand Forecasting in Fashion Retailing.” European Journal of Operational Research 269 (3): 860–869.
  • Lordahl, A. E., and J. H. Bookbinder. 1994. “Order-statistic Calculation, Costs, and Service in An (s, Q) Inventory System.” Naval Research Logistics 41: 81–97.
  • Lütkepohl, H. 2011. “Forecasting Aggregated Time Series Variables: A Survey.” OECD Journal: Journal of Business Cycle Measurement and Analysis 2010 (2): 1–26.
  • Makridakis, S., C. Chatfield, M. Hibon, M. Lawrence, T. Mills, K. Ord, and L. F. Simmons. 1993. “The M2-competition: A Real-time Judgmentally Based Forecasting Study.” International Journal of Forecasting 9 (1): 5–22.
  • Makridakis, S., E. Spiliotis, and V. Assimakopoulos. 2020. “The M5 Accuracy Competition: Results, Findings and Conclusions.” Working Paper. https://bit.ly/3iKWwj9.
  • Makridakis, S., E. Spiliotis, V. Assimakopoulos, Z. Chen, A. Gaba, I. Tsetlin, and R. Winkler. 2021. “The M5 Uncertainty Competition: Results, Findings and Conclusions.” Working paper. https://bit.ly/3ruWT5F.
  • Meza-Peralta, K., J. Gonzalez-Feliu, J. R. Montoya-Torres, and A. Khodadad-Saryazdi. 2020. “A Unified Typology of Urban Logistics Spaces As Interfaces for Freight Transport: A Systematic Literature Review.” Supply Chain Forum: An International Journal 21 (4): 274–289.
  • Mircetic, D., B. Rostami-Tabar, S. Nikolicic, and M. Maslaric. 2021. “Forecasting Hierarchical Time Series in Supply Chains: An Empirical Investigation.” International Journal of Production Research, 1–20.
  • Mohammadipour, M., and J. E. Boylan. 2012. “Forecast Horizon Aggregation in Integer Autoregressive Moving Average (inarma) Models.” Omega 40 (6): 703–712.
  • Moon, S., C. Hicks, and A. Simpson. 2012. “The Development of a Novel Hierarchical Forecasting Method for Predicting Spare Parts Demand in the South Korean Navy -- a Case Study.” International Journal of Production Economics 140: 794–802.
  • Moon, S., A. Simpson, and C. Hicks. 2013. “The Development of a Classification Model for Predicting the Performance of Forecasting Methods for Naval Spare Parts Demand.” International Journal of Production Economics 143: 449–453.
  • Murray, P. W., B. Agard, and M. A. Barajas. 2018a. “Asact-data Preparation for Forecasting: A Method to Substitute Transaction Data for Unavailable Product Consumption Data.” International Journal of Production Economics 203: 264–275.
  • Murray, P. W., B. Agard, and M. A. Barajas. 2018b. “Forecast of Individual Customer's Demand From a Large and Noisy Dataset.” Computers & Industrial Engineering 118: 33–43.
  • Narayanan, P., W. J. Verhagen, and V. V. Dhanisetty. 2019. “Identifying Strategic Maintenance Capacity for Accidental Damage Occurrence in Aircraft Operations.” Journal of Management Analytics 6 (1): 30–48.
  • Nenova, Z. D., and J. H. May. 2016. “Determining An Optimal Hierarchical Forecasting Model Based on the Characteristics of the Data Set, Technical Note.” Journal of Operations Management 44: 62–68.
  • Nikolopoulos, K., A. A. Syntetos, J. E. Boylan, F. Petropoulos, and V. Assimakopoulos. 2011. “An Aggregate–disaggregate Intermittent Demand Approach (adida) to Forecasting: An Empirical Proposition and Analysis.” Journal of the Operational Research Society 62 (3): 544–554.
  • Orcutt, G. H., H. W. Watts, and J. B. Edwards. 1968. “Data Aggregation and Information Loss.” The American Economic Review 58 (4): 773–787.
  • Ouwehand, P., K. H. Van Donselaar, and A. de Kok. 2005. “The Impact of Forecasting Horizon When Forecasting With Group Seasonal Indices.” Technical report.
  • Panagiotelis, A., G. Athanasopoulos, P. Gamakumara, and R. J. Hyndman. 2021. “Forecast Reconciliation: A Geometric View with New Insights on Bias Correction.” International Journal of Forecasting 37 (1): 343–359.
  • Panagiotelis, A., P. Gamakumara, G. Athanasopoulos, and R. Hyndman. 2020. “Probabilistic Forecast Reconciliation: Properties, Evaluation and Score Optimisation.” Technical report, Monash University, Department of Econometrics and Business Statistics.
  • Park, D., and T. R. Willemain. 1999. “The Threshold Bootstrap and Threshold Jackknife.” Computational Statistics & Data Analysis 31 (2): 187–202.
  • Pennings, C. L., and J. Van Dalen. 2017. “Integrated Hierarchical Forecasting.” European Journal of Operational Research 263 (2): 412–418.
  • Pennings, C., J. van Dalen, and E. van der Laan. 2017. “Exploiting Elapsed Time for Managing Intermittent Demand for Spare Parts.” European Journal of Operational Research 258: 958–969.
  • Petropoulos, F., and N. Kourentzes. 2014. “Forecast Combinations for Intermittent Demand.” Journal of the Operational Research Society 66 (6): 914–924.
  • Petropoulos, F., N. Kourentzes, and K. Nikolopoulos. 2016. “Another Look At Estimators for Intermittent Demand.” International Journal of Production Economics 181: 154–161.
  • Petropoulos, F., X. Wang, and S. M. Disney. 2019. “The Inventory Performance of Forecasting Methods: Evidence From the M3 Competition Data.” International Journal of Forecasting 35 (1): 251–265.
  • Porras, E., and R. Dekker. 2008. “An Inventory Control System for Spare Parts At a Refinery: An Empirical Comparison of Different Re-order Point Methods.” European Journal of Operational Research184 (1): 101–132.
  • Punia, S., S. P. Singh, and J. K. Madaan. 2020. “A Cross-temporal Hierarchical Framework and Deep Learning for Supply Chain Forecasting.” Computers & Industrial Engineering 149: 106796.
  • Quenouille, M. 1958. “Discrete Autoregressive Schemes with Varying Time-intervals.” Metrika 1 (1): 21–27.
  • Ray, W. 1980. “The Significance of Correlated Demands and Variable Lead Times for Stock Control Policies.” Journal of the Operational Research Society 31: 187–190.
  • Razi, M., I. Kurtulus, and C. Smith. 2004. “Development and Evaluation of An Inventory Model for Low-demand Spare Parts.” International Journal of Industrial Engineering-Theory Applications and Practice 11 (1): 90–98.
  • Rego, J., and M. Mesquita. 2015. “Demand Forecasting and Inventory Control: A Simulation Study on Automotive Spare Parts.” International Journal of Production Economics 161: 1–16.
  • Rostami-Tabar, B., M. Z. Babai, Y. Ducq, and A. Syntetos. 2015. “Non-stationary Demand Forecasting by Cross-sectional Aggregation.” International Journal of Production Economics 170: 297–309.
  • Rostami-Tabar, B., M. Babai, and A. Syntetos. 2021. “To Aggregate or not to Aggregate: Forecasting of Finite Autocorrelated Demand.” Journal of Economic Surveys arXiv: 2103.16310.
  • Rostami-Tabar, B., M. Z. Babai, A. Syntetos, and Y. Ducq. 2013. “Demand Forecasting by Temporal Aggregation.” Naval Research Logistics (NRL) 60 (6): 479–498.
  • Rostami-Tabar, B., M. Z. Babai, A. Syntetos, and Y. Ducq. 2014. “A Note on the Forecast Performance of Temporal Aggregation.” Naval Research Logistics (NRL) 61 (7): 489–500.
  • Sbrana, G., and A. Silvestrini. 2013. “Forecasting Aggregate Demand: Analytical Comparison of Top-down and Bottom-up Approaches in a Multivariate Exponential Smoothing Framework.” International Journal of Production Economics 146 (1): 185–198.
  • Schwarzkopf, A. B., R. J. Tersine, and J. S. Morris. 1988. “Top-down Versus Bottom-up Forecasting Strategies.” The International Journal Of Production Research 26 (11): 1833–1843.
  • Shlifer, E., and R. W. Wolff. 1979. “Aggregation and Proration in Forecasting.” Management Science 25 (6): 594–603.
  • Silvestrini, A., and D. Veredas. 2008. “Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey.” Journal of Economic Surveys 22 (3): 458–497.
  • Smith, M., and M. Z. Babai. 2011. “A Review of Bootstrapping for Spare Parts Forecasting.” In Service Parts Management: Demand Forecasting and Inventory Control, edited by N. Altay and L. Litteral, chapter 6, 125–141. London: Springer.
  • Spiliotis, E., M. Abolghasemi, R. J. Hyndman, F. Petropoulos, and V. Assimakopoulos. 2020. “Hierarchical Forecast Reconciliation With Machine Learning.” arXiv preprint arXiv:2006.02043.
  • Spithourakis, G. P., F. Petropoulos, M. Z. Babai, K. Nikolopoulos, and V. Assimakopoulos. 2011. “Improving the Performance of Popular Supply Chain Forecasting Techniques.” In Supply Chain Forum: An International Journal. Vol. 12. 16–25. Taylor & Francis.
  • Spithourakis, G. P., F. Petropoulos, K. Nikolopoulos, and V. Assimakopoulos. 2014. “A Systemic View of the Adida Framework.” IMA Journal of Management Mathematics 25 (2): 125–137.
  • Strijbosch, L., R. M. Heuts, and J. J. Moors. 2008. “Hierarchical Estimation As a Basis for Hierarchical Forecasting.” IMA Journal of Management Mathematics 19 (2): 193–205.
  • Syntetos, A. A., Z. Babai, J. E. Boylan, S. Kolassa, and K. Nikolopoulos. 2016. “Supply Chain Forecasting: Theory, Practice, Their Gap and the Future.” European Journal of Operational Research 252 (1): 1–26.
  • Syntetos, A. A., M. Z. Babai, and E. Gardner. 2015. “Forecasting Intermittent Inventory Demands: Simple Parametric Methods Vs. Bootstrapping.” Journal of Business Research 68: 1746–1752.
  • Syntetos, A. A., and J. E. Boylan. 2005. “The Accuracy of Intermittent Demand Estimates.” International Journal of Forecasting 21: 303–314.
  • Syntetos, A. A., J. E. Boylan, and J. D. Croston. 2005. “On the Categorization of Demand Patterns.” Journal of the Operational Research Society 56: 495–503.
  • Syntetos, A. A., J. E. Boylan, and S. M. Disney. 2009. “Forecasting for Inventory Planning: A 50-year Review.” Journal of the Operational Research Society 60 (sup1): S149–S160.
  • Taieb, S. B., J. W. Taylor, and R. J. Hyndman. 2017. “Coherent Probabilistic Forecasts for Hierarchical Time Series.” In International Conference on Machine Learning, 3348–3357. PMLR.
  • Taieb, S. B., J. W. Taylor, and R. J. Hyndman. 2021. “Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data.” Journal of the American Statistical Association 116 (533), 1–17.
  • Teunter, R., and L. Duncan. 2009. “Forecasting Intermittent Demand: A Comparative Study.” Journal of the Operational Research Society 60: 321–329.
  • Theil, H. 1954. “Linear Aggregation of Economic Relations”.North-Holland Pub. Co.
  • Tukey, J. 1958. “Bias and Confidence in Not-quite Large Samples. (Abstract).” Annals of Mathematical Statistics 29: 614.
  • Van Wingerden, E., R. Basten, R. Dekker, and W. Rustenberg. 2014. “More Grip on Inventory Control Through Improved Forecasting: A Comparative Study At Three Companies.” International Journal of Production Economics 157: 220–237.
  • Verstaete, G., E.-H. Aghezzaf, and B. Desmet. 2019. “A Data-driven Framework for Predicting Weather Impact on High-volume Low-margin Retail Products.” Journal of Retail and Consumer Services 48: 169–177.
  • Villegas, M. A., D. J. Pedregal, and J. R. Trapero. 2018. “A Support Vector Machine for Model Selection in Demand Forecasting Applications.” Computers & Industrial Engineering 121: 1–7.
  • Viswanathan, S., H. Widiarta, and R. Piplani. 2008. “Forecasting Aggregate Time Series with Intermittent Subaggregate Components: Top-down Versus Bottom-up Forecasting.” IMA Journal of Management Mathematics 19 (3): 275–287.
  • Wang, X., and S. M. Disney. 2016. “The Bullwhip Effect: Progress, Trends and Directions.” European Journal of Operational Research 250 (3): 691–701.
  • Wang, M.-C., and S. Rao. 1992. “Estimating Reorder Points and Other Management Science Applications by Bootstrap Procedure.” European Journal of Operational Research 56: 332–342.
  • Weatherford, L. R., S. E. Kimes, and D. A. Scott. 2001. “Forecasting for Hotel Revenue Management: Testing Aggregation Against Disaggregation.” Cornell Hotel and Restaurant Administration Quarterly 42 (4): 53–64.
  • Wei, W. W. 1978. “Some Consequences of Temporal Aggregation in Seasonal Time Series Models.” In Seasonal Analysis of Economic Time Series, 433–448. NBER.
  • Weiss, A. A. 1984. “Systematic Sampling and Temporal Aggregation in Time Series Models.” Journal of Econometrics 26 (3): 271–281.
  • Wickramasuriya, S. L., G. Athanasopoulos, and R. J. Hyndman. 2019. “Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization.” Journal of the American Statistical Association 114 (526): 804–819.
  • Widiarta, H., S. Viswanathan, and R. Piplani. 2007. “On the Effectiveness of Top-down Strategy for Forecasting Autoregressive Demands.” Naval Research Logistics (NRL) 54 (2): 176–188.
  • Widiarta, H., S. Viswanathan, and R. Piplani. 2008. “Forecasting Item-level Demands: An Analytical Evaluation of Top–down Versus Bottom-up Forecasting in a Production-planning Framework.” IMA Journal of Management Mathematics 19 (2): 207–218.
  • Widiarta, H., S. Viswanathan, and R. Piplani. 2009. “Forecasting Aggregate Demand: An Analytical Evaluation of Top-down Versus Bottom-up Forecasting in a Production Planning Framework.” International Journal of Production Economics 118 (1): 87–94.
  • Willemain, T., and C. Smart. 2001. “System and Method for Forecasting Intermittent Demand”. Patent US6205431B1, United States.
  • Willemain, T., C. Smart, J. Schocker, and P. DeSautels. 1994. “Forecasting Intermittent Demand in Manufacturing.” International Journal of Forecasting 10: 529–538.
  • Willemain, T., C. Smart, and H. Schwarz. 2004. “A New Approach of Forecasting Intermittent Demand for Service Parts Inventories.” International Journal of Forecasting 20: 375–387.
  • Withycombe, R. 1989. “Forecasting with Combined Seasonal Indexes.” International Journal of Forecasting 5: 547–552.
  • Zellner, A., and J. Tobias. 1998. “A Note on Aggregation, Disaggregation and Forecasting Performance.” Technical report.
  • Zhou, S., P. Jackson, R. O. Roundy, and R. Q. Zhang. 2007. “The Evolution of Family Level Sales Forecasts Into Product Level Forecasts: Modeling and Estimation.” IIE Transactions 39 (9): 831–843.
  • Zhou, C., and S. Viswanathan. 2011. “Comparison of a New Bootstrapping Method with Parametric Approaches for Safety Stock Determination in Service Parts Inventory Systems.” International Journal of Production Economics 133: 481–485.
  • Zhu, S., R. Dekker, W. Van Jaarsveld, R. W. Renjie, and A. J. Koning. 2017. “An Improved Method for Forecasting Spare Parts Demand Using Extreme Value Theory.” European Journal of Operational Research 261 (1): 169–181.
  • Zotteri, G., and M. Kalchschmidt. 2007. “A Model for Selecting the Appropriate Level of Aggregation in Forecasting Processes.” International Journal of Production Economics 108 (1-2): 74–83.
  • Zotteri, G., M. Kalchschmidt, and F. Caniato. 2005. “The Impact of Aggregation Level on Forecasting Performance.” International Journal of Production Economics 93: 479–491.

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