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machine learning applications in retail
eBook

Latest Machine Learning Applications In Retail Inventory Management

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

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inventory management platform
Blog

Platform Retailing: No Push, No Pull, Just Flow

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

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retail dimensions
Blog

New Retail Dimensions

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

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machine learning applications in retail
Blog

How can machine learning improve retail inventory management?

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,

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understock-overstock
Blog

Understock and Overstock Explained

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

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