Platform9, the leader in multi-cloud Kubernetes-as-a-Service, announced that Norna, a leading applied artificial intelligence company, experienced a ten-fold productivity improvement and a 78% total cost of operations (TCO) reduction after implementing Platform9's Managed Kubernetes-as-a-Service to power the company's retail fashion AI technology. Norna's unique AI-driven service helps fashion retailers with assortment planning and pricing through near real-time insights into changes in competitor pricing and offerings. Norna turned to Platform9 to solve two major challenges the company was facing in using a public cloud platform – the rapidly escalating costs for its public cloud-based infrastructure and the high demands on the team's time to manage its Kubernetes infrastructure. Platform9's Managed Kubernetes-as-a-Service provided Norna with the simplest and fastest path to running its production, cloud-native data harvesting, and processing applications, enabling Norna to quickly deploy Kubernetes clusters with a rich set of pre-built, cloud-native services and infrastructure plug-ins. Rather than having to spend valuable engineering cycles on Kubernetes platform operations, Norna is now able to focus on its mission of becoming the world leader in applied AI. "As AI specialists, we cannot have in-house talent spending time becoming production Kubernetes experts," said Jonas Saric, founder and CEO of Norna.

Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes. Accurate forecasts are important to ensure retailers' profitability and to reduce environmental damage caused by disposal of unsold inventory. It is challenging because most products are new in a season and have short life cycles, huge sales variations and long lead-times. In this paper, we present a novel product age based forecast model, where product age refers to the number of weeks since its launch, and show that it outperforms existing models. We demonstrate the robust performance of the approach through real world use case of a multinational fashion retailer having over 300 stores, 35k items and around 40 categories. The main contributions of this work include unique and significant feature engineering for product attribute values, accurate demand forecast 6-12 months in advance and extending our approach to recommend product launch time for the next season. We use our fashion assortment optimization model to produce list and quantity of items to be listed in a store for the next season that maximizes total revenue and satisfies business constraints. We found a revenue uplift of 41% from our framework in comparison to the retailer's plan. We also compare our forecast results with the current methods and show that it outperforms existing models. Our framework leads to better ordering, inventory planning, assortment planning and overall increase in profit for the retailer's supply chain.

Artificial intelligence at its most basic level provides steroids for retail data. Suppose a regional fashion retailer with an eCommerce presence has 100,000 customers in its data base. And suppose those records are fairly basic: most recent transactions, average purchase per visit, demographic information and website traffic history. Now suppose those records are supplemented by a third-party anonymized database. AI will take that data and match it to an algorithm that then allows all the data to be supplemented by data within the category or for competitive purchases.

The fashion industry all across the globe is moving swiftly. There is no doubt in the fact that by nature the field of fashion is extremely malleable. Almost every season, it transforms into something completely new. This industry is revolving around the perpetual dynamics of transformations and a continuous introduction to fresh ideas. Over the last few years, the fashion runways have been flourished with a plethora of innovative changes. Out of all, artificial intelligence has to be the main game-changer.

Fashion retailers are increasingly turning to artificial intelligence (AI) for help in the quest to give customers what they want. AI-driven retail enables brands to compete in the economy of the 21st century and meet modern customer demands by personalizing the shopping experience. As more retail companies are taking their business from the traditional brick and mortar retail stores into e-commerce, they are able to get more insight into their customers' preferences to serve the demand. Talking about AI in retail brings us to one of the most beloved brands in the fashion industry – H&M. H&M Group has been heavily investing in artificial intelligence to stay on top of fashion cycles, and also to support its massive growth.

Exposure across product discovery channels is essential for fashion retailers. To state the obvious: if customers don't see your products when they're searching, they're probably not going to buy them. So, retailers spend millions every year trying to make sure that they're top of the pile, or at least present in the search results of whichever channels they think are most important. Unfortunately, many of them are leaving money on the table by failing to optimise the process by which products get digitised and fed into channels like Google Shopping.

Retailers are rolling out new ways for consumers to look for items online. Forever 21, in particular, is moving beyond text-based online search for its customers with visual search. In an announcement for the feature, Forever 21 President Alex Ok said the technology "bridges the gap" between online and offline worlds, while enabling "customers to search for clothing in the same way they think about it -- using visuals, not words." Forever 21 is not alone in its efforts to integrate the latest in AI: A myriad of merchants are using the technology in new and exciting ways. Some retailers even possess the computing power and storage capacity to treat each customer as an individual rather than part of a segment.

Success in fashion retail was once predicated on having an instinct for the next big thing. But in an omnichannel, social media-saturated consumer environment, human powers of divination alone – even those informed by trend forecasters – are not enough. The best fashion businesses are increasingly combining human with artificial intelligence (AI) to improve customer service, identify consumer trends, offer greater personalisation and to ensure their supply chain is as responsive as it can be. Last month, Marks & Spencer announced it was replacing switchboard staff with AI technology to increase the speed it deals with customer complaints and queries. The system recognises human speech and routes calls to relevant departments, and will be used in all 640 M&S UK stores by the end of this month, as well as its 13 UK call centres.

Profits and gross margins are under pressure for fashion retailers. But could Artificial Intelligence hold the key to optimising markdowns and unlocking value in the supply chain? Fashion retailing is a dynamic, complex and highly competitive business – and it's not getting any easier. Margins are coming under increasing pressure as a number of factors play-out: Consumers are becoming more demanding, rising inflation coupled with stagnant pay-growth is impacting buying power, costs are going up and online competition continues to gather pace. A particular challenge for bricks & mortar retailers is the incessant growth of online sales. According to the IMRG Capgemini Online Retail Sales Index for August 2017, clothing sales grew by 17.9%, year on year, and figures from the UK's Office for National Statistics (ONS) indicate that 15.3% of all retail spending is now conducted online.

There have been a number of buzzwords and defining technology trends in retail over the last decade: from Big Data, to omnichannel, and the ubiquitous, omni-present Cloud. And now the internet of things (IoT) and artificial intelligence (AI) have seemingly become the latest crazes and talk of the town. Forrester expects investment in AI to triple this year. By 2020, 85% of customer interactions will be managed by AI according to research by Gartner. The value of AI is estimated to be worth $36.8bn globally by 2025 predicts US market intelligence firm Tractica.