OTTAWA, ON, June 15, 2020 /CNW/ - Kinaxis Inc. (TSX: KXS), the authority in driving agility for fast, confident decision-making in an unpredictable world, has signed a definitive agreement to acquire Toronto-based Rubikloud, a disruptive, emerging provider of AI solutions that automate supply chain prescriptive analytics and decision-making in the retail and consumer packaged goods (CPG) industries. Globally-recognized retailers and CPG manufacturers in the health and beauty, household and grocery segments use Rubikloud's AI-based products today. Their offerings include demand forecasting and automation to manage and optimize trade promotions, pricing and assortment to drive product demand and dramatically improve financial results. Kinaxis will enhance RapidResponse's demand planning capabilities with the Rubikloud offerings, anticipating initial opportunities in the company's rapidly-growing CPG customer base and over time for other industries such as life sciences. The acquisition also offers Kinaxis a springboard into the enterprise retail industry.

Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable to have this pricing policy be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in slow adoption in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation, results are a mixed bag. While many AI applications have yielded increased ROI -- this case study of AI in retail marketing segmentation is one example -- others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.

The recent report on "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026" offered by Researchmoz.us, Additionally, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the "Artificial Intelligence(AI) in Retail market". This is the most recent report inclusive of the COVID-19 effects on the functioning of the market. It is well known that some changes, for the worse, were administered by the pandemic on all industries. The current scenario of the business sector and pandemic's impact on the past and future of the industry are covered in this report.

The sense of discovery and surprise at something that matches an unrecognized yearning can be exhilarating. But the sheer size of many stores is exhausting -- with some, I need a site map, flashlight, and overnight bag to take it all in. In others, the inventory is specialized and packed in so tightly that online seems a better way to find what I want. If you're primarily an online shopper, you may be asking how relevant brick-and-mortar stores are. The answer is: quite relevant.

About Blue Ridge Blue Ridge Supply Chain Planning and Price Optimization solutions empower distributors and retailers to tap into undiscovered margin through enterprise-wide inventory intelligence, automation and synchronization. Blue Ridge uniquely combines demand forecasting with pricing strategy, so that businesses can proactively understand the unpredictable and allocate the right inventory – right-priced across the entire mix – to accelerate top- and bottom-line results. In a world where the only constant is change, Blue Ridge provides more certainty, more speed, and more assurance – so companies can see the why behind the buy, and respond faster to the unexpected. That's why major retailers and distributors rely on Blue Ridge for a more foreseeable future. For more information, go to www.blueridgeglobal.com.

"We needed to invest in a building of that type of size and scale so we can deliver the convenience, in terms of delivery speed, to the Australian customer base."Mr Fuller said while the centre would likely improve Amazon's delivery times across most of its Australian customers, the retailer would not know the material benefits of the centre until its completion in 2021.When we launched in Australia there were lots of unknowns…we had to learn the nuances of the Australian marketplaceCraig Fuller, Amazon Australia's director of operationsWhile Amazon operates around 30 robotic fulfilment centres internationally, this will be its first in Australia. The centre will still use humans to pick and pack items, but instead of workers walking to the shelves to pick the items, robotic units take the shelves to them, improving fulfilment time and reducing the amount of walking workers have to do.Amazon has faced criticism in the past over the treatment of its distribution centre workers, who have described working conditions at its Melbourne centre as a "hellscape" due to allegedly unrealistic performance targets.New South Wales Premier Gladys Berejiklian said the jobs created by the new centre come at a time the Australian economy " …

Artificial Intelligence (AI) has a market full of hype, with vendors, customers, and media speaking non stop about the abilities of AI on worldwide and their contributions individually. Blockchain is also generally hyped in the market, with technology providers and clients claiming all sorts of abilities that may or may not be possible. Combining AI and blockchain can obtain double the hype? On the other hand, AI is implemented real, the actual value in many endless ways we talk about every day. Likewise, blockchain is starting to show value across a variety of applications and businesses.

XGBoost is a popular and efficient machine learning (ML) algorithm for regression and classification tasks on tabular datasets. It implements a technique known as gradient boosting on trees and performs remarkably well in ML competitions. Since its launch, Amazon SageMaker has supported XGBoost as a built-in managed algorithm. For more information, see Simplify machine learning with XGBoost and Amazon SageMaker. As of this writing, you can take advantage of the open-source Amazon SageMaker XGBoost container, which has improved flexibility, scalability, extensibility, and Managed Spot Training.