Abstract
Precise prediction of the goods demand is an important element of the supply chain management because we can optimise the level of stock based on predicted demand. Demand of goods may vary influenced by numerous factors, including price elasticity and weather, which we focus in this paper. We analysed daily sales data of consumer goods collected from Point of Sale (POS) systems of Japanese retailers, mostly supermarkets, which consist of records of price and quantity sold for each item, spanning several years. Demand may change according to regional preferences, so we built prediction models for each region by estimating demand curve of each item by employing linear regression and neural networks. We show that there are regional differences of the demand itself and also regional differences of the effect of weather.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
