Supply chain forecasting when information is not shared

Ali, Mohammad M., Babai, Mohamed Zied, Boylan, John E. and Syntetos, A. A. ORCID: https://orcid.org/0000-0003-4639-0756 2017. Supply chain forecasting when information is not shared. European Journal of Operational Research 260 (3) , pp. 984-994. 10.1016/j.ejor.2016.11.046

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Abstract

The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HF Commerce
Uncontrolled Keywords:	Supply chain management; Information sharing; Simple Moving Average; ARIMA; Downstream demand inference
Publisher:	Elsevier
ISSN:	0377-2217
Date of First Compliant Deposit:	20 January 2017
Date of Acceptance:	25 November 2016
Last Modified:	19 Nov 2024 19:30
URI:	https://orca.cardiff.ac.uk/id/eprint/97512

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