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Machine Learning Applications in Retail Inventory Management With improved computation and digital storage technology, those in the retail sector will have to learn how to utilize machine learning solutions to obtain maximum value from the data at their disposal, in order to remain competitive players. Content of the ebook In this ebook, we will touch
Last week, I attended a webinar, titled: Retail buying in uncertain times, aimed at multi-brand retailers of fashion and sporting goods. Multi-brand retailers buy from the brands and sell to consumers in a seasonal rhythm, where a season lasts roughly 26 weeks, including the sale period, where ‘everything must go’. The cash to cash cycle between concept and
When I write this, most brick and mortar retail locations we serve are closed and we, like our clients, are having to deal with a significant drop in monthly revenue and have taken necessary steps to reduce expenses and preserve cash positions. At Retailisation, we renamed ‘customer success’ to ‘customer care’ and are preparing for
Introduction The current interest in artificial intelligence and machine learning has been generated by advances in computer technology and the abundance of data. Applications of this new technology are well known to be part of the operations of tech giants such as Google, Facebook, Linkedin, etc. But what about the retail industry? In this article,
While working as a merchandiser for big brands and retailers, I learned that work was never ‘done’. Seasons and deadlines came and went, and plans were made and beaten – most of the time ;-). We would look for market signals and make decisions on how to interpret them and then on how to act.
In one of my previous blogs, I explained that the biggest dilemma of retail inventory management is ‘to ship or not to ship’. Too much inventory leads to markdowns and unnecessary employment of capital and too little leads to lost sales and erosion of market share. Since then, I have received several questions about how