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Determining an optimal hierarchical forecasting model based on the characteristics of the data set: Technical note
Author(s) -
Nenova Zlatana D.,
May Jerrold H.
Publication year - 2016
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2016.04.001
Subject(s) - computer science , hierarchy , hierarchical database model , set (abstract data type) , data mining , task (project management) , multilevel model , tree (set theory) , process (computing) , data set , machine learning , artificial intelligence , mathematics , operating system , mathematical analysis , management , economics , market economy , programming language
The efficient flow of goods and services involves addressing multilevel forecast questions, and careful consideration when aggregating or disaggregating hierarchical estimates. Assessing all possible aggregation alternatives helps to determine the statistically most accurate way of consolidating multilevel forecasts. However, doing so in a multilevel and multiproduct supply chain may prove to be a very computationally intensive and time‐consuming task. In this paper, we present a new, two‐level oblique linear discriminant tree model, which identifies the optimal hierarchical forecast technique for a given hierarchical database in a very time‐efficient manner. We induced our model from a real‐world dataset, and it separates all historical time series into the four aggregation mechanisms considered. The separation process is a function of both the positive and negative correlation groups' variances at the lowest level of the hierarchical datasets. Our primary contributions are: (1) establishing a clear‐cut relationship between the correlation metrics at the lowest level of the hierarchy and the optimal aggregation mechanism for a product/service hierarchy, and (2) developing an analytical model for personalized forecast aggregation decisions, based on characteristics of a hierarchical dataset.