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

Traditionally, manufacturers use past orders (received from some downstreamsupply chain level) to forecast future ones, before turning such forecasts intoappropriate inventory and production optimization decisions. With recentadvances in information sharing technologies, upstream supply chain (SC)companies may have access to downstream point of sales (POS) data. Such datacan be used as an alternative source of information for forecasting. There area few studies that investigate the benefits of using orders versus POS data inupstream SC forecasting; the results are mixed and empirical evidence islacking, particularly in the context of multi-echelon SCs and in the presenceof promotions. We investigate an actual three-echelon SC with 684 series wherethe manufacturer aims to forecast orders received from distribution centers(DCs) using either aggregated POS data at DC level or historical ordersreceived from the DCs. Our results show that the order-based methods outperformthe POS-based ones by 6\%-15\%. We find that low values of mean, variance,non-linearity and entropy of POS data, and promotion presence negatively impactthe performance of the POS-based forecasts. Such findings are useful fordetermining the appropriate source of data and the impact of seriescharacteristics for order forecasting in SCs.

The value of point of sales information in upstream supply chain forecasting: an empirical investigation