This fall, Nordstrom opened a new store in West Hollywood, Calif., called Nordstrom Local, that didn't actually stock clothes you can buy. Instead, you can get manicures, try on clothes, talk to stylists and consultants about fashion, and meet with friends over coffee or perhaps a glass of Pinot Grigio (yes, there's a bar). If you want to buy something, it'll be available for same-day pickup or delivery. As bizarre as the concept might seem, it's likely a glimpse into the way many stores will soon function. Forward-thinking retail experts say that over the next decade or so the act of physical shopping will transform in two key ways. One, shopping will become more of an entertaining experience for trying out new products and services rather than a repetitive chore for purchasing and picking up things to bring home.

One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accurately predict the sales of 111 potentially weather-sensitive products around the time of major weather events at 45 of Walmart retails locations in the U.S. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it is difficult for replenishment managers to predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. While they rely on a variety of vendor tools to predict sales around extreme weather events, they mostly employ a time-consuming process that lacks a systematic measure of effectiveness. We employ all the methods critical to any analytics project and start with data exploration. Critical features are extracted from the raw historical dataset for demand forecasting accuracy and robustness. In particular, we employ Artificial Neural Network for forecasting demand for each product sold around the time of major weather events. Finally, we evaluate our model to evaluate their accuracy and robustness.

Bossa Nova Robotics, a Silicon Valley developer of autonomous service robots for the retail industry, announced the close of a $17.5 million Series B funding round led by Paxion Capital Partners and participation by Intel Capital, WRV Capital, Lucas Venture Group (LVG), and Cota Capital. This round brings Bossa Nova's total funding to date to $41.7 million. Bossa Nova helps large scale stores automate the collection and analysis of on-shelf inventory data by driving their sensor-laden mobile robots autonomously through aisles, navigating safely among customers and store associates. The robots capture images of store shelves and use AI to analyze the data and calculate the status of each product including location, price, and out-of-stocks which is then aggregated and delivered to management in the form of a restock action plan. They recently began testing their robots and analytic services in 50 Walmart stores across the US.

If you're reading this article, chances are that you're pretty aware of the fact that many things are moving from the physical world to the digital one -- from editorial content to retail. But sometimes, those online experiences still leave something to be desired; a trend we've noticed over the past couple of years is retailers harnessing technology in an attempt to mimic the level of customer service and personalization you might get from a really good, attentive salesperson IRL. While shopping online is supposed to be convenient, it can often be overwhelming. Many online retailers boast tens or hundreds of thousands of brands and SKUs, and if you don't know exactly what you're looking for, and how it will fit you, the experience can be pretty frustrating. To mitigate that, retailers with the resources to do so are working to use data collection and, in some cases, AI to create more personalized shopping experiences -- i.e., showing you products it thinks you will like based on what you've purchased before, sort of like fashion's version of Spotify Discover Weekly or Apple Music's For You tab.

Xiaomi has chosen to focus its efforts on dozens of markets outside the US that very few of its products ever make it stateside. It has slowly been bringing its offerings into the country, though, and has started selling four new products on Amazon (with one more to come) just in time for this year's holiday shopping season. The priciest one you can add to your Christmas list is Xiaomi's GoPro rival, the $300 Mi Sphere Camera kit, which is a 23.88-megapixel 360-degree camera that can record videos in 3.5K. Xiaomi has also released a $140 robot builder kit with Lego-like components and electronic parts like high-speed motors and engine that you can use to build robotic dinosaurs, Transformer-like machines or pretty much anything you want. You can write programs for your creations with the kit's accompanying app even if you don't know how to write code.

The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate a phenomenon known as the bullwhip effect. The game consists of a serial supply chain network with four players--a retailer, a wholesaler, a distributor, and a manufacturer. In each period of the game, the retailer experiences a random demand from customers. Then the four players each decide how much inventory of "beer" to order. The retailer orders from the wholesaler, the wholesaler orders from the distributor, the distributor from the manufacturer, and the manufacturer orders from an external supplier that is not a player in the game.

Jefferson Graham offers a countdown to Black Friday deals on Talking Tech. Get out your credit cards and let the shopping begin. Sure, many of the so-called "sales" started this week, but let's get real--the action really begins early Thursday, like just after midnight. Remember when people would camp outside big-box stores, hoping to be the first person to rush inside and save $100 or $200 off the price of a big TV on Thanksgiving and the day after? Black Friday can mean getting great deals, but it can also get you in financial trouble.

In my role as an industry analyst, I get asked all the time about retailers' investment plans when it comes to artificial intelligence. My answer usually covers the two ways that AI figured into the average retail enterprise these days. One way comes through investments where the AI component is completely unknown to the retailer. Usually, we're talking about personalization or some kind of optimization technology that contains a machine learning or natural language processing component to it. Retailers want the sophisticated solution, but don't realize they are actually investing in what qualifies as AI.

Apple Inc. said Friday that it's pushing back its plans for a Siri-powered smart speaker until sometime early next year. The HomePod speaker was announced in June, with an initial launch date set for December. Apple said that its smart speaker will be able to control home appliances and take basic orders. In June, some analysts questioned whether a December launch would be too late for Apple to compete with Amazon.com But Apple promised that the speaker would have all of Siri's smarts and stand out from the pack by offering superior sound quality.

OK, Google, and hey, Alexa, you won't have Siri to worry about in the living room for the holidays. Apple on Friday delayed the pending release of its high-end HomePod connected speaker until 2018, saying it wasn't ready for shipment. When the company announced HomePod in June as a higher-fidelity answer to Amazon's Echo and Google Home -- and its Siri personal digital assistant on board -- December had been its scheduled release. In a statement, Apple said "We can't wait for people to experience HomePod...but we need a little more time before it's ready for our customers." It also put off the release of its AirPods wireless earbuds in 2016 by two months.