English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Temporal aggregation is an intuitively appealing approach to deal with demanduncertainty. There are two types of temporal aggregation: non-overlapping andoverlapping. Most of the supply chain forecasting literature has focused so faron the former and there is no research that analyses the latter forauto-correlated demands. In addition, most of the analytical research to-dateassumes infinite demand series' lengths whereas, in practice, forecasting isbased on finite demand histories. The length of the demand history is animportant determinant of the comparative performance of the two approaches buthas not been given sufficient attention in the literature. In this paper weexamine the effectiveness of temporal aggregation for forecasting finiteauto-correlated demand. We do so by means of an analytical study of theforecast accuracy of aggregation and non-aggregation approaches based on meansquared error. We complement this with a numerical analysis to explore theimpact of demand parameters and the length of the series on (comparative)performance. We also conduct an empirical evaluation to validate the analyticalresults using monthly time series of the M4-competition dataset. We find thedegree of auto-correlation, the forecast horizon and the length of the seriesto be important determinants of forecast accuracy. We discuss the merits ofeach approach and highlight their implications for real world practices.

To aggregate or not to aggregate: Forecasting of finite autocorrelated demand