We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as operation research or urban science, and more generally, it is crucial for businesses' planning and decision making. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location provided their features and the potential customers' characteristics in a given region. BSIM explicitly accounts for the competition among the competitive facilities through a probability value determined by evaluating a store-specific Gaussian distribution at a given customer location. We propose a scalable variational inference framework that, while being significantly faster than competing Markov Chain Monte Carlo inference schemes, exhibits comparable performances in terms of parameters identification and uncertainty quantification. We demonstrate the benefits of BSIM in various synthetic settings characterised by an increasing number of stores and customers. Finally, we construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions. We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.

The year 2019 seemed to be the year of unpredictability, not the least of which was the seemingly ever-changing foreign trade policy of major world economies. Interestingly, it's that same unpredictable nature of foreign trade policy that serves as a springboard for supply chain predictions for 2020. Here are the top five predictions that will have a major impact on the world's global supply chains. Historically, digital transformation of the supply chain has taken place by targeting various functional silos within their own walls. This approach lacked the ability to evaluate the interconnected nature of supply chain decisions.

Jeremy Stanley is giving a talk, "How Instacart is Using AI to Create the Most Efficient Shoppers Ever," at the O'Reilly Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart.

Growing customer expectations related to personalization and transparency are putting unprecedented pressures on businesses to deliver reliable outcomes, at exactly the right time. But companies relying on traditional methods to plan and deliver products won't keep pace with these changing market demands. Forward-thinking businesses are finding ways to tap into the data they need to be able to predict--with high levels of accuracy--what customers are going to demand, when they want it, by what channel, and most importantly, where they want it available. By combining location intelligence and artificial intelligence (AI), companies can bridge the traditional gap between supply chain forecasting and actual consumer demand. Today's merchandise planning spans the entire complex network in dynamic iterations that reflect real-time trends.

The conventional wisdom is that being in the right location is critical to success in retail. As one Japanese convenience store pursues an expansion, it may be getting some non-human help to decide where its stores should go. Convenience store chain Lawson is considering using artificial intelligence (AI) to determine where to place its new store locations, according to the Japan Times. The chain plans to use AI to collect marketing data, such as household distribution patterns and traffic volume, to determine a given store's chances of success in an area. Generally, the chain makes such decisions based on information gathering and analysis of an area carried out by staff.

No one wants to go grocery shopping. It's all heavy carts, complex layouts, and unfathomable product placement decisions – why is the sherry vinegar next to the olive bar? The daunting, sisyphean task takes up enough time to make you resentful. Making you hate it less, and helping you find exactly what you're looking for, is Instacart's raison d'etre, says Jeremy Stanley, the company's VP of data science, and chief wielder of the machine learned algorthims behind the scenes. See the full line-up and grab last-minute tickets.] Instacart represents an advancement in online grocery shopping service: It gives you millions of products from hundreds of retail partners, and then hooks you up with a personal shopper who makes that grocery list land on your doorstep.

How do we bring order to the chaos? In the remainder of this post, we'll first introduce the logistics problem Instacart is solving, outline the architecture of our systems and describe the GPS data we collect. Visualizations like these help us to build intuition about our system, generate hypotheses for improvements, sanity check our changes, identify best practices and improve our operations. But before we get too caught up in these visualizations, let's first quickly cover the problem we are solving. When using our app to order groceries, you first choose a retailer, and then shop for groceries to be delivered.

Stores are large and have complex layouts that are confusing to navigate. The hummus you want could be in the dairy section, the deli section, or somewhere else entirely. Efficiently navigating a store can be a daunting task. At Instacart, our customers can order millions of products from hundreds of retail partners. Our fleet of tens of thousands of personal shoppers must find these items at thousands of store locations.

NEW YORK, NY - 20 Jul 2016: Today, Macy's announced the pilot of "Macy's On Call," a mobile web tool that allows customers to interact with an AI-powered platform, via their mobile devices. "Macy's On Call" taps IBM (NYSE: IBM) Watson, via Satisfi, an intelligent engagement platform, to deliver a first-of-its-kind solution that will enhance the customer in-store shopping experience at 10 test locations nationwide. A Macy's team member tests out Macy's On Call, a new mobile web tool powered by IBM Watson and Satisfi. Macy's On Call allows customers to input questions in natural language about each participating store's unique product assortment, services and facilities and then receive a customized response to the inquiry. Macy's is currently piloting the new tool in 10 store locations across the country.

Location  plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semi-supervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach  in performance prediction of  certain candidate locations for new convenience store opening.